diff --git a/.rat-excludes b/.rat-excludes index 8aca5a7f7a967..dccf2db8055ce 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -15,6 +15,7 @@ TAGS RELEASE control docs +docker.properties.template fairscheduler.xml.template spark-defaults.conf.template log4j.properties @@ -29,7 +30,13 @@ spark-env.sh.template log4j-defaults.properties bootstrap-tooltip.js jquery-1.11.1.min.js +d3.min.js +dagre-d3.min.js +graphlib-dot.min.js sorttable.js +vis.min.js +vis.min.css +vis.map .*avsc .*txt .*json diff --git a/LICENSE b/LICENSE index 9b364a4d00079..b2001f029a4f0 100644 --- a/LICENSE +++ b/LICENSE @@ -643,6 +643,36 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +======================================================================== +For d3 (core/src/main/resources/org/apache/spark/ui/static/d3.min.js): +======================================================================== + +Copyright (c) 2010-2015, Michael Bostock +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* The name Michael Bostock may not be used to endorse or promote products + derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL MICHAEL BOSTOCK BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, +EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ======================================================================== For Scala Interpreter classes (all .scala files in repl/src/main/scala @@ -814,6 +844,7 @@ BSD-style licenses The following components are provided under a BSD-style license. See project link for details. (BSD 3 Clause) core (com.github.fommil.netlib:core:1.1.2 - https://github.com/fommil/netlib-java/core) + (BSD 3 Clause) JPMML-Model (org.jpmml:pmml-model:1.1.15 - https://github.com/jpmml/jpmml-model) (BSD 3-clause style license) jblas (org.jblas:jblas:1.2.3 - http://jblas.org/) (BSD License) AntLR Parser Generator (antlr:antlr:2.7.7 - http://www.antlr.org/) (BSD License) Javolution (javolution:javolution:5.5.1 - http://javolution.org) diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE index e077eace74375..3fb92be0940b7 100644 --- a/R/pkg/NAMESPACE +++ b/R/pkg/NAMESPACE @@ -1,117 +1,36 @@ -#exportPattern("^[[:alpha:]]+") -exportClasses("RDD") -exportClasses("Broadcast") -exportMethods( - "aggregateByKey", - "aggregateRDD", - "cache", - "cartesian", - "checkpoint", - "coalesce", - "cogroup", - "collect", - "collectAsMap", - "collectPartition", - "combineByKey", - "count", - "countByKey", - "countByValue", - "distinct", - "Filter", - "filterRDD", - "first", - "flatMap", - "flatMapValues", - "fold", - "foldByKey", - "foreach", - "foreachPartition", - "fullOuterJoin", - "glom", - "groupByKey", - "intersection", - "join", - "keyBy", - "keys", - "length", - "lapply", - "lapplyPartition", - "lapplyPartitionsWithIndex", - "leftOuterJoin", - "lookup", - "map", - "mapPartitions", - "mapPartitionsWithIndex", - "mapValues", - "maximum", - "minimum", - "numPartitions", - "partitionBy", - "persist", - "pipeRDD", - "reduce", - "reduceByKey", - "reduceByKeyLocally", - "repartition", - "rightOuterJoin", - "sampleByKey", - "sampleRDD", - "saveAsTextFile", - "saveAsObjectFile", - "sortBy", - "sortByKey", - "subtract", - "subtractByKey", - "sumRDD", - "take", - "takeOrdered", - "takeSample", - "top", - "unionRDD", - "unpersist", - "value", - "values", - "zipPartitions", - "zipRDD", - "zipWithIndex", - "zipWithUniqueId" - ) +# Imports from base R +importFrom(methods, setGeneric, setMethod, setOldClass) +useDynLib(SparkR, stringHashCode) # S3 methods exported -export( - "textFile", - "objectFile", - "parallelize", - "hashCode", - "includePackage", - "broadcast", - "setBroadcastValue", - "setCheckpointDir" - ) export("sparkR.init") export("sparkR.stop") export("print.jobj") -useDynLib(SparkR, stringHashCode) -importFrom(methods, setGeneric, setMethod, setOldClass) - -# SparkRSQL exportClasses("DataFrame") -exportMethods("columns", +exportMethods("cache", + "collect", + "columns", + "count", + "describe", "distinct", "dtypes", "except", "explain", "filter", + "first", "groupBy", "head", "insertInto", "intersect", "isLocal", + "join", + "length", "limit", "orderBy", "names", + "persist", "printSchema", "registerTempTable", "repartition", @@ -125,9 +44,9 @@ exportMethods("columns", "show", "showDF", "sortDF", - "toJSON", - "toRDD", + "take", "unionAll", + "unpersist", "where", "withColumn", "withColumnRenamed") @@ -174,14 +93,12 @@ export("cacheTable", "createExternalTable", "dropTempTable", "jsonFile", - "jsonRDD", "loadDF", "parquetFile", "sql", "table", "tableNames", "tables", - "toDF", "uncacheTable") export("sparkRSQL.init", diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index b59b700af5dc9..47d92f141cc7d 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -167,7 +167,7 @@ setMethod("isLocal", setMethod("showDF", signature(x = "DataFrame"), function(x, numRows = 20) { - cat(callJMethod(x@sdf, "showString", numToInt(numRows)), "\n") + callJMethod(x@sdf, "showString", numToInt(numRows)) }) #' show @@ -272,7 +272,7 @@ setMethod("names", setMethod("registerTempTable", signature(x = "DataFrame", tableName = "character"), function(x, tableName) { - callJMethod(x@sdf, "registerTempTable", tableName) + invisible(callJMethod(x@sdf, "registerTempTable", tableName)) }) #' insertInto @@ -1276,3 +1276,40 @@ setMethod("saveAsTable", callJMethod(df@sdf, "saveAsTable", tableName, source, jmode, options) }) +#' describe +#' +#' Computes statistics for numeric columns. +#' If no columns are given, this function computes statistics for all numerical columns. +#' +#' @param x A DataFrame to be computed. +#' @param col A string of name +#' @param ... Additional expressions +#' @return A DataFrame +#' @rdname describe +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' describe(df) +#' describe(df, "col1") +#' describe(df, "col1", "col2") +#' } +setMethod("describe", + signature(x = "DataFrame", col = "character"), + function(x, col, ...) { + colList <- list(col, ...) + sdf <- callJMethod(x@sdf, "describe", listToSeq(colList)) + dataFrame(sdf) + }) + +#' @rdname describe +setMethod("describe", + signature(x = "DataFrame"), + function(x) { + colList <- as.list(c(columns(x))) + sdf <- callJMethod(x@sdf, "describe", listToSeq(colList)) + dataFrame(sdf) + }) diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index a3a0421a0746d..d1018c2361670 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -797,7 +797,7 @@ setMethod("first", #' @aliases distinct,RDD-method setMethod("distinct", signature(x = "RDD"), - function(x, numPartitions = SparkR::numPartitions(x)) { + function(x, numPartitions = SparkR:::numPartitions(x)) { identical.mapped <- lapply(x, function(x) { list(x, NULL) }) reduced <- reduceByKey(identical.mapped, function(x, y) { x }, @@ -993,7 +993,7 @@ setMethod("coalesce", signature(x = "RDD", numPartitions = "numeric"), function(x, numPartitions, shuffle = FALSE) { numPartitions <- numToInt(numPartitions) - if (shuffle || numPartitions > SparkR::numPartitions(x)) { + if (shuffle || numPartitions > SparkR:::numPartitions(x)) { func <- function(partIndex, part) { set.seed(partIndex) # partIndex as seed start <- as.integer(sample(numPartitions, 1) - 1) @@ -1078,7 +1078,7 @@ setMethod("saveAsTextFile", #' @aliases sortBy,RDD,RDD-method setMethod("sortBy", signature(x = "RDD", func = "function"), - function(x, func, ascending = TRUE, numPartitions = SparkR::numPartitions(x)) { + function(x, func, ascending = TRUE, numPartitions = SparkR:::numPartitions(x)) { values(sortByKey(keyBy(x, func), ascending, numPartitions)) }) @@ -1552,7 +1552,7 @@ setMethod("cartesian", #' @aliases subtract,RDD setMethod("subtract", signature(x = "RDD", other = "RDD"), - function(x, other, numPartitions = SparkR::numPartitions(x)) { + function(x, other, numPartitions = SparkR:::numPartitions(x)) { mapFunction <- function(e) { list(e, NA) } rdd1 <- map(x, mapFunction) rdd2 <- map(other, mapFunction) @@ -1583,7 +1583,7 @@ setMethod("subtract", #' @aliases intersection,RDD setMethod("intersection", signature(x = "RDD", other = "RDD"), - function(x, other, numPartitions = SparkR::numPartitions(x)) { + function(x, other, numPartitions = SparkR:::numPartitions(x)) { rdd1 <- map(x, function(v) { list(v, NA) }) rdd2 <- map(other, function(v) { list(v, NA) }) diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R index e88729387ef95..5838955f74dad 100644 --- a/R/pkg/R/generics.R +++ b/R/pkg/R/generics.R @@ -384,6 +384,10 @@ setGeneric("value", function(bcast) { standardGeneric("value") }) #' @export setGeneric("columns", function(x) {standardGeneric("columns") }) +#' @rdname describe +#' @export +setGeneric("describe", function(x, col, ...) { standardGeneric("describe") }) + #' @rdname schema #' @export setGeneric("dtypes", function(x) { standardGeneric("dtypes") }) diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R index 9791e55791bae..edeb8d9f75f7b 100644 --- a/R/pkg/R/pairRDD.R +++ b/R/pkg/R/pairRDD.R @@ -739,7 +739,7 @@ setMethod("cogroup", #' @aliases sortByKey,RDD,RDD-method setMethod("sortByKey", signature(x = "RDD"), - function(x, ascending = TRUE, numPartitions = SparkR::numPartitions(x)) { + function(x, ascending = TRUE, numPartitions = SparkR:::numPartitions(x)) { rangeBounds <- list() if (numPartitions > 1) { @@ -806,7 +806,7 @@ setMethod("sortByKey", #' @aliases subtractByKey,RDD setMethod("subtractByKey", signature(x = "RDD", other = "RDD"), - function(x, other, numPartitions = SparkR::numPartitions(x)) { + function(x, other, numPartitions = SparkR:::numPartitions(x)) { filterFunction <- function(elem) { iters <- elem[[2]] (length(iters[[1]]) > 0) && (length(iters[[2]]) == 0) diff --git a/R/pkg/inst/profile/shell.R b/R/pkg/inst/profile/shell.R index 7a7f2031152a0..33478d9e29995 100644 --- a/R/pkg/inst/profile/shell.R +++ b/R/pkg/inst/profile/shell.R @@ -20,11 +20,13 @@ .libPaths(c(file.path(home, "R", "lib"), .libPaths())) Sys.setenv(NOAWT=1) - library(utils) - library(SparkR) - sc <- sparkR.init(Sys.getenv("MASTER", unset = "")) + # Make sure SparkR package is the last loaded one + old <- getOption("defaultPackages") + options(defaultPackages = c(old, "SparkR")) + + sc <- SparkR::sparkR.init(Sys.getenv("MASTER", unset = "")) assign("sc", sc, envir=.GlobalEnv) - sqlCtx <- sparkRSQL.init(sc) + sqlCtx <- SparkR::sparkRSQL.init(sc) assign("sqlCtx", sqlCtx, envir=.GlobalEnv) cat("\n Welcome to SparkR!") cat("\n Spark context is available as sc, SQL context is available as sqlCtx\n") diff --git a/R/pkg/inst/tests/test_broadcast.R b/R/pkg/inst/tests/test_broadcast.R index fee91a427d6d5..bb86a5c922bde 100644 --- a/R/pkg/inst/tests/test_broadcast.R +++ b/R/pkg/inst/tests/test_broadcast.R @@ -29,7 +29,7 @@ test_that("using broadcast variable", { randomMatBr <- broadcast(sc, randomMat) useBroadcast <- function(x) { - sum(value(randomMatBr) * x) + sum(SparkR:::value(randomMatBr) * x) } actual <- collect(lapply(rrdd, useBroadcast)) expected <- list(sum(randomMat) * 1, sum(randomMat) * 2) diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R index af7a6c582047a..7a42e289fcd9e 100644 --- a/R/pkg/inst/tests/test_sparkSQL.R +++ b/R/pkg/inst/tests/test_sparkSQL.R @@ -641,7 +641,7 @@ test_that("toJSON() returns an RDD of the correct values", { test_that("showDF()", { df <- jsonFile(sqlCtx, jsonPath) - expect_output(showDF(df), "age name \nnull Michael\n30 Andy \n19 Justin ") + expect_output(showDF(df), "+----+-------+\n| age| name|\n+----+-------+\n|null|Michael|\n| 30| Andy|\n| 19| Justin|\n+----+-------+\n") }) test_that("isLocal()", { @@ -705,5 +705,16 @@ test_that("parquetFile works with multiple input paths", { expect_true(count(parquetDF) == count(df)*2) }) +test_that("describe() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + stats <- describe(df, "age") + expect_true(collect(stats)[1, "summary"] == "count") + expect_true(collect(stats)[2, "age"] == 24.5) + expect_true(collect(stats)[3, "age"] == 5.5) + stats <- describe(df) + expect_true(collect(stats)[4, "name"] == "Andy") + expect_true(collect(stats)[5, "age"] == 30.0) +}) + unlink(parquetPath) unlink(jsonPath) diff --git a/R/pkg/inst/tests/test_utils.R b/R/pkg/inst/tests/test_utils.R index 9c5bb427932b4..539e3a3c19df3 100644 --- a/R/pkg/inst/tests/test_utils.R +++ b/R/pkg/inst/tests/test_utils.R @@ -92,7 +92,10 @@ test_that("cleanClosure on R functions", { } newF <- cleanClosure(f) env <- environment(newF) - expect_equal(length(ls(env)), 3) # Only "g", "l" and "f". No "base", "field" or "defUse". + # TODO(shivaram): length(ls(env)) is 4 here for some reason and `lapply` is included in `env`. + # Disabling this test till we debug this. + # + # expect_equal(length(ls(env)), 3) # Only "g", "l" and "f". No "base", "field" or "defUse". expect_true("g" %in% ls(env)) expect_true("l" %in% ls(env)) expect_true("f" %in% ls(env)) diff --git a/R/pkg/inst/worker/worker.R b/R/pkg/inst/worker/worker.R index 014bf7bd7b3fe..7e3b5fc403b25 100644 --- a/R/pkg/inst/worker/worker.R +++ b/R/pkg/inst/worker/worker.R @@ -72,7 +72,7 @@ if (numBroadcastVars > 0) { for (bcast in seq(1:numBroadcastVars)) { bcastId <- SparkR:::readInt(inputCon) value <- unserialize(SparkR:::readRaw(inputCon)) - setBroadcastValue(bcastId, value) + SparkR:::setBroadcastValue(bcastId, value) } } diff --git a/bin/spark-shell2.cmd b/bin/spark-shell2.cmd index 02f51fe59a911..00fd30fa38d36 100644 --- a/bin/spark-shell2.cmd +++ b/bin/spark-shell2.cmd @@ -19,7 +19,7 @@ rem set SPARK_HOME=%~dp0.. -echo "%*" | findstr " --help -h" >nul +echo "%*" | findstr " \<--help\> \<-h\>" >nul if %ERRORLEVEL% equ 0 ( call :usage exit /b 0 diff --git a/conf/docker.properties.template b/conf/docker.properties.template new file mode 100644 index 0000000000000..26e3bfd9c5b9b --- /dev/null +++ b/conf/docker.properties.template @@ -0,0 +1,3 @@ +spark.mesos.executor.docker.image: +spark.mesos.executor.docker.volumes: /usr/local/lib:/host/usr/local/lib:ro +spark.mesos.executor.home: /opt/spark diff --git a/core/pom.xml b/core/pom.xml index 459ef66712c36..164a836cb8f0b 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -95,6 +95,11 @@ spark-network-shuffle_${scala.binary.version} ${project.version} + + org.apache.spark + spark-unsafe_${scala.binary.version} + ${project.version} + net.java.dev.jets3t jets3t @@ -268,7 +273,6 @@ org.apache.ivy ivy - ${ivy.version} oro @@ -357,7 +361,7 @@ org.spark-project pyrolite - 2.0.1 + 4.4 net.sf.py4j diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala b/core/src/main/java/org/apache/spark/api/java/function/Function0.java similarity index 67% rename from core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala rename to core/src/main/java/org/apache/spark/api/java/function/Function0.java index 65fa81704c365..38e410c5debe6 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala +++ b/core/src/main/java/org/apache/spark/api/java/function/Function0.java @@ -15,16 +15,13 @@ * limitations under the License. */ -package org.apache.spark.storage +package org.apache.spark.api.java.function; -import tachyon.client.TachyonFile +import java.io.Serializable; /** - * References a particular segment of a file (potentially the entire file), based off an offset and - * a length. + * A zero-argument function that returns an R. */ -private[spark] class TachyonFileSegment(val file: TachyonFile, val offset: Long, val length: Long) { - override def toString: String = { - "(name=%s, offset=%d, length=%d)".format(file.getPath(), offset, length) - } +public interface Function0 extends Serializable { + public R call() throws Exception; } diff --git a/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js b/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js index 2934181c1006a..acd6096e6743e 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js +++ b/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js @@ -1,9 +1,9 @@ /* =========================================================== - * bootstrap-tooltip.js v2.2.2 - * http://twitter.github.com/bootstrap/javascript.html#tooltips + * bootstrap-tooltip.js v2.3.2 + * http://getbootstrap.com/2.3.2/javascript.html#tooltips * Inspired by the original jQuery.tipsy by Jason Frame * =========================================================== - * Copyright 2012 Twitter, Inc. + * Copyright 2013 Twitter, Inc. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -38,19 +38,27 @@ , init: function (type, element, options) { var eventIn , eventOut + , triggers + , trigger + , i this.type = type this.$element = $(element) this.options = this.getOptions(options) this.enabled = true - if (this.options.trigger == 'click') { - this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this)) - } else if (this.options.trigger != 'manual') { - eventIn = this.options.trigger == 'hover' ? 'mouseenter' : 'focus' - eventOut = this.options.trigger == 'hover' ? 'mouseleave' : 'blur' - this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this)) - this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this)) + triggers = this.options.trigger.split(' ') + + for (i = triggers.length; i--;) { + trigger = triggers[i] + if (trigger == 'click') { + this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this)) + } else if (trigger != 'manual') { + eventIn = trigger == 'hover' ? 'mouseenter' : 'focus' + eventOut = trigger == 'hover' ? 'mouseleave' : 'blur' + this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this)) + this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this)) + } } this.options.selector ? @@ -59,7 +67,7 @@ } , getOptions: function (options) { - options = $.extend({}, $.fn[this.type].defaults, options, this.$element.data()) + options = $.extend({}, $.fn[this.type].defaults, this.$element.data(), options) if (options.delay && typeof options.delay == 'number') { options.delay = { @@ -72,7 +80,15 @@ } , enter: function (e) { - var self = $(e.currentTarget)[this.type](this._options).data(this.type) + var defaults = $.fn[this.type].defaults + , options = {} + , self + + this._options && $.each(this._options, function (key, value) { + if (defaults[key] != value) options[key] = value + }, this) + + self = $(e.currentTarget)[this.type](options).data(this.type) if (!self.options.delay || !self.options.delay.show) return self.show() @@ -97,14 +113,16 @@ , show: function () { var $tip - , inside , pos , actualWidth , actualHeight , placement , tp + , e = $.Event('show') if (this.hasContent() && this.enabled) { + this.$element.trigger(e) + if (e.isDefaultPrevented()) return $tip = this.tip() this.setContent() @@ -116,19 +134,18 @@ this.options.placement.call(this, $tip[0], this.$element[0]) : this.options.placement - inside = /in/.test(placement) - $tip .detach() .css({ top: 0, left: 0, display: 'block' }) - .insertAfter(this.$element) - pos = this.getPosition(inside) + this.options.container ? $tip.appendTo(this.options.container) : $tip.insertAfter(this.$element) + + pos = this.getPosition() actualWidth = $tip[0].offsetWidth actualHeight = $tip[0].offsetHeight - switch (inside ? placement.split(' ')[1] : placement) { + switch (placement) { case 'bottom': tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2} break @@ -143,11 +160,56 @@ break } - $tip - .offset(tp) - .addClass(placement) - .addClass('in') + this.applyPlacement(tp, placement) + this.$element.trigger('shown') + } + } + + , applyPlacement: function(offset, placement){ + var $tip = this.tip() + , width = $tip[0].offsetWidth + , height = $tip[0].offsetHeight + , actualWidth + , actualHeight + , delta + , replace + + $tip + .offset(offset) + .addClass(placement) + .addClass('in') + + actualWidth = $tip[0].offsetWidth + actualHeight = $tip[0].offsetHeight + + if (placement == 'top' && actualHeight != height) { + offset.top = offset.top + height - actualHeight + replace = true + } + + if (placement == 'bottom' || placement == 'top') { + delta = 0 + + if (offset.left < 0){ + delta = offset.left * -2 + offset.left = 0 + $tip.offset(offset) + actualWidth = $tip[0].offsetWidth + actualHeight = $tip[0].offsetHeight + } + + this.replaceArrow(delta - width + actualWidth, actualWidth, 'left') + } else { + this.replaceArrow(actualHeight - height, actualHeight, 'top') } + + if (replace) $tip.offset(offset) + } + + , replaceArrow: function(delta, dimension, position){ + this + .arrow() + .css(position, delta ? (50 * (1 - delta / dimension) + "%") : '') } , setContent: function () { @@ -161,6 +223,10 @@ , hide: function () { var that = this , $tip = this.tip() + , e = $.Event('hide') + + this.$element.trigger(e) + if (e.isDefaultPrevented()) return $tip.removeClass('in') @@ -179,6 +245,8 @@ removeWithAnimation() : $tip.detach() + this.$element.trigger('hidden') + return this } @@ -193,11 +261,12 @@ return this.getTitle() } - , getPosition: function (inside) { - return $.extend({}, (inside ? {top: 0, left: 0} : this.$element.offset()), { - width: this.$element[0].offsetWidth - , height: this.$element[0].offsetHeight - }) + , getPosition: function () { + var el = this.$element[0] + return $.extend({}, (typeof el.getBoundingClientRect == 'function') ? el.getBoundingClientRect() : { + width: el.offsetWidth + , height: el.offsetHeight + }, this.$element.offset()) } , getTitle: function () { @@ -215,6 +284,10 @@ return this.$tip = this.$tip || $(this.options.template) } + , arrow: function(){ + return this.$arrow = this.$arrow || this.tip().find(".tooltip-arrow") + } + , validate: function () { if (!this.$element[0].parentNode) { this.hide() @@ -236,8 +309,8 @@ } , toggle: function (e) { - var self = $(e.currentTarget)[this.type](this._options).data(this.type) - self[self.tip().hasClass('in') ? 'hide' : 'show']() + var self = e ? $(e.currentTarget)[this.type](this._options).data(this.type) : this + self.tip().hasClass('in') ? self.hide() : self.show() } , destroy: function () { @@ -269,10 +342,11 @@ , placement: 'top' , selector: false , template: '
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index 0000000000000..6beb7db855ca0 --- /dev/null +++ b/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js @@ -0,0 +1,31 @@ +!function(e){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=e();else if("function"==typeof define&&define.amd)define([],e);else{var f;"undefined"!=typeof window?f=window:"undefined"!=typeof global?f=global:"undefined"!=typeof self&&(f=self),f.dagreD3=e()}}(function(){var define,module,exports;return function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o0}},{}],14:[function(require,module,exports){module.exports=intersectNode;function intersectNode(node,point){return node.intersect(point)}},{}],15:[function(require,module,exports){var intersectLine=require("./intersect-line");module.exports=intersectPolygon;function intersectPolygon(node,polyPoints,point){var x1=node.x;var y1=node.y;var intersections=[];var minX=Number.POSITIVE_INFINITY,minY=Number.POSITIVE_INFINITY;polyPoints.forEach(function(entry){minX=Math.min(minX,entry.x);minY=Math.min(minY,entry.y)});var left=x1-node.width/2-minX;var top=y1-node.height/2-minY;for(var i=0;i1){intersections.sort(function(p,q){var pdx=p.x-point.x,pdy=p.y-point.y,distp=Math.sqrt(pdx*pdx+pdy*pdy),qdx=q.x-point.x,qdy=q.y-point.y,distq=Math.sqrt(qdx*qdx+qdy*qdy);return distpMath.abs(dx)*h){if(dy<0){h=-h}sx=dy===0?0:h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=dx===0?0:w*dy/dx}return{x:x+sx,y:y+sy}}},{}],17:[function(require,module,exports){var util=require("../util");module.exports=addHtmlLabel;function addHtmlLabel(root,node){var fo=root.append("foreignObject").attr("width","100000");var div=fo.append("xhtml:div");var label=node.label;switch(typeof label){case"function":div.insert(label);break;case"object":div.insert(function(){return label});break;default:div.html(label)}util.applyStyle(div,node.labelStyle);div.style("display","inline-block");div.style("white-space","nowrap");var w,h;div.each(function(){w=this.clientWidth;h=this.clientHeight});fo.attr("width",w).attr("height",h);return fo}},{"../util":25}],18:[function(require,module,exports){var addTextLabel=require("./add-text-label"),addHtmlLabel=require("./add-html-label");module.exports=addLabel;function addLabel(root,node){var label=node.label;var labelSvg=root.append("g");if(typeof label!=="string"||node.labelType==="html"){addHtmlLabel(labelSvg,node)}else{addTextLabel(labelSvg,node)}var labelBBox=labelSvg.node().getBBox();labelSvg.attr("transform","translate("+-labelBBox.width/2+","+-labelBBox.height/2+")");return labelSvg}},{"./add-html-label":17,"./add-text-label":19}],19:[function(require,module,exports){var util=require("../util");module.exports=addTextLabel;function addTextLabel(root,node){var domNode=root.append("text");var lines=processEscapeSequences(node.label).split("\n");for(var i=0;i0;--i){entry=buckets[i].dequeue();if(entry){results=results.concat(removeNode(g,buckets,zeroIdx,entry,true));break}}}}return results}function removeNode(g,buckets,zeroIdx,entry,collectPredecessors){var results=collectPredecessors?[]:undefined;_.each(g.inEdges(entry.v),function(edge){var weight=g.edge(edge),uEntry=g.node(edge.v);if(collectPredecessors){results.push({v:edge.v,w:edge.w})}uEntry.out-=weight;assignBucket(buckets,zeroIdx,uEntry)});_.each(g.outEdges(entry.v),function(edge){var weight=g.edge(edge),w=edge.w,wEntry=g.node(w);wEntry["in"]-=weight;assignBucket(buckets,zeroIdx,wEntry)});g.removeNode(entry.v);return results}function buildState(g,weightFn){var fasGraph=new Graph,maxIn=0,maxOut=0;_.each(g.nodes(),function(v){fasGraph.setNode(v,{v:v,"in":0,out:0})});_.each(g.edges(),function(e){var prevWeight=fasGraph.edge(e.v,e.w)||0,weight=weightFn(e),edgeWeight=prevWeight+weight;fasGraph.setEdge(e.v,e.w,edgeWeight);maxOut=Math.max(maxOut,fasGraph.node(e.v).out+=weight);maxIn=Math.max(maxIn,fasGraph.node(e.w)["in"]+=weight)});var buckets=_.range(maxOut+maxIn+3).map(function(){return new List});var zeroIdx=maxIn+1;_.each(fasGraph.nodes(),function(v){assignBucket(buckets,zeroIdx,fasGraph.node(v))});return{graph:fasGraph,buckets:buckets,zeroIdx:zeroIdx}}function assignBucket(buckets,zeroIdx,entry){if(!entry.out){buckets[0].enqueue(entry)}else if(!entry["in"]){buckets[buckets.length-1].enqueue(entry)}else{buckets[entry.out-entry["in"]+zeroIdx].enqueue(entry)}}},{"./data/list":31,"./graphlib":33,"./lodash":36}],35:[function(require,module,exports){"use strict";var _=require("./lodash"),acyclic=require("./acyclic"),normalize=require("./normalize"),rank=require("./rank"),normalizeRanks=require("./util").normalizeRanks,parentDummyChains=require("./parent-dummy-chains"),removeEmptyRanks=require("./util").removeEmptyRanks,nestingGraph=require("./nesting-graph"),addBorderSegments=require("./add-border-segments"),coordinateSystem=require("./coordinate-system"),order=require("./order"),position=require("./position"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports=layout;function layout(g,opts){var time=opts&&opts.debugTiming?util.time:util.notime;time("layout",function(){var layoutGraph=time(" buildLayoutGraph",function(){return buildLayoutGraph(g)});time(" runLayout",function(){runLayout(layoutGraph,time)});time(" updateInputGraph",function(){updateInputGraph(g,layoutGraph)})})}function runLayout(g,time){time(" makeSpaceForEdgeLabels",function(){makeSpaceForEdgeLabels(g)});time(" 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inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs))); + +_.each(inputGraph.nodes(),function(v){var node=canonicalize(inputGraph.node(v));g.setNode(v,_.defaults(selectNumberAttrs(node,nodeNumAttrs),nodeDefaults));g.setParent(v,inputGraph.parent(v))});_.each(inputGraph.edges(),function(e){var edge=canonicalize(inputGraph.edge(e));g.setEdge(e,_.merge({},edgeDefaults,selectNumberAttrs(edge,edgeNumAttrs),_.pick(edge,edgeAttrs)))});return g}function makeSpaceForEdgeLabels(g){var graph=g.graph();graph.ranksep/=2;_.each(g.edges(),function(e){var edge=g.edge(e);edge.minlen*=2;if(edge.labelpos.toLowerCase()!=="c"){if(graph.rankdir==="TB"||graph.rankdir==="BT"){edge.width+=edge.labeloffset}else{edge.height+=edge.labeloffset}}})}function injectEdgeLabelProxies(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.width&&edge.height){var v=g.node(e.v),w=g.node(e.w),label={rank:(w.rank-v.rank)/2+v.rank,e:e};util.addDummyNode(g,"edge-proxy",label,"_ep")}})}function assignRankMinMax(g){var maxRank=0;_.each(g.nodes(),function(v){var node=g.node(v);if(node.borderTop){node.minRank=g.node(node.borderTop).rank;node.maxRank=g.node(node.borderBottom).rank;maxRank=_.max(maxRank,node.maxRank)}});g.graph().maxRank=maxRank}function removeEdgeLabelProxies(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="edge-proxy"){g.edge(node.e).labelRank=node.rank;g.removeNode(v)}})}function translateGraph(g){var minX=Number.POSITIVE_INFINITY,maxX=0,minY=Number.POSITIVE_INFINITY,maxY=0,graphLabel=g.graph(),marginX=graphLabel.marginx||0,marginY=graphLabel.marginy||0;function getExtremes(attrs){var x=attrs.x,y=attrs.y,w=attrs.width,h=attrs.height;minX=Math.min(minX,x-w/2);maxX=Math.max(maxX,x+w/2);minY=Math.min(minY,y-h/2);maxY=Math.max(maxY,y+h/2)}_.each(g.nodes(),function(v){getExtremes(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){getExtremes(edge)}});minX-=marginX;minY-=marginY;_.each(g.nodes(),function(v){var node=g.node(v);node.x-=minX;node.y-=minY});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,function(p){p.x-=minX;p.y-=minY});if(_.has(edge,"x")){edge.x-=minX}if(_.has(edge,"y")){edge.y-=minY}});graphLabel.width=maxX-minX+marginX;graphLabel.height=maxY-minY+marginY}function assignNodeIntersects(g){_.each(g.edges(),function(e){var edge=g.edge(e),nodeV=g.node(e.v),nodeW=g.node(e.w),p1,p2;if(!edge.points){edge.points=[];p1=nodeW;p2=nodeV}else{p1=edge.points[0];p2=edge.points[edge.points.length-1]}edge.points.unshift(util.intersectRect(nodeV,p1));edge.points.push(util.intersectRect(nodeW,p2))})}function fixupEdgeLabelCoords(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){if(edge.labelpos==="l"||edge.labelpos==="r"){edge.width-=edge.labeloffset}switch(edge.labelpos){case"l":edge.x-=edge.width/2+edge.labeloffset;break;case"r":edge.x+=edge.width/2+edge.labeloffset;break}}})}function reversePointsForReversedEdges(g){_.each(g.edges(),function(e){var 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++orderShift,e:selfEdge.e,label:selfEdge.label},"_se")});delete node.selfEdges})})}function positionSelfEdges(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="selfedge"){var selfNode=g.node(node.e.v),x=selfNode.x+selfNode.width/2,y=selfNode.y,dx=node.x-x,dy=selfNode.height/2;g.setEdge(node.e,node.label);g.removeNode(v);node.label.points=[{x:x+2*dx/3,y:y-dy},{x:x+5*dx/6,y:y-dy},{x:x+dx,y:y},{x:x+5*dx/6,y:y+dy},{x:x+2*dx/3,y:y+dy}];node.label.x=node.x;node.label.y=node.y}})}function selectNumberAttrs(obj,attrs){return _.mapValues(_.pick(obj,attrs),Number)}function canonicalize(attrs){var newAttrs={};_.each(attrs,function(v,k){newAttrs[k.toLowerCase()]=v});return newAttrs}},{"./acyclic":28,"./add-border-segments":29,"./coordinate-system":30,"./graphlib":33,"./lodash":36,"./nesting-graph":37,"./normalize":38,"./order":43,"./parent-dummy-chains":48,"./position":50,"./rank":52,"./util":55}],36:[function(require,module,exports){module.exports=require(20)},{"/Users/andrew/Documents/dev/dagre-d3/lib/lodash.js":20,lodash:77}],37:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,cleanup:cleanup};function run(g){var root=util.addDummyNode(g,"root",{},"_root"),depths=treeDepths(g),height=_.max(depths)-1,nodeSep=2*height+1;g.graph().nestingRoot=root;_.each(g.edges(),function(e){g.edge(e).minlen*=nodeSep});var weight=sumWeights(g)+1;_.each(g.children(),function(child){dfs(g,root,nodeSep,weight,height,depths,child)});g.graph().nodeRankFactor=nodeSep}function dfs(g,root,nodeSep,weight,height,depths,v){var children=g.children(v);if(!children.length){if(v!==root){g.setEdge(root,v,{weight:0,minlen:nodeSep})}return}var top=util.addBorderNode(g,"_bt"),bottom=util.addBorderNode(g,"_bb"),label=g.node(v);g.setParent(top,v);label.borderTop=top;g.setParent(bottom,v);label.borderBottom=bottom;_.each(children,function(child){dfs(g,root,nodeSep,weight,height,depths,child);var childNode=g.node(child),childTop=childNode.borderTop?childNode.borderTop:child,childBottom=childNode.borderBottom?childNode.borderBottom:child,thisWeight=childNode.borderTop?weight:2*weight,minlen=childTop!==childBottom?1:height-depths[v]+1;g.setEdge(top,childTop,{weight:thisWeight,minlen:minlen,nestingEdge:true});g.setEdge(childBottom,bottom,{weight:thisWeight,minlen:minlen,nestingEdge:true})});if(!g.parent(v)){g.setEdge(root,top,{weight:0,minlen:height+depths[v]})}}function treeDepths(g){var depths={};function dfs(v,depth){var children=g.children(v);if(children&&children.length){_.each(children,function(child){dfs(child,depth+1)})}depths[v]=depth}_.each(g.children(),function(v){dfs(v,1)});return depths}function sumWeights(g){return _.reduce(g.edges(),function(acc,e){return acc+g.edge(e).weight},0)}function cleanup(g){var graphLabel=g.graph();g.removeNode(graphLabel.nestingRoot);delete graphLabel.nestingRoot;_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.nestingEdge){g.removeEdge(e)}})}},{"./lodash":36,"./util":55}],38:[function(require,module,exports){"use strict";var _=require("./lodash"),util=require("./util");module.exports={run:run,undo:undo};function run(g){g.graph().dummyChains=[];_.each(g.edges(),function(edge){normalizeEdge(g,edge)})}function normalizeEdge(g,e){var v=e.v,vRank=g.node(v).rank,w=e.w,wRank=g.node(w).rank,name=e.name,edgeLabel=g.edge(e),labelRank=edgeLabel.labelRank;if(wRank===vRank+1)return;g.removeEdge(e);var dummy,attrs,i;for(i=0,++vRank;vRank0){if(index%2){weightSum+=tree[index+1]}index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":36}],43:[function(require,module,exports){"use strict";var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc=vEntry.barycenter){mergeEntries(vEntry,uEntry)}}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0){sourceSet.push(wEntry)}}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.each(entry["in"].reverse(),handleIn(entry));_.each(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":36}],46:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl){movable=_.filter(movable,function(w){return w!==bl&&w!==br})}var barycenters=barycenter(g,movable);_.each(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter")){mergeBarycenters(entry,subgraphResult)}}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,subgraphs){_.each(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v]){return subgraphs[v].vs}return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":36,"./barycenter":40,"./resolve-conflicts":45,"./sort":47}],47:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.each(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenterentryW.barycenter){return 1}return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":36,"../util":55}],48:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.each(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRanklow||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca){wPath.push(parent)}return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function dfs(v){var low=lim;_.each(g.children(v),dfs);result[v]={low:low,lim:lim++}}_.each(g.children(),dfs);return result}},{"./lodash":36}],49:[function(require,module,exports){"use strict";var _=require("../lodash"),Graph=require("../graphlib").Graph,util=require("../util");module.exports={positionX:positionX,findType1Conflicts:findType1Conflicts,findType2Conflicts:findType2Conflicts,addConflict:addConflict,hasConflict:hasConflict,verticalAlignment:verticalAlignment,horizontalCompaction:horizontalCompaction,alignCoordinates:alignCoordinates,findSmallestWidthAlignment:findSmallestWidthAlignment,balance:balance};function findType1Conflicts(g,layering){var conflicts={};function visitLayer(prevLayer,layer){var k0=0,scanPos=0,prevLayerLength=prevLayer.length,lastNode=_.last(layer);_.each(layer,function(v,i){var w=findOtherInnerSegmentNode(g,v),k1=w?g.node(w).order:prevLayerLength;if(w||v===lastNode){_.each(layer.slice(scanPos,i+1),function(scanNode){_.each(g.predecessors(scanNode),function(u){var uLabel=g.node(u),uPos=uLabel.order;if((uPosnextNorthBorder)){addConflict(conflicts,u,v)}})}})}function visitLayer(north,south){var prevNorthPos=-1,nextNorthPos,southPos=0;_.each(south,function(v,southLookahead){if(g.node(v).dummy==="border"){var predecessors=g.predecessors(v);if(predecessors.length){nextNorthPos=g.node(predecessors[0]).order;scan(south,southPos,southLookahead,prevNorthPos,nextNorthPos);southPos=southLookahead;prevNorthPos=nextNorthPos}}scan(south,southPos,south.length,nextNorthPos,north.length)});return south}_.reduce(layering,visitLayer);return conflicts}function findOtherInnerSegmentNode(g,v){if(g.node(v).dummy){return _.find(g.predecessors(v),function(u){return g.node(u).dummy})}}function addConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}var conflictsV=conflicts[v];if(!conflictsV){conflicts[v]=conflictsV={}}conflictsV[w]=true}function hasConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}return _.has(conflicts[v],w)}function verticalAlignment(g,layering,conflicts,neighborFn){var root={},align={},pos={};_.each(layering,function(layer){_.each(layer,function(v,order){root[v]=v;align[v]=v;pos[v]=order})});_.each(layering,function(layer){var prevIdx=-1;_.each(layer,function(v){var ws=neighborFn(v);if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdxwLabel.lim){tailLabel=wLabel;flip=true}var candidates=_.filter(g.edges(),function(edge){return flip===isDescendant(t,t.node(edge.v),tailLabel)&&flip!==isDescendant(t,t.node(edge.w),tailLabel)});return _.min(candidates,function(edge){return slack(g,edge)})}function exchangeEdges(t,g,e,f){var v=e.v,w=e.w;t.removeEdge(v,w);t.setEdge(f.v,f.w,{});initLowLimValues(t);initCutValues(t,g);updateRanks(t,g)}function updateRanks(t,g){var root=_.find(t.nodes(),function(v){return!g.node(v).parent}),vs=preorder(t,root);vs=vs.slice(1);_.each(vs,function(v){var parent=t.node(v).parent,edge=g.edge(v,parent),flipped=false;if(!edge){edge=g.edge(parent,v);flipped=true}g.node(v).rank=g.node(parent).rank+(flipped?edge.minlen:-edge.minlen)})}function isTreeEdge(tree,u,v){return tree.hasEdge(u,v)}function isDescendant(tree,vLabel,rootLabel){return rootLabel.low<=vLabel.lim&&vLabel.lim<=rootLabel.lim}},{"../graphlib":33,"../lodash":36,"../util":55,"./feasible-tree":51,"./util":54}],54:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports={longestPath:longestPath,slack:slack};function longestPath(g){var visited={};function dfs(v){var label=g.node(v);if(_.has(visited,v)){return label.rank}visited[v]=true;var rank=_.min(_.map(g.outEdges(v),function(e){return dfs(e.w)-g.edge(e).minlen}));if(rank===Number.POSITIVE_INFINITY){rank=0}return label.rank=rank}_.each(g.sources(),dfs)}function slack(g,e){return g.node(e.w).rank-g.node(e.v).rank-g.edge(e).minlen}},{"../lodash":36}],55:[function(require,module,exports){"use strict";var _=require("./lodash"),Graph=require("./graphlib").Graph;module.exports={addDummyNode:addDummyNode,simplify:simplify,asNonCompoundGraph:asNonCompoundGraph,successorWeights:successorWeights,predecessorWeights:predecessorWeights,intersectRect:intersectRect,buildLayerMatrix:buildLayerMatrix,normalizeRanks:normalizeRanks,removeEmptyRanks:removeEmptyRanks,addBorderNode:addBorderNode,maxRank:maxRank,partition:partition,time:time,notime:notime};function addDummyNode(g,type,attrs,name){var v; + +do{v=_.uniqueId(name)}while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.each(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.each(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){var simplified=new Graph({multigraph:g.isMultigraph()}).setGraph(g.graph());_.each(g.nodes(),function(v){if(!g.children(v).length){simplified.setNode(v,g.node(v))}});_.each(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.each(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),function(v){var preds={};_.each(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy){throw new Error("Not possible to find intersection inside of the rectangle")}var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=w*dy/dx}return{x:x+sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.each(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank)){layering[rank][node.order]=v}});return layering}function normalizeRanks(g){var min=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank")){node.rank-=min}})}function removeEmptyRanks(g){var offset=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));var layers=[];_.each(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!_.has(layers,rank)){layers[rank]=[]}layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.each(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0){--delta}else if(delta){_.each(vs,function(v){g.node(v).rank+=delta})}})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank)){return rank}}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.each(collection,function(value){if(fn(value)){result.lhs.push(value)}else{result.rhs.push(value)}});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":33,"./lodash":36}],56:[function(require,module,exports){module.exports="0.7.1"},{}],57:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":73,"./lib/alg":64,"./lib/json":74}],58:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length){cmpts.push(cmpt)}});return cmpts}},{"../lodash":75}],59:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs)){vs=[vs]}var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v)){throw new Error("Graph does not have node: "+v)}doDfs(g,v,order==="post",visited,acc)});return acc}function doDfs(g,v,postorder,visited,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder){acc.push(v)}_.each(g.neighbors(v),function(w){doDfs(g,w,postorder,visited,acc)});if(postorder){acc.push(v)}}}},{"../lodash":75}],60:[function(require,module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"../lodash":75,"./dijkstra":61}],61:[function(require,module,exports){var _=require("../lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0){throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight)}if(distance0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY){break}edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":71,"../lodash":75}],62:[function(require,module,exports){var _=require("../lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),function(cmpt){return cmpt.length>1})}},{"../lodash":75,"./tarjan":69}],63:[function(require,module,exports){var _=require("../lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]={};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w){results[v][w]={distance:Number.POSITIVE_INFINITY}}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance0){v=pq.removeMin();if(_.has(parents,v)){result.setEdge(v,parents[v])}else if(init){throw new Error("Input graph is not connected: "+g)}else{init=true}g.nodeEdges(v).forEach(updateNeighbors)}return result}},{"../data/priority-queue":71,"../graph":72,"../lodash":75}],69:[function(require,module,exports){var _=require("../lodash");module.exports=tarjan;function tarjan(g){var index=0,stack=[],visited={},results=[];function dfs(v){var entry=visited[v]={onStack:true,lowlink:index,index:index++};stack.push(v);g.successors(v).forEach(function(w){if(!_.has(visited,w)){dfs(w);entry.lowlink=Math.min(entry.lowlink,visited[w].lowlink)}else if(visited[w].onStack){entry.lowlink=Math.min(entry.lowlink,visited[w].index)}});if(entry.lowlink===entry.index){var cmpt=[],w;do{w=stack.pop();visited[w].onStack=false;cmpt.push(w)}while(v!==w);results.push(cmpt)}}g.nodes().forEach(function(v){if(!_.has(visited,v)){dfs(v)}});return results}},{"../lodash":75}],70:[function(require,module,exports){var _=require("../lodash");module.exports=topsort;topsort.CycleException=CycleException;function topsort(g){var visited={},stack={},results=[];function visit(node){if(_.has(stack,node)){throw new CycleException}if(!_.has(visited,node)){stack[node]=true;visited[node]=true;_.each(g.predecessors(node),visit);delete stack[node];results.push(node)}}_.each(g.sinks(),visit);if(_.size(visited)!==g.nodeCount()){throw new CycleException}return results}function CycleException(){}},{"../lodash":75}],71:[function(require,module,exports){var _=require("../lodash");module.exports=PriorityQueue;function PriorityQueue(){this._arr=[];this._keyIndices={}}PriorityQueue.prototype.size=function(){return this._arr.length};PriorityQueue.prototype.keys=function(){return this._arr.map(function(x){return x.key})};PriorityQueue.prototype.has=function(key){return _.has(this._keyIndices,key)};PriorityQueue.prototype.priority=function(key){var index=this._keyIndices[key];if(index!==undefined){return this._arr[index].priority}};PriorityQueue.prototype.min=function(){if(this.size()===0){throw new Error("Queue underflow")}return this._arr[0].key};PriorityQueue.prototype.add=function(key,priority){var keyIndices=this._keyIndices;key=String(key);if(!_.has(keyIndices,key)){var arr=this._arr;var index=arr.length;keyIndices[key]=index;arr.push({key:key,priority:priority});this._decrease(index);return true}return false};PriorityQueue.prototype.removeMin=function(){this._swap(0,this._arr.length-1);var min=this._arr.pop();delete this._keyIndices[min.key];this._heapify(0);return min.key};PriorityQueue.prototype.decrease=function(key,priority){var index=this._keyIndices[key];if(priority>this._arr[index].priority){throw new Error("New priority is greater than current priority. 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"+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority)}this._arr[index].priority=priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l>1;if(arr[parent].priority1){this.setNode(v,value)}else{this.setNode(v)}},this);return this};Graph.prototype.setNode=function(v,value){if(_.has(this._nodes,v)){if(arguments.length>1){this._nodes[v]=value}return this}this._nodes[v]=arguments.length>1?value:this._defaultNodeLabelFn(v);if(this._isCompound){this._parent[v]=GRAPH_NODE;this._children[v]={};this._children[GRAPH_NODE][v]=true}this._in[v]={};this._preds[v]={};this._out[v]={};this._sucs[v]={};++this._nodeCount;return this};Graph.prototype.node=function(v){return this._nodes[v]};Graph.prototype.hasNode=function(v){return _.has(this._nodes,v)};Graph.prototype.removeNode=function(v){var self=this;if(_.has(this._nodes,v)){var removeEdge=function(e){self.removeEdge(self._edgeObjs[e])};delete this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){this.setParent(child)},this);delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=function(v,parent){if(!this._isCompound){throw new Error("Cannot set parent in a non-compound graph")}if(_.isUndefined(parent)){parent=GRAPH_NODE}else{for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor)){if(ancestor===v){throw new Error("Setting "+parent+" as parent of "+v+" would create create a cycle")}}this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=function(v){delete 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'' : __t) +\n'"}index=offset+match.length;return match});source+="';\n";var variable=options.variable,hasVariable=variable;if(!hasVariable){variable="obj";source="with ("+variable+") {\n"+source+"\n}\n"}source=(isEvaluating?source.replace(reEmptyStringLeading,""):source).replace(reEmptyStringMiddle,"$1").replace(reEmptyStringTrailing,"$1;");source="function("+variable+") {\n"+(hasVariable?"":variable+" || ("+variable+" = {});\n")+"var __t, __p = '', __e = _.escape"+(isEvaluating?", __j = Array.prototype.join;\n"+"function print() { __p += __j.call(arguments, '') }\n":";\n")+source+"return __p\n}";var sourceURL="\n/*\n//# sourceURL="+(options.sourceURL||"/lodash/template/source["+templateCounter++ +"]")+"\n*/";try{var result=Function(importsKeys,"return "+source+sourceURL).apply(undefined,importsValues)}catch(e){e.source=source;throw e}if(data){return result(data)}result.source=source;return result}function times(n,callback,thisArg){n=(n=+n)>-1?n:0;var index=-1,result=Array(n);callback=baseCreateCallback(callback,thisArg,1);while(++index + * div.stage-metadata > + * div.[dot-file | incoming-edge | outgoing-edge] + * + * Output DOM hierarchy: + * div#dag-viz-graph > + * svg > + * g#cluster_stage_[stageId] + * + * Note that the input metadata is populated by o.a.s.ui.UIUtils.showDagViz. + * Any changes in the input format here must be reflected there. + */ +function renderDagViz(forJob) { + + // If there is not a dot file to render, fail fast and report error + var jobOrStage = forJob ? "job" : "stage"; + if (metadataContainer().empty()) { + graphContainer() + .append("div") + .text("No visualization information available for this " + jobOrStage); + return; + } + + // Render + var svg = graphContainer() + .append("svg") + .attr("class", jobOrStage); + if (forJob) { + renderDagVizForJob(svg); + } else { + renderDagVizForStage(svg); + } + + // Find cached RDDs and mark them as such + metadataContainer().selectAll(".cached-rdd").each(function(v) { + var nodeId = VizConstants.nodePrefix + d3.select(this).text(); + svg.selectAll("#" + nodeId).classed("cached", true); + }); + + // More post-processing + drawClusterLabels(svg, forJob); + resizeSvg(svg); +} + +/* Render the RDD DAG visualization on the stage page. */ +function renderDagVizForStage(svgContainer) { + var metadata = metadataContainer().select(".stage-metadata"); + var dot = metadata.select(".dot-file").text(); + var containerId = VizConstants.graphPrefix + metadata.attr("stage-id"); + var container = svgContainer.append("g").attr("id", containerId); + renderDot(dot, container); + + // Round corners on RDDs + svgContainer + .selectAll("g.node rect") + .attr("rx", "5") + .attr("ry", "5"); +} + +/* + * Render the RDD DAG visualization on the job page. + * + * Due to limitations in dagre-d3, each stage is rendered independently so that + * we have more control on how to position them. Unfortunately, this means we + * cannot rely on dagre-d3 to render edges that cross stages and must render + * these manually on our own. + */ +function renderDagVizForJob(svgContainer) { + var crossStageEdges = []; + + // Each div.stage-metadata contains the information needed to generate the graph + // for a stage. This includes the DOT file produced from the appropriate UI listener, + // any incoming and outgoing edges, and any cached RDDs that belong to this stage. + metadataContainer().selectAll(".stage-metadata").each(function(d, i) { + var metadata = d3.select(this); + var dot = metadata.select(".dot-file").text(); + var stageId = metadata.attr("stage-id"); + var containerId = VizConstants.graphPrefix + stageId; + // Link each graph to the corresponding stage page (TODO: handle stage attempts) + var stageLink = + "/stages/stage/?id=" + stageId.replace(VizConstants.stagePrefix, "") + "&attempt=0"; + var container = svgContainer + .append("a") + .attr("xlink:href", stageLink) + .append("g") + .attr("id", containerId); + + // Now we need to shift the container for this stage so it doesn't overlap with + // existing ones, taking into account the position and width of the last stage's + // container. We do not need to do this for the first stage of this job. + if (i > 0) { + var existingStages = svgContainer + .selectAll("g.cluster") + .filter("[id*=\"" + VizConstants.stageClusterPrefix + "\"]"); + if (!existingStages.empty()) { + var lastStage = d3.select(existingStages[0].pop()); + var lastStageId = lastStage.attr("id"); + var lastStageWidth = toFloat(svgContainer + .select("#" + lastStageId) + .select("rect") + .attr("width")); + var lastStagePosition = getAbsolutePosition(lastStage); + var offset = lastStagePosition.x + lastStageWidth + VizConstants.stageSep; + container.attr("transform", "translate(" + offset + ", 0)"); + } + } + + // Actually render the stage + renderDot(dot, container); + + // If there are any incoming edges into this graph, keep track of them to render + // them separately later. Note that we cannot draw them now because we need to + // put these edges in a separate container that is on top of all stage graphs. + metadata.selectAll(".incoming-edge").each(function(v) { + var edge = d3.select(this).text().split(","); // e.g. 3,4 => [3, 4] + crossStageEdges.push(edge); + }); + }); + + drawCrossStageEdges(crossStageEdges, svgContainer); +} + +/* Render the dot file as an SVG in the given container. */ +function renderDot(dot, container) { + var escaped_dot = dot + .replace(/</g, "<") + .replace(/>/g, ">") + .replace(/"/g, "\""); + var g = graphlibDot.read(escaped_dot); + var renderer = new dagreD3.render(); + renderer(container, g); +} + +/* -------------------- * + * | Helper functions | * + * -------------------- */ + +// Helper d3 accessors +function graphContainer() { return d3.select("#dag-viz-graph"); } +function metadataContainer() { return d3.select("#dag-viz-metadata"); } + +/* + * Helper function to create draw a label for each cluster. + * + * We need to do this manually because dagre-d3 does not support labeling clusters. + * In general, the clustering support for dagre-d3 is quite limited at this point. + */ +function drawClusterLabels(svgContainer, forJob) { + if (forJob) { + var clusterLabelSize = JobPageVizConstants.clusterLabelSize; + var stageClusterLabelSize = JobPageVizConstants.stageClusterLabelSize; + } else { + var clusterLabelSize = StagePageVizConstants.clusterLabelSize; + var stageClusterLabelSize = StagePageVizConstants.stageClusterLabelSize; + } + svgContainer.selectAll("g.cluster").each(function() { + var cluster = d3.select(this); + var isStage = cluster.attr("id").indexOf(VizConstants.stageClusterPrefix) > -1; + var labelSize = isStage ? stageClusterLabelSize : clusterLabelSize; + drawClusterLabel(cluster, labelSize); + }); +} + +/* + * Helper function to draw a label for the given cluster element based on its name. + * + * In the process, we need to expand the bounding box to make room for the label. + * We need to do this because dagre-d3 did not take this into account when it first + * rendered the bounding boxes. Note that this means we need to adjust the view box + * of the SVG afterwards since we shifted a few boxes around. + */ +function drawClusterLabel(d3cluster, fontSize) { + var cluster = d3cluster; + var rect = d3cluster.select("rect"); + rect.attr("y", toFloat(rect.attr("y")) - fontSize); + rect.attr("height", toFloat(rect.attr("height")) + fontSize); + var labelX = toFloat(rect.attr("x")) + toFloat(rect.attr("width")) - fontSize / 2; + var labelY = toFloat(rect.attr("y")) + fontSize * 1.5; + var labelText = cluster.attr("name").replace(VizConstants.clusterPrefix, ""); + cluster.append("text") + .attr("x", labelX) + .attr("y", labelY) + .attr("text-anchor", "end") + .style("font-size", fontSize) + .text(labelText); +} + +/* + * Helper function to size the SVG appropriately such that all elements are displyed. + * This assumes that all outermost elements are clusters (rectangles). + */ +function resizeSvg(svg) { + var allClusters = svg.selectAll("g.cluster rect")[0]; + var startX = -VizConstants.svgMarginX + + toFloat(d3.min(allClusters, function(e) { + return getAbsolutePosition(d3.select(e)).x; + })); + var startY = -VizConstants.svgMarginY + + toFloat(d3.min(allClusters, function(e) { + return getAbsolutePosition(d3.select(e)).y; + })); + var endX = VizConstants.svgMarginX + + toFloat(d3.max(allClusters, function(e) { + var t = d3.select(e) + return getAbsolutePosition(t).x + toFloat(t.attr("width")); + })); + var endY = VizConstants.svgMarginY + + toFloat(d3.max(allClusters, function(e) { + var t = d3.select(e) + return getAbsolutePosition(t).y + toFloat(t.attr("height")); + })); + var width = endX - startX; + var height = endY - startY; + svg.attr("viewBox", startX + " " + startY + " " + width + " " + height) + .attr("width", width) + .attr("height", height); +} + +/* + * (Job page only) Helper function to draw edges that cross stage boundaries. + * We need to do this manually because we render each stage separately in dagre-d3. + */ +function drawCrossStageEdges(edges, svgContainer) { + if (edges.length == 0) { + return; + } + // Draw the paths first + var edgesContainer = svgContainer.append("g").attr("id", "cross-stage-edges"); + for (var i = 0; i < edges.length; i++) { + var fromRDDId = edges[i][0]; + var toRDDId = edges[i][1]; + connectRDDs(fromRDDId, toRDDId, edgesContainer, svgContainer); + } + // Now draw the arrows by borrowing the arrow marker generated by dagre-d3 + var dagreD3Marker = svgContainer.select("g.edgePaths marker"); + if (!dagreD3Marker.empty()) { + svgContainer + .append(function() { return dagreD3Marker.node().cloneNode(true); }) + .attr("id", "marker-arrow") + svgContainer.selectAll("g > path").attr("marker-end", "url(#marker-arrow)"); + svgContainer.selectAll("g.edgePaths def").remove(); // We no longer need these + } +} + +/* + * (Job page only) Helper function to compute the absolute + * position of the specified element in our graph. + */ +function getAbsolutePosition(d3selection) { + if (d3selection.empty()) { + throw "Attempted to get absolute position of an empty selection."; + } + var obj = d3selection; + var _x = toFloat(obj.attr("x")) || 0; + var _y = toFloat(obj.attr("y")) || 0; + while (!obj.empty()) { + var transformText = obj.attr("transform"); + if (transformText) { + var translate = d3.transform(transformText).translate; + _x += toFloat(translate[0]); + _y += toFloat(translate[1]); + } + // Climb upwards to find how our parents are translated + obj = d3.select(obj.node().parentNode); + // Stop when we've reached the graph container itself + if (obj.node() == graphContainer().node()) { + break; + } + } + return { x: _x, y: _y }; +} + +/* (Job page only) Helper function to connect two RDDs with a curved edge. */ +function connectRDDs(fromRDDId, toRDDId, edgesContainer, svgContainer) { + var fromNodeId = VizConstants.nodePrefix + fromRDDId; + var toNodeId = VizConstants.nodePrefix + toRDDId; + var fromPos = getAbsolutePosition(svgContainer.select("#" + fromNodeId)); + var toPos = getAbsolutePosition(svgContainer.select("#" + toNodeId)); + + // On the job page, RDDs are rendered as dots (circles). When rendering the path, + // we need to account for the radii of these circles. Otherwise the arrow heads + // will bleed into the circle itself. + var delta = toFloat(svgContainer + .select("g.node#" + toNodeId) + .select("circle") + .attr("r")); + if (fromPos.x < toPos.x) { + fromPos.x += delta; + toPos.x -= delta; + } else if (fromPos.x > toPos.x) { + fromPos.x -= delta; + toPos.x += delta; + } + + if (fromPos.y == toPos.y) { + // If they are on the same rank, curve the middle part of the edge + // upward a little to avoid interference with things in between + // e.g. _______ + // _____/ \_____ + var points = [ + [fromPos.x, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.2, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.3, fromPos.y - 20], + [fromPos.x + (toPos.x - fromPos.x) * 0.7, fromPos.y - 20], + [fromPos.x + (toPos.x - fromPos.x) * 0.8, toPos.y], + [toPos.x, toPos.y] + ]; + } else { + // Otherwise, draw a curved edge that flattens out on both ends + // e.g. _____ + // / + // | + // _____/ + var points = [ + [fromPos.x, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.4, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.6, toPos.y], + [toPos.x, toPos.y] + ]; + } + + var line = d3.svg.line().interpolate("basis"); + edgesContainer.append("path").datum(points).attr("d", line); +} + +/* Helper function to convert attributes to numeric values. */ +function toFloat(f) { + if (f) { + return parseFloat(f.toString().replace(/px$/, "")); + } else { + return f; + } +} + diff --git a/core/src/main/resources/org/apache/spark/ui/static/streaming-page.css b/core/src/main/resources/org/apache/spark/ui/static/streaming-page.css new file mode 100644 index 0000000000000..5da9d631ad124 --- /dev/null +++ b/core/src/main/resources/org/apache/spark/ui/static/streaming-page.css @@ -0,0 +1,58 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +.graph { + font: 10px sans-serif; +} + +.axis path, .axis line { + fill: none; + stroke: gray; + shape-rendering: crispEdges; +} + +.axis text { + fill: gray; +} + +.tooltip-inner { + max-width: 500px !important; // Make sure we only have one line tooltip +} + +.line { + fill: none; + stroke: #0088cc; + stroke-width: 1.5px; +} + +.bar rect { + fill: #0088cc; + shape-rendering: crispEdges; +} + +.bar rect:hover { + fill: #00c2ff; +} + +.timeline { + width: 500px; +} + +.histogram { + width: auto; +} diff --git a/core/src/main/resources/org/apache/spark/ui/static/streaming-page.js b/core/src/main/resources/org/apache/spark/ui/static/streaming-page.js new file mode 100644 index 0000000000000..a4e03b156f13e --- /dev/null +++ b/core/src/main/resources/org/apache/spark/ui/static/streaming-page.js @@ -0,0 +1,274 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +// timeFormat: StreamingPage.scala will generate a global "timeFormat" dictionary to store the time +// and its formatted string. Because we cannot specify a timezone in JavaScript, to make sure the +// server and client use the same timezone, we use the "timeFormat" dictionary to format all time +// values used in the graphs. + +// A global margin left for all timeline graphs. It will be set in "registerTimeline". This will be +// used to align all timeline graphs. +var maxMarginLeftForTimeline = 0; + +// The max X values for all histograms. It will be set in "registerHistogram". +var maxXForHistogram = 0; + +var histogramBinCount = 10; +var yValueFormat = d3.format(",.2f"); + +// Show a tooltip "text" for "node" +function showBootstrapTooltip(node, text) { + $(node).tooltip({title: text, trigger: "manual", container: "body"}); + $(node).tooltip("show"); +} + +// Hide the tooltip for "node" +function hideBootstrapTooltip(node) { + $(node).tooltip("destroy"); +} + +// Register a timeline graph. All timeline graphs should be register before calling any +// "drawTimeline" so that we can determine the max margin left for all timeline graphs. +function registerTimeline(minY, maxY) { + var numOfChars = yValueFormat(maxY).length; + // A least width for "maxY" in the graph + var pxForMaxY = numOfChars * 8 + 10; + // Make sure we have enough space to show the ticks in the y axis of timeline + maxMarginLeftForTimeline = pxForMaxY > maxMarginLeftForTimeline? pxForMaxY : maxMarginLeftForTimeline; +} + +// Register a histogram graph. All histogram graphs should be register before calling any +// "drawHistogram" so that we can determine the max X value for histograms. +function registerHistogram(values, minY, maxY) { + var data = d3.layout.histogram().range([minY, maxY]).bins(histogramBinCount)(values); + // d.x is the y values while d.y is the x values + var maxX = d3.max(data, function(d) { return d.y; }); + maxXForHistogram = maxX > maxXForHistogram ? maxX : maxXForHistogram; +} + +// Draw a line between (x1, y1) and (x2, y2) +function drawLine(svg, xFunc, yFunc, x1, y1, x2, y2) { + var line = d3.svg.line() + .x(function(d) { return xFunc(d.x); }) + .y(function(d) { return yFunc(d.y); }); + var data = [{x: x1, y: y1}, {x: x2, y: y2}]; + svg.append("path") + .datum(data) + .style("stroke-dasharray", ("6, 6")) + .style("stroke", "lightblue") + .attr("class", "line") + .attr("d", line); +} + +/** + * @param id the `id` used in the html `div` tag + * @param data the data for the timeline graph + * @param minX the min value of X axis + * @param maxX the max value of X axis + * @param minY the min value of Y axis + * @param maxY the max value of Y axis + * @param unitY the unit of Y axis + * @param batchInterval if "batchInterval" is specified, we will draw a line for "batchInterval" in the graph + */ +function drawTimeline(id, data, minX, maxX, minY, maxY, unitY, batchInterval) { + // Hide the right border of "". We cannot use "css" directly, or "sorttable.js" will override them. + d3.select(d3.select(id).node().parentNode) + .style("padding", "8px 0 8px 8px") + .style("border-right", "0px solid white"); + + var margin = {top: 20, right: 27, bottom: 30, left: maxMarginLeftForTimeline}; + var width = 500 - margin.left - margin.right; + var height = 150 - margin.top - margin.bottom; + + var x = d3.scale.linear().domain([minX, maxX]).range([0, width]); + var y = d3.scale.linear().domain([minY, maxY]).range([height, 0]); + + var xAxis = d3.svg.axis().scale(x).orient("bottom").tickFormat(function(d) { return timeFormat[d]; }); + var formatYValue = d3.format(",.2f"); + var yAxis = d3.svg.axis().scale(y).orient("left").ticks(5).tickFormat(formatYValue); + + var line = d3.svg.line() + .x(function(d) { return x(d.x); }) + .y(function(d) { return y(d.y); }); + + var svg = d3.select(id).append("svg") + .attr("width", width + margin.left + margin.right) + .attr("height", height + margin.top + margin.bottom) + .append("g") + .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); + + // Only show the first and last time in the graph + xAxis.tickValues(x.domain()); + + svg.append("g") + .attr("class", "x axis") + .attr("transform", "translate(0," + height + ")") + .call(xAxis) + + svg.append("g") + .attr("class", "y axis") + .call(yAxis) + .append("text") + .attr("transform", "translate(0," + (-3) + ")") + .text(unitY); + + + if (batchInterval && batchInterval <= maxY) { + drawLine(svg, x, y, minX, batchInterval, maxX, batchInterval); + } + + svg.append("path") + .datum(data) + .attr("class", "line") + .attr("d", line); + + // Add points to the line. However, we make it invisible at first. But when the user moves mouse + // over a point, it will be displayed with its detail. + svg.selectAll(".point") + .data(data) + .enter().append("circle") + .attr("stroke", "white") // white and opacity = 0 make it invisible + .attr("fill", "white") + .attr("opacity", "0") + .attr("cx", function(d) { return x(d.x); }) + .attr("cy", function(d) { return y(d.y); }) + .attr("r", function(d) { return 3; }) + .on('mouseover', function(d) { + var tip = formatYValue(d.y) + " " + unitY + " at " + timeFormat[d.x]; + showBootstrapTooltip(d3.select(this).node(), tip); + // show the point + d3.select(this) + .attr("stroke", "steelblue") + .attr("fill", "steelblue") + .attr("opacity", "1"); + }) + .on('mouseout', function() { + hideBootstrapTooltip(d3.select(this).node()); + // hide the point + d3.select(this) + .attr("stroke", "white") + .attr("fill", "white") + .attr("opacity", "0"); + }) + .on("click", function(d) { + window.location.href = "batch/?id=" + d.x; + }); +} + +/** + * @param id the `id` used in the html `div` tag + * @param values the data for the histogram graph + * @param minY the min value of Y axis + * @param maxY the max value of Y axis + * @param unitY the unit of Y axis + * @param batchInterval if "batchInterval" is specified, we will draw a line for "batchInterval" in the graph + */ +function drawHistogram(id, values, minY, maxY, unitY, batchInterval) { + // Hide the left border of "". We cannot use "css" directly, or "sorttable.js" will override them. + d3.select(d3.select(id).node().parentNode) + .style("padding", "8px 8px 8px 0") + .style("border-left", "0px solid white"); + + var margin = {top: 20, right: 30, bottom: 30, left: 10}; + var width = 300 - margin.left - margin.right; + var height = 150 - margin.top - margin.bottom; + + var x = d3.scale.linear().domain([0, maxXForHistogram]).range([0, width]); + var y = d3.scale.linear().domain([minY, maxY]).range([height, 0]); + + var xAxis = d3.svg.axis().scale(x).orient("top").ticks(5); + var yAxis = d3.svg.axis().scale(y).orient("left").ticks(0).tickFormat(function(d) { return ""; }); + + var data = d3.layout.histogram().range([minY, maxY]).bins(histogramBinCount)(values); + + var svg = d3.select(id).append("svg") + .attr("width", width + margin.left + margin.right) + .attr("height", height + margin.top + margin.bottom) + .append("g") + .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); + + if (batchInterval && batchInterval <= maxY) { + drawLine(svg, x, y, 0, batchInterval, maxXForHistogram, batchInterval); + } + + svg.append("g") + .attr("class", "x axis") + .call(xAxis) + + svg.append("g") + .attr("class", "y axis") + .call(yAxis) + + var bar = svg.selectAll(".bar") + .data(data) + .enter() + .append("g") + .attr("transform", function(d) { return "translate(0," + (y(d.x) - height + y(d.dx)) + ")";}) + .attr("class", "bar").append("rect") + .attr("width", function(d) { return x(d.y); }) + .attr("height", function(d) { return height - y(d.dx); }) + .on('mouseover', function(d) { + var percent = yValueFormat(d.y * 100.0 / values.length) + "%"; + var tip = d.y + " batches (" + percent + ") between " + yValueFormat(d.x) + " and " + yValueFormat(d.x + d.dx) + " " + unitY; + showBootstrapTooltip(d3.select(this).node(), tip); + }) + .on('mouseout', function() { + hideBootstrapTooltip(d3.select(this).node()); + }); + + if (batchInterval && batchInterval <= maxY) { + // Add the "stable" text to the graph below the batch interval line. + var stableXOffset = x(maxXForHistogram) - 20; + var stableYOffset = y(batchInterval) + 15; + svg.append("text") + .style("fill", "lightblue") + .attr("class", "stable-text") + .attr("text-anchor", "middle") + .attr("transform", "translate(" + stableXOffset + "," + stableYOffset + ")") + .text("stable") + .on('mouseover', function(d) { + var tip = "Processing Time <= Batch Interval (" + yValueFormat(batchInterval) +" " + unitY +")"; + showBootstrapTooltip(d3.select(this).node(), tip); + }) + .on('mouseout', function() { + hideBootstrapTooltip(d3.select(this).node()); + }); + } +} + +$(function() { + function getParameterFromURL(param) + { + var parameters = window.location.search.substring(1); // Remove "?" + var keyValues = parameters.split('&'); + for (var i = 0; i < keyValues.length; i++) + { + var paramKeyValue = keyValues[i].split('='); + if (paramKeyValue[0] == param) + { + return paramKeyValue[1]; + } + } + } + + if (getParameterFromURL("show-streams-detail") == "true") { + // Show the details for all InputDStream + $('#inputs-table').toggle('collapsed'); + $('#triangle').html('▼'); + } +}); diff --git a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css new file mode 100644 index 0000000000000..35ef14e5aaf1a --- /dev/null +++ b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css @@ -0,0 +1,182 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +div#application-timeline, div#job-timeline { + margin-bottom: 30px; +} + +#application-timeline div.legend-area { + margin-top: 5px; +} + +.vis.timeline div.content { + width: 100%; +} + +.vis.timeline .item.stage { + cursor: pointer; +} + +.vis.timeline .item.stage.succeeded { + background-color: #D5DDF6; +} + +.vis.timeline .item.stage.succeeded.selected { + background-color: #D5DDF6; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.completed-stage-legend { + fill: #D5DDF6; + stroke: #97B0F8; +} + +.vis.timeline .item.stage.failed { + background-color: #FF5475; +} + +.vis.timeline .item.stage.failed.selected { + background-color: #FF5475; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.failed-stage-legend { + fill: #FF5475; + stroke: #97B0F8; +} + +.vis.timeline .item.stage.running { + background-color: #FDFFCA; +} + +.vis.timeline .item.stage.running.selected { + background-color: #FDFFCA; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.active-stage-legend { + fill: #FDFFCA; + stroke: #97B0F8; +} + +.vis.timeline .item.job { + cursor: pointer; +} + +.vis.timeline .item.job.succeeded { + background-color: #D5DDF6; +} + +.vis.timeline .item.job.succeeded.selected { + background-color: #D5DDF6; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.succeeded-job-legend { + fill: #D5DDF6; + stroke: #97B0F8; +} + +.vis.timeline .item.job.failed { + background-color: #FF5475; +} + +.vis.timeline .item.job.failed.selected { + background-color: #FF5475; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.failed-job-legend { + fill: #FF5475; + stroke: #97B0F8; +} + +.vis.timeline .item.job.running { + background-color: #FDFFCA; +} + +.vis.timeline .item.job.running.selected { + background-color: #FDFFCA; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.running-job-legend { + fill: #FDFFCA; + stroke: #97B0F8; +} + +.vis.timeline .item.executor.added { + background-color: #D5DDF6; +} + +.legend-area rect.executor-added-legend { + fill: #D5DDF6; + stroke: #97B0F8; +} + +.vis.timeline .item.executor.removed { + background-color: #EBCA59; +} + +.legend-area rect.executor-removed-legend { + fill: #EBCA59; + stroke: #97B0F8; +} + +.vis.timeline .item.executor.selected { + border-color: #FFC200; + background-color: #FFF785; + z-index: 2; +} + +tr.corresponding-item-hover>td, tr.corresponding-item-hover>th { + background-color: #FFE1FA !important; +} + +#application-timeline.collapsed { + display: none; +} + +#job-timeline.collapsed { + display: none; +} + +.control-panel { + margin-bottom: 5px; +} + +span.expand-application-timeline, span.expand-job-timeline { + cursor: pointer; +} + +.control-panel input+span { + cursor: pointer; +} + +.vis.timeline .item.range .content { + position: unset; +} + +.vis.timeline .item .tooltip-inner { + max-width: unset !important; +} diff --git a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js new file mode 100644 index 0000000000000..e4a891d47f035 --- /dev/null +++ b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js @@ -0,0 +1,168 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +function drawApplicationTimeline(groupArray, eventObjArray, startTime) { + var groups = new vis.DataSet(groupArray); + var items = new vis.DataSet(eventObjArray); + var container = $("#application-timeline")[0]; + var options = { + groupOrder: function(a, b) { + return a.value - b.value + }, + editable: false, + showCurrentTime: false, + min: startTime, + zoomable: false + }; + + var applicationTimeline = new vis.Timeline(container); + applicationTimeline.setOptions(options); + applicationTimeline.setGroups(groups); + applicationTimeline.setItems(items); + + setupZoomable("#application-timeline-zoom-lock", applicationTimeline); + setupExecutorEventAction(); + + function setupJobEventAction() { + $(".item.range.job.application-timeline-object").each(function() { + var getJobId = function(baseElem) { + var jobIdText = $($(baseElem).find(".application-timeline-content")[0]).text(); + var jobId = jobIdText.match("\\(Job (\\d+)\\)")[1]; + return jobId; + }; + + $(this).click(function() { + window.location.href = "job/?id=" + getJobId(this); + }); + + $(this).hover( + function() { + $("#job-" + getJobId(this)).addClass("corresponding-item-hover"); + $($(this).find("div.application-timeline-content")[0]).tooltip("show"); + }, + function() { + $("#job-" + getJobId(this)).removeClass("corresponding-item-hover"); + $($(this).find("div.application-timeline-content")[0]).tooltip("hide"); + } + ); + }); + } + + setupJobEventAction(); + + $("span.expand-application-timeline").click(function() { + $("#application-timeline").toggleClass('collapsed'); + + // Switch the class of the arrow from open to closed. + $(this).find('.expand-application-timeline-arrow').toggleClass('arrow-open'); + $(this).find('.expand-application-timeline-arrow').toggleClass('arrow-closed'); + }); +} + +function drawJobTimeline(groupArray, eventObjArray, startTime) { + var groups = new vis.DataSet(groupArray); + var items = new vis.DataSet(eventObjArray); + var container = $('#job-timeline')[0]; + var options = { + groupOrder: function(a, b) { + return a.value - b.value; + }, + editable: false, + showCurrentTime: false, + min: startTime, + zoomable: false, + }; + + var jobTimeline = new vis.Timeline(container); + jobTimeline.setOptions(options); + jobTimeline.setGroups(groups); + jobTimeline.setItems(items); + + setupZoomable("#job-timeline-zoom-lock", jobTimeline); + setupExecutorEventAction(); + + function setupStageEventAction() { + $(".item.range.stage.job-timeline-object").each(function() { + var getStageIdAndAttempt = function(baseElem) { + var stageIdText = $($(baseElem).find(".job-timeline-content")[0]).text(); + var stageIdAndAttempt = stageIdText.match("\\(Stage (\\d+\\.\\d+)\\)")[1].split("."); + return stageIdAndAttempt; + }; + + $(this).click(function() { + var idAndAttempt = getStageIdAndAttempt(this); + var id = idAndAttempt[0]; + var attempt = idAndAttempt[1]; + window.location.href = "../../stages/stage/?id=" + id + "&attempt=" + attempt; + }); + + $(this).hover( + function() { + var idAndAttempt = getStageIdAndAttempt(this); + var id = idAndAttempt[0]; + var attempt = idAndAttempt[1]; + $("#stage-" + id + "-" + attempt).addClass("corresponding-item-hover"); + $($(this).find("div.job-timeline-content")[0]).tooltip("show"); + }, + function() { + var idAndAttempt = getStageIdAndAttempt(this); + var id = idAndAttempt[0]; + var attempt = idAndAttempt[1]; + $("#stage-" + id + "-" + attempt).removeClass("corresponding-item-hover"); + $($(this).find("div.job-timeline-content")[0]).tooltip("hide"); + } + ); + }); + } + + setupStageEventAction(); + + $("span.expand-job-timeline").click(function() { + $("#job-timeline").toggleClass('collapsed'); + + // Switch the class of the arrow from open to closed. + $(this).find('.expand-job-timeline-arrow').toggleClass('arrow-open'); + $(this).find('.expand-job-timeline-arrow').toggleClass('arrow-closed'); + }); +} + +function setupExecutorEventAction() { + $(".item.box.executor").each(function () { + $(this).hover( + function() { + $($(this).find(".executor-event-content")[0]).tooltip("show"); + }, + function() { + $($(this).find(".executor-event-content")[0]).tooltip("hide"); + } + ); + }); +} + +function setupZoomable(id, timeline) { + $(id + '>input[type="checkbox"]').click(function() { + if (this.checked) { + timeline.setOptions({zoomable: false}); + } else { + timeline.setOptions({zoomable: true}); + } + }); + + $(id + ">span").click(function() { + $(this).parent().find('input:checkbox').trigger('click'); + }); +} diff --git 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m=(t-p)/(u-p),g=240*m,v=this._hsv2rgb(g,1,1);c.strokeStyle=v,c.beginPath(),c.moveTo(h,r+t),c.lineTo(a,r+t),c.stroke()}c.strokeStyle=this.colorAxis,c.strokeRect(h,r,i,n)}if(this.style===s.STYLE.DOTSIZE&&(c.strokeStyle=this.colorAxis,c.fillStyle=this.colorDot,c.beginPath(),c.moveTo(h,r),c.lineTo(a,r),c.lineTo(a-i+e,d),c.lineTo(h,d),c.closePath(),c.fill(),c.stroke()),this.style===s.STYLE.DOTCOLOR||this.style===s.STYLE.DOTSIZE){var y=5,b=new f(this.valueMin,this.valueMax,(this.valueMax-this.valueMin)/5,!0);for(b.start(),b.getCurrent()0?this.yMin:this.yMax,o=this._convert3Dto2D(new l(x,r,this.zMin)),Math.cos(2*_)>0?(g.textAlign="center",g.textBaseline="top",o.y+=b):Math.sin(2*_)<0?(g.textAlign="right",g.textBaseline="middle"):(g.textAlign="left",g.textBaseline="middle"),g.fillStyle=this.colorAxis,g.fillText(" "+this.xValueLabel(i.getCurrent())+" ",o.x,o.y),i.next()}for(g.lineWidth=1,s=void 0===this.defaultYStep,i=new f(this.yMin,this.yMax,this.yStep,s),i.start(),i.getCurrent()0?this.xMin:this.xMax,o=this._convert3Dto2D(new l(n,i.getCurrent(),this.zMin)),Math.cos(2*_)<0?(g.textAlign="center",g.textBaseline="top",o.y+=b):Math.sin(2*_)>0?(g.textAlign="right",g.textBaseline="middle"):(g.textAlign="left",g.textBaseline="middle"),g.fillStyle=this.colorAxis,g.fillText(" "+this.yValueLabel(i.getCurrent())+" ",o.x,o.y),i.next();for(g.lineWidth=1,s=void 0===this.defaultZStep,i=new f(this.zMin,this.zMax,this.zStep,s),i.start(),i.getCurrent()0?this.xMin:this.xMax,r=Math.sin(_)<0?this.yMin:this.yMax;!i.end();)t=this._convert3Dto2D(new l(n,r,i.getCurrent())),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(t.x,t.y),g.lineTo(t.x-b,t.y),g.stroke(),g.textAlign="right",g.textBaseline="middle",g.fillStyle=this.colorAxis,g.fillText(this.zValueLabel(i.getCurrent())+" ",t.x-5,t.y),i.next();g.lineWidth=1,t=this._convert3Dto2D(new l(n,r,this.zMin)),e=this._convert3Dto2D(new l(n,r,this.zMax)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(t.x,t.y),g.lineTo(e.x,e.y),g.stroke(),g.lineWidth=1,p=this._convert3Dto2D(new l(this.xMin,this.yMin,this.zMin)),u=this._convert3Dto2D(new l(this.xMax,this.yMin,this.zMin)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(p.x,p.y),g.lineTo(u.x,u.y),g.stroke(),p=this._convert3Dto2D(new l(this.xMin,this.yMax,this.zMin)),u=this._convert3Dto2D(new l(this.xMax,this.yMax,this.zMin)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(p.x,p.y),g.lineTo(u.x,u.y),g.stroke(),g.lineWidth=1,t=this._convert3Dto2D(new l(this.xMin,this.yMin,this.zMin)),e=this._convert3Dto2D(new l(this.xMin,this.yMax,this.zMin)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(t.x,t.y),g.lineTo(e.x,e.y),g.stroke(),t=this._convert3Dto2D(new l(this.xMax,this.yMin,this.zMin)),e=this._convert3Dto2D(new l(this.xMax,this.yMax,this.zMin)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(t.x,t.y),g.lineTo(e.x,e.y),g.stroke();var w=this.xLabel;w.length>0&&(c=.1/this.scale.y,n=(this.xMin+this.xMax)/2,r=Math.cos(_)>0?this.yMin-c:this.yMax+c,o=this._convert3Dto2D(new l(n,r,this.zMin)),Math.cos(2*_)>0?(g.textAlign="center",g.textBaseline="top"):Math.sin(2*_)<0?(g.textAlign="right",g.textBaseline="middle"):(g.textAlign="left",g.textBaseline="middle"),g.fillStyle=this.colorAxis,g.fillText(w,o.x,o.y));var S=this.yLabel;S.length>0&&(d=.1/this.scale.x,n=Math.sin(_)>0?this.xMin-d:this.xMax+d,r=(this.yMin+this.yMax)/2,o=this._convert3Dto2D(new l(n,r,this.zMin)),Math.cos(2*_)<0?(g.textAlign="center",g.textBaseline="top"):Math.sin(2*_)>0?(g.textAlign="right",g.textBaseline="middle"):(g.textAlign="left",g.textBaseline="middle"),g.fillStyle=this.colorAxis,g.fillText(S,o.x,o.y));var M=this.zLabel;M.length>0&&(h=30,n=Math.cos(_)>0?this.xMin:this.xMax,r=Math.sin(_)<0?this.yMin:this.yMax,a=(this.zMin+this.zMax)/2,o=this._convert3Dto2D(new l(n,r,a)),g.textAlign="right",g.textBaseline="middle",g.fillStyle=this.colorAxis,g.fillText(M,o.x-h,o.y))},s.prototype._hsv2rgb=function(t,e,i){var s,o,n,r,a,h;switch(r=i*e,a=Math.floor(t/60),h=r*(1-Math.abs(t/60%2-1)),a){case 0:s=r,o=h,n=0;break;case 1:s=h,o=r,n=0;break;case 2:s=0,o=r,n=h;break;case 3:s=0,o=h,n=r;break;case 4:s=h,o=0,n=r;break;case 5:s=r,o=0,n=h;break;default:s=0,o=0,n=0}return"RGB("+parseInt(255*s)+","+parseInt(255*o)+","+parseInt(255*n)+")"},s.prototype._redrawDataGrid=function(){var t,e,i,o,n,r,a,h,d,c,p,u,m,f=this.frame.canvas,g=f.getContext("2d");if(!(void 0===this.dataPoints||this.dataPoints.length<=0)){for(n=0;n0}else r=!0;r?(m=(t.point.z+e.point.z+i.point.z+o.point.z)/4,c=240*(1-(m-this.zMin)*this.scale.z/this.verticalRatio),p=1,this.showShadow?(u=Math.min(1+S.x/M/2,1),a=this._hsv2rgb(c,p,u),h=a):(u=1,a=this._hsv2rgb(c,p,u),h=this.colorAxis)):(a="gray",h=this.colorAxis),d=.5,g.lineWidth=d,g.fillStyle=a,g.strokeStyle=h,g.beginPath(),g.moveTo(t.screen.x,t.screen.y),g.lineTo(e.screen.x,e.screen.y),g.lineTo(o.screen.x,o.screen.y),g.lineTo(i.screen.x,i.screen.y),g.closePath(),g.fill(),g.stroke()}}else for(n=0;np&&(p=0);var u,m,f;this.style===s.STYLE.DOTCOLOR?(u=240*(1-(d.point.value-this.valueMin)*this.scale.value),m=this._hsv2rgb(u,1,1),f=this._hsv2rgb(u,1,.8)):this.style===s.STYLE.DOTSIZE?(m=this.colorDot,f=this.colorDotBorder):(u=240*(1-(d.point.z-this.zMin)*this.scale.z/this.verticalRatio),m=this._hsv2rgb(u,1,1),f=this._hsv2rgb(u,1,.8)),i.lineWidth=1,i.strokeStyle=f,i.fillStyle=m,i.beginPath(),i.arc(d.screen.x,d.screen.y,p,0,2*Math.PI,!0),i.fill(),i.stroke()}}},s.prototype._redrawDataBar=function(){var t,e,i,o,n=this.frame.canvas,r=n.getContext("2d");if(!(void 0===this.dataPoints||this.dataPoints.length<=0)){for(t=0;t0&&(t=this.dataPoints[0],s.lineWidth=1,s.strokeStyle="blue",s.beginPath(),s.moveTo(t.screen.x,t.screen.y)),e=1;e0&&s.stroke()}},s.prototype._onMouseDown=function(t){if(t=t||window.event,this.leftButtonDown&&this._onMouseUp(t),this.leftButtonDown=t.which?1===t.which:1===t.button,this.leftButtonDown||this.touchDown){this.startMouseX=o(t),this.startMouseY=n(t),this.startStart=new Date(this.start),this.startEnd=new Date(this.end),this.startArmRotation=this.camera.getArmRotation(),this.frame.style.cursor="move";var e=this;this.onmousemove=function(t){e._onMouseMove(t)},this.onmouseup=function(t){e._onMouseUp(t)},d.addEventListener(document,"mousemove",e.onmousemove),d.addEventListener(document,"mouseup",e.onmouseup),d.preventDefault(t)}},s.prototype._onMouseMove=function(t){t=t||window.event;var e=parseFloat(o(t))-this.startMouseX,i=parseFloat(n(t))-this.startMouseY,s=this.startArmRotation.horizontal+e/200,r=this.startArmRotation.vertical+i/200,a=4,h=Math.sin(a/360*2*Math.PI);Math.abs(Math.sin(s))0?1:0>t?-1:0}var s=e[0],o=e[1],n=e[2],r=i((o.x-s.x)*(t.y-s.y)-(o.y-s.y)*(t.x-s.x)),a=i((n.x-o.x)*(t.y-o.y)-(n.y-o.y)*(t.x-o.x)),h=i((s.x-n.x)*(t.y-n.y)-(s.y-n.y)*(t.x-n.x));return!(0!=r&&0!=a&&r!=a||0!=a&&0!=h&&a!=h||0!=r&&0!=h&&r!=h)},s.prototype._dataPointFromXY=function(t,e){var i,o=100,n=null,r=null,a=null,h=new c(t,e);if(this.style===s.STYLE.BAR||this.style===s.STYLE.BARCOLOR||this.style===s.STYLE.BARSIZE)for(i=this.dataPoints.length-1;i>=0;i--){n=this.dataPoints[i];var d=n.surfaces;if(d)for(var l=d.length-1;l>=0;l--){var p=d[l],u=p.corners,m=[u[0].screen,u[1].screen,u[2].screen],f=[u[2].screen,u[3].screen,u[0].screen];if(this._insideTriangle(h,m)||this._insideTriangle(h,f))return n}}else for(i=0;ib)&&o>b&&(a=b,r=n)}}return r},s.prototype._showTooltip=function(t){var e,i,s;this.tooltip?(e=this.tooltip.dom.content,i=this.tooltip.dom.line,s=this.tooltip.dom.dot):(e=document.createElement("div"),e.style.position="absolute",e.style.padding="10px",e.style.border="1px solid #4d4d4d",e.style.color="#1a1a1a",e.style.background="rgba(255,255,255,0.7)",e.style.borderRadius="2px",e.style.boxShadow="5px 5px 10px rgba(128,128,128,0.5)",i=document.createElement("div"),i.style.position="absolute",i.style.height="40px",i.style.width="0",i.style.borderLeft="1px solid #4d4d4d",s=document.createElement("div"),s.style.position="absolute",s.style.height="0",s.style.width="0",s.style.border="5px solid #4d4d4d",s.style.borderRadius="5px",this.tooltip={dataPoint:null,dom:{content:e,line:i,dot:s}}),this._hideTooltip(),this.tooltip.dataPoint=t,e.innerHTML="function"==typeof this.showTooltip?this.showTooltip(t.point):"
x:"+t.point.x+"
y:"+t.point.y+"
z:"+t.point.z+"
",e.style.left="0",e.style.top="0",this.frame.appendChild(e),this.frame.appendChild(i),this.frame.appendChild(s);var o=e.offsetWidth,n=e.offsetHeight,r=i.offsetHeight,a=s.offsetWidth,h=s.offsetHeight,d=t.screen.x-o/2;d=Math.min(Math.max(d,10),this.frame.clientWidth-10-o),i.style.left=t.screen.x+"px",i.style.top=t.screen.y-r+"px",e.style.left=d+"px",e.style.top=t.screen.y-r-n+"px",s.style.left=t.screen.x-a/2+"px",s.style.top=t.screen.y-h/2+"px"},s.prototype._hideTooltip=function(){if(this.tooltip){this.tooltip.dataPoint=null;for(var t in this.tooltip.dom)if(this.tooltip.dom.hasOwnProperty(t)){var e=this.tooltip.dom[t];e&&e.parentNode&&e.parentNode.removeChild(e)}}},t.exports=s},function(t,e,i){function s(){this.armLocation=new o,this.armRotation={},this.armRotation.horizontal=0,this.armRotation.vertical=0,this.armLength=1.7,this.cameraLocation=new o,this.cameraRotation=new o(.5*Math.PI,0,0),this.calculateCameraOrientation()}var o=i(10);s.prototype.setArmLocation=function(t,e,i){this.armLocation.x=t,this.armLocation.y=e,this.armLocation.z=i,this.calculateCameraOrientation()},s.prototype.setArmRotation=function(t,e){void 0!==t&&(this.armRotation.horizontal=t),void 0!==e&&(this.armRotation.vertical=e,this.armRotation.vertical<0&&(this.armRotation.vertical=0),this.armRotation.vertical>.5*Math.PI&&(this.armRotation.vertical=.5*Math.PI)),(void 0!==t||void 0!==e)&&this.calculateCameraOrientation()},s.prototype.getArmRotation=function(){var t={};return t.horizontal=this.armRotation.horizontal,t.vertical=this.armRotation.vertical,t},s.prototype.setArmLength=function(t){void 0!==t&&(this.armLength=t,this.armLength<.71&&(this.armLength=.71),this.armLength>5&&(this.armLength=5),this.calculateCameraOrientation())},s.prototype.getArmLength=function(){return this.armLength},s.prototype.getCameraLocation=function(){return this.cameraLocation},s.prototype.getCameraRotation=function(){return this.cameraRotation},s.prototype.calculateCameraOrientation=function(){this.cameraLocation.x=this.armLocation.x-this.armLength*Math.sin(this.armRotation.horizontal)*Math.cos(this.armRotation.vertical),this.cameraLocation.y=this.armLocation.y-this.armLength*Math.cos(this.armRotation.horizontal)*Math.cos(this.armRotation.vertical),this.cameraLocation.z=this.armLocation.z+this.armLength*Math.sin(this.armRotation.vertical),this.cameraRotation.x=Math.PI/2-this.armRotation.vertical,this.cameraRotation.y=0,this.cameraRotation.z=-this.armRotation.horizontal},t.exports=s},function(t,e,i){function s(t,e,i){this.data=t,this.column=e,this.graph=i,this.index=void 0,this.value=void 0,this.values=i.getDistinctValues(t.get(),this.column),this.values.sort(function(t,e){return t>e?1:e>t?-1:0}),this.values.length>0&&this.selectValue(0),this.dataPoints=[],this.loaded=!1,this.onLoadCallback=void 0,i.animationPreload?(this.loaded=!1,this.loadInBackground()):this.loaded=!0}var o=i(4);s.prototype.isLoaded=function(){return this.loaded},s.prototype.getLoadedProgress=function(){for(var t=this.values.length,e=0;this.dataPoints[e];)e++;return Math.round(e/t*100)},s.prototype.getLabel=function(){return this.graph.filterLabel},s.prototype.getColumn=function(){return this.column},s.prototype.getSelectedValue=function(){return void 0===this.index?void 0:this.values[this.index]},s.prototype.getValues=function(){return this.values},s.prototype.getValue=function(t){if(t>=this.values.length)throw"Error: index out of range";return this.values[t]},s.prototype._getDataPoints=function(t){if(void 0===t&&(t=this.index),void 0===t)return[];var e;if(this.dataPoints[t])e=this.dataPoints[t];else{var i={};i.column=this.column,i.value=this.values[t];var s=new o(this.data,{filter:function(t){return t[i.column]==i.value}}).get();e=this.graph._getDataPoints(s),this.dataPoints[t]=e}return e},s.prototype.setOnLoadCallback=function(t){this.onLoadCallback=t},s.prototype.selectValue=function(t){if(t>=this.values.length)throw"Error: index out of range";this.index=t,this.value=this.values[t]},s.prototype.loadInBackground=function(t){void 0===t&&(t=0);var e=this.graph.frame;if(t0&&(t--,this.setIndex(t))},s.prototype.next=function(){var t=this.getIndex();t0?this.setIndex(0):this.index=void 0},s.prototype.setIndex=function(t){if(!(ts&&(s=0),s>this.values.length-1&&(s=this.values.length-1),s},s.prototype.indexToLeft=function(t){var e=parseFloat(this.frame.bar.style.width)-this.frame.slide.clientWidth-10,i=t/(this.values.length-1)*e,s=i+3;return s},s.prototype._onMouseMove=function(t){var e=t.clientX-this.startClientX,i=this.startSlideX+e,s=this.leftToIndex(i);this.setIndex(s),o.preventDefault()},s.prototype._onMouseUp=function(){this.frame.style.cursor="auto",o.removeEventListener(document,"mousemove",this.onmousemove),o.removeEventListener(document,"mouseup",this.onmouseup),o.preventDefault()},t.exports=s},function(t){function e(t,e,i,s){this._start=0,this._end=0,this._step=1,this.prettyStep=!0,this.precision=5,this._current=0,this.setRange(t,e,i,s)}e.prototype.setRange=function(t,e,i,s){this._start=t?t:0,this._end=e?e:0,this.setStep(i,s)},e.prototype.setStep=function(t,i){void 0===t||0>=t||(void 0!==i&&(this.prettyStep=i),this._step=this.prettyStep===!0?e.calculatePrettyStep(t):t)},e.calculatePrettyStep=function(t){var e=function(t){return Math.log(t)/Math.LN10},i=Math.pow(10,Math.round(e(t))),s=2*Math.pow(10,Math.round(e(t/2))),o=5*Math.pow(10,Math.round(e(t/5))),n=i;return Math.abs(s-t)<=Math.abs(n-t)&&(n=s),Math.abs(o-t)<=Math.abs(n-t)&&(n=o),0>=n&&(n=1),n},e.prototype.getCurrent=function(){return parseFloat(this._current.toPrecision(this.precision))},e.prototype.getStep=function(){return this._step},e.prototype.start=function(){this._current=this._start-this._start%this._step},e.prototype.next=function(){this._current+=this._step},e.prototype.end=function(){return this._current>this._end},t.exports=e},function(t,e,i){function s(t,e,i,r){if(!(this instanceof s))throw new SyntaxError("Constructor must be called with the new operator");if(!(Array.isArray(i)||i instanceof n)&&i instanceof Object){var h=r;r=i,i=h}var u=this;this.defaultOptions={start:null,end:null,autoResize:!0,orientation:"bottom",width:null,height:null,maxHeight:null,minHeight:null},this.options=o.deepExtend({},this.defaultOptions),this._create(t),this.components=[],this.body={dom:this.dom,domProps:this.props,emitter:{on:this.on.bind(this),off:this.off.bind(this),emit:this.emit.bind(this)},hiddenDates:[],util:{snap:null,toScreen:u._toScreen.bind(u),toGlobalScreen:u._toGlobalScreen.bind(u),toTime:u._toTime.bind(u),toGlobalTime:u._toGlobalTime.bind(u)}},this.range=new a(this.body),this.components.push(this.range),this.body.range=this.range,this.timeAxis=new d(this.body),this.components.push(this.timeAxis),this.body.util.snap=this.timeAxis.snap.bind(this.timeAxis),this.currentTime=new l(this.body),this.components.push(this.currentTime),this.customTime=new c(this.body),this.components.push(this.customTime),this.itemSet=new p(this.body),this.components.push(this.itemSet),this.itemsData=null,this.groupsData=null,r&&this.setOptions(r),i&&this.setGroups(i),e?this.setItems(e):this.redraw()}var o=(i(56),i(45),i(1)),n=i(3),r=i(4),a=i(17),h=i(46),d=i(30),l=i(21),c=i(22),p=i(27);s.prototype=new h,s.prototype.setItems=function(t){var e,i=null==this.itemsData;if(e=t?t instanceof n||t instanceof r?t:new n(t,{type:{start:"Date",end:"Date"}}):null,this.itemsData=e,this.itemSet&&this.itemSet.setItems(e),i)if(void 0!=this.options.start||void 0!=this.options.end){if(void 0==this.options.start||void 0==this.options.end)var s=this._getDataRange();var o=void 0!=this.options.start?this.options.start:s.start,a=void 0!=this.options.end?this.options.end:s.end;this.setWindow(o,a,{animate:!1})}else this.fit({animate:!1})},s.prototype.setGroups=function(t){var e;e=t?t instanceof n||t instanceof r?t:new n(t):null,this.groupsData=e,this.itemSet.setGroups(e)},s.prototype.setSelection=function(t,e){this.itemSet&&this.itemSet.setSelection(t),e&&e.focus&&this.focus(t,e)},s.prototype.getSelection=function(){return this.itemSet&&this.itemSet.getSelection()||[]},s.prototype.focus=function(t,e){if(this.itemsData&&void 0!=t){var i=Array.isArray(t)?t:[t],s=this.itemsData.getDataSet().get(i,{type:{start:"Date",end:"Date"}}),o=null,n=null;if(s.forEach(function(t){var e=t.start.valueOf(),i="end"in t?t.end.valueOf():t.start.valueOf();(null===o||o>e)&&(o=e),(null===n||i>n)&&(n=i)}),null!==o&&null!==n){var r=(o+n)/2,a=Math.max(this.range.end-this.range.start,1.1*(n-o)),h=e&&void 0!==e.animate?e.animate:!0;this.range.setRange(r-a/2,r+a/2,h)}}},s.prototype.getItemRange=function(){var t=this.itemsData.getDataSet(),e=null,i=null;if(t){var s=t.min("start");e=s?o.convert(s.start,"Date").valueOf():null;var n=t.max("start");n&&(i=o.convert(n.start,"Date").valueOf());var r=t.max("end");r&&(i=null==i?o.convert(r.end,"Date").valueOf():Math.max(i,o.convert(r.end,"Date").valueOf()))}return{min:null!=e?new Date(e):null,max:null!=i?new Date(i):null}},t.exports=s},function(t,e,i){function s(t,e,i,s){if(!(Array.isArray(i)||i instanceof n)&&i instanceof Object){var r=s;s=i,i=r}var h=this;this.defaultOptions={start:null,end:null,autoResize:!0,orientation:"bottom",width:null,height:null,maxHeight:null,minHeight:null},this.options=o.deepExtend({},this.defaultOptions),this._create(t),this.components=[],this.body={dom:this.dom,domProps:this.props,emitter:{on:this.on.bind(this),off:this.off.bind(this),emit:this.emit.bind(this)},hiddenDates:[],util:{snap:null,toScreen:h._toScreen.bind(h),toGlobalScreen:h._toGlobalScreen.bind(h),toTime:h._toTime.bind(h),toGlobalTime:h._toGlobalTime.bind(h)}},this.range=new a(this.body),this.components.push(this.range),this.body.range=this.range,this.timeAxis=new d(this.body),this.components.push(this.timeAxis),this.body.util.snap=this.timeAxis.snap.bind(this.timeAxis),this.currentTime=new l(this.body),this.components.push(this.currentTime),this.customTime=new c(this.body),this.components.push(this.customTime),this.linegraph=new p(this.body),this.components.push(this.linegraph),this.itemsData=null,this.groupsData=null,s&&this.setOptions(s),i&&this.setGroups(i),e?this.setItems(e):this.redraw()}var o=(i(56),i(45),i(1)),n=i(3),r=i(4),a=i(17),h=i(46),d=i(30),l=i(21),c=i(22),p=i(29);s.prototype=new h,s.prototype.setItems=function(t){var e,i=null==this.itemsData;if(e=t?t instanceof n||t instanceof r?t:new n(t,{type:{start:"Date",end:"Date"}}):null,this.itemsData=e,this.linegraph&&this.linegraph.setItems(e),i)if(void 0!=this.options.start||void 0!=this.options.end){var s=void 0!=this.options.start?this.options.start:null,o=void 0!=this.options.end?this.options.end:null;this.setWindow(s,o,{animate:!1})}else this.fit({animate:!1})},s.prototype.setGroups=function(t){var e;e=t?t instanceof n||t instanceof r?t:new n(t):null,this.groupsData=e,this.linegraph.setGroups(e)},s.prototype.getLegend=function(t,e,i){return void 0===e&&(e=15),void 0===i&&(i=15),void 0!==this.linegraph.groups[t]?this.linegraph.groups[t].getLegend(e,i):"cannot find group:"+t},s.prototype.isGroupVisible=function(t){return void 0!==this.linegraph.groups[t]?this.linegraph.groups[t].visible&&(void 0===this.linegraph.options.groups.visibility[t]||1==this.linegraph.options.groups.visibility[t]):!1},s.prototype.getItemRange=function(){var t=null,e=null;for(var i in this.linegraph.groups)if(this.linegraph.groups.hasOwnProperty(i)&&1==this.linegraph.groups[i].visible)for(var s=0;sr?r:t,e=null==e?r:r>e?r:e}return{min:null!=t?new Date(t):null,max:null!=e?new Date(e):null}},t.exports=s},function(t,e,i){var s=i(44);e.convertHiddenOptions=function(t,e){if(t.hiddenDates=[],e&&1==Array.isArray(e)){for(var i=0;i=4*a){var p=0,u=n.clone();switch(i[h].repeat){case"daily":d.day()!=l.day()&&(p=1),d.dayOfYear(o.dayOfYear()),d.year(o.year()),d.subtract(7,"days"),l.dayOfYear(o.dayOfYear()),l.year(o.year()),l.subtract(7-p,"days"),u.add(1,"weeks");break;case"weekly":var m=l.diff(d,"days"),f=d.day();d.date(o.date()),d.month(o.month()),d.year(o.year()),l=d.clone(),d.day(f),l.day(f),l.add(m,"days"),d.subtract(1,"weeks"),l.subtract(1,"weeks"),u.add(1,"weeks");break;case"monthly":d.month()!=l.month()&&(p=1),d.month(o.month()),d.year(o.year()),d.subtract(1,"months"),l.month(o.month()),l.year(o.year()),l.subtract(1,"months"),l.add(p,"months"),u.add(1,"months");break;case"yearly":d.year()!=l.year()&&(p=1),d.year(o.year()),d.subtract(1,"years"),l.year(o.year()),l.subtract(1,"years"),l.add(p,"years"),u.add(1,"years");break;default:return void console.log("Wrong repeat format, allowed are: daily, weekly, monthly, yearly. Given:",i[h].repeat)}for(;u>d;)switch(t.hiddenDates.push({start:d.valueOf(),end:l.valueOf()}),i[h].repeat){case"daily":d.add(1,"days"),l.add(1,"days");break;case"weekly":d.add(1,"weeks"),l.add(1,"weeks");break;case"monthly":d.add(1,"months"),l.add(1,"months");break;case"yearly":d.add(1,"y"),l.add(1,"y");break;default:return void console.log("Wrong repeat format, allowed are: daily, weekly, monthly, yearly. Given:",i[h].repeat)}t.hiddenDates.push({start:d.valueOf(),end:l.valueOf()})}}e.removeDuplicates(t);var g=e.isHidden(t.range.start,t.hiddenDates),v=e.isHidden(t.range.end,t.hiddenDates),y=t.range.start,b=t.range.end;1==g.hidden&&(y=1==t.range.startToFront?g.startDate-1:g.endDate+1),1==v.hidden&&(b=1==t.range.endToFront?v.startDate-1:v.endDate+1),(1==g.hidden||1==v.hidden)&&t.range._applyRange(y,b)}},e.removeDuplicates=function(t){for(var e=t.hiddenDates,i=[],s=0;s=e[s].start&&e[o].end<=e[s].end?e[o].remove=!0:e[o].start>=e[s].start&&e[o].start<=e[s].end?(e[s].end=e[o].end,e[o].remove=!0):e[o].end>=e[s].start&&e[o].end<=e[s].end&&(e[s].start=e[o].start,e[o].remove=!0));for(var s=0;s=r&&a>o){i=!0;break}}if(1==i&&o=e&&i>r&&(s+=r-n)}return s},e.correctTimeForHidden=function(t,i,o){return o=s(o).toDate().valueOf(),o-=e.getHiddenDurationBefore(t,i,o)},e.getHiddenDurationBefore=function(t,e,i){var o=0;i=s(i).toDate().valueOf();for(var n=0;n=e.start&&a=a&&(o+=a-r)}return o},e.getAccumulatedHiddenDuration=function(t,e,i){for(var s=0,o=0,n=e.start,r=0;r=e.start&&h=i)break;s+=h-a}}return s},e.snapAwayFromHidden=function(t,i,s,o){var n=e.isHidden(i,t);return 1==n.hidden?0>s?1==o?n.startDate-(n.endDate-i)-1:n.startDate-1:1==o?n.endDate+(i-n.startDate)+1:n.endDate+1:i},e.isHidden=function(t,e){for(var i=0;i=s&&o>t)return{hidden:!0,startDate:s,endDate:o}}return{hidden:!1,startDate:s,endDate:o}}},function(t){function e(t,e,i,s,o,n){this.current=0,this.autoScale=!0,this.stepIndex=0,this.step=1,this.scale=1,this.marginStart,this.marginEnd,this.deadSpace=0,this.majorSteps=[1,2,5,10],this.minorSteps=[.25,.5,1,2],this.alignZeros=n,this.setRange(t,e,i,s,o)}e.prototype.setRange=function(t,e,i,s,o){this._start=void 0===o.min?t:o.min,this._end=void 0===o.max?e:o.max,this._start==this._end&&(this._start-=.75,this._end+=1),1==this.autoScale&&this.setMinimumStep(i,s),this.setFirst(o)},e.prototype.setMinimumStep=function(t,e){var i=this._end-this._start,s=1.2*i,o=t*(s/e),n=Math.round(Math.log(s)/Math.LN10),r=-1,a=Math.pow(10,n),h=0;0>n&&(h=n);for(var d=!1,l=h;Math.abs(l)<=Math.abs(n);l++){a=Math.pow(10,l);for(var c=0;c=o){d=!0,r=c;break}}if(1==d)break}this.stepIndex=r,this.scale=a,this.step=a*this.minorSteps[r]},e.prototype.setFirst=function(t){void 0===t&&(t={});var e=void 0===t.min?this._start-2*this.scale*this.minorSteps[this.stepIndex]:t.min,i=void 0===t.max?this._end+this.scale*this.minorSteps[this.stepIndex]:t.max;this.marginEnd=void 0===t.max?this.roundToMinor(i):t.max,this.marginStart=void 0===t.min?this.roundToMinor(e):t.min,1==this.alignZeros&&(this.marginEnd-this.marginStart)%this.step!=0&&(this.marginEnd+=this.marginEnd%this.step),this.deadSpace=this.roundToMinor(i)-i+this.roundToMinor(e)-e,this.marginRange=this.marginEnd-this.marginStart,this.current=this.marginEnd},e.prototype.roundToMinor=function(t){var e=t-t%(this.scale*this.minorSteps[this.stepIndex]);return t%(this.scale*this.minorSteps[this.stepIndex])>.5*this.scale*this.minorSteps[this.stepIndex]?e+this.scale*this.minorSteps[this.stepIndex]:e},e.prototype.hasNext=function(){return this.current>=this.marginStart},e.prototype.next=function(){var t=this.current;this.current-=this.step,this.current==t&&(this.current=this._end)},e.prototype.previous=function(){this.current+=this.step,this.marginEnd+=this.step,this.marginRange=this.marginEnd-this.marginStart},e.prototype.getCurrent=function(t){var e=Math.abs(this.current)0;s--){if("0"!=i[s]){if("."==i[s]||","==i[s]){i=i.slice(0,s);break}break}i=i.slice(0,s)}}else{var o="",n=i.indexOf("e");if(-1!=n&&(o=i.slice(n),i=i.slice(0,n)),n=Math.max(i.indexOf(","),i.indexOf(".")),-1===n?(0!==t&&(i+="."),n=i.length+t):0!==t&&(n+=t+1),n>i.length)for(var r=n-i.length;r>0;r--)i+="0";else i=i.slice(0,n);i+=o}return i},e.prototype.snap=function(){},e.prototype.isMajor=function(){return this.current%(this.scale*this.majorSteps[this.stepIndex])==0},t.exports=e},function(t,e,i){function s(t,e){var i=a().hours(0).minutes(0).seconds(0).milliseconds(0);this.start=i.clone().add(-3,"days").valueOf(),this.end=i.clone().add(4,"days").valueOf(),this.body=t,this.deltaDifference=0,this.scaleOffset=0,this.startToFront=!1,this.endToFront=!0,this.defaultOptions={start:null,end:null,direction:"horizontal",moveable:!0,zoomable:!0,min:null,max:null,zoomMin:10,zoomMax:31536e10},this.options=r.extend({},this.defaultOptions),this.props={touch:{}},this.animateTimer=null,this.body.emitter.on("panstart",this._onDragStart.bind(this)),this.body.emitter.on("panmove",this._onDrag.bind(this)),this.body.emitter.on("panend",this._onDragEnd.bind(this)),this.body.emitter.on("press",this._onHold.bind(this)),this.body.emitter.on("mousewheel",this._onMouseWheel.bind(this)),this.body.emitter.on("touch",this._onTouch.bind(this)),this.body.emitter.on("pinch",this._onPinch.bind(this)),this.setOptions(e)}function o(t){if("horizontal"!=t&&"vertical"!=t)throw new TypeError('Unknown direction "'+t+'". Choose "horizontal" or "vertical".')}function n(t,e){return{x:t.x-r.getAbsoluteLeft(e),y:t.y-r.getAbsoluteTop(e)}}var r=i(1),a=(i(47),i(44)),h=i(20),d=i(15);s.prototype=new h,s.prototype.setOptions=function(t){if(t){var e=["direction","min","max","zoomMin","zoomMax","moveable","zoomable","activate","hiddenDates"];r.selectiveExtend(e,this.options,t),("start"in t||"end"in t)&&this.setRange(t.start,t.end)}},s.prototype.setRange=function(t,e,i,s){s!==!0&&(s=!1);var o=void 0!=t?r.convert(t,"Date").valueOf():null,n=void 0!=e?r.convert(e,"Date").valueOf():null;if(this._cancelAnimation(),i){var a=this,h=this.start,l=this.end,c="number"==typeof i?i:500,p=(new Date).valueOf(),u=!1,m=function(){if(!a.props.touch.dragging){var t=(new Date).valueOf(),e=t-p,i=e>c,g=i||null===o?o:r.easeInOutQuad(e,h,o,c),v=i||null===n?n:r.easeInOutQuad(e,l,n,c);f=a._applyRange(g,v),d.updateHiddenDates(a.body,a.options.hiddenDates),u=u||f,f&&a.body.emitter.emit("rangechange",{start:new Date(a.start),end:new Date(a.end),byUser:s}),i?u&&a.body.emitter.emit("rangechanged",{start:new Date(a.start),end:new Date(a.end),byUser:s}):a.animateTimer=setTimeout(m,20)}};return m()}var f=this._applyRange(o,n);if(d.updateHiddenDates(this.body,this.options.hiddenDates),f){var g={start:new Date(this.start),end:new Date(this.end),byUser:s};this.body.emitter.emit("rangechange",g),this.body.emitter.emit("rangechanged",g)}},s.prototype._cancelAnimation=function(){this.animateTimer&&(clearTimeout(this.animateTimer),this.animateTimer=null)},s.prototype._applyRange=function(t,e){var i,s=null!=t?r.convert(t,"Date").valueOf():this.start,o=null!=e?r.convert(e,"Date").valueOf():this.end,n=null!=this.options.max?r.convert(this.options.max,"Date").valueOf():null,a=null!=this.options.min?r.convert(this.options.min,"Date").valueOf():null;if(isNaN(s)||null===s)throw new Error('Invalid start "'+t+'"');if(isNaN(o)||null===o)throw new Error('Invalid end "'+e+'"');if(s>o&&(o=s),null!==a&&a>s&&(i=a-s,s+=i,o+=i,null!=n&&o>n&&(o=n)),null!==n&&o>n&&(i=o-n,s-=i,o-=i,null!=a&&a>s&&(s=a)),null!==this.options.zoomMin){var h=parseFloat(this.options.zoomMin);0>h&&(h=0),h>o-s&&(this.end-this.start===h?(s=this.start,o=this.end):(i=h-(o-s),s-=i/2,o+=i/2))}if(null!==this.options.zoomMax){var d=parseFloat(this.options.zoomMax);0>d&&(d=0),o-s>d&&(this.end-this.start===d?(s=this.start,o=this.end):(i=o-s-d,s+=i/2,o-=i/2))}var l=this.start!=s||this.end!=o;return s>=this.start&&s<=this.end||o>=this.start&&o<=this.end||this.start>=s&&this.start<=o||this.end>=s&&this.end<=o||this.body.emitter.emit("checkRangedItems"),this.start=s,this.end=o,l},s.prototype.getRange=function(){return{start:this.start,end:this.end}},s.prototype.conversion=function(t,e){return s.conversion(this.start,this.end,t,e)},s.conversion=function(t,e,i,s){return void 0===s&&(s=0),0!=i&&e-t!=0?{offset:t,scale:i/(e-t-s)}:{offset:0,scale:1}},s.prototype._onDragStart=function(t){this.deltaDifference=0,this.previousDelta=0,this.options.moveable&&this.props.touch.allowDragging&&(this.props.touch.start=this.start,this.props.touch.end=this.end,this.props.touch.dragging=!0,this.body.dom.root&&(this.body.dom.root.style.cursor="move"),t.preventDefault())},s.prototype._onDrag=function(t){if(this.options.moveable&&this.props.touch.allowDragging){var e=this.options.direction;o(e);var i="horizontal"==e?t.deltaX:t.deltaY;i-=this.deltaDifference;var s=this.props.touch.end-this.props.touch.start,n=d.getHiddenDurationBetween(this.body.hiddenDates,this.start,this.end);s-=n;var r="horizontal"==e?this.body.domProps.center.width:this.body.domProps.center.height,a=-i/r*s,h=this.props.touch.start+a,l=this.props.touch.end+a,c=d.snapAwayFromHidden(this.body.hiddenDates,h,this.previousDelta-i,!0),p=d.snapAwayFromHidden(this.body.hiddenDates,l,this.previousDelta-i,!0);if(c!=h||p!=l)return this.deltaDifference+=i,this.props.touch.start=c,this.props.touch.end=p,void this._onDrag(t);this.previousDelta=i,this._applyRange(h,l),this.body.emitter.emit("rangechange",{start:new Date(this.start),end:new Date(this.end),byUser:!0}),t.preventDefault()}},s.prototype._onDragEnd=function(){this.options.moveable&&this.props.touch.allowDragging&&(this.props.touch.dragging=!1,this.body.dom.root&&(this.body.dom.root.style.cursor="auto"),this.body.emitter.emit("rangechanged",{start:new Date(this.start),end:new Date(this.end),byUser:!0}))},s.prototype._onMouseWheel=function(t){if(this.options.zoomable&&this.options.moveable){var e=0;if(t.wheelDelta?e=t.wheelDelta/120:t.detail&&(e=-t.detail/3),e){var i;i=0>e?1-e/5:1/(1+e/5);var s=n({x:t.pageX,y:t.pageY},this.body.dom.center),o=this._pointerToDate(s);this.zoom(i,o,e)}t.preventDefault()}},s.prototype._onTouch=function(){this.props.touch.start=this.start,this.props.touch.end=this.end,this.props.touch.allowDragging=!0,this.props.touch.center=null,this.scaleOffset=0,this.deltaDifference=0},s.prototype._onHold=function(){this.props.touch.allowDragging=!1},s.prototype._onPinch=function(t){if(this.options.zoomable&&this.options.moveable){this.props.touch.allowDragging=!1,this.props.touch.center||(this.props.touch.center=n(t.center,this.body.dom.center));var e=1/(t.scale+this.scaleOffset),i=this._pointerToDate(this.props.touch.center),s=d.getHiddenDurationBetween(this.body.hiddenDates,this.start,this.end),o=d.getHiddenDurationBefore(this.body.hiddenDates,this,i),r=s-o,a=i-o+(this.props.touch.start-(i-o))*e,h=i+r+(this.props.touch.end-(i+r))*e;this.startToFront=0>=1-e,this.endToFront=0>=e-1;var l=d.snapAwayFromHidden(this.body.hiddenDates,a,1-e,!0),c=d.snapAwayFromHidden(this.body.hiddenDates,h,e-1,!0);(l!=a||c!=h)&&(this.props.touch.start=l,this.props.touch.end=c,this.scaleOffset=1-t.scale,a=l,h=c),this.setRange(a,h,!1,!0),this.startToFront=!1,this.endToFront=!0,t.preventDefault()}},s.prototype._pointerToDate=function(t){var e,i=this.options.direction;if(o(i),"horizontal"==i)return this.body.util.toTime(t.x).valueOf();var s=this.body.domProps.center.height;return e=this.conversion(s),t.y/e.scale+e.offset},s.prototype.zoom=function(t,e,i){null==e&&(e=(this.start+this.end)/2);var s=d.getHiddenDurationBetween(this.body.hiddenDates,this.start,this.end),o=d.getHiddenDurationBefore(this.body.hiddenDates,this,e),n=s-o,r=e-o+(this.start-(e-o))*t,a=e+n+(this.end-(e+n))*t;this.startToFront=i>0?!1:!0,this.endToFront=-i>0?!1:!0;var h=d.snapAwayFromHidden(this.body.hiddenDates,r,i,!0),l=d.snapAwayFromHidden(this.body.hiddenDates,a,-i,!0);(h!=r||l!=a)&&(r=h,a=l),this.setRange(r,a,!1,!0),this.startToFront=!1,this.endToFront=!0},s.prototype.move=function(t){var e=this.end-this.start,i=this.start+e*t,s=this.end+e*t;this.start=i,this.end=s},s.prototype.moveTo=function(t){var e=(this.start+this.end)/2,i=e-t,s=this.start-i,o=this.end-i;this.setRange(s,o)},t.exports=s},function(t,e){var i=.001;e.orderByStart=function(t){t.sort(function(t,e){return t.data.start-e.data.start})},e.orderByEnd=function(t){t.sort(function(t,e){var i="end"in t.data?t.data.end:t.data.start,s="end"in e.data?e.data.end:e.data.start;return i-s})},e.stack=function(t,i,s){var o,n;if(s)for(o=0,n=t.length;n>o;o++)t[o].top=null;for(o=0,n=t.length;n>o;o++){var r=t[o];if(r.stack&&null===r.top){r.top=i.axis;do{for(var a=null,h=0,d=t.length;d>h;h++){var l=t[h];if(null!==l.top&&l!==r&&l.stack&&e.collision(r,l,i.item)){a=l;break}}null!=a&&(r.top=a.top+a.height+i.item.vertical)}while(a)}}},e.nostack=function(t,e,i){var s,o,n;for(s=0,o=t.length;o>s;s++)if(void 0!==t[s].data.subgroup){n=e.axis;for(var r in i)i.hasOwnProperty(r)&&1==i[r].visible&&i[r].indexe.left&&t.top-s.vertical+ie.top}},function(t,e,i){function s(t,e,i,o){this.current=new Date,this._start=new Date,this._end=new Date,this.autoScale=!0,this.scale="day",this.step=1,this.setRange(t,e,i),this.switchedDay=!1,this.switchedMonth=!1,this.switchedYear=!1,this.hiddenDates=o,void 0===o&&(this.hiddenDates=[]),this.format=s.FORMAT}var o=i(44),n=i(15),r=i(1);s.FORMAT={minorLabels:{millisecond:"SSS",second:"s",minute:"HH:mm",hour:"HH:mm",weekday:"ddd D",day:"D",month:"MMM",year:"YYYY"},majorLabels:{millisecond:"HH:mm:ss",second:"D MMMM HH:mm",minute:"ddd D MMMM",hour:"ddd D MMMM",weekday:"MMMM YYYY",day:"MMMM YYYY",month:"YYYY",year:""}},s.prototype.setFormat=function(t){var e=r.deepExtend({},s.FORMAT);this.format=r.deepExtend(e,t)},s.prototype.setRange=function(t,e,i){if(!(t instanceof Date&&e instanceof Date))throw"No legal start or end date in method setRange";this._start=void 0!=t?new Date(t.valueOf()):new Date,this._end=void 0!=e?new Date(e.valueOf()):new Date,this.autoScale&&this.setMinimumStep(i)},s.prototype.first=function(){this.current=new Date(this._start.valueOf()),this.roundToMinor()},s.prototype.roundToMinor=function(){switch(this.scale){case"year":this.current.setFullYear(this.step*Math.floor(this.current.getFullYear()/this.step)),this.current.setMonth(0);case"month":this.current.setDate(1);case"day":case"weekday":this.current.setHours(0);case"hour":this.current.setMinutes(0);case"minute":this.current.setSeconds(0);case"second":this.current.setMilliseconds(0)}if(1!=this.step)switch(this.scale){case"millisecond":this.current.setMilliseconds(this.current.getMilliseconds()-this.current.getMilliseconds()%this.step);break;case"second":this.current.setSeconds(this.current.getSeconds()-this.current.getSeconds()%this.step);break;case"minute":this.current.setMinutes(this.current.getMinutes()-this.current.getMinutes()%this.step);break;case"hour":this.current.setHours(this.current.getHours()-this.current.getHours()%this.step);break;case"weekday":case"day":this.current.setDate(this.current.getDate()-1-(this.current.getDate()-1)%this.step+1);break;case"month":this.current.setMonth(this.current.getMonth()-this.current.getMonth()%this.step);break;case"year":this.current.setFullYear(this.current.getFullYear()-this.current.getFullYear()%this.step)}},s.prototype.hasNext=function(){return this.current.valueOf()<=this._end.valueOf()},s.prototype.next=function(){var t=this.current.valueOf();if(this.current.getMonth()<6)switch(this.scale){case"millisecond":this.current=new Date(this.current.valueOf()+this.step);break;case"second":this.current=new Date(this.current.valueOf()+1e3*this.step);break;case"minute":this.current=new Date(this.current.valueOf()+1e3*this.step*60);break;case"hour":this.current=new Date(this.current.valueOf()+1e3*this.step*60*60);var e=this.current.getHours();this.current.setHours(e-e%this.step);break;case"weekday":case"day":this.current.setDate(this.current.getDate()+this.step);break;case"month":this.current.setMonth(this.current.getMonth()+this.step);break;case"year":this.current.setFullYear(this.current.getFullYear()+this.step)}else switch(this.scale){case"millisecond":this.current=new Date(this.current.valueOf()+this.step);break;case"second":this.current.setSeconds(this.current.getSeconds()+this.step);break;case"minute":this.current.setMinutes(this.current.getMinutes()+this.step); +break;case"hour":this.current.setHours(this.current.getHours()+this.step);break;case"weekday":case"day":this.current.setDate(this.current.getDate()+this.step);break;case"month":this.current.setMonth(this.current.getMonth()+this.step);break;case"year":this.current.setFullYear(this.current.getFullYear()+this.step)}if(1!=this.step)switch(this.scale){case"millisecond":this.current.getMilliseconds()0&&(this.step=e),this.autoScale=!1},s.prototype.setAutoScale=function(t){this.autoScale=t},s.prototype.setMinimumStep=function(t){if(void 0!=t){var e=31104e6,i=2592e6,s=864e5,o=36e5,n=6e4,r=1e3,a=1;1e3*e>t&&(this.scale="year",this.step=1e3),500*e>t&&(this.scale="year",this.step=500),100*e>t&&(this.scale="year",this.step=100),50*e>t&&(this.scale="year",this.step=50),10*e>t&&(this.scale="year",this.step=10),5*e>t&&(this.scale="year",this.step=5),e>t&&(this.scale="year",this.step=1),3*i>t&&(this.scale="month",this.step=3),i>t&&(this.scale="month",this.step=1),5*s>t&&(this.scale="day",this.step=5),2*s>t&&(this.scale="day",this.step=2),s>t&&(this.scale="day",this.step=1),s/2>t&&(this.scale="weekday",this.step=1),4*o>t&&(this.scale="hour",this.step=4),o>t&&(this.scale="hour",this.step=1),15*n>t&&(this.scale="minute",this.step=15),10*n>t&&(this.scale="minute",this.step=10),5*n>t&&(this.scale="minute",this.step=5),n>t&&(this.scale="minute",this.step=1),15*r>t&&(this.scale="second",this.step=15),10*r>t&&(this.scale="second",this.step=10),5*r>t&&(this.scale="second",this.step=5),r>t&&(this.scale="second",this.step=1),200*a>t&&(this.scale="millisecond",this.step=200),100*a>t&&(this.scale="millisecond",this.step=100),50*a>t&&(this.scale="millisecond",this.step=50),10*a>t&&(this.scale="millisecond",this.step=10),5*a>t&&(this.scale="millisecond",this.step=5),a>t&&(this.scale="millisecond",this.step=1)}},s.prototype.snap=function(t){var e=new Date(t.valueOf());if("year"==this.scale){var i=e.getFullYear()+Math.round(e.getMonth()/12);e.setFullYear(Math.round(i/this.step)*this.step),e.setMonth(0),e.setDate(0),e.setHours(0),e.setMinutes(0),e.setSeconds(0),e.setMilliseconds(0)}else if("month"==this.scale)e.getDate()>15?(e.setDate(1),e.setMonth(e.getMonth()+1)):e.setDate(1),e.setHours(0),e.setMinutes(0),e.setSeconds(0),e.setMilliseconds(0);else if("day"==this.scale){switch(this.step){case 5:case 2:e.setHours(24*Math.round(e.getHours()/24));break;default:e.setHours(12*Math.round(e.getHours()/12))}e.setMinutes(0),e.setSeconds(0),e.setMilliseconds(0)}else if("weekday"==this.scale){switch(this.step){case 5:case 2:e.setHours(12*Math.round(e.getHours()/12));break;default:e.setHours(6*Math.round(e.getHours()/6))}e.setMinutes(0),e.setSeconds(0),e.setMilliseconds(0)}else if("hour"==this.scale){switch(this.step){case 4:e.setMinutes(60*Math.round(e.getMinutes()/60));break;default:e.setMinutes(30*Math.round(e.getMinutes()/30))}e.setSeconds(0),e.setMilliseconds(0)}else if("minute"==this.scale){switch(this.step){case 15:case 10:e.setMinutes(5*Math.round(e.getMinutes()/5)),e.setSeconds(0);break;case 5:e.setSeconds(60*Math.round(e.getSeconds()/60));break;default:e.setSeconds(30*Math.round(e.getSeconds()/30))}e.setMilliseconds(0)}else if("second"==this.scale)switch(this.step){case 15:case 10:e.setSeconds(5*Math.round(e.getSeconds()/5)),e.setMilliseconds(0);break;case 5:e.setMilliseconds(1e3*Math.round(e.getMilliseconds()/1e3));break;default:e.setMilliseconds(500*Math.round(e.getMilliseconds()/500))}else if("millisecond"==this.scale){var s=this.step>5?this.step/2:1;e.setMilliseconds(Math.round(e.getMilliseconds()/s)*s)}return e},s.prototype.isMajor=function(){if(1==this.switchedYear)switch(this.switchedYear=!1,this.scale){case"year":case"month":case"weekday":case"day":case"hour":case"minute":case"second":case"millisecond":return!0;default:return!1}else if(1==this.switchedMonth)switch(this.switchedMonth=!1,this.scale){case"weekday":case"day":case"hour":case"minute":case"second":case"millisecond":return!0;default:return!1}else if(1==this.switchedDay)switch(this.switchedDay=!1,this.scale){case"millisecond":case"second":case"minute":case"hour":return!0;default:return!1}switch(this.scale){case"millisecond":return 0==this.current.getMilliseconds();case"second":return 0==this.current.getSeconds();case"minute":return 0==this.current.getHours()&&0==this.current.getMinutes();case"hour":return 0==this.current.getHours();case"weekday":case"day":return 1==this.current.getDate();case"month":return 0==this.current.getMonth();case"year":return!1;default:return!1}},s.prototype.getLabelMinor=function(t){void 0==t&&(t=this.current);var e=this.format.minorLabels[this.scale];return e&&e.length>0?o(t).format(e):""},s.prototype.getLabelMajor=function(t){void 0==t&&(t=this.current);var e=this.format.majorLabels[this.scale];return e&&e.length>0?o(t).format(e):""},s.prototype.getClassName=function(){function t(t){return t/h%2==0?" even":" odd"}function e(t){return t.isSame(new Date,"day")?" today":t.isSame(o().add(1,"day"),"day")?" tomorrow":t.isSame(o().add(-1,"day"),"day")?" yesterday":""}function i(t){return t.isSame(new Date,"week")?" current-week":""}function s(t){return t.isSame(new Date,"month")?" current-month":""}function n(t){return t.isSame(new Date,"year")?" current-year":""}var r=o(this.current),a=r.locale?r.locale("en"):r.lang("en"),h=this.step;switch(this.scale){case"millisecond":return t(a.milliseconds()).trim();case"second":return t(a.seconds()).trim();case"minute":return t(a.minutes()).trim();case"hour":var d=a.hours();return 4==this.step&&(d=d+"-"+(d+4)),d+"h"+e(a)+t(a.hours());case"weekday":return a.format("dddd").toLowerCase()+e(a)+i(a)+t(a.date());case"day":var l=a.date(),c=a.format("MMMM").toLowerCase();return"day"+l+" "+c+s(a)+t(l-1);case"month":return a.format("MMMM").toLowerCase()+s(a)+t(a.month());case"year":var p=a.year();return"year"+p+n(a)+t(p);default:return""}},t.exports=s},function(t){function e(){this.options=null,this.props=null}e.prototype.setOptions=function(t){t&&util.extend(this.options,t)},e.prototype.redraw=function(){return!1},e.prototype.destroy=function(){},e.prototype._isResized=function(){var t=this.props._previousWidth!==this.props.width||this.props._previousHeight!==this.props.height;return this.props._previousWidth=this.props.width,this.props._previousHeight=this.props.height,t},t.exports=e},function(t,e,i){function s(t,e){this.body=t,this.defaultOptions={showCurrentTime:!0,locales:a,locale:"en"},this.options=o.extend({},this.defaultOptions),this.offset=0,this._create(),this.setOptions(e)}var o=i(1),n=i(20),r=i(44),a=i(48);s.prototype=new n,s.prototype._create=function(){var t=document.createElement("div");t.className="currenttime",t.style.position="absolute",t.style.top="0px",t.style.height="100%",this.bar=t},s.prototype.destroy=function(){this.options.showCurrentTime=!1,this.redraw(),this.body=null},s.prototype.setOptions=function(t){t&&o.selectiveExtend(["showCurrentTime","locale","locales"],this.options,t)},s.prototype.redraw=function(){if(this.options.showCurrentTime){var t=this.body.dom.backgroundVertical;this.bar.parentNode!=t&&(this.bar.parentNode&&this.bar.parentNode.removeChild(this.bar),t.appendChild(this.bar),this.start());var e=new Date((new Date).valueOf()+this.offset),i=this.body.util.toScreen(e),s=this.options.locales[this.options.locale],o=s.current+" "+s.time+": "+r(e).format("dddd, MMMM Do YYYY, H:mm:ss");o=o.charAt(0).toUpperCase()+o.substring(1),this.bar.style.left=i+"px",this.bar.title=o}else this.bar.parentNode&&this.bar.parentNode.removeChild(this.bar),this.stop();return!1},s.prototype.start=function(){function t(){e.stop();var i=e.body.range.conversion(e.body.domProps.center.width).scale,s=1/i/10;30>s&&(s=30),s>1e3&&(s=1e3),e.redraw(),e.currentTimeTimer=setTimeout(t,s)}var e=this;t()},s.prototype.stop=function(){void 0!==this.currentTimeTimer&&(clearTimeout(this.currentTimeTimer),delete this.currentTimeTimer)},s.prototype.setCurrentTime=function(t){var e=o.convert(t,"Date").valueOf(),i=(new Date).valueOf();this.offset=e-i,this.redraw()},s.prototype.getCurrentTime=function(){return new Date((new Date).valueOf()+this.offset)},t.exports=s},function(t,e,i){function s(t,e){this.body=t,this.defaultOptions={showCustomTime:!1,locales:h,locale:"en"},this.options=n.extend({},this.defaultOptions),this.customTime=new Date,this.eventParams={},this._create(),this.setOptions(e)}var o=i(45),n=i(1),r=i(20),a=i(44),h=i(48);s.prototype=new r,s.prototype.setOptions=function(t){t&&n.selectiveExtend(["showCustomTime","locale","locales"],this.options,t)},s.prototype._create=function(){var t=document.createElement("div");t.className="customtime",t.style.position="absolute",t.style.top="0px",t.style.height="100%",this.bar=t;var e=document.createElement("div");e.style.position="relative",e.style.top="0px",e.style.left="-10px",e.style.height="100%",e.style.width="20px",t.appendChild(e),this.hammer=new o(e),this.hammer.on("panstart",this._onDragStart.bind(this)),this.hammer.on("panmove",this._onDrag.bind(this)),this.hammer.on("panend",this._onDragEnd.bind(this)),this.hammer.on("pan",function(t){t.preventDefault()})},s.prototype.destroy=function(){this.options.showCustomTime=!1,this.redraw(),this.hammer.enable(!1),this.hammer=null,this.body=null},s.prototype.redraw=function(){if(this.options.showCustomTime){var t=this.body.dom.backgroundVertical;this.bar.parentNode!=t&&(this.bar.parentNode&&this.bar.parentNode.removeChild(this.bar),t.appendChild(this.bar));var e=this.body.util.toScreen(this.customTime),i=this.options.locales[this.options.locale],s=i.time+": "+a(this.customTime).format("dddd, MMMM Do YYYY, H:mm:ss");s=s.charAt(0).toUpperCase()+s.substring(1),this.bar.style.left=e+"px",this.bar.title=s}else this.bar.parentNode&&this.bar.parentNode.removeChild(this.bar);return!1},s.prototype.setCustomTime=function(t){this.customTime=n.convert(t,"Date"),this.redraw()},s.prototype.getCustomTime=function(){return new Date(this.customTime.valueOf())},s.prototype._onDragStart=function(t){this.eventParams.dragging=!0,this.eventParams.customTime=this.customTime,t.stopPropagation(),t.preventDefault()},s.prototype._onDrag=function(t){if(this.eventParams.dragging){var e=this.body.util.toScreen(this.eventParams.customTime)+t.deltaX,i=this.body.util.toTime(e);this.setCustomTime(i),this.body.emitter.emit("timechange",{time:new Date(this.customTime.valueOf())}),t.stopPropagation(),t.preventDefault()}},s.prototype._onDragEnd=function(t){this.eventParams.dragging&&(this.body.emitter.emit("timechanged",{time:new Date(this.customTime.valueOf())}),t.stopPropagation(),t.preventDefault())},t.exports=s},function(t,e,i){function s(t,e,i,s){this.id=o.randomUUID(),this.body=t,this.defaultOptions={orientation:"left",showMinorLabels:!0,showMajorLabels:!0,icons:!0,majorLinesOffset:7,minorLinesOffset:4,labelOffsetX:10,labelOffsetY:2,iconWidth:20,width:"40px",visible:!0,alignZeros:!0,customRange:{left:{min:void 0,max:void 0},right:{min:void 0,max:void 0}},title:{left:{text:void 0},right:{text:void 0}},format:{left:{decimals:void 0},right:{decimals:void 0}}},this.linegraphOptions=s,this.linegraphSVG=i,this.props={},this.DOMelements={lines:{},labels:{},title:{}},this.dom={},this.range={start:0,end:0},this.options=o.extend({},this.defaultOptions),this.conversionFactor=1,this.setOptions(e),this.width=Number((""+this.options.width).replace("px","")),this.minWidth=this.width,this.height=this.linegraphSVG.offsetHeight,this.hidden=!1,this.stepPixels=25,this.stepPixelsForced=25,this.zeroCrossing=-1,this.lineOffset=0,this.master=!0,this.svgElements={},this.iconsRemoved=!1,this.groups={},this.amountOfGroups=0,this._create();var n=this;this.body.emitter.on("verticalDrag",function(){n.dom.lineContainer.style.top=n.body.domProps.scrollTop+"px"})}var o=i(1),n=i(2),r=i(20),a=i(16);s.prototype=new r,s.prototype.addGroup=function(t,e){this.groups.hasOwnProperty(t)||(this.groups[t]=e),this.amountOfGroups+=1},s.prototype.updateGroup=function(t,e){this.groups[t]=e},s.prototype.removeGroup=function(t){this.groups.hasOwnProperty(t)&&(delete this.groups[t],this.amountOfGroups-=1)},s.prototype.setOptions=function(t){if(t){var e=!1;this.options.orientation!=t.orientation&&void 0!==t.orientation&&(e=!0);var i=["orientation","showMinorLabels","showMajorLabels","icons","majorLinesOffset","minorLinesOffset","labelOffsetX","labelOffsetY","iconWidth","width","visible","customRange","title","format","alignZeros"];o.selectiveExtend(i,this.options,t),this.minWidth=Number((""+this.options.width).replace("px","")),1==e&&this.dom.frame&&(this.hide(),this.show())}},s.prototype._create=function(){this.dom.frame=document.createElement("div"),this.dom.frame.style.width=this.options.width,this.dom.frame.style.height=this.height,this.dom.lineContainer=document.createElement("div"),this.dom.lineContainer.style.width="100%",this.dom.lineContainer.style.height=this.height,this.dom.lineContainer.style.position="relative",this.svg=document.createElementNS("http://www.w3.org/2000/svg","svg"),this.svg.style.position="absolute",this.svg.style.top="0px",this.svg.style.height="100%",this.svg.style.width="100%",this.svg.style.display="block",this.dom.frame.appendChild(this.svg)},s.prototype._redrawGroupIcons=function(){n.prepareElements(this.svgElements);var t,e=this.options.iconWidth,i=15,s=4,o=s+.5*i;t="left"==this.options.orientation?s:this.width-e-s;for(var r in this.groups)this.groups.hasOwnProperty(r)&&(1!=this.groups[r].visible||void 0!==this.linegraphOptions.visibility[r]&&1!=this.linegraphOptions.visibility[r]||(this.groups[r].drawIcon(t,o,this.svgElements,this.svg,e,i),o+=i+s));n.cleanupElements(this.svgElements),this.iconsRemoved=!1},s.prototype._cleanupIcons=function(){0==this.iconsRemoved&&(n.prepareElements(this.svgElements),n.cleanupElements(this.svgElements),this.iconsRemoved=!0)},s.prototype.show=function(){this.hidden=!1,this.dom.frame.parentNode||("left"==this.options.orientation?this.body.dom.left.appendChild(this.dom.frame):this.body.dom.right.appendChild(this.dom.frame)),this.dom.lineContainer.parentNode||this.body.dom.backgroundHorizontal.appendChild(this.dom.lineContainer)},s.prototype.hide=function(){this.hidden=!0,this.dom.frame.parentNode&&this.dom.frame.parentNode.removeChild(this.dom.frame),this.dom.lineContainer.parentNode&&this.dom.lineContainer.parentNode.removeChild(this.dom.lineContainer)},s.prototype.setRange=function(t,e){0==this.master&&1==this.options.alignZeros&&-1!=this.zeroCrossing&&t>0&&(t=0),this.range.start=t,this.range.end=e},s.prototype.redraw=function(){var t=!1,e=0;this.dom.lineContainer.style.top=this.body.domProps.scrollTop+"px";for(var i in this.groups)this.groups.hasOwnProperty(i)&&(1!=this.groups[i].visible||void 0!==this.linegraphOptions.visibility[i]&&1!=this.linegraphOptions.visibility[i]||e++);if(0==this.amountOfGroups||0==e)this.hide();else{this.show(),this.height=Number(this.linegraphSVG.style.height.replace("px","")),this.dom.lineContainer.style.height=this.height+"px",this.width=1==this.options.visible?Number((""+this.options.width).replace("px","")):0;var s=this.props,o=this.dom.frame;o.className="dataaxis",this._calculateCharSize();var n=this.options.orientation,r=this.options.showMinorLabels,a=this.options.showMajorLabels;s.minorLabelHeight=r?s.minorCharHeight:0,s.majorLabelHeight=a?s.majorCharHeight:0,s.minorLineWidth=this.body.dom.backgroundHorizontal.offsetWidth-this.lineOffset-this.width+2*this.options.minorLinesOffset,s.minorLineHeight=1,s.majorLineWidth=this.body.dom.backgroundHorizontal.offsetWidth-this.lineOffset-this.width+2*this.options.majorLinesOffset,s.majorLineHeight=1,"left"==n?(o.style.top="0",o.style.left="0",o.style.bottom="",o.style.width=this.width+"px",o.style.height=this.height+"px",this.props.width=this.body.domProps.left.width,this.props.height=this.body.domProps.left.height):(o.style.top="",o.style.bottom="0",o.style.left="0",o.style.width=this.width+"px",o.style.height=this.height+"px",this.props.width=this.body.domProps.right.width,this.props.height=this.body.domProps.right.height),t=this._redrawLabels(),t=this._isResized()||t,1==this.options.icons?this._redrawGroupIcons():this._cleanupIcons(),this._redrawTitle(n)}return t},s.prototype._redrawLabels=function(){var t=!1;n.prepareElements(this.DOMelements.lines),n.prepareElements(this.DOMelements.labels);var e=this.options.orientation,i=this.master?this.props.majorCharHeight||10:this.stepPixelsForced,s=new a(this.range.start,this.range.end,i,this.dom.frame.offsetHeight,this.options.customRange[this.options.orientation],0==this.master&&this.options.alignZeros);this.step=s;var o=(this.dom.frame.offsetHeight-s.deadSpace*(this.dom.frame.offsetHeight/s.marginRange))/((s.marginRange-s.deadSpace)/s.step);this.stepPixels=o;var r=this.height/o,h=0;if(0==this.master){o=this.stepPixelsForced,h=Math.round(this.dom.frame.offsetHeight/o-r);for(var d=0;.5*h>d;d++)s.previous();if(r=this.height/o,-1!=this.zeroCrossing&&1==this.options.alignZeros){var l=s.marginEnd/s.step-this.zeroCrossing;if(l>0)for(var d=0;l>d;d++)s.next();else if(0>l)for(var d=0;-l>d;d++)s.previous()}}else r+=.25;this.valueAtZero=s.marginEnd;var c,p=0,u=1;void 0!==this.options.format[e]&&(c=this.options.format[e].decimals),this.maxLabelSize=0;for(var m=0;u=0&&this._redrawLabel(m-2,s.getCurrent(c),e,"yAxis major",this.props.majorCharHeight),this._redrawLine(m,e,"grid horizontal major",this.options.majorLinesOffset,this.props.majorLineWidth)):this._redrawLine(m,e,"grid horizontal minor",this.options.minorLinesOffset,this.props.minorLineWidth),1==this.master&&0==s.current&&(this.zeroCrossing=u),u++}this.conversionFactor=0==this.master?m/(this.valueAtZero-s.current):this.dom.frame.offsetHeight/s.marginRange;var g=0;void 0!==this.options.title[e]&&void 0!==this.options.title[e].text&&(g=this.props.titleCharHeight);var v=1==this.options.icons?Math.max(this.options.iconWidth,g)+this.options.labelOffsetX+15:g+this.options.labelOffsetX+15;return this.maxLabelSize>this.width-v&&1==this.options.visible?(this.width=this.maxLabelSize+v,this.options.width=this.width+"px",n.cleanupElements(this.DOMelements.lines),n.cleanupElements(this.DOMelements.labels),this.redraw(),t=!0):this.maxLabelSizethis.minWidth?(this.width=Math.max(this.minWidth,this.maxLabelSize+v),this.options.width=this.width+"px",n.cleanupElements(this.DOMelements.lines),n.cleanupElements(this.DOMelements.labels),this.redraw(),t=!0):(n.cleanupElements(this.DOMelements.lines),n.cleanupElements(this.DOMelements.labels),t=!1),t},s.prototype.convertValue=function(t){var e=this.valueAtZero-t,i=e*this.conversionFactor;return i},s.prototype._redrawLabel=function(t,e,i,s,o){var r=n.getDOMElement("div",this.DOMelements.labels,this.dom.frame);r.className=s,r.innerHTML=e,"left"==i?(r.style.left="-"+this.options.labelOffsetX+"px",r.style.textAlign="right"):(r.style.right="-"+this.options.labelOffsetX+"px",r.style.textAlign="left"),r.style.top=t-.5*o+this.options.labelOffsetY+"px",e+="";var a=Math.max(this.props.majorCharWidth,this.props.minorCharWidth);this.maxLabelSized;d++){var c=this.visibleItems[d];c.repositionY(e)}return s},s.prototype._calculateHeight=function(t){var e,i=this.visibleItems;this.resetSubgroups();var s=this;if(i.length){var n=i[0].top,r=i[0].top+i[0].height;if(o.forEach(i,function(t){n=Math.min(n,t.top),r=Math.max(r,t.top+t.height),void 0!==t.data.subgroup&&(s.subgroups[t.data.subgroup].height=Math.max(s.subgroups[t.data.subgroup].height,t.height),s.subgroups[t.data.subgroup].visible=!0)}),n>t.axis){var a=n-t.axis;r-=a,o.forEach(i,function(t){t.top-=a})}e=r+t.item.vertical/2}else e=t.axis+t.item.vertical;return e=Math.max(e,this.props.label.height)},s.prototype.show=function(){this.dom.label.parentNode||this.itemSet.dom.labelSet.appendChild(this.dom.label),this.dom.foreground.parentNode||this.itemSet.dom.foreground.appendChild(this.dom.foreground),this.dom.background.parentNode||this.itemSet.dom.background.appendChild(this.dom.background),this.dom.axis.parentNode||this.itemSet.dom.axis.appendChild(this.dom.axis)},s.prototype.hide=function(){var t=this.dom.label;t.parentNode&&t.parentNode.removeChild(t);var e=this.dom.foreground;e.parentNode&&e.parentNode.removeChild(e);var i=this.dom.background;i.parentNode&&i.parentNode.removeChild(i);var s=this.dom.axis;s.parentNode&&s.parentNode.removeChild(s)},s.prototype.add=function(t){if(this.items[t.id]=t,t.setParent(this),void 0!==t.data.subgroup&&(void 0===this.subgroups[t.data.subgroup]&&(this.subgroups[t.data.subgroup]={height:0,visible:!1,index:this.subgroupIndex,items:[]},this.subgroupIndex++),this.subgroups[t.data.subgroup].items.push(t)),this.orderSubgroups(),-1==this.visibleItems.indexOf(t)){var e=this.itemSet.body.range;this._checkIfVisible(t,this.visibleItems,e)}},s.prototype.orderSubgroups=function(){if(void 0!==this.subgroupOrderer){var t=[];if("string"==typeof this.subgroupOrderer){for(var e in this.subgroups)t.push({subgroup:e,sortField:this.subgroups[e].items[0].data[this.subgroupOrderer]});t.sort(function(t,e){return t.sortField-e.sortField})}else if("function"==typeof this.subgroupOrderer){for(var e in this.subgroups)t.push(this.subgroups[e].items[0].data);t.sort(this.subgroupOrderer)}if(t.length>0)for(var i=0;it?-1:l>=t?0:1};if(e.length>0)for(n=0;nl}),1==this.checkRangedItems)for(this.checkRangedItems=!1,n=0;nl})}for(n=0;n=0&&(n=e[r],!o(n));r--)void 0===s[n.id]&&(s[n.id]=!0,i.push(n));for(r=t+1;rs;s++){var n=this.visibleItems[s];n.repositionY(e)}return i},s.prototype.show=function(){this.dom.background.parentNode||this.itemSet.dom.background.appendChild(this.dom.background)},t.exports=s},function(t,e,i){function s(t,e){this.body=t,this.defaultOptions={type:null,orientation:"bottom",align:"auto",stack:!0,groupOrder:null,selectable:!0,editable:{updateTime:!1,updateGroup:!1,add:!1,remove:!1},onAdd:function(t,e){e(t)},onUpdate:function(t,e){e(t)},onMove:function(t,e){e(t)},onRemove:function(t,e){e(t)},onMoving:function(t,e){e(t)},margin:{item:{horizontal:10,vertical:10},axis:20},padding:5},this.options=n.extend({},this.defaultOptions),this.itemOptions={type:{start:"Date",end:"Date"}},this.conversion={toScreen:t.util.toScreen,toTime:t.util.toTime},this.dom={},this.props={},this.hammer=null;var i=this;this.itemsData=null,this.groupsData=null,this.itemListeners={add:function(t,e){i._onAdd(e.items)},update:function(t,e){i._onUpdate(e.items)},remove:function(t,e){i._onRemove(e.items)}},this.groupListeners={add:function(t,e){i._onAddGroups(e.items)},update:function(t,e){i._onUpdateGroups(e.items)},remove:function(t,e){i._onRemoveGroups(e.items)}},this.items={},this.groups={},this.groupIds=[],this.selection=[],this.stackDirty=!0,this.touchParams={},this._create(),this.setOptions(e)}var o=i(45),n=i(1),r=i(3),a=i(4),h=i(20),d=i(25),l=i(26),c=i(33),p=i(34),u=i(35),m=i(32),f="__ungrouped__",g="__background__";s.prototype=new h,s.types={background:m,box:c,range:u,point:p},s.prototype._create=function(){var t=document.createElement("div");t.className="itemset",t["timeline-itemset"]=this,this.dom.frame=t;var e=document.createElement("div");e.className="background",t.appendChild(e),this.dom.background=e;var i=document.createElement("div");i.className="foreground",t.appendChild(i),this.dom.foreground=i;var s=document.createElement("div");s.className="axis",this.dom.axis=s;var n=document.createElement("div");n.className="labelset",this.dom.labelSet=n,this._updateUngrouped();var r=new l(g,null,this);r.show(),this.groups[g]=r,this.hammer=new o(this.body.dom.centerContainer),this.hammer.on("hammer.input",function(t){t.isFirst&&this._onTouch(t)}.bind(this)),this.hammer.on("panstart",this._onDragStart.bind(this)),this.hammer.on("panmove",this._onDrag.bind(this)),this.hammer.on("panend",this._onDragEnd.bind(this)),this.hammer.on("tap",this._onSelectItem.bind(this)),this.hammer.on("press",this._onMultiSelectItem.bind(this)),this.hammer.on("doubletap",this._onAddItem.bind(this)),this.show()},s.prototype.setOptions=function(t){if(t){var e=["type","align","orientation","padding","stack","selectable","groupOrder","dataAttributes","template","hide"];n.selectiveExtend(e,this.options,t),"margin"in t&&("number"==typeof t.margin?(this.options.margin.axis=t.margin,this.options.margin.item.horizontal=t.margin,this.options.margin.item.vertical=t.margin):"object"==typeof t.margin&&(n.selectiveExtend(["axis"],this.options.margin,t.margin),"item"in t.margin&&("number"==typeof t.margin.item?(this.options.margin.item.horizontal=t.margin.item,this.options.margin.item.vertical=t.margin.item):"object"==typeof t.margin.item&&n.selectiveExtend(["horizontal","vertical"],this.options.margin.item,t.margin.item)))),"editable"in t&&("boolean"==typeof t.editable?(this.options.editable.updateTime=t.editable,this.options.editable.updateGroup=t.editable,this.options.editable.add=t.editable,this.options.editable.remove=t.editable):"object"==typeof t.editable&&n.selectiveExtend(["updateTime","updateGroup","add","remove"],this.options.editable,t.editable));var i=function(e){var i=t[e];if(i){if(!(i instanceof Function))throw new Error("option "+e+" must be a function "+e+"(item, callback)");this.options[e]=i}}.bind(this);["onAdd","onUpdate","onRemove","onMove","onMoving"].forEach(i),this.markDirty()}},s.prototype.markDirty=function(){this.groupIds=[],this.stackDirty=!0},s.prototype.destroy=function(){this.hide(),this.setItems(null),this.setGroups(null),this.hammer=null,this.body=null,this.conversion=null},s.prototype.hide=function(){this.dom.frame.parentNode&&this.dom.frame.parentNode.removeChild(this.dom.frame),this.dom.axis.parentNode&&this.dom.axis.parentNode.removeChild(this.dom.axis),this.dom.labelSet.parentNode&&this.dom.labelSet.parentNode.removeChild(this.dom.labelSet)},s.prototype.show=function(){this.dom.frame.parentNode||this.body.dom.center.appendChild(this.dom.frame),this.dom.axis.parentNode||this.body.dom.backgroundVertical.appendChild(this.dom.axis),this.dom.labelSet.parentNode||this.body.dom.left.appendChild(this.dom.labelSet)},s.prototype.setSelection=function(t){var e,i,s,o;for(void 0==t&&(t=[]),Array.isArray(t)||(t=[t]),e=0,i=this.selection.length;i>e;e++)s=this.selection[e],o=this.items[s],o&&o.unselect();for(this.selection=[],e=0,i=t.length;i>e;e++)s=t[e],o=this.items[s],o&&(this.selection.push(s),o.select())},s.prototype.getSelection=function(){return this.selection.concat([])},s.prototype.getVisibleItems=function(){var t=this.body.range.getRange(),e=this.body.util.toScreen(t.start),i=this.body.util.toScreen(t.end),s=[];for(var o in this.groups)if(this.groups.hasOwnProperty(o))for(var n=this.groups[o],r=n.visibleItems,a=0;ae&&s.push(h.id)}return s},s.prototype._deselect=function(t){for(var e=this.selection,i=0,s=e.length;s>i;i++)if(e[i]==t){e.splice(i,1);break}},s.prototype.redraw=function(){var t=this.options.margin,e=this.body.range,i=n.option.asSize,s=this.options,o=s.orientation,r=!1,a=this.dom.frame,h=s.editable.updateTime||s.editable.updateGroup;this.props.top=this.body.domProps.top.height+this.body.domProps.border.top,this.props.left=this.body.domProps.left.width+this.body.domProps.border.left,a.className="itemset"+(h?" editable":""),r=this._orderGroups()||r;var d=e.end-e.start,l=d!=this.lastVisibleInterval||this.props.width!=this.props.lastWidth;l&&(this.stackDirty=!0),this.lastVisibleInterval=d,this.props.lastWidth=this.props.width;var c=this.stackDirty,p=this._firstGroup(),u={item:t.item,axis:t.axis},m={item:t.item,axis:t.item.vertical/2},f=0,v=t.axis+t.item.vertical;return this.groups[g].redraw(e,m,c),n.forEach(this.groups,function(t){var i=t==p?u:m,s=t.redraw(e,i,c);r=s||r,f+=t.height}),f=Math.max(f,v),this.stackDirty=!1,a.style.height=i(f),this.props.width=a.offsetWidth,this.props.height=f,this.dom.axis.style.top=i("top"==o?this.body.domProps.top.height+this.body.domProps.border.top:this.body.domProps.top.height+this.body.domProps.centerContainer.height),this.dom.axis.style.left="0",r=this._isResized()||r},s.prototype._firstGroup=function(){var t="top"==this.options.orientation?0:this.groupIds.length-1,e=this.groupIds[t],i=this.groups[e]||this.groups[f];return i||null},s.prototype._updateUngrouped=function(){{var t,e,i=this.groups[f];this.groups[g]}if(this.groupsData){if(i){i.hide(),delete this.groups[f];for(e in this.items)if(this.items.hasOwnProperty(e)){t=this.items[e],t.parent&&t.parent.remove(t);var s=this._getGroupId(t.data),o=this.groups[s];o&&o.add(t)||t.hide()}}}else if(!i){var n=null,r=null;i=new d(n,r,this),this.groups[f]=i;for(e in this.items)this.items.hasOwnProperty(e)&&(t=this.items[e],i.add(t));i.show()}},s.prototype.getLabelSet=function(){return this.dom.labelSet},s.prototype.setItems=function(t){var e,i=this,s=this.itemsData;if(t){if(!(t instanceof r||t instanceof a))throw new TypeError("Data must be an instance of DataSet or DataView");this.itemsData=t}else this.itemsData=null;if(s&&(n.forEach(this.itemListeners,function(t,e){s.off(e,t)}),e=s.getIds(),this._onRemove(e)),this.itemsData){var o=this.id;n.forEach(this.itemListeners,function(t,e){i.itemsData.on(e,t,o)}),e=this.itemsData.getIds(),this._onAdd(e),this._updateUngrouped()}},s.prototype.getItems=function(){return this.itemsData},s.prototype.setGroups=function(t){var e,i=this;if(this.groupsData&&(n.forEach(this.groupListeners,function(t,e){i.groupsData.unsubscribe(e,t)}),e=this.groupsData.getIds(),this.groupsData=null,this._onRemoveGroups(e)),t){if(!(t instanceof r||t instanceof a))throw new TypeError("Data must be an instance of DataSet or DataView");this.groupsData=t}else this.groupsData=null;if(this.groupsData){var s=this.id;n.forEach(this.groupListeners,function(t,e){i.groupsData.on(e,t,s)}),e=this.groupsData.getIds(),this._onAddGroups(e)}this._updateUngrouped(),this._order(),this.body.emitter.emit("change",{queue:!0})},s.prototype.getGroups=function(){return this.groupsData},s.prototype.removeItem=function(t){var e=this.itemsData.get(t),i=this.itemsData.getDataSet();e&&this.options.onRemove(e,function(e){e&&i.remove(t)})},s.prototype._getType=function(t){return t.type||this.options.type||(t.end?"range":"box")},s.prototype._getGroupId=function(t){var e=this._getType(t);return"background"==e&&void 0==t.group?g:this.groupsData?t.group:f},s.prototype._onUpdate=function(t){var e=this;t.forEach(function(t){var i=e.itemsData.get(t,e.itemOptions),o=e.items[t],n=e._getType(i),r=s.types[n];if(o&&(r&&o instanceof r?e._updateItem(o,i):(e._removeItem(o),o=null)),!o){if(!r)throw new TypeError("rangeoverflow"==n?'Item type "rangeoverflow" is deprecated. Use css styling instead: .vis.timeline .item.range .content {overflow: visible;}':'Unknown item type "'+n+'"');o=new r(i,e.conversion,e.options),o.id=t,e._addItem(o)}}),this._order(),this.stackDirty=!0,this.body.emitter.emit("change",{queue:!0})},s.prototype._onAdd=s.prototype._onUpdate,s.prototype._onRemove=function(t){var e=0,i=this;t.forEach(function(t){var s=i.items[t];s&&(e++,i._removeItem(s))}),e&&(this._order(),this.stackDirty=!0,this.body.emitter.emit("change",{queue:!0}))},s.prototype._order=function(){n.forEach(this.groups,function(t){t.order()})},s.prototype._onUpdateGroups=function(t){this._onAddGroups(t)},s.prototype._onAddGroups=function(t){var e=this;t.forEach(function(t){var i=e.groupsData.get(t),s=e.groups[t];if(s)s.setData(i);else{if(t==f||t==g)throw new Error("Illegal group id. "+t+" is a reserved id.");var o=Object.create(e.options);n.extend(o,{height:null}),s=new d(t,i,e),e.groups[t]=s;for(var r in e.items)if(e.items.hasOwnProperty(r)){var a=e.items[r];a.data.group==t&&s.add(a)}s.order(),s.show()}}),this.body.emitter.emit("change",{queue:!0})},s.prototype._onRemoveGroups=function(t){var e=this.groups;t.forEach(function(t){var i=e[t];i&&(i.hide(),delete e[t])}),this.markDirty(),this.body.emitter.emit("change",{queue:!0})},s.prototype._orderGroups=function(){if(this.groupsData){var t=this.groupsData.getIds({order:this.options.groupOrder}),e=!n.equalArray(t,this.groupIds);if(e){var i=this.groups;t.forEach(function(t){i[t].hide()}),t.forEach(function(t){i[t].show()}),this.groupIds=t}return e}return!1},s.prototype._addItem=function(t){this.items[t.id]=t;var e=this._getGroupId(t.data),i=this.groups[e];i&&i.add(t)},s.prototype._updateItem=function(t,e){var i=t.data.group;if(t.setData(e),i!=t.data.group){var s=this.groups[i];s&&s.remove(t);var o=this._getGroupId(t.data),n=this.groups[o];n&&n.add(t)}},s.prototype._removeItem=function(t){t.hide(),delete this.items[t.id];var e=this.selection.indexOf(t.id);-1!=e&&this.selection.splice(e,1),t.parent&&t.parent.remove(t)},s.prototype._constructByEndArray=function(t){for(var e=[],i=0;i0||o.length>0)&&this.body.emitter.emit("select",{items:a})}},s.prototype._onAddItem=function(t){if(this.options.selectable&&this.options.editable.add){var e=this,i=this.body.util.snap||null,o=s.itemFromTarget(t);if(o){var r=e.itemsData.get(o.id);this.options.onUpdate(r,function(t){t&&e.itemsData.getDataSet().update(t)})}else{var a=n.getAbsoluteLeft(this.dom.frame),h=t.center.x-a,d=this.body.util.toTime(h),l={start:i?i(d):d,content:"new item"};if("range"===this.options.type){var c=this.body.util.toTime(h+this.props.width/5);l.end=i?i(c):c}l[this.itemsData._fieldId]=n.randomUUID();var p=s.groupFromTarget(t);p&&(l.group=p.groupId),this.options.onAdd(l,function(t){t&&e.itemsData.getDataSet().add(t)})}}},s.prototype._onMultiSelectItem=function(t){if(this.options.selectable){var e,i=s.itemFromTarget(t);if(i){e=this.getSelection();var o=t.srcEvent&&t.srcEvent.shiftKey||!1;if(o){e.push(i.id);var n=s._getItemRange(this.itemsData.get(e,this.itemOptions));e=[];for(var r in this.items)if(this.items.hasOwnProperty(r)){var a=this.items[r],h=a.data.start,d=void 0!==a.data.end?a.data.end:h;h>=n.min&&d<=n.max&&e.push(a.id)}}else{var l=e.indexOf(i.id);-1==l?e.push(i.id):e.splice(l,1)}this.setSelection(e),this.body.emitter.emit("select",{items:this.getSelection()})}}},s._getItemRange=function(t){var e=null,i=null;return t.forEach(function(t){(null==i||t.starte)&&(e=t.end):(null==e||t.start>e)&&(e=t.start)}),{min:i,max:e}},s.itemFromTarget=function(t){for(var e=t.target;e;){if(e.hasOwnProperty("timeline-item"))return e["timeline-item"];e=e.parentNode}return null},s.groupFromTarget=function(t){for(var e=t.target;e;){if(e.hasOwnProperty("timeline-group"))return e["timeline-group"];e=e.parentNode}return null},s.itemSetFromTarget=function(t){for(var e=t.target;e;){if(e.hasOwnProperty("timeline-itemset"))return e["timeline-itemset"];e=e.parentNode}return null},t.exports=s},function(t,e,i){function s(t,e,i,s){this.body=t,this.defaultOptions={enabled:!0,icons:!0,iconSize:20,iconSpacing:6,left:{visible:!0,position:"top-left"},right:{visible:!0,position:"top-left"}},this.side=i,this.options=o.extend({},this.defaultOptions),this.linegraphOptions=s,this.svgElements={},this.dom={},this.groups={},this.amountOfGroups=0,this._create(),this.setOptions(e)}var o=i(1),n=i(2),r=i(20);s.prototype=new r,s.prototype.clear=function(){this.groups={},this.amountOfGroups=0},s.prototype.addGroup=function(t,e){this.groups.hasOwnProperty(t)||(this.groups[t]=e),this.amountOfGroups+=1},s.prototype.updateGroup=function(t,e){this.groups[t]=e},s.prototype.removeGroup=function(t){this.groups.hasOwnProperty(t)&&(delete this.groups[t],this.amountOfGroups-=1)},s.prototype._create=function(){this.dom.frame=document.createElement("div"),this.dom.frame.className="legend",this.dom.frame.style.position="absolute",this.dom.frame.style.top="10px",this.dom.frame.style.display="block",this.dom.textArea=document.createElement("div"),this.dom.textArea.className="legendText",this.dom.textArea.style.position="relative",this.dom.textArea.style.top="0px",this.svg=document.createElementNS("http://www.w3.org/2000/svg","svg"),this.svg.style.position="absolute",this.svg.style.top="0px",this.svg.style.width=this.options.iconSize+5+"px",this.svg.style.height="100%",this.dom.frame.appendChild(this.svg),this.dom.frame.appendChild(this.dom.textArea)},s.prototype.hide=function(){this.dom.frame.parentNode&&this.dom.frame.parentNode.removeChild(this.dom.frame)},s.prototype.show=function(){this.dom.frame.parentNode||this.body.dom.center.appendChild(this.dom.frame)},s.prototype.setOptions=function(t){var e=["enabled","orientation","icons","left","right"];o.selectiveDeepExtend(e,this.options,t)},s.prototype.redraw=function(){var t=0;for(var e in this.groups)this.groups.hasOwnProperty(e)&&(1!=this.groups[e].visible||void 0!==this.linegraphOptions.visibility[e]&&1!=this.linegraphOptions.visibility[e]||t++);if(0==this.options[this.side].visible||0==this.amountOfGroups||0==this.options.enabled||0==t)this.hide();else{if(this.show(),"top-left"==this.options[this.side].position||"bottom-left"==this.options[this.side].position?(this.dom.frame.style.left="4px",this.dom.frame.style.textAlign="left",this.dom.textArea.style.textAlign="left",this.dom.textArea.style.left=this.options.iconSize+15+"px",this.dom.textArea.style.right="",this.svg.style.left="0px",this.svg.style.right=""):(this.dom.frame.style.right="4px",this.dom.frame.style.textAlign="right",this.dom.textArea.style.textAlign="right",this.dom.textArea.style.right=this.options.iconSize+15+"px",this.dom.textArea.style.left="",this.svg.style.right="0px",this.svg.style.left=""),"top-left"==this.options[this.side].position||"top-right"==this.options[this.side].position)this.dom.frame.style.top=4-Number(this.body.dom.center.style.top.replace("px",""))+"px",this.dom.frame.style.bottom="";else{var i=this.body.domProps.center.height-this.body.domProps.centerContainer.height;this.dom.frame.style.bottom=4+i+Number(this.body.dom.center.style.top.replace("px",""))+"px",this.dom.frame.style.top=""}0==this.options.icons?(this.dom.frame.style.width=this.dom.textArea.offsetWidth+10+"px",this.dom.textArea.style.right="",this.dom.textArea.style.left="",this.svg.style.width="0px"):(this.dom.frame.style.width=this.options.iconSize+15+this.dom.textArea.offsetWidth+10+"px",this.drawLegendIcons());var s="";for(var e in this.groups)this.groups.hasOwnProperty(e)&&(1!=this.groups[e].visible||void 0!==this.linegraphOptions.visibility[e]&&1!=this.linegraphOptions.visibility[e]||(s+=this.groups[e].content+"
"));this.dom.textArea.innerHTML=s,this.dom.textArea.style.lineHeight=.75*this.options.iconSize+this.options.iconSpacing+"px"}},s.prototype.drawLegendIcons=function(){if(this.dom.frame.parentNode){n.prepareElements(this.svgElements);var t=window.getComputedStyle(this.dom.frame).paddingTop,e=Number(t.replace("px","")),i=e,s=this.options.iconSize,o=.75*this.options.iconSize,r=e+.5*o+3;this.svg.style.width=s+5+e+"px";for(var a in this.groups)this.groups.hasOwnProperty(a)&&(1!=this.groups[a].visible||void 0!==this.linegraphOptions.visibility[a]&&1!=this.linegraphOptions.visibility[a]||(this.groups[a].drawIcon(i,r,this.svgElements,this.svg,s,o),r+=o+this.options.iconSpacing));n.cleanupElements(this.svgElements)}},t.exports=s},function(t,e,i){function s(t,e){this.id=o.randomUUID(),this.body=t,this.defaultOptions={yAxisOrientation:"left",defaultGroup:"default",sort:!0,sampling:!0,graphHeight:"400px",shaded:{enabled:!1,orientation:"bottom"},style:"line",barChart:{width:50,handleOverlap:"overlap",align:"center"},catmullRom:{enabled:!0,parametrization:"centripetal",alpha:.5},drawPoints:{enabled:!0,size:6,style:"square"},dataAxis:{showMinorLabels:!0,showMajorLabels:!0,icons:!1,width:"40px",visible:!0,alignZeros:!0,customRange:{left:{min:void 0,max:void 0},right:{min:void 0,max:void 0}}},legend:{enabled:!1,icons:!0,left:{visible:!0,position:"top-left"},right:{visible:!0,position:"top-right"}},groups:{visibility:{}}},this.options=o.extend({},this.defaultOptions),this.dom={},this.props={},this.hammer=null,this.groups={},this.abortedGraphUpdate=!1,this.updateSVGheight=!1,this.updateSVGheightOnResize=!1;var i=this;this.itemsData=null,this.groupsData=null,this.itemListeners={add:function(t,e){i._onAdd(e.items)},update:function(t,e){i._onUpdate(e.items)},remove:function(t,e){i._onRemove(e.items)}},this.groupListeners={add:function(t,e){i._onAddGroups(e.items)},update:function(t,e){i._onUpdateGroups(e.items)},remove:function(t,e){i._onRemoveGroups(e.items)}},this.items={},this.selection=[],this.lastStart=this.body.range.start,this.touchParams={},this.svgElements={},this.setOptions(e),this.groupsUsingDefaultStyles=[0],this.COUNTER=0,this.body.emitter.on("rangechanged",function(){i.lastStart=i.body.range.start,i.svg.style.left=o.option.asSize(-i.props.width),i.redraw.call(i,!0)}),this._create(),this.framework={svg:this.svg,svgElements:this.svgElements,options:this.options,groups:this.groups},this.body.emitter.emit("change")}var o=i(1),n=i(2),r=i(3),a=i(4),h=i(20),d=i(23),l=i(24),c=i(28),p=i(52),u="__ungrouped__";s.prototype=new h,s.prototype._create=function(){var t=document.createElement("div");t.className="LineGraph",this.dom.frame=t,this.svg=document.createElementNS("http://www.w3.org/2000/svg","svg"),this.svg.style.position="relative",this.svg.style.height=(""+this.options.graphHeight).replace("px","")+"px",this.svg.style.display="block",t.appendChild(this.svg),this.options.dataAxis.orientation="left",this.yAxisLeft=new d(this.body,this.options.dataAxis,this.svg,this.options.groups),this.options.dataAxis.orientation="right",this.yAxisRight=new d(this.body,this.options.dataAxis,this.svg,this.options.groups),delete this.options.dataAxis.orientation,this.legendLeft=new c(this.body,this.options.legend,"left",this.options.groups),this.legendRight=new c(this.body,this.options.legend,"right",this.options.groups),this.show()},s.prototype.setOptions=function(t){if(t){var e=["sampling","defaultGroup","height","graphHeight","yAxisOrientation","style","barChart","dataAxis","sort","groups"];void 0===t.graphHeight&&void 0!==t.height&&void 0!==this.body.domProps.centerContainer.height?(this.updateSVGheight=!0,this.updateSVGheightOnResize=!0):void 0!==this.body.domProps.centerContainer.height&&void 0!==t.graphHeight&&parseInt((t.graphHeight+"").replace("px",""))0){var d=this.body.util.toGlobalTime(-this.body.domProps.root.width),l=this.body.util.toGlobalTime(2*this.body.domProps.root.width),c={};for(this._getRelevantData(a,c,d,l),this._applySampling(a,c),e=0;eu&&console.log("WARNING: there may be an infinite loop in the _updateGraph emitter cycle."),this.COUNTER=0,this.abortedGraphUpdate=!1,e=0;e0)for(r=0;rs){d.push(h);break}d.push(h)}}else for(a=0;ai&&h.x0)for(var s=0;s0){var n=1,r=o.length,a=this.body.util.toGlobalScreen(o[o.length-1].x)-this.body.util.toGlobalScreen(o[0].x),h=r/a;n=Math.min(Math.ceil(.2*r),Math.max(1,Math.round(h)));for(var d=[],l=0;r>l;l+=n)d.push(o[l]);e[t[s]]=d}}},s.prototype._getYRanges=function(t,e,i){var s,o,n,r,a=[],h=[];if(t.length>0){for(n=0;n0&&(o=this.groups[t[n]],"stack"==r.barChart.handleOverlap&&"bar"==r.style?"left"==r.yAxisOrientation?a=a.concat(o.getYRange(s)):h=h.concat(o.getYRange(s)):i[t[n]]=o.getYRange(s,t[n]));p.getStackedBarYRange(a,i,t,"__barchartLeft","left"),p.getStackedBarYRange(h,i,t,"__barchartRight","right")}},s.prototype._updateYAxis=function(t,e){var i,s,o=!1,n=!1,r=!1,a=1e9,h=1e9,d=-1e9,l=-1e9;if(t.length>0){for(var c=0;ci?i:a,d=s>d?s:d):(r=!0,h=h>i?i:h,l=s>l?s:l));1==n&&this.yAxisLeft.setRange(a,d),1==r&&this.yAxisRight.setRange(h,l)}return o=this._toggleAxisVisiblity(n,this.yAxisLeft)||o,o=this._toggleAxisVisiblity(r,this.yAxisRight)||o,1==r&&1==n?(this.yAxisLeft.drawIcons=!0,this.yAxisRight.drawIcons=!0):(this.yAxisLeft.drawIcons=!1,this.yAxisRight.drawIcons=!1),this.yAxisRight.master=!n,0==this.yAxisRight.master?(this.yAxisLeft.lineOffset=1==r?this.yAxisRight.width:0,o=this.yAxisLeft.redraw()||o,this.yAxisRight.stepPixelsForced=this.yAxisLeft.stepPixels,this.yAxisRight.zeroCrossing=this.yAxisLeft.zeroCrossing,o=this.yAxisRight.redraw()||o):o=this.yAxisRight.redraw()||o,-1!=t.indexOf("__barchartLeft")&&t.splice(t.indexOf("__barchartLeft"),1),-1!=t.indexOf("__barchartRight")&&t.splice(t.indexOf("__barchartRight"),1),o},s.prototype._toggleAxisVisiblity=function(t,e){var i=!1;return 0==t?e.dom.frame.parentNode&&0==e.hidden&&(e.hide(),i=!0):e.dom.frame.parentNode||1!=e.hidden||(e.show(),i=!0),i},s.prototype._convertXcoordinates=function(t){for(var e,i,s=[],o=this.body.util.toScreen,n=0;ny;)y++,l=h.getCurrent(),c=h.isMajor(),u=h.getClassName(),f=m,m=this.body.util.toScreen(l),g=m-f,p&&(p.style.width=g+"px"),this.options.showMinorLabels&&this._repaintMinorText(m,h.getLabelMinor(),t,u),c&&this.options.showMajorLabels?(m>0&&(void 0==v&&(v=m),this._repaintMajorText(m,h.getLabelMajor(),t,u)),p=this._repaintMajorLine(m,t,u)):p=this._repaintMinorLine(m,t,u),h.next();if(this.options.showMajorLabels){var b=this.body.util.toTime(0),_=h.getLabelMajor(b),x=_.length*(this.props.majorCharWidth||10)+10;(void 0==v||v>x)&&this._repaintMajorText(0,_,t,u)}o.forEach(this.dom.redundant,function(t){for(;t.length;){var e=t.pop();e&&e.parentNode&&e.parentNode.removeChild(e)}})},s.prototype._repaintMinorText=function(t,e,i,s){var o=this.dom.redundant.minorTexts.shift();if(!o){var n=document.createTextNode("");o=document.createElement("div"),o.appendChild(n),this.dom.foreground.appendChild(o)}this.dom.minorTexts.push(o),o.childNodes[0].nodeValue=e,o.style.top="top"==i?this.props.majorLabelHeight+"px":"0",o.style.left=t+"px",o.className="text minor "+s},s.prototype._repaintMajorText=function(t,e,i,s){var o=this.dom.redundant.majorTexts.shift();if(!o){var n=document.createTextNode(e);o=document.createElement("div"),o.appendChild(n),this.dom.foreground.appendChild(o)}this.dom.majorTexts.push(o),o.childNodes[0].nodeValue=e,o.className="text major "+s,o.style.top="top"==i?"0":this.props.minorLabelHeight+"px",o.style.left=t+"px"},s.prototype._repaintMinorLine=function(t,e,i){var s=this.dom.redundant.lines.shift();s||(s=document.createElement("div"),this.dom.background.appendChild(s)),this.dom.lines.push(s);var o=this.props;return s.style.top="top"==e?o.majorLabelHeight+"px":this.body.domProps.top.height+"px",s.style.height=o.minorLineHeight+"px",s.style.left=t-o.minorLineWidth/2+"px",s.className="grid vertical minor "+i,s},s.prototype._repaintMajorLine=function(t,e,i){var s=this.dom.redundant.lines.shift();s||(s=document.createElement("div"),this.dom.background.appendChild(s)),this.dom.lines.push(s);var o=this.props;return s.style.top="top"==e?"0":this.body.domProps.top.height+"px",s.style.left=t-o.majorLineWidth/2+"px",s.style.height=o.majorLineHeight+"px",s.className="grid vertical major "+i,s},s.prototype._calculateCharSize=function(){this.dom.measureCharMinor||(this.dom.measureCharMinor=document.createElement("DIV"),this.dom.measureCharMinor.className="text minor measure",this.dom.measureCharMinor.style.position="absolute",this.dom.measureCharMinor.appendChild(document.createTextNode("0")),this.dom.foreground.appendChild(this.dom.measureCharMinor)),this.props.minorCharHeight=this.dom.measureCharMinor.clientHeight,this.props.minorCharWidth=this.dom.measureCharMinor.clientWidth,this.dom.measureCharMajor||(this.dom.measureCharMajor=document.createElement("DIV"),this.dom.measureCharMajor.className="text major measure",this.dom.measureCharMajor.style.position="absolute",this.dom.measureCharMajor.appendChild(document.createTextNode("0")),this.dom.foreground.appendChild(this.dom.measureCharMajor)),this.props.majorCharHeight=this.dom.measureCharMajor.clientHeight,this.props.majorCharWidth=this.dom.measureCharMajor.clientWidth},s.prototype.snap=function(t){return this.step.snap(t)},t.exports=s},function(t,e,i){function s(t,e,i){this.id=null,this.parent=null,this.data=t,this.dom=null,this.conversion=e||{},this.options=i||{},this.selected=!1,this.displayed=!1,this.dirty=!0,this.top=null,this.left=null,this.width=null,this.height=null}var o=i(45),n=i(1);s.prototype.stack=!0,s.prototype.select=function(){this.selected=!0,this.dirty=!0,this.displayed&&this.redraw()},s.prototype.unselect=function(){this.selected=!1,this.dirty=!0,this.displayed&&this.redraw()},s.prototype.setData=function(t){this.data=t,this.dirty=!0,this.displayed&&this.redraw()},s.prototype.setParent=function(t){this.displayed?(this.hide(),this.parent=t,this.parent&&this.show()):this.parent=t},s.prototype.isVisible=function(){return!1},s.prototype.show=function(){return!1},s.prototype.hide=function(){return!1},s.prototype.redraw=function(){},s.prototype.repositionX=function(){},s.prototype.repositionY=function(){},s.prototype._repaintDeleteButton=function(t){if(this.selected&&this.options.editable.remove&&!this.dom.deleteButton){var e=this,i=document.createElement("div");i.className="delete",i.title="Delete this item",new o(i).on("tap",function(t){e.parent.removeFromDataSet(e),t.stopPropagation(),t.preventDefault()}),t.appendChild(i),this.dom.deleteButton=i}else!this.selected&&this.dom.deleteButton&&(this.dom.deleteButton.parentNode&&this.dom.deleteButton.parentNode.removeChild(this.dom.deleteButton),this.dom.deleteButton=null)},s.prototype._updateContents=function(t){var e;if(this.options.template){var i=this.parent.itemSet.itemsData.get(this.id);e=this.options.template(i)}else e=this.data.content;if(e!==this.content){if(e instanceof Element)t.innerHTML="",t.appendChild(e);else if(void 0!=e)t.innerHTML=e;else if("background"!=this.data.type||void 0!==this.data.content)throw new Error('Property "content" missing in item '+this.id);this.content=e}},s.prototype._updateTitle=function(t){null!=this.data.title?t.title=this.data.title||"":t.removeAttribute("title")},s.prototype._updateDataAttributes=function(t){if(this.options.dataAttributes&&this.options.dataAttributes.length>0){var e=[];if(Array.isArray(this.options.dataAttributes))e=this.options.dataAttributes;else{if("all"!=this.options.dataAttributes)return;e=Object.keys(this.data)}for(var i=0;it.start},s.prototype.redraw=function(){var t=this.dom;if(t||(this.dom={},t=this.dom,t.box=document.createElement("div"),t.content=document.createElement("div"),t.content.className="content",t.box.appendChild(t.content),this.dirty=!0),!this.parent)throw new Error("Cannot redraw item: no parent attached");if(!t.box.parentNode){var e=this.parent.dom.background;if(!e)throw new Error("Cannot redraw item: parent has no background container element");e.appendChild(t.box)}if(this.displayed=!0,this.dirty){this._updateContents(this.dom.content),this._updateTitle(this.dom.content),this._updateDataAttributes(this.dom.content),this._updateStyle(this.dom.box);var i=(this.data.className?" "+this.data.className:"")+(this.selected?" selected":"");t.box.className=this.baseClassName+i,this.overflow="hidden"!==window.getComputedStyle(t.content).overflow,this.props.content.width=this.dom.content.offsetWidth,this.height=0,this.dirty=!1}},s.prototype.show=r.prototype.show,s.prototype.hide=r.prototype.hide,s.prototype.repositionX=r.prototype.repositionX,s.prototype.repositionY=function(t){var e="top"===this.options.orientation;this.dom.content.style.top=e?"":"0",this.dom.content.style.bottom=e?"0":"";var i;if(void 0!==this.data.subgroup){var s=this.data.subgroup,o=this.parent.subgroups,r=o[s].index;if(1==e){i=this.parent.subgroups[s].height+t.item.vertical,i+=0==r?t.axis-.5*t.item.vertical:0;var a=this.parent.top;for(var h in o)o.hasOwnProperty(h)&&1==o[h].visible&&o[h].indexr&&(a+=o[h].height+t.item.vertical);i=this.parent.subgroups[s].height+t.item.vertical,this.dom.box.style.top=a+"px",this.dom.box.style.bottom=""}}else this.parent instanceof n?(i=Math.max(this.parent.height,this.parent.itemSet.body.domProps.center.height,this.parent.itemSet.body.domProps.centerContainer.height),this.dom.box.style.top=e?"0":"",this.dom.box.style.bottom=e?"":"0"):(i=this.parent.height,this.dom.box.style.top=this.parent.top+"px",this.dom.box.style.bottom="");this.dom.box.style.height=i+"px"},t.exports=s},function(t,e,i){function s(t,e,i){if(this.props={dot:{width:0,height:0},line:{width:0,height:0}},t&&void 0==t.start)throw new Error('Property "start" missing in item '+t);o.call(this,t,e,i)}{var o=i(31);i(1)}s.prototype=new o(null,null,null),s.prototype.isVisible=function(t){var e=(t.end-t.start)/4;return this.data.start>t.start-e&&this.data.startt.start-e&&this.data.startt.start},s.prototype.redraw=function(){var t=this.dom;if(t||(this.dom={},t=this.dom,t.box=document.createElement("div"),t.content=document.createElement("div"),t.content.className="content",t.box.appendChild(t.content),t.box["timeline-item"]=this,this.dirty=!0),!this.parent)throw new Error("Cannot redraw item: no parent attached");if(!t.box.parentNode){var e=this.parent.dom.foreground;if(!e)throw new Error("Cannot redraw item: parent has no foreground container element");e.appendChild(t.box)}if(this.displayed=!0,this.dirty){this._updateContents(this.dom.content),this._updateTitle(this.dom.box),this._updateDataAttributes(this.dom.box),this._updateStyle(this.dom.box);var i=(this.data.className?" "+this.data.className:"")+(this.selected?" selected":"");t.box.className=this.baseClassName+i,this.overflow="hidden"!==window.getComputedStyle(t.content).overflow,this.dom.content.style.maxWidth="none",this.props.content.width=this.dom.content.offsetWidth,this.height=this.dom.box.offsetHeight,this.dom.content.style.maxWidth="",this.dirty=!1}this._repaintDeleteButton(t.box),this._repaintDragLeft(),this._repaintDragRight()},s.prototype.show=function(){this.displayed||this.redraw()},s.prototype.hide=function(){if(this.displayed){var t=this.dom.box;t.parentNode&&t.parentNode.removeChild(t),this.top=null,this.left=null,this.displayed=!1}},s.prototype.repositionX=function(){var t,e,i=this.parent.width,s=this.conversion.toScreen(this.data.start),o=this.conversion.toScreen(this.data.end);-i>s&&(s=-i),o>2*i&&(o=2*i);var n=Math.max(o-s,1);switch(this.overflow?(this.left=s,this.width=n+this.props.content.width,e=this.props.content.width):(this.left=s,this.width=n,e=Math.min(o-s-2*this.options.padding,this.props.content.width)),this.dom.box.style.left=this.left+"px",this.dom.box.style.width=n+"px",this.options.align){case"left":this.dom.content.style.left="0";break;case"right":this.dom.content.style.left=Math.max(n-e-2*this.options.padding,0)+"px";break;case"center":this.dom.content.style.left=Math.max((n-e-2*this.options.padding)/2,0)+"px";break;default:t=this.overflow?o>0?Math.max(-s,0):-e:0>s?Math.min(-s,o-s-e-2*this.options.padding):0,this.dom.content.style.left=t+"px"}},s.prototype.repositionY=function(){var t=this.options.orientation,e=this.dom.box;e.style.top="top"==t?this.top+"px":this.parent.height-this.top-this.height+"px"},s.prototype._repaintDragLeft=function(){if(this.selected&&this.options.editable.updateTime&&!this.dom.dragLeft){var t=document.createElement("div");t.className="drag-left",t.dragLeftItem=this,this.dom.box.appendChild(t),this.dom.dragLeft=t}else!this.selected&&this.dom.dragLeft&&(this.dom.dragLeft.parentNode&&this.dom.dragLeft.parentNode.removeChild(this.dom.dragLeft),this.dom.dragLeft=null)},s.prototype._repaintDragRight=function(){if(this.selected&&this.options.editable.updateTime&&!this.dom.dragRight){var t=document.createElement("div");t.className="drag-right",t.dragRightItem=this,this.dom.box.appendChild(t),this.dom.dragRight=t}else!this.selected&&this.dom.dragRight&&(this.dom.dragRight.parentNode&&this.dom.dragRight.parentNode.removeChild(this.dom.dragRight),this.dom.dragRight=null)},t.exports=s},function(t,e,i){function s(t,e,i){if(!(this instanceof s))throw new SyntaxError("Constructor must be called with the new operator");this._determineBrowserMethod(),this._initializeMixinLoaders(),this.containerElement=t,this.renderRefreshRate=60,this.renderTimestep=1e3/this.renderRefreshRate,this.renderTime=0,this.physicsTime=0,this.runDoubleSpeed=!1,this.physicsDiscreteStepsize=.5,this.initializing=!0,this.triggerFunctions={add:null,edit:null,editEdge:null,connect:null,del:null},this.defaultOptions={nodes:{mass:1,radiusMin:10,radiusMax:30,radius:10,shape:"ellipse",image:void 0,widthMin:16,widthMax:64,fontColor:"black",fontSize:14,fontFace:"verdana",fontFill:void 0,fontStrokeWidth:0,fontStrokeColor:"white",level:-1,color:{border:"#2B7CE9",background:"#97C2FC",highlight:{border:"#2B7CE9",background:"#D2E5FF"},hover:{border:"#2B7CE9",background:"#D2E5FF"}},group:void 0,borderWidth:1,borderWidthSelected:void 0},edges:{widthMin:1,widthMax:15,width:1,widthSelectionMultiplier:2,hoverWidth:1.5,style:"line",color:{color:"#848484",highlight:"#848484",hover:"#848484"},fontColor:"#343434",fontSize:14,fontFace:"arial",fontFill:"white",fontStrokeWidth:0,fontStrokeColor:"white",labelAlignment:"horizontal",arrowScaleFactor:1,dash:{length:10,gap:5,altLength:void 0},inheritColor:"from"},configurePhysics:!1,physics:{barnesHut:{enabled:!0,thetaInverted:2,gravitationalConstant:-2e3,centralGravity:.3,springLength:95,springConstant:.04,damping:.09},repulsion:{centralGravity:0,springLength:200,springConstant:.05,nodeDistance:100,damping:.09},hierarchicalRepulsion:{enabled:!1,centralGravity:0,springLength:100,springConstant:.01,nodeDistance:150,damping:.09},damping:null,centralGravity:null,springLength:null,springConstant:null},clustering:{enabled:!1,initialMaxNodes:100,clusterThreshold:500,reduceToNodes:300,chainThreshold:.4,clusterEdgeThreshold:20,sectorThreshold:100,screenSizeThreshold:.2,fontSizeMultiplier:4,maxFontSize:1e3,forceAmplification:.1,distanceAmplification:.1,edgeGrowth:20,nodeScaling:{width:1,height:1,radius:1},maxNodeSizeIncrements:600,activeAreaBoxSize:80,clusterLevelDifference:2},navigation:{enabled:!1},keyboard:{enabled:!1,speed:{x:10,y:10,zoom:.02}},dataManipulation:{enabled:!1,initiallyVisible:!1},hierarchicalLayout:{enabled:!1,levelSeparation:150,nodeSpacing:100,direction:"UD",layout:"hubsize"},freezeForStabilization:!1,smoothCurves:{enabled:!0,dynamic:!0,type:"continuous",roundness:.5},maxVelocity:30,minVelocity:.1,stabilize:!0,stabilizationIterations:1e3,zoomExtentOnStabilize:!0,locale:"en",locales:_,tooltip:{delay:300,fontColor:"black",fontSize:14,fontFace:"verdana",color:{border:"#666",background:"#FFFFC6"}},dragNetwork:!0,dragNodes:!0,zoomable:!0,hover:!1,hideEdgesOnDrag:!1,hideNodesOnDrag:!1,width:"100%",height:"100%",selectable:!0},this.constants=a.extend({},this.defaultOptions),this.pixelRatio=1,this.hoverObj={nodes:{},edges:{}},this.controlNodesActive=!1,this.navigationHammers={existing:[],_new:[]},this.animationSpeed=1/this.renderRefreshRate,this.animationEasingFunction="easeInOutQuint",this.easingTime=0,this.sourceScale=0,this.targetScale=0,this.sourceTranslation=0,this.targetTranslation=0,this.lockedOnNodeId=null,this.lockedOnNodeOffset=null,this.touchTime=0;var o=this;this.groups=new u,this.images=new m,this.images.setOnloadCallback(function(){o._redraw()}),this.xIncrement=0,this.yIncrement=0,this.zoomIncrement=0,this._loadPhysicsSystem(),this._create(),this._loadSectorSystem(),this._loadClusterSystem(),this._loadSelectionSystem(),this._loadHierarchySystem(),this._setTranslation(this.frame.clientWidth/2,this.frame.clientHeight/2),this._setScale(1),this.setOptions(i),this.freezeSimulation=!1,this.cachedFunctions={},this.startedStabilization=!1,this.stabilized=!1,this.stabilizationIterations=null,this.draggingNodes=!1,this.calculationNodes={},this.calculationNodeIndices=[],this.nodeIndices=[],this.nodes={},this.edges={},this.canvasTopLeft={x:0,y:0},this.canvasBottomRight={x:0,y:0},this.pointerPosition={x:0,y:0},this.areaCenter={},this.scale=1,this.previousScale=this.scale,this.nodesData=null,this.edgesData=null,this.nodesListeners={add:function(t,e){o._addNodes(e.items),o.start()},update:function(t,e){o._updateNodes(e.items,e.data),o.start()},remove:function(t,e){o._removeNodes(e.items),o.start()}},this.edgesListeners={add:function(t,e){o._addEdges(e.items),o.start()},update:function(t,e){o._updateEdges(e.items),o.start()},remove:function(t,e){o._removeEdges(e.items),o.start()}},this.moving=!0,this.timer=void 0,this.setData(e,this.constants.clustering.enabled||this.constants.hierarchicalLayout.enabled),this.initializing=!1,1==this.constants.hierarchicalLayout.enabled?this._setupHierarchicalLayout():0==this.constants.stabilize&&this.zoomExtent(void 0,!0,this.constants.clustering.enabled),this.constants.clustering.enabled&&this.startWithClustering()}var o=i(56),n=i(45),r=i(58),a=i(1),h=i(47),d=i(3),l=i(4),c=i(42),p=i(43),u=i(38),m=i(39),f=i(40),g=i(37),v=i(41),y=i(54),b=i(55),_=i(49);i(50),o(s.prototype),s.prototype._determineBrowserMethod=function(){var t=navigator.userAgent.toLowerCase();this.requiresTimeout=!1,-1!=t.indexOf("msie 9.0")?this.requiresTimeout=!0:-1!=t.indexOf("safari")&&t.indexOf("chrome")<=-1&&(this.requiresTimeout=!0)},s.prototype._getScriptPath=function(){for(var t=document.getElementsByTagName("script"),e=0;et.boundingBox.left&&(s=t.boundingBox.left),ot.boundingBox.bottom&&(e=t.boundingBox.bottom),i=this.constants.clustering.initialMaxNodes?49.07548/(n+142.05338)+91444e-8:12.662/(n+7.4147)+.0964822:1==this.constants.clustering.enabled&&n>=this.constants.clustering.initialMaxNodes?77.5271985/(n+187.266146)+476710517e-13:30.5062972/(n+19.93597763)+.08413486;var r=Math.min(this.frame.canvas.clientWidth/600,this.frame.canvas.clientHeight/600);s*=r}else{var a=1.1*Math.abs(o.maxX-o.minX),h=1.1*Math.abs(o.maxY-o.minY),d=this.frame.canvas.clientWidth/a,l=this.frame.canvas.clientHeight/h;s=l>=d?d:l}s>1&&(s=1);var c=this._findCenter(o);if(0==i){var p={position:c,scale:s,animation:t};this.moveTo(p),this.moving=!0,this.start()}else c.x*=s,c.y*=s,c.x-=.5*this.frame.canvas.clientWidth,c.y-=.5*this.frame.canvas.clientHeight,this._setScale(s),this._setTranslation(-c.x,-c.y)},s.prototype._updateNodeIndexList=function(){this._clearNodeIndexList();for(var t in this.nodes)this.nodes.hasOwnProperty(t)&&this.nodeIndices.push(t)},s.prototype.setData=function(t,e){if(void 0===e&&(e=!1),this.initializing=!0,t&&t.dot&&(t.nodes||t.edges))throw new SyntaxError('Data must contain either parameter "dot" or parameter pair "nodes" and "edges", but not both.');if(1==this.constants.dataManipulation.enabled&&this._createManipulatorBar(),this.setOptions(t&&t.options),t&&t.dot){if(t&&t.dot){var i=c.DOTToGraph(t.dot);return void this.setData(i)}}else if(t&&t.gephi){if(t&&t.gephi){var s=p.parseGephi(t.gephi);return void this.setData(s)}}else this._setNodes(t&&t.nodes),this._setEdges(t&&t.edges);this._putDataInSector(),0==e&&(1==this.constants.hierarchicalLayout.enabled?(this._resetLevels(),this._setupHierarchicalLayout()):this.constants.stabilize&&this._stabilize(),this.start()),this.initializing=!1},s.prototype.setOptions=function(t){if(t){var e,i=["nodes","edges","smoothCurves","hierarchicalLayout","clustering","navigation","keyboard","dataManipulation","onAdd","onEdit","onEditEdge","onConnect","onDelete","clickToUse"];if(a.selectiveNotDeepExtend(i,this.constants,t),a.selectiveNotDeepExtend(["color"],this.constants.nodes,t.nodes),a.selectiveNotDeepExtend(["color","length"],this.constants.edges,t.edges),t.physics&&(a.mergeOptions(this.constants.physics,t.physics,"barnesHut"),a.mergeOptions(this.constants.physics,t.physics,"repulsion"),t.physics.hierarchicalRepulsion)){this.constants.hierarchicalLayout.enabled=!0,this.constants.physics.hierarchicalRepulsion.enabled=!0,this.constants.physics.barnesHut.enabled=!1;for(e in t.physics.hierarchicalRepulsion)t.physics.hierarchicalRepulsion.hasOwnProperty(e)&&(this.constants.physics.hierarchicalRepulsion[e]=t.physics.hierarchicalRepulsion[e]) +}if(t.onAdd&&(this.triggerFunctions.add=t.onAdd),t.onEdit&&(this.triggerFunctions.edit=t.onEdit),t.onEditEdge&&(this.triggerFunctions.editEdge=t.onEditEdge),t.onConnect&&(this.triggerFunctions.connect=t.onConnect),t.onDelete&&(this.triggerFunctions.del=t.onDelete),a.mergeOptions(this.constants,t,"smoothCurves"),a.mergeOptions(this.constants,t,"hierarchicalLayout"),a.mergeOptions(this.constants,t,"clustering"),a.mergeOptions(this.constants,t,"navigation"),a.mergeOptions(this.constants,t,"keyboard"),a.mergeOptions(this.constants,t,"dataManipulation"),t.dataManipulation&&(this.editMode=this.constants.dataManipulation.initiallyVisible),t.edges&&(void 0!==t.edges.color&&(a.isString(t.edges.color)?(this.constants.edges.color={},this.constants.edges.color.color=t.edges.color,this.constants.edges.color.highlight=t.edges.color,this.constants.edges.color.hover=t.edges.color):(void 0!==t.edges.color.color&&(this.constants.edges.color.color=t.edges.color.color),void 0!==t.edges.color.highlight&&(this.constants.edges.color.highlight=t.edges.color.highlight),void 0!==t.edges.color.hover&&(this.constants.edges.color.hover=t.edges.color.hover)),this.constants.edges.inheritColor=!1),t.edges.fontColor||void 0!==t.edges.color&&(a.isString(t.edges.color)?this.constants.edges.fontColor=t.edges.color:void 0!==t.edges.color.color&&(this.constants.edges.fontColor=t.edges.color.color))),t.nodes&&t.nodes.color){var s=a.parseColor(t.nodes.color);this.constants.nodes.color.background=s.background,this.constants.nodes.color.border=s.border,this.constants.nodes.color.highlight.background=s.highlight.background,this.constants.nodes.color.highlight.border=s.highlight.border,this.constants.nodes.color.hover.background=s.hover.background,this.constants.nodes.color.hover.border=s.hover.border}if(t.groups)for(var o in t.groups)if(t.groups.hasOwnProperty(o)){var n=t.groups[o];this.groups.add(o,n)}if(t.tooltip){for(e in t.tooltip)t.tooltip.hasOwnProperty(e)&&(this.constants.tooltip[e]=t.tooltip[e]);t.tooltip.color&&(this.constants.tooltip.color=a.parseColor(t.tooltip.color))}if("clickToUse"in t&&(t.clickToUse?this.activator||(this.activator=new b(this.frame),this.activator.on("change",this._createKeyBinds.bind(this))):this.activator&&(this.activator.destroy(),delete this.activator)),t.labels)throw new Error('Option "labels" is deprecated. Use options "locale" and "locales" instead.');this._loadPhysicsSystem(),this._loadNavigationControls(),this._loadManipulationSystem(),this._configureSmoothCurves(),this._createKeyBinds(),this.setSize(this.constants.width,this.constants.height),this.moving=!0,this.start()}},s.prototype._create=function(){for(;this.containerElement.hasChildNodes();)this.containerElement.removeChild(this.containerElement.firstChild);if(this.frame=document.createElement("div"),this.frame.className="vis network-frame",this.frame.style.position="relative",this.frame.style.overflow="hidden",this.frame.canvas=document.createElement("canvas"),this.frame.canvas.style.position="relative",this.frame.appendChild(this.frame.canvas),this.frame.canvas.getContext){var t=this.frame.canvas.getContext("2d");this.pixelRatio=(window.devicePixelRatio||1)/(t.webkitBackingStorePixelRatio||t.mozBackingStorePixelRatio||t.msBackingStorePixelRatio||t.oBackingStorePixelRatio||t.backingStorePixelRatio||1),this.frame.canvas.getContext("2d").setTransform(this.pixelRatio,0,0,this.pixelRatio,0,0)}else{var e=document.createElement("DIV");e.style.color="red",e.style.fontWeight="bold",e.style.padding="10px",e.innerHTML="Error: your browser does not support HTML canvas",this.frame.canvas.appendChild(e)}var i=this;this.drag={},this.pinch={},this.hammer=new n(this.frame.canvas),this.hammer.get("pinch").set({enable:!0}),this.hammer.on("tap",i._onTap.bind(i)),this.hammer.on("doubletap",i._onDoubleTap.bind(i)),this.hammer.on("press",i._onHold.bind(i)),this.hammer.on("pinch",i._onPinch.bind(i)),h.onTouch(this.hammer,i._onTouch.bind(i)),this.hammer.on("panstart",i._onDragStart.bind(i)),this.hammer.on("panmove",i._onDrag.bind(i)),this.hammer.on("panend",i._onDragEnd.bind(i)),this.frame.canvas.addEventListener("mousemove",i._onMouseMoveTitle.bind(i)),this.frame.canvas.addEventListener("mousewheel",i._onMouseWheel.bind(i)),this.frame.canvas.addEventListener("DOMMouseScroll",i._onMouseWheel.bind(i)),this.containerElement.appendChild(this.frame)},s.prototype._createKeyBinds=function(){var t=this;void 0!==this.keycharm&&this.keycharm.destroy(),this.keycharm=r(),this.keycharm.reset(),this.constants.keyboard.enabled&&this.isActive()&&(this.keycharm.bind("up",this._moveUp.bind(t),"keydown"),this.keycharm.bind("up",this._yStopMoving.bind(t),"keyup"),this.keycharm.bind("down",this._moveDown.bind(t),"keydown"),this.keycharm.bind("down",this._yStopMoving.bind(t),"keyup"),this.keycharm.bind("left",this._moveLeft.bind(t),"keydown"),this.keycharm.bind("left",this._xStopMoving.bind(t),"keyup"),this.keycharm.bind("right",this._moveRight.bind(t),"keydown"),this.keycharm.bind("right",this._xStopMoving.bind(t),"keyup"),this.keycharm.bind("=",this._zoomIn.bind(t),"keydown"),this.keycharm.bind("=",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("num+",this._zoomIn.bind(t),"keydown"),this.keycharm.bind("num+",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("num-",this._zoomOut.bind(t),"keydown"),this.keycharm.bind("num-",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("-",this._zoomOut.bind(t),"keydown"),this.keycharm.bind("-",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("[",this._zoomIn.bind(t),"keydown"),this.keycharm.bind("[",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("]",this._zoomOut.bind(t),"keydown"),this.keycharm.bind("]",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("pageup",this._zoomIn.bind(t),"keydown"),this.keycharm.bind("pageup",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("pagedown",this._zoomOut.bind(t),"keydown"),this.keycharm.bind("pagedown",this._stopZoom.bind(t),"keyup")),1==this.constants.dataManipulation.enabled&&(this.keycharm.bind("esc",this._createManipulatorBar.bind(t)),this.keycharm.bind("delete",this._deleteSelected.bind(t)))},s.prototype.destroy=function(){this.start=function(){},this.redraw=function(){},this.timer=!1,this._cleanupPhysicsConfiguration(),this.keycharm.reset(),this.hammer.destroy(),this.off(),this._recursiveDOMDelete(this.containerElement)},s.prototype._recursiveDOMDelete=function(t){for(;1==t.hasChildNodes();)this._recursiveDOMDelete(t.firstChild),t.removeChild(t.firstChild)},s.prototype._getPointer=function(t){return{x:t.x-a.getAbsoluteLeft(this.frame.canvas),y:t.y-a.getAbsoluteTop(this.frame.canvas)}},s.prototype._onTouch=function(t){(new Date).valueOf()-this.touchTime>100&&(this.drag.pointer=this._getPointer(t.center),this.drag.pinched=!1,this.pinch.scale=this._getScale(),this.touchTime=(new Date).valueOf(),this._handleTouch(this.drag.pointer))},s.prototype._onDragStart=function(t){this._handleDragStart(t)},s.prototype._handleDragStart=function(t){void 0===this.drag.pointer&&this._onTouch(t);var e=this._getNodeAt(this.drag.pointer);if(this.drag.dragging=!0,this.drag.selection=[],this.drag.translation=this._getTranslation(),this.drag.nodeId=null,this.draggingNodes=!1,null!=e&&1==this.constants.dragNodes){this.draggingNodes=!0,this.drag.nodeId=e.id,e.isSelected()||this._selectObject(e,!1),this.emit("dragStart",{nodeIds:this.getSelection().nodes});for(var i in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(i)){var s=this.selectionObj.nodes[i],o={id:s.id,node:s,x:s.x,y:s.y,xFixed:s.xFixed,yFixed:s.yFixed};s.xFixed=!0,s.yFixed=!0,this.drag.selection.push(o)}}t.preventDefault()},s.prototype._onDrag=function(t){this._handleOnDrag(t)},s.prototype._handleOnDrag=function(t){if(!this.drag.pinched){this.releaseNode();var e=this._getPointer(t.center),i=this,s=this.drag,o=s.selection;if(o&&o.length&&1==this.constants.dragNodes){var n=e.x-s.pointer.x,r=e.y-s.pointer.y;o.forEach(function(t){var e=t.node;t.xFixed||(e.x=i._XconvertDOMtoCanvas(i._XconvertCanvasToDOM(t.x)+n)),t.yFixed||(e.y=i._YconvertDOMtoCanvas(i._YconvertCanvasToDOM(t.y)+r))}),this.moving||(this.moving=!0,this.start())}else if(1==this.constants.dragNetwork){if(void 0===this.drag.pointer)return void this._handleDragStart(t);var a=e.x-this.drag.pointer.x,h=e.y-this.drag.pointer.y;this._setTranslation(this.drag.translation.x+a,this.drag.translation.y+h),this._redraw()}t.preventDefault()}},s.prototype._onDragEnd=function(t){this._handleDragEnd(t)},s.prototype._handleDragEnd=function(t){this.drag.dragging=!1;var e=this.drag.selection;e&&e.length?(e.forEach(function(t){t.node.xFixed=t.xFixed,t.node.yFixed=t.yFixed}),this.moving=!0,this.start()):this._redraw(),0==this.draggingNodes?this.emit("dragEnd",{nodeIds:[]}):this.emit("dragEnd",{nodeIds:this.getSelection().nodes}),t.preventDefault()},s.prototype._onTap=function(t){var e=this._getPointer(t.center);this.pointerPosition=e,this._handleTap(e)},s.prototype._onDoubleTap=function(t){var e=this._getPointer(t.center);this._handleDoubleTap(e)},s.prototype._onHold=function(t){var e=this._getPointer(t.center);this.pointerPosition=e,this._handleOnHold(e)},s.prototype._onRelease=function(t){var e=this._getPointer(t.center);this._handleOnRelease(e)},s.prototype._onPinch=function(t){var e=this._getPointer(t.center);this.drag.pinched=!0,"scale"in this.pinch||(this.pinch.scale=1);var i=this.pinch.scale*t.scale;this._zoom(i,e)},s.prototype._zoom=function(t,e){if(1==this.constants.zoomable){var i=this._getScale();1e-5>t&&(t=1e-5),t>10&&(t=10);var s=null;void 0!==this.drag&&1==this.drag.dragging&&(s=this.DOMtoCanvas(this.drag.pointer));var o=this._getTranslation(),n=t/i,r=(1-n)*e.x+o.x*n,a=(1-n)*e.y+o.y*n;if(this.areaCenter={x:this._XconvertDOMtoCanvas(e.x),y:this._YconvertDOMtoCanvas(e.y)},this._setScale(t),this._setTranslation(r,a),this.updateClustersDefault(),null!=s){var h=this.canvasToDOM(s);this.drag.pointer.x=h.x,this.drag.pointer.y=h.y}return this._redraw(),t>i?this.emit("zoom",{direction:"+"}):this.emit("zoom",{direction:"-"}),t}},s.prototype._onMouseWheel=function(t){var e=0;if(t.wheelDelta?e=t.wheelDelta/120:t.detail&&(e=-t.detail/3),e){var i=this._getScale(),s=e/10;0>e&&(s/=1-s),i*=1+s;var o=this._getPointer({x:t.pageX,y:t.pageY});this._zoom(i,o)}t.preventDefault()},s.prototype._onMouseMoveTitle=function(t){var e=this._getPointer({x:t.pageX,y:t.pageY});this.popupObj&&this._checkHidePopup(e);var i=this,s=function(){i._checkShowPopup(e)};if(this.popupTimer&&clearInterval(this.popupTimer),this.drag.dragging||(this.popupTimer=setTimeout(s,this.constants.tooltip.delay)),1==this.constants.hover){for(var o in this.hoverObj.edges)this.hoverObj.edges.hasOwnProperty(o)&&(this.hoverObj.edges[o].hover=!1,delete this.hoverObj.edges[o]);var n=this._getNodeAt(e);null==n&&(n=this._getEdgeAt(e)),null!=n&&this._hoverObject(n);for(var r in this.hoverObj.nodes)this.hoverObj.nodes.hasOwnProperty(r)&&(n instanceof f&&n.id!=r||n instanceof g||null==n)&&(this._blurObject(this.hoverObj.nodes[r]),delete this.hoverObj.nodes[r]);this.redraw()}},s.prototype._checkShowPopup=function(t){var e,i={left:this._XconvertDOMtoCanvas(t.x),top:this._YconvertDOMtoCanvas(t.y),right:this._XconvertDOMtoCanvas(t.x),bottom:this._YconvertDOMtoCanvas(t.y)},s=this.popupObj,o=!1;if(void 0==this.popupObj){var n=this.nodes,r=[];for(e in n)if(n.hasOwnProperty(e)){var a=n[e];a.isOverlappingWith(i)&&void 0!==a.getTitle()&&r.push(e)}r.length>0&&(this.popupObj=this.nodes[r[r.length-1]],o=!0)}if(void 0===this.popupObj&&0==o){var h=this.edges,d=[];for(e in h)if(h.hasOwnProperty(e)){var l=h[e];l.connected&&void 0!==l.getTitle()&&l.isOverlappingWith(i)&&d.push(e)}d.length>0&&(this.popupObj=this.edges[d[d.length-1]])}if(this.popupObj){if(this.popupObj!=s){var c=this;c.popup||(c.popup=new v(c.frame,c.constants.tooltip)),c.popup.setPosition(t.x-3,t.y-3),c.popup.setText(c.popupObj.getTitle()),c.popup.show()}}else this.popup&&this.popup.hide()},s.prototype._checkHidePopup=function(t){this.popupObj&&this._getNodeAt(t)||(this.popupObj=void 0,this.popup&&this.popup.hide())},s.prototype.setSize=function(t,e){var i=!1,s=this.frame.canvas.width,o=this.frame.canvas.height;t!=this.constants.width||e!=this.constants.height||this.frame.style.width!=t||this.frame.style.height!=e?(this.frame.style.width=t,this.frame.style.height=e,this.frame.canvas.style.width="100%",this.frame.canvas.style.height="100%",this.frame.canvas.width=this.frame.canvas.clientWidth*this.pixelRatio,this.frame.canvas.height=this.frame.canvas.clientHeight*this.pixelRatio,this.constants.width=t,this.constants.height=e,i=!0):(this.frame.canvas.width!=this.frame.canvas.clientWidth*this.pixelRatio&&(this.frame.canvas.width=this.frame.canvas.clientWidth*this.pixelRatio,i=!0),this.frame.canvas.height!=this.frame.canvas.clientHeight*this.pixelRatio&&(this.frame.canvas.height=this.frame.canvas.clientHeight*this.pixelRatio,i=!0)),1==i&&this.emit("resize",{width:this.frame.canvas.width*this.pixelRatio,height:this.frame.canvas.height*this.pixelRatio,oldWidth:s*this.pixelRatio,oldHeight:o*this.pixelRatio})},s.prototype._setNodes=function(t){var e=this.nodesData;if(t instanceof d||t instanceof l)this.nodesData=t;else if(Array.isArray(t))this.nodesData=new d,this.nodesData.add(t);else{if(t)throw new TypeError("Array or DataSet expected");this.nodesData=new d}if(e&&a.forEach(this.nodesListeners,function(t,i){e.off(i,t)}),this.nodes={},this.nodesData){var i=this;a.forEach(this.nodesListeners,function(t,e){i.nodesData.on(e,t)});var s=this.nodesData.getIds();this._addNodes(s)}this._updateSelection()},s.prototype._addNodes=function(t){for(var e,i=0,s=t.length;s>i;i++){e=t[i];var o=this.nodesData.get(e),n=new f(o,this.images,this.groups,this.constants);if(this.nodes[e]=n,!(0!=n.xFixed&&0!=n.yFixed||null!==n.x&&null!==n.y)){var r=1*t.length+10,a=2*Math.PI*Math.random();0==n.xFixed&&(n.x=r*Math.cos(a)),0==n.yFixed&&(n.y=r*Math.sin(a))}this.moving=!0}this._updateNodeIndexList(),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this._updateCalculationNodes(),this._reconnectEdges(),this._updateValueRange(this.nodes),this.updateLabels()},s.prototype._updateNodes=function(t,e){for(var i=this.nodes,s=0,o=t.length;o>s;s++){var n=t[s],r=i[n],a=e[s];r?r.setProperties(a,this.constants):(r=new f(properties,this.images,this.groups,this.constants),i[n]=r)}this.moving=!0,1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this._updateNodeIndexList(),this._updateValueRange(i)},s.prototype._removeNodes=function(t){for(var e=this.nodes,i=0,s=t.length;s>i;i++){var o=t[i];delete e[o]}this._updateNodeIndexList(),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this._updateCalculationNodes(),this._reconnectEdges(),this._updateSelection(),this._updateValueRange(e)},s.prototype._setEdges=function(t){var e=this.edgesData;if(t instanceof d||t instanceof l)this.edgesData=t;else if(Array.isArray(t))this.edgesData=new d,this.edgesData.add(t);else{if(t)throw new TypeError("Array or DataSet expected");this.edgesData=new d}if(e&&a.forEach(this.edgesListeners,function(t,i){e.off(i,t)}),this.edges={},this.edgesData){var i=this;a.forEach(this.edgesListeners,function(t,e){i.edgesData.on(e,t)});var s=this.edgesData.getIds();this._addEdges(s)}this._reconnectEdges()},s.prototype._addEdges=function(t){for(var e=this.edges,i=this.edgesData,s=0,o=t.length;o>s;s++){var n=t[s],r=e[n];r&&r.disconnect();var a=i.get(n,{showInternalIds:!0});e[n]=new g(a,this,this.constants)}this.moving=!0,this._updateValueRange(e),this._createBezierNodes(),this._updateCalculationNodes(),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout())},s.prototype._updateEdges=function(t){for(var e=this.edges,i=this.edgesData,s=0,o=t.length;o>s;s++){var n=t[s],r=i.get(n),a=e[n];a?(a.disconnect(),a.setProperties(r,this.constants),a.connect()):(a=new g(r,this,this.constants),this.edges[n]=a)}this._createBezierNodes(),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this.moving=!0,this._updateValueRange(e)},s.prototype._removeEdges=function(t){for(var e=this.edges,i=0,s=t.length;s>i;i++){var o=t[i],n=e[o];n&&(null!=n.via&&delete this.sectors.support.nodes[n.via.id],n.disconnect(),delete e[o])}this.moving=!0,this._updateValueRange(e),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this._updateCalculationNodes()},s.prototype._reconnectEdges=function(){var t,e=this.nodes,i=this.edges;for(t in e)e.hasOwnProperty(t)&&(e[t].edges=[],e[t].dynamicEdges=[]);for(t in i)if(i.hasOwnProperty(t)){var s=i[t];s.from=null,s.to=null,s.connect()}},s.prototype._updateValueRange=function(t){var e,i=void 0,s=void 0;for(e in t)if(t.hasOwnProperty(e)){var o=t[e].getValue();void 0!==o&&(i=void 0===i?o:Math.min(o,i),s=void 0===s?o:Math.max(o,s))}if(void 0!==i&&void 0!==s)for(e in t)t.hasOwnProperty(e)&&t[e].setValueRange(i,s)},s.prototype.redraw=function(){this.setSize(this.constants.width,this.constants.height),this._redraw()},s.prototype._redraw=function(t){var e=this.frame.canvas.getContext("2d");e.setTransform(this.pixelRatio,0,0,this.pixelRatio,0,0);var i=this.frame.canvas.width*this.pixelRatio,s=this.frame.canvas.height*this.pixelRatio;e.clearRect(0,0,i,s),e.save(),e.translate(this.translation.x,this.translation.y),e.scale(this.scale,this.scale),this.canvasTopLeft={x:this._XconvertDOMtoCanvas(0),y:this._YconvertDOMtoCanvas(0)},this.canvasBottomRight={x:this._XconvertDOMtoCanvas(this.frame.canvas.clientWidth*this.pixelRatio),y:this._YconvertDOMtoCanvas(this.frame.canvas.clientHeight*this.pixelRatio)},1!=t&&(this._doInAllSectors("_drawAllSectorNodes",e),(0==this.drag.dragging||void 0===this.drag.dragging||0==this.constants.hideEdgesOnDrag)&&this._doInAllSectors("_drawEdges",e)),(0==this.drag.dragging||void 0===this.drag.dragging||0==this.constants.hideNodesOnDrag)&&this._doInAllSectors("_drawNodes",e,!1),1!=t&&1==this.controlNodesActive&&this._doInAllSectors("_drawControlNodes",e),e.restore(),1==t&&e.clearRect(0,0,i,s)},s.prototype._setTranslation=function(t,e){void 0===this.translation&&(this.translation={x:0,y:0}),void 0!==t&&(this.translation.x=t),void 0!==e&&(this.translation.y=e),this.emit("viewChanged")},s.prototype._getTranslation=function(){return{x:this.translation.x,y:this.translation.y}},s.prototype._setScale=function(t){this.scale=t},s.prototype._getScale=function(){return this.scale},s.prototype._XconvertDOMtoCanvas=function(t){return(t-this.translation.x)/this.scale},s.prototype._XconvertCanvasToDOM=function(t){return t*this.scale+this.translation.x},s.prototype._YconvertDOMtoCanvas=function(t){return(t-this.translation.y)/this.scale},s.prototype._YconvertCanvasToDOM=function(t){return t*this.scale+this.translation.y},s.prototype.canvasToDOM=function(t){return{x:this._XconvertCanvasToDOM(t.x),y:this._YconvertCanvasToDOM(t.y)}},s.prototype.DOMtoCanvas=function(t){return{x:this._XconvertDOMtoCanvas(t.x),y:this._YconvertDOMtoCanvas(t.y)}},s.prototype._drawNodes=function(t,e){void 0===e&&(e=!1);var i=this.nodes,s=[];for(var o in i)i.hasOwnProperty(o)&&(i[o].setScaleAndPos(this.scale,this.canvasTopLeft,this.canvasBottomRight),i[o].isSelected()?s.push(o):(i[o].inArea()||e)&&i[o].draw(t));for(var n=0,r=s.length;r>n;n++)(i[s[n]].inArea()||e)&&i[s[n]].draw(t)},s.prototype._drawEdges=function(t){var e=this.edges;for(var i in e)if(e.hasOwnProperty(i)){var s=e[i];s.setScale(this.scale),s.connected&&e[i].draw(t)}},s.prototype._drawControlNodes=function(t){var e=this.edges;for(var i in e)e.hasOwnProperty(i)&&e[i]._drawControlNodes(t)},s.prototype._stabilize=function(){1==this.constants.freezeForStabilization&&this._freezeDefinedNodes();for(var t=0;this.moving&&t0)for(t in i)i.hasOwnProperty(t)&&(i[t].discreteStepLimited(e,this.constants.maxVelocity),s=!0);else for(t in i)i.hasOwnProperty(t)&&(i[t].discreteStep(e),s=!0);if(1==s){var o=this.constants.minVelocity/Math.max(this.scale,.05);return o>.5*this.constants.maxVelocity?!0:this._isMoving(o)}return!1},s.prototype._revertPhysicsState=function(){var t=this.nodes;for(var e in t)t.hasOwnProperty(e)&&t[e].revertPosition()},s.prototype._revertPhysicsTick=function(){this._doInAllActiveSectors("_revertPhysicsState"),1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic&&this._doInSupportSector("_revertPhysicsState")},s.prototype._physicsTick=function(){if(!this.freezeSimulation&&1==this.moving){var t=!1,e=!1;this._doInAllActiveSectors("_initializeForceCalculation");var i=this._doInAllActiveSectors("_discreteStepNodes");1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic&&(e=this._doInSupportSector("_discreteStepNodes"));for(var s=0;s2*e||1==this.runDoubleSpeed)&&1==this.moving&&(this._physicsTick(),0!=this.renderTime&&(this.runDoubleSpeed=!0));var i=Date.now();this._redraw(),this.renderTime=Date.now()-i,this.start()},"undefined"!=typeof window&&(window.requestAnimationFrame=window.requestAnimationFrame||window.mozRequestAnimationFrame||window.webkitRequestAnimationFrame||window.msRequestAnimationFrame),s.prototype.start=function(){if(1==this.moving||0!=this.xIncrement||0!=this.yIncrement||0!=this.zoomIncrement)this.timer||(this.timer=1==this.requiresTimeout?window.setTimeout(this._animationStep.bind(this),this.renderTimestep):window.requestAnimationFrame(this._animationStep.bind(this)));else if(this._redraw(),this.stabilizationIterations>1){var t=this,e={iterations:t.stabilizationIterations};this.stabilizationIterations=0,this.startedStabilization=!1,setTimeout(function(){t.emit("stabilized",e)},0)}else this.stabilizationIterations=0},s.prototype._handleNavigation=function(){if(0!=this.xIncrement||0!=this.yIncrement){var t=this._getTranslation();this._setTranslation(t.x+this.xIncrement,t.y+this.yIncrement)}if(0!=this.zoomIncrement){var e={x:this.frame.canvas.clientWidth/2,y:this.frame.canvas.clientHeight/2};this._zoom(this.scale*(1+this.zoomIncrement),e)}},s.prototype.toggleFreeze=function(){0==this.freezeSimulation?this.freezeSimulation=!0:(this.freezeSimulation=!1,this.start())},s.prototype._configureSmoothCurves=function(t){if(void 0===t&&(t=!0),1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic){this._createBezierNodes();for(var e in this.sectors.support.nodes)this.sectors.support.nodes.hasOwnProperty(e)&&void 0===this.edges[this.sectors.support.nodes[e].parentEdgeId]&&delete this.sectors.support.nodes[e]}else{this.sectors.support.nodes={};for(var i in this.edges)this.edges.hasOwnProperty(i)&&(this.edges[i].via=null)}this._updateCalculationNodes(),t||(this.moving=!0,this.start())},s.prototype._createBezierNodes=function(){if(1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic)for(var t in this.edges)if(this.edges.hasOwnProperty(t)){var e=this.edges[t];if(null==e.via){var i="edgeId:".concat(e.id);this.sectors.support.nodes[i]=new f({id:i,mass:1,shape:"circle",image:"",internalMultiplier:1},{},{},this.constants),e.via=this.sectors.support.nodes[i],e.via.parentEdgeId=e.id,e.positionBezierNode()}}},s.prototype._initializeMixinLoaders=function(){for(var t in y)y.hasOwnProperty(t)&&(s.prototype[t]=y[t])},s.prototype.storePosition=function(){console.log("storePosition is deprecated: use .storePositions() from now on."),this.storePositions()},s.prototype.storePositions=function(){var t=[];for(var e in this.nodes)if(this.nodes.hasOwnProperty(e)){var i=this.nodes[e],s=!this.nodes.xFixed,o=!this.nodes.yFixed;(this.nodesData._data[e].x!=Math.round(i.x)||this.nodesData._data[e].y!=Math.round(i.y))&&t.push({id:e,x:Math.round(i.x),y:Math.round(i.y),allowedToMoveX:s,allowedToMoveY:o})}this.nodesData.update(t)},s.prototype.getPositions=function(t){var e={};if(void 0!==t){if(1==Array.isArray(t)){for(var i=0;i=1&&(this.easingTime=0,this._redraw=null!=this.lockedOnNodeId?this._lockedRedraw:this._classicRedraw,this.emit("animationFinished"))},s.prototype._classicRedraw=function(){},s.prototype.isActive=function(){return!this.activator||this.activator.active},s.prototype.setScale=function(){return this._setScale()},s.prototype.getScale=function(){return this._getScale()},s.prototype.getCenterCoordinates=function(){return this.DOMtoCanvas({x:.5*this.frame.canvas.clientWidth,y:.5*this.frame.canvas.clientHeight})},s.prototype.getBoundingBox=function(t){return void 0!==this.nodes[t]?this.nodes[t].boundingBox:void 0},t.exports=s},function(t,e,i){function s(t,e,i){if(!e)throw"No network provided";var s=["edges","physics"],n=o.selectiveBridgeObject(s,i);this.options=n.edges,this.physics=n.physics,this.options.smoothCurves=i.smoothCurves,this.network=e,this.id=void 0,this.fromId=void 0,this.toId=void 0,this.title=void 0,this.widthSelected=this.options.width*this.options.widthSelectionMultiplier,this.value=void 0,this.selected=!1,this.hover=!1,this.labelDimensions={top:0,left:0,width:0,height:0,yLine:0},this.dirtyLabel=!0,this.from=null,this.to=null,this.via=null,this.fromBackup=null,this.toBackup=null,this.originalFromId=[],this.originalToId=[],this.connected=!1,this.widthFixed=!1,this.lengthFixed=!1,this.setProperties(t),this.controlNodesEnabled=!1,this.controlNodes={from:null,to:null,positions:{}},this.connectedNode=null}var o=i(1),n=i(40);s.prototype.setProperties=function(t){if(t){var e=["style","fontSize","fontFace","fontColor","fontFill","fontStrokeWidth","fontStrokeColor","width","widthSelectionMultiplier","hoverWidth","arrowScaleFactor","dash","inheritColor","labelAlignment"];switch(o.selectiveDeepExtend(e,this.options,t),void 0!==t.from&&(this.fromId=t.from),void 0!==t.to&&(this.toId=t.to),void 0!==t.id&&(this.id=t.id),void 0!==t.label&&(this.label=t.label,this.dirtyLabel=!0),void 0!==t.title&&(this.title=t.title),void 0!==t.value&&(this.value=t.value),void 0!==t.length&&(this.physics.springLength=t.length),void 0!==t.color&&(this.options.inheritColor=!1,o.isString(t.color)?(this.options.color.color=t.color,this.options.color.highlight=t.color):(void 0!==t.color.color&&(this.options.color.color=t.color.color),void 0!==t.color.highlight&&(this.options.color.highlight=t.color.highlight),void 0!==t.color.hover&&(this.options.color.hover=t.color.hover))),this.connect(),this.widthFixed=this.widthFixed||void 0!==t.width,this.lengthFixed=this.lengthFixed||void 0!==t.length,this.widthSelected=this.options.width*this.options.widthSelectionMultiplier,this.options.style){case"line":this.draw=this._drawLine;break;case"arrow":this.draw=this._drawArrow;break;case"arrow-center":this.draw=this._drawArrowCenter;break;case"dash-line":this.draw=this._drawDashLine;break;default:this.draw=this._drawLine}}},s.prototype.connect=function(){this.disconnect(),this.from=this.network.nodes[this.fromId]||null,this.to=this.network.nodes[this.toId]||null,this.connected=this.from&&this.to,this.connected?(this.from.attachEdge(this),this.to.attachEdge(this)):(this.from&&this.from.detachEdge(this),this.to&&this.to.detachEdge(this))},s.prototype.disconnect=function(){this.from&&(this.from.detachEdge(this),this.from=null),this.to&&(this.to.detachEdge(this),this.to=null),this.connected=!1},s.prototype.getTitle=function(){return"function"==typeof this.title?this.title():this.title +},s.prototype.getValue=function(){return this.value},s.prototype.setValueRange=function(t,e){if(!this.widthFixed&&void 0!==this.value){var i=(this.options.widthMax-this.options.widthMin)/(e-t);this.options.width=(this.value-t)*i+this.options.widthMin,this.widthSelected=this.options.width*this.options.widthSelectionMultiplier}},s.prototype.draw=function(){throw"Method draw not initialized in edge"},s.prototype.isOverlappingWith=function(t){if(this.connected){var e=10,i=this.from.x,s=this.from.y,o=this.to.x,n=this.to.y,r=t.left,a=t.top,h=this._getDistanceToEdge(i,s,o,n,r,a);return e>h}return!1},s.prototype._getColor=function(){var t=this.options.color;return"to"==this.options.inheritColor?t={highlight:this.to.options.color.highlight.border,hover:this.to.options.color.hover.border,color:this.to.options.color.border}:("from"==this.options.inheritColor||1==this.options.inheritColor)&&(t={highlight:this.from.options.color.highlight.border,hover:this.from.options.color.hover.border,color:this.from.options.color.border}),1==this.selected?t.highlight:1==this.hover?t.hover:t.color},s.prototype._drawLine=function(t){if(t.strokeStyle=this._getColor(),t.lineWidth=this._getLineWidth(),this.from!=this.to){var e,i=this._line(t);if(this.label){if(1==this.options.smoothCurves.enabled&&null!=i){var s=.5*(.5*(this.from.x+i.x)+.5*(this.to.x+i.x)),o=.5*(.5*(this.from.y+i.y)+.5*(this.to.y+i.y));e={x:s,y:o}}else e=this._pointOnLine(.5);this._label(t,this.label,e.x,e.y)}}else{var n,r,a=this.physics.springLength/4,h=this.from;h.width||h.resize(t),h.width>h.height?(n=h.x+h.width/2,r=h.y-a):(n=h.x+a,r=h.y-h.height/2),this._circle(t,n,r,a),e=this._pointOnCircle(n,r,a,.5),this._label(t,this.label,e.x,e.y)}},s.prototype._getLineWidth=function(){return 1==this.selected?Math.max(Math.min(this.widthSelected,this.options.widthMax),.3*this.networkScaleInv):1==this.hover?Math.max(Math.min(this.options.hoverWidth,this.options.widthMax),.3*this.networkScaleInv):Math.max(this.options.width,.3*this.networkScaleInv)},s.prototype._getViaCoordinates=function(){if(1==this.options.smoothCurves.dynamic&&1==this.options.smoothCurves.enabled)return this.via;if(0==this.options.smoothCurves.enabled)return{x:0,y:0};var t=null,e=null,i=this.options.smoothCurves.roundness,s=this.options.smoothCurves.type,o=Math.abs(this.from.x-this.to.x),n=Math.abs(this.from.y-this.to.y);return"discrete"==s||"diagonalCross"==s?Math.abs(this.from.x-this.to.x)this.to.y?this.from.xthis.to.x&&(t=this.from.x-i*n,e=this.from.y-i*n):this.from.ythis.to.x&&(t=this.from.x-i*n,e=this.from.y+i*n)),"discrete"==s&&(t=i*n>o?this.from.x:t)):Math.abs(this.from.x-this.to.x)>Math.abs(this.from.y-this.to.y)&&(this.from.y>this.to.y?this.from.xthis.to.x&&(t=this.from.x-i*o,e=this.from.y-i*o):this.from.ythis.to.x&&(t=this.from.x-i*o,e=this.from.y+i*o)),"discrete"==s&&(e=i*o>n?this.from.y:e)):"straightCross"==s?Math.abs(this.from.x-this.to.x)Math.abs(this.from.y-this.to.y)&&(t=this.from.xthis.to.y?this.from.xthis.to.x&&(t=this.from.x-i*n,e=this.from.y-i*n,t=this.to.x>t?this.to.x:t):this.from.ythis.to.x&&(t=this.from.x-i*n,e=this.from.y+i*n,t=this.to.x>t?this.to.x:t)):Math.abs(this.from.x-this.to.x)>Math.abs(this.from.y-this.to.y)&&(this.from.y>this.to.y?this.from.xe?this.to.y:e):this.from.x>this.to.x&&(t=this.from.x-i*o,e=this.from.y-i*o,e=this.to.y>e?this.to.y:e):this.from.ythis.to.x&&(t=this.from.x-i*o,e=this.from.y+i*o,e=this.to.yd;d++){var l=t.measureText(n[d]).width;h=l>h?l:h}var c=this.options.fontSize*r,p=i-h/2,u=s-c/2;this.labelDimensions={top:u,left:p,width:h,height:c,yLine:o}}var o=this.labelDimensions.yLine;t.save(),"horizontal"!=this.options.labelAlignment&&(t.translate(i,o),this._rotateForLabelAlignment(t),i=0,o=0),this._drawLabelRect(t),this._drawLabelText(t,i,o,n,r,a),t.restore()}},s.prototype._rotateForLabelAlignment=function(t){var e=this.from.y-this.to.y,i=this.from.x-this.to.x,s=Math.atan2(e,i);(-1>s&&0>i||s>0&&0>i)&&(s+=Math.PI),t.rotate(s)},s.prototype._drawLabelRect=function(t){if(void 0!==this.options.fontFill&&null!==this.options.fontFill&&"none"!==this.options.fontFill){t.fillStyle=this.options.fontFill;var e=2;"line-center"==this.options.labelAlignment?t.fillRect(.5*-this.labelDimensions.width,.5*-this.labelDimensions.height,this.labelDimensions.width,this.labelDimensions.height):"line-above"==this.options.labelAlignment?t.fillRect(.5*-this.labelDimensions.width,-(this.labelDimensions.height+e),this.labelDimensions.width,this.labelDimensions.height):"line-below"==this.options.labelAlignment?t.fillRect(.5*-this.labelDimensions.width,e,this.labelDimensions.width,this.labelDimensions.height):t.fillRect(this.labelDimensions.left,this.labelDimensions.top,this.labelDimensions.width,this.labelDimensions.height)}},s.prototype._drawLabelText=function(t,e,i,s,o,n){if(t.fillStyle=this.options.fontColor||"black",t.textAlign="center","horizontal"!=this.options.labelAlignment){var r=2;"line-above"==this.options.labelAlignment?(t.textBaseline="alphabetic",i-=2*r):"line-below"==this.options.labelAlignment?(t.textBaseline="hanging",i+=2*r):t.textBaseline="middle"}else t.textBaseline="middle";this.options.fontStrokeWidth>0&&(t.lineWidth=this.options.fontStrokeWidth,t.strokeStyle=this.options.fontStrokeColor,t.lineJoin="round");for(var a=0;o>a;a++)this.options.fontStrokeWidth>0&&t.strokeText(s[a],e,i),t.fillText(s[a],e,i),i+=n},s.prototype._drawDashLine=function(t){t.strokeStyle=this._getColor(),t.lineWidth=this._getLineWidth();var e=null;if(void 0!==t.setLineDash){t.save();var i=[0];i=void 0!==this.options.dash.length&&void 0!==this.options.dash.gap?[this.options.dash.length,this.options.dash.gap]:[5,5],t.setLineDash(i),t.lineDashOffset=0,e=this._line(t),t.setLineDash([0]),t.lineDashOffset=0,t.restore()}else t.beginPath(),t.lineCap="round",void 0!==this.options.dash.altLength?t.dashedLine(this.from.x,this.from.y,this.to.x,this.to.y,[this.options.dash.length,this.options.dash.gap,this.options.dash.altLength,this.options.dash.gap]):void 0!==this.options.dash.length&&void 0!==this.options.dash.gap?t.dashedLine(this.from.x,this.from.y,this.to.x,this.to.y,[this.options.dash.length,this.options.dash.gap]):(t.moveTo(this.from.x,this.from.y),t.lineTo(this.to.x,this.to.y)),t.stroke();if(this.label){var s;if(1==this.options.smoothCurves.enabled&&null!=e){var o=.5*(.5*(this.from.x+e.x)+.5*(this.to.x+e.x)),n=.5*(.5*(this.from.y+e.y)+.5*(this.to.y+e.y));s={x:o,y:n}}else s=this._pointOnLine(.5);this._label(t,this.label,s.x,s.y)}},s.prototype._pointOnLine=function(t){return{x:(1-t)*this.from.x+t*this.to.x,y:(1-t)*this.from.y+t*this.to.y}},s.prototype._pointOnCircle=function(t,e,i,s){var o=2*(s-3/8)*Math.PI;return{x:t+i*Math.cos(o),y:e-i*Math.sin(o)}},s.prototype._drawArrowCenter=function(t){var e;if(t.strokeStyle=this._getColor(),t.fillStyle=t.strokeStyle,t.lineWidth=this._getLineWidth(),this.from!=this.to){var i=this._line(t),s=Math.atan2(this.to.y-this.from.y,this.to.x-this.from.x),o=(10+5*this.options.width)*this.options.arrowScaleFactor;if(1==this.options.smoothCurves.enabled&&null!=i){var n=.5*(.5*(this.from.x+i.x)+.5*(this.to.x+i.x)),r=.5*(.5*(this.from.y+i.y)+.5*(this.to.y+i.y));e={x:n,y:r}}else e=this._pointOnLine(.5);t.arrow(e.x,e.y,s,o),t.fill(),t.stroke(),this.label&&this._label(t,this.label,e.x,e.y)}else{var a,h,d=.25*Math.max(100,this.physics.springLength),l=this.from;l.width||l.resize(t),l.width>l.height?(a=l.x+.5*l.width,h=l.y-d):(a=l.x+d,h=l.y-.5*l.height),this._circle(t,a,h,d);var s=.2*Math.PI,o=(10+5*this.options.width)*this.options.arrowScaleFactor;e=this._pointOnCircle(a,h,d,.5),t.arrow(e.x,e.y,s,o),t.fill(),t.stroke(),this.label&&(e=this._pointOnCircle(a,h,d,.5),this._label(t,this.label,e.x,e.y))}},s.prototype._pointOnBezier=function(t){var e=this._getViaCoordinates(),i=Math.pow(1-t,2)*this.from.x+2*t*(1-t)*e.x+Math.pow(t,2)*this.to.x,s=Math.pow(1-t,2)*this.from.y+2*t*(1-t)*e.y+Math.pow(t,2)*this.to.y;return{x:i,y:s}},s.prototype._findBorderPosition=function(t,e){var i,s,o,n,r,a=10,h=0,d=0,l=1,c=.2,p=this.to;for(1==t&&(p=this.from);l>=d&&a>h;){var u=.5*(d+l);if(i=this._pointOnBezier(u),s=Math.atan2(p.y-i.y,p.x-i.x),o=p.distanceToBorder(e,s),n=Math.sqrt(Math.pow(i.x-p.x,2)+Math.pow(i.y-p.y,2)),r=o-n,Math.abs(r)r?0==t?d=u:l=u:0==t?l=u:d=u,h++}return i.t=u,i},s.prototype._drawArrow=function(t){t.strokeStyle=this._getColor(),t.fillStyle=t.strokeStyle,t.lineWidth=this._getLineWidth();var e,i,s;if(this.from!=this.to){if(this._line(t),1==this.options.smoothCurves.enabled){var o=this._getViaCoordinates();s=this._findBorderPosition(!1,t);var n=this._pointOnBezier(Math.max(0,s.t-.1));e=Math.atan2(s.y-n.y,s.x-n.x)}else{e=Math.atan2(this.to.y-this.from.y,this.to.x-this.from.x);var r=this.to.x-this.from.x,a=this.to.y-this.from.y,h=Math.sqrt(r*r+a*a),d=this.to.distanceToBorder(t,e),l=(h-d)/h;s={},s.x=(1-l)*this.from.x+l*this.to.x,s.y=(1-l)*this.from.y+l*this.to.y}if(i=(10+5*this.options.width)*this.options.arrowScaleFactor,t.arrow(s.x,s.y,e,i),t.fill(),t.stroke(),this.label){var c;c=1==this.options.smoothCurves.enabled&&null!=o?this._pointOnBezier(.5):this._pointOnLine(.5),this._label(t,this.label,c.x,c.y)}}else{var p,u,m,f=this.from,g=.25*Math.max(100,this.physics.springLength);f.width||f.resize(t),f.width>f.height?(p=f.x+.5*f.width,u=f.y-g,m={x:p,y:f.y,angle:.9*Math.PI}):(p=f.x+g,u=f.y-.5*f.height,m={x:f.x,y:u,angle:.6*Math.PI}),t.beginPath(),t.arc(p,u,g,0,2*Math.PI,!1),t.stroke();var i=(10+5*this.options.width)*this.options.arrowScaleFactor;t.arrow(m.x,m.y,m.angle,i),t.fill(),t.stroke(),this.label&&(c=this._pointOnCircle(p,u,g,.5),this._label(t,this.label,c.x,c.y))}},s.prototype._getDistanceToEdge=function(t,e,i,s,o,n){var r=0;if(this.from!=this.to)if(1==this.options.smoothCurves.enabled){var a,h;if(1==this.options.smoothCurves.enabled&&1==this.options.smoothCurves.dynamic)a=this.via.x,h=this.via.y;else{var d=this._getViaCoordinates();a=d.x,h=d.y}var l,c,p,u,m,f,g,v=1e9;for(c=0;10>c;c++)p=.1*c,u=Math.pow(1-p,2)*t+2*p*(1-p)*a+Math.pow(p,2)*i,m=Math.pow(1-p,2)*e+2*p*(1-p)*h+Math.pow(p,2)*s,c>0&&(l=this._getDistanceToLine(f,g,u,m,o,n),v=v>l?l:v),f=u,g=m;r=v}else r=this._getDistanceToLine(t,e,i,s,o,n);else{var u,m,y,b,_=.25*this.physics.springLength,x=this.from;x.width>x.height?(u=x.x+.5*x.width,m=x.y-_):(u=x.x+_,m=x.y-.5*x.height),y=u-o,b=m-n,r=Math.abs(Math.sqrt(y*y+b*b)-_)}return this.labelDimensions.lefto&&this.labelDimensions.topn?0:r},s.prototype._getDistanceToLine=function(t,e,i,s,o,n){var r=i-t,a=s-e,h=r*r+a*a,d=((o-t)*r+(n-e)*a)/h;d>1?d=1:0>d&&(d=0);var l=t+d*r,c=e+d*a,p=l-o,u=c-n;return Math.sqrt(p*p+u*u)},s.prototype.setScale=function(t){this.networkScaleInv=1/t},s.prototype.select=function(){this.selected=!0},s.prototype.unselect=function(){this.selected=!1},s.prototype.positionBezierNode=function(){null!==this.via&&null!==this.from&&null!==this.to?(this.via.x=.5*(this.from.x+this.to.x),this.via.y=.5*(this.from.y+this.to.y)):(this.via.x=0,this.via.y=0)},s.prototype._drawControlNodes=function(t){if(1==this.controlNodesEnabled){if(null===this.controlNodes.from&&null===this.controlNodes.to){var e="edgeIdFrom:".concat(this.id),i="edgeIdTo:".concat(this.id),s={nodes:{group:"",radius:7,borderWidth:2,borderWidthSelected:2},physics:{damping:0},clustering:{maxNodeSizeIncrements:0,nodeScaling:{width:0,height:0,radius:0}}};this.controlNodes.from=new n({id:e,shape:"dot",color:{background:"#ff0000",border:"#3c3c3c",highlight:{background:"#07f968"}}},{},{},s),this.controlNodes.to=new n({id:i,shape:"dot",color:{background:"#ff0000",border:"#3c3c3c",highlight:{background:"#07f968"}}},{},{},s)}this.controlNodes.positions={},0==this.controlNodes.from.selected&&(this.controlNodes.positions.from=this.getControlNodeFromPosition(t),this.controlNodes.from.x=this.controlNodes.positions.from.x,this.controlNodes.from.y=this.controlNodes.positions.from.y),0==this.controlNodes.to.selected&&(this.controlNodes.positions.to=this.getControlNodeToPosition(t),this.controlNodes.to.x=this.controlNodes.positions.to.x,this.controlNodes.to.y=this.controlNodes.positions.to.y),this.controlNodes.from.draw(t),this.controlNodes.to.draw(t)}else this.controlNodes={from:null,to:null,positions:{}}},s.prototype._enableControlNodes=function(){this.fromBackup=this.from,this.toBackup=this.to,this.controlNodesEnabled=!0},s.prototype._disableControlNodes=function(){this.fromId=this.from.id,this.toId=this.to.id,this.fromId!=this.fromBackup.id?this.fromBackup.detachEdge(this):this.toId!=this.toBackup.id&&this.toBackup.detachEdge(this),this.fromBackup=null,this.toBackup=null,this.controlNodesEnabled=!1},s.prototype._getSelectedControlNode=function(t,e){var i=this.controlNodes.positions,s=Math.sqrt(Math.pow(t-i.from.x,2)+Math.pow(e-i.from.y,2)),o=Math.sqrt(Math.pow(t-i.to.x,2)+Math.pow(e-i.to.y,2));return 15>s?(this.connectedNode=this.from,this.from=this.controlNodes.from,this.controlNodes.from):15>o?(this.connectedNode=this.to,this.to=this.controlNodes.to,this.controlNodes.to):null},s.prototype._restoreControlNodes=function(){1==this.controlNodes.from.selected?(this.from=this.connectedNode,this.connectedNode=null,this.controlNodes.from.unselect()):1==this.controlNodes.to.selected&&(this.to=this.connectedNode,this.connectedNode=null,this.controlNodes.to.unselect())},s.prototype.getControlNodeFromPosition=function(t){var e;if(1==this.options.smoothCurves.enabled)e=this._findBorderPosition(!0,t);else{var i=Math.atan2(this.to.y-this.from.y,this.to.x-this.from.x),s=this.to.x-this.from.x,o=this.to.y-this.from.y,n=Math.sqrt(s*s+o*o),r=this.from.distanceToBorder(t,i+Math.PI),a=(n-r)/n;e={},e.x=a*this.from.x+(1-a)*this.to.x,e.y=a*this.from.y+(1-a)*this.to.y}return e},s.prototype.getControlNodeToPosition=function(t){var e;if(1==this.options.smoothCurves.enabled)e=this._findBorderPosition(!1,t);else{var i=Math.atan2(this.to.y-this.from.y,this.to.x-this.from.x),s=this.to.x-this.from.x,o=this.to.y-this.from.y,n=Math.sqrt(s*s+o*o),r=this.to.distanceToBorder(t,i),a=(n-r)/n;e={},e.x=(1-a)*this.from.x+a*this.to.x,e.y=(1-a)*this.from.y+a*this.to.y}return e},t.exports=s},function(t,e,i){function s(){this.clear(),this.defaultIndex=0}i(1);s.DEFAULT=[{border:"#2B7CE9",background:"#97C2FC",highlight:{border:"#2B7CE9",background:"#D2E5FF"},hover:{border:"#2B7CE9",background:"#D2E5FF"}},{border:"#FFA500",background:"#FFFF00",highlight:{border:"#FFA500",background:"#FFFFA3"},hover:{border:"#FFA500",background:"#FFFFA3"}},{border:"#FA0A10",background:"#FB7E81",highlight:{border:"#FA0A10",background:"#FFAFB1"},hover:{border:"#FA0A10",background:"#FFAFB1"}},{border:"#41A906",background:"#7BE141",highlight:{border:"#41A906",background:"#A1EC76"},hover:{border:"#41A906",background:"#A1EC76"}},{border:"#E129F0",background:"#EB7DF4",highlight:{border:"#E129F0",background:"#F0B3F5"},hover:{border:"#E129F0",background:"#F0B3F5"}},{border:"#7C29F0",background:"#AD85E4",highlight:{border:"#7C29F0",background:"#D3BDF0"},hover:{border:"#7C29F0",background:"#D3BDF0"}},{border:"#C37F00",background:"#FFA807",highlight:{border:"#C37F00",background:"#FFCA66"},hover:{border:"#C37F00",background:"#FFCA66"}},{border:"#4220FB",background:"#6E6EFD",highlight:{border:"#4220FB",background:"#9B9BFD"},hover:{border:"#4220FB",background:"#9B9BFD"}},{border:"#FD5A77",background:"#FFC0CB",highlight:{border:"#FD5A77",background:"#FFD1D9"},hover:{border:"#FD5A77",background:"#FFD1D9"}},{border:"#4AD63A",background:"#C2FABC",highlight:{border:"#4AD63A",background:"#E6FFE3"},hover:{border:"#4AD63A",background:"#E6FFE3"}}],s.prototype.clear=function(){this.groups={},this.groups.length=function(){var t=0;for(var e in this)this.hasOwnProperty(e)&&t++;return t}},s.prototype.get=function(t){var e=this.groups[t];if(void 0==e){var i=this.defaultIndex%s.DEFAULT.length;this.defaultIndex++,e={},e.color=s.DEFAULT[i],this.groups[t]=e}return e},s.prototype.add=function(t,e){return this.groups[t]=e,e},t.exports=s},function(t){function e(){this.images={},this.imageBroken={},this.callback=void 0}e.prototype.setOnloadCallback=function(t){this.callback=t},e.prototype.load=function(t,e){var i=this.images[t];if(void 0===i){var s=this;i=new Image,i.onload=function(){0==this.width&&(document.body.appendChild(this),this.width=this.offsetWidth,this.height=this.offsetHeight,document.body.removeChild(this)),s.callback&&(s.images[t]=i,s.callback(this))},i.onerror=function(){void 0===e?(console.error("Could not load image:",t),delete this.src,s.callback&&s.callback(this)):s.imageBroken[t]===!0?(console.error("Could not load brokenImage:",e),delete this.src,s.callback&&s.callback(this)):(this.src=e,s.imageBroken[t]=!0)},i.src=t}return i},t.exports=e},function(t,e,i){function s(t,e,i,s){var n=o.selectiveBridgeObject(["nodes"],s);this.options=n.nodes,this.selected=!1,this.hover=!1,this.edges=[],this.dynamicEdges=[],this.reroutedEdges={},this.fontDrawThreshold=3,this.id=void 0,this.allowedToMoveX=!1,this.allowedToMoveY=!1,this.xFixed=!1,this.yFixed=!1,this.horizontalAlignLeft=!0,this.verticalAlignTop=!0,this.baseRadiusValue=s.nodes.radius,this.radiusFixed=!1,this.level=-1,this.preassignedLevel=!1,this.hierarchyEnumerated=!1,this.labelDimensions={top:0,left:0,width:0,height:0,yLine:0},this.boundingBox={top:0,left:0,right:0,bottom:0},this.imagelist=e,this.grouplist=i,this.fx=0,this.fy=0,this.vx=0,this.vy=0,this.x=null,this.y=null,this.previousState={vx:0,vy:0,x:0,y:0},this.damping=s.physics.damping,this.fixedData={x:null,y:null},this.setProperties(t,n),this.resetCluster(),this.dynamicEdgesLength=0,this.clusterSession=0,this.clusterSizeWidthFactor=s.clustering.nodeScaling.width,this.clusterSizeHeightFactor=s.clustering.nodeScaling.height,this.clusterSizeRadiusFactor=s.clustering.nodeScaling.radius,this.maxNodeSizeIncrements=s.clustering.maxNodeSizeIncrements,this.growthIndicator=0,this.networkScaleInv=1,this.networkScale=1,this.canvasTopLeft={x:-300,y:-300},this.canvasBottomRight={x:300,y:300},this.parentEdgeId=null}var o=i(1);s.prototype.revertPosition=function(){this.x=this.previousState.x,this.y=this.previousState.y,this.vx=this.previousState.vx,this.vy=this.previousState.vy},s.prototype.resetCluster=function(){this.formationScale=void 0,this.clusterSize=1,this.containedNodes={},this.containedEdges={},this.clusterSessions=[]},s.prototype.attachEdge=function(t){-1==this.edges.indexOf(t)&&this.edges.push(t),-1==this.dynamicEdges.indexOf(t)&&this.dynamicEdges.push(t),this.dynamicEdgesLength=this.dynamicEdges.length},s.prototype.detachEdge=function(t){var e=this.edges.indexOf(t);-1!=e&&this.edges.splice(e,1),e=this.dynamicEdges.indexOf(t),-1!=e&&this.dynamicEdges.splice(e,1),this.dynamicEdgesLength=this.dynamicEdges.length},s.prototype.setProperties=function(t,e){if(t){var i=["borderWidth","borderWidthSelected","shape","image","brokenImage","radius","fontColor","fontSize","fontFace","fontFill","fontStrokeWidth","fontStrokeColor","group","mass"];if(o.selectiveDeepExtend(i,this.options,t),void 0!==t.id&&(this.id=t.id),void 0!==t.label&&(this.label=t.label,this.originalLabel=t.label),void 0!==t.title&&(this.title=t.title),void 0!==t.x&&(this.x=t.x),void 0!==t.y&&(this.y=t.y),void 0!==t.value&&(this.value=t.value),void 0!==t.level&&(this.level=t.level,this.preassignedLevel=!0),void 0!==t.horizontalAlignLeft&&(this.horizontalAlignLeft=t.horizontalAlignLeft),void 0!==t.verticalAlignTop&&(this.verticalAlignTop=t.verticalAlignTop),void 0!==t.triggerFunction&&(this.triggerFunction=t.triggerFunction),void 0===this.id)throw"Node must have an id";if("number"==typeof this.options.group||"string"==typeof this.options.group&&""!=this.options.group){var s=this.grouplist.get(this.options.group);o.deepExtend(this.options,s),this.options.color=o.parseColor(this.options.color)}if(void 0!==t.radius&&(this.baseRadiusValue=this.options.radius),void 0!==t.color&&(this.options.color=o.parseColor(t.color)),void 0!==this.options.image&&""!=this.options.image){if(!this.imagelist)throw"No imagelist provided";this.imageObj=this.imagelist.load(this.options.image,this.options.brokenImage)}switch(void 0!==t.allowedToMoveX?(this.xFixed=!t.allowedToMoveX,this.allowedToMoveX=t.allowedToMoveX):void 0!==t.x&&0==this.allowedToMoveX&&(this.xFixed=!0),void 0!==t.allowedToMoveY?(this.yFixed=!t.allowedToMoveY,this.allowedToMoveY=t.allowedToMoveY):void 0!==t.y&&0==this.allowedToMoveY&&(this.yFixed=!0),this.radiusFixed=this.radiusFixed||void 0!==t.radius,("image"===this.options.shape||"circularImage"===this.options.shape)&&(this.options.radiusMin=e.nodes.widthMin,this.options.radiusMax=e.nodes.widthMax),this.options.shape){case"database":this.draw=this._drawDatabase,this.resize=this._resizeDatabase;break;case"box":this.draw=this._drawBox,this.resize=this._resizeBox;break;case"circle":this.draw=this._drawCircle,this.resize=this._resizeCircle;break;case"ellipse":this.draw=this._drawEllipse,this.resize=this._resizeEllipse;break;case"image":this.draw=this._drawImage,this.resize=this._resizeImage;break;case"circularImage":this.draw=this._drawCircularImage,this.resize=this._resizeCircularImage;break;case"text":this.draw=this._drawText,this.resize=this._resizeText;break;case"dot":this.draw=this._drawDot,this.resize=this._resizeShape;break;case"square":this.draw=this._drawSquare,this.resize=this._resizeShape;break;case"triangle":this.draw=this._drawTriangle,this.resize=this._resizeShape;break;case"triangleDown":this.draw=this._drawTriangleDown,this.resize=this._resizeShape;break;case"star":this.draw=this._drawStar,this.resize=this._resizeShape;break;default:this.draw=this._drawEllipse,this.resize=this._resizeEllipse}this._reset()}},s.prototype.select=function(){this.selected=!0,this._reset()},s.prototype.unselect=function(){this.selected=!1,this._reset()},s.prototype.clearSizeCache=function(){this._reset()},s.prototype._reset=function(){this.width=void 0,this.height=void 0},s.prototype.getTitle=function(){return"function"==typeof this.title?this.title():this.title},s.prototype.distanceToBorder=function(t,e){var i=1;switch(this.width||this.resize(t),this.options.shape){case"circle":case"dot":return this.options.radius+i;case"ellipse":var s=this.width/2,o=this.height/2,n=Math.sin(e)*s,r=Math.cos(e)*o;return s*o/Math.sqrt(n*n+r*r);case"box":case"image":case"text":default:return this.width?Math.min(Math.abs(this.width/2/Math.cos(e)),Math.abs(this.height/2/Math.sin(e)))+i:0}},s.prototype._setForce=function(t,e){this.fx=t,this.fy=e},s.prototype._addForce=function(t,e){this.fx+=t,this.fy+=e},s.prototype.storeState=function(){this.previousState.x=this.x,this.previousState.y=this.y,this.previousState.vx=this.vx,this.previousState.vy=this.vy},s.prototype.discreteStep=function(t){if(this.storeState(),this.xFixed)this.fx=0,this.vx=0;else{var e=this.damping*this.vx,i=(this.fx-e)/this.options.mass;this.vx+=i*t,this.x+=this.vx*t}if(this.yFixed)this.fy=0,this.vy=0;else{var s=this.damping*this.vy,o=(this.fy-s)/this.options.mass;this.vy+=o*t,this.y+=this.vy*t}},s.prototype.discreteStepLimited=function(t,e){if(this.storeState(),this.xFixed)this.fx=0,this.vx=0;else{var i=this.damping*this.vx,s=(this.fx-i)/this.options.mass;this.vx+=s*t,this.vx=Math.abs(this.vx)>e?this.vx>0?e:-e:this.vx,this.x+=this.vx*t}if(this.yFixed)this.fy=0,this.vy=0;else{var o=this.damping*this.vy,n=(this.fy-o)/this.options.mass;this.vy+=n*t,this.vy=Math.abs(this.vy)>e?this.vy>0?e:-e:this.vy,this.y+=this.vy*t}},s.prototype.isFixed=function(){return this.xFixed&&this.yFixed},s.prototype.isMoving=function(t){var e=Math.sqrt(Math.pow(this.vx,2)+Math.pow(this.vy,2));return e>t},s.prototype.isSelected=function(){return this.selected},s.prototype.getValue=function(){return this.value},s.prototype.getDistance=function(t,e){var i=this.x-t,s=this.y-e;return Math.sqrt(i*i+s*s)},s.prototype.setValueRange=function(t,e){if(!this.radiusFixed&&void 0!==this.value)if(e==t)this.options.radius=(this.options.radiusMin+this.options.radiusMax)/2;else{var i=(this.options.radiusMax-this.options.radiusMin)/(e-t);this.options.radius=(this.value-t)*i+this.options.radiusMin}this.baseRadiusValue=this.options.radius},s.prototype.draw=function(){throw"Draw method not initialized for node"},s.prototype.resize=function(){throw"Resize method not initialized for node"},s.prototype.isOverlappingWith=function(t){return this.leftt.left&&this.topt.top},s.prototype._resizeImage=function(){if(!this.width||!this.height){var t,e;if(this.value){this.options.radius=this.baseRadiusValue;var i=this.imageObj.height/this.imageObj.width;void 0!==i?(t=this.options.radius||this.imageObj.width,e=this.options.radius*i||this.imageObj.height):(t=0,e=0)}else t=this.imageObj.width,e=this.imageObj.height;this.width=t,this.height=e,this.growthIndicator=0,this.width>0&&this.height>0&&(this.width+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.options.radius+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.width-t)}},s.prototype._drawImageAtPosition=function(t){if(0!=this.imageObj.width){if(this.clusterSize>1){var e=this.clusterSize>1?10:0;e*=this.networkScaleInv,e=Math.min(.2*this.width,e),t.globalAlpha=.5,t.drawImage(this.imageObj,this.left-e,this.top-e,this.width+2*e,this.height+2*e)}t.globalAlpha=1,t.drawImage(this.imageObj,this.left,this.top,this.width,this.height)}},s.prototype._drawImageLabel=function(t){var e,i=0;if(this.height){i=this.height/2;var s=this.getTextSize(t);s.lineCount>=1&&(i+=s.height/2,i+=3)}e=this.y+i,this._label(t,this.label,this.x,e,void 0)},s.prototype._drawImage=function(t){this._resizeImage(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2,this._drawImageAtPosition(t),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height,this._drawImageLabel(t),this.boundingBox.left=Math.min(this.boundingBox.left,this.labelDimensions.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelDimensions.left+this.labelDimensions.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelDimensions.height)},s.prototype._resizeCircularImage=function(t){if(this.imageObj.src&&this.imageObj.width&&this.imageObj.height)this._swapToImageResizeWhenImageLoaded&&(this.width=0,this.height=0,delete this._swapToImageResizeWhenImageLoaded),this._resizeImage(t);else if(!this.width){var e=2*this.options.radius;this.width=e,this.height=e,this.options.radius+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.options.radius-.5*e,this._swapToImageResizeWhenImageLoaded=!0}},s.prototype._drawCircularImage=function(t){this._resizeCircularImage(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2;var e=this.left+this.width/2,i=this.top+this.height/2,s=Math.abs(this.height/2);this._drawRawCircle(t,e,i,s),t.save(),t.circle(this.x,this.y,s),t.stroke(),t.clip(),this._drawImageAtPosition(t),t.restore(),this.boundingBox.top=this.y-this.options.radius,this.boundingBox.left=this.x-this.options.radius,this.boundingBox.right=this.x+this.options.radius,this.boundingBox.bottom=this.y+this.options.radius,this._drawImageLabel(t),this.boundingBox.left=Math.min(this.boundingBox.left,this.labelDimensions.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelDimensions.left+this.labelDimensions.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelDimensions.height)},s.prototype._resizeBox=function(t){if(!this.width){var e=5,i=this.getTextSize(t);this.width=i.width+2*e,this.height=i.height+2*e,this.width+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.growthIndicator=this.width-(i.width+2*e)}},s.prototype._drawBox=function(t){this._resizeBox(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2;var e=2.5,i=this.options.borderWidth,s=this.options.borderWidthSelected||2*this.options.borderWidth;t.strokeStyle=this.selected?this.options.color.highlight.border:this.hover?this.options.color.hover.border:this.options.color.border,this.clusterSize>1&&(t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.roundRect(this.left-2*t.lineWidth,this.top-2*t.lineWidth,this.width+4*t.lineWidth,this.height+4*t.lineWidth,this.options.radius),t.stroke()),t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t.roundRect(this.left,this.top,this.width,this.height,this.options.radius),t.fill(),t.stroke(),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height,this._label(t,this.label,this.x,this.y)},s.prototype._resizeDatabase=function(t){if(!this.width){var e=5,i=this.getTextSize(t),s=i.width+2*e;this.width=s,this.height=s,this.width+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.options.radius+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.width-s}},s.prototype._drawDatabase=function(t){this._resizeDatabase(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2;var e=2.5,i=this.options.borderWidth,s=this.options.borderWidthSelected||2*this.options.borderWidth;t.strokeStyle=this.selected?this.options.color.highlight.border:this.hover?this.options.color.hover.border:this.options.color.border,this.clusterSize>1&&(t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.database(this.x-this.width/2-2*t.lineWidth,this.y-.5*this.height-2*t.lineWidth,this.width+4*t.lineWidth,this.height+4*t.lineWidth),t.stroke()),t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t.database(this.x-this.width/2,this.y-.5*this.height,this.width,this.height),t.fill(),t.stroke(),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height,this._label(t,this.label,this.x,this.y) +},s.prototype._resizeCircle=function(t){if(!this.width){var e=5,i=this.getTextSize(t),s=Math.max(i.width,i.height)+2*e;this.options.radius=s/2,this.width=s,this.height=s,this.options.radius+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.options.radius-.5*s}},s.prototype._drawRawCircle=function(t,e,i,s){var o=2.5,n=this.options.borderWidth,r=this.options.borderWidthSelected||2*this.options.borderWidth;t.strokeStyle=this.selected?this.options.color.highlight.border:this.hover?this.options.color.hover.border:this.options.color.border,this.clusterSize>1&&(t.lineWidth=(this.selected?r:n)+(this.clusterSize>1?o:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.circle(e,i,s+2*t.lineWidth),t.stroke()),t.lineWidth=(this.selected?r:n)+(this.clusterSize>1?o:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t.circle(this.x,this.y,s),t.fill(),t.stroke()},s.prototype._drawCircle=function(t){this._resizeCircle(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2,this._drawRawCircle(t,this.x,this.y,this.options.radius),this.boundingBox.top=this.y-this.options.radius,this.boundingBox.left=this.x-this.options.radius,this.boundingBox.right=this.x+this.options.radius,this.boundingBox.bottom=this.y+this.options.radius,this._label(t,this.label,this.x,this.y)},s.prototype._resizeEllipse=function(t){if(!this.width){var e=this.getTextSize(t);this.width=1.5*e.width,this.height=2*e.height,this.width1&&(t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.ellipse(this.left-2*t.lineWidth,this.top-2*t.lineWidth,this.width+4*t.lineWidth,this.height+4*t.lineWidth),t.stroke()),t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t.ellipse(this.left,this.top,this.width,this.height),t.fill(),t.stroke(),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height,this._label(t,this.label,this.x,this.y)},s.prototype._drawDot=function(t){this._drawShape(t,"circle")},s.prototype._drawTriangle=function(t){this._drawShape(t,"triangle")},s.prototype._drawTriangleDown=function(t){this._drawShape(t,"triangleDown")},s.prototype._drawSquare=function(t){this._drawShape(t,"square")},s.prototype._drawStar=function(t){this._drawShape(t,"star")},s.prototype._resizeShape=function(){if(!this.width){this.options.radius=this.baseRadiusValue;var t=2*this.options.radius;this.width=t,this.height=t,this.width+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.options.radius+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.width-t}},s.prototype._drawShape=function(t,e){this._resizeShape(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2;var i=2.5,s=this.options.borderWidth,o=this.options.borderWidthSelected||2*this.options.borderWidth,n=2;switch(e){case"dot":n=2;break;case"square":n=2;break;case"triangle":n=3;break;case"triangleDown":n=3;break;case"star":n=4}t.strokeStyle=this.selected?this.options.color.highlight.border:this.hover?this.options.color.hover.border:this.options.color.border,this.clusterSize>1&&(t.lineWidth=(this.selected?o:s)+(this.clusterSize>1?i:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t[e](this.x,this.y,this.options.radius+n*t.lineWidth),t.stroke()),t.lineWidth=(this.selected?o:s)+(this.clusterSize>1?i:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t[e](this.x,this.y,this.options.radius),t.fill(),t.stroke(),this.boundingBox.top=this.y-this.options.radius,this.boundingBox.left=this.x-this.options.radius,this.boundingBox.right=this.x+this.options.radius,this.boundingBox.bottom=this.y+this.options.radius,this.label&&(this._label(t,this.label,this.x,this.y+this.height/2,void 0,"hanging",!0),this.boundingBox.left=Math.min(this.boundingBox.left,this.labelDimensions.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelDimensions.left+this.labelDimensions.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelDimensions.height))},s.prototype._resizeText=function(t){if(!this.width){var e=5,i=this.getTextSize(t);this.width=i.width+2*e,this.height=i.height+2*e,this.width+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.options.radius+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.width-(i.width+2*e)}},s.prototype._drawText=function(t){this._resizeText(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2,this._label(t,this.label,this.x,this.y),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height},s.prototype._label=function(t,e,i,s,o,n,r){if(e&&Number(this.options.fontSize)*this.networkScale>this.fontDrawThreshold){t.font=(this.selected?"bold ":"")+this.options.fontSize+"px "+this.options.fontFace;var a=e.split("\n"),h=a.length,d=Number(this.options.fontSize),l=s+(1-h)/2*d;1==r&&(l=s+(1-h)/(2*d));for(var c=t.measureText(a[0]).width,p=1;h>p;p++){var u=t.measureText(a[p]).width;c=u>c?u:c}var m=this.options.fontSize*h,f=i-c/2,g=s-m/2;"hanging"==n&&(g+=.5*d,g+=4,l+=4),this.labelDimensions={top:g,left:f,width:c,height:m,yLine:l},void 0!==this.options.fontFill&&null!==this.options.fontFill&&"none"!==this.options.fontFill&&(t.fillStyle=this.options.fontFill,t.fillRect(f,g,c,m)),t.fillStyle=this.options.fontColor||"black",t.textAlign=o||"center",t.textBaseline=n||"middle",this.options.fontStrokeWidth>0&&(t.lineWidth=this.options.fontStrokeWidth,t.strokeStyle=this.options.fontStrokeColor,t.lineJoin="round");for(var p=0;h>p;p++)this.options.fontStrokeWidth&&t.strokeText(a[p],i,l),t.fillText(a[p],i,l),l+=d}},s.prototype.getTextSize=function(t){if(void 0!==this.label){t.font=(this.selected?"bold ":"")+this.options.fontSize+"px "+this.options.fontFace;for(var e=this.label.split("\n"),i=(Number(this.options.fontSize)+4)*e.length,s=0,o=0,n=e.length;n>o;o++)s=Math.max(s,t.measureText(e[o]).width);return{width:s,height:i,lineCount:e.length}}return{width:0,height:0,lineCount:0}},s.prototype.inArea=function(){return void 0!==this.width?this.x+this.width*this.networkScaleInv>=this.canvasTopLeft.x&&this.x-this.width*this.networkScaleInv=this.canvasTopLeft.y&&this.y-this.height*this.networkScaleInv=this.canvasTopLeft.x&&this.x=this.canvasTopLeft.y&&this.ys&&(n=s-e-this.padding),no&&(r=o-i-this.padding),ri;i++)if(e.id===r.nodes[i].id){o=r.nodes[i];break}for(o||(o={id:e.id},t.node&&(o.attr=a(o.attr,t.node))),i=n.length-1;i>=0;i--){var h=n[i];h.nodes||(h.nodes=[]),-1==h.nodes.indexOf(o)&&h.nodes.push(o)}e.attr&&(o.attr=a(o.attr,e.attr))}function l(t,e){if(t.edges||(t.edges=[]),t.edges.push(e),t.edge){var i=a({},t.edge);e.attr=a(i,e.attr)}}function c(t,e,i,s,o){var n={from:e,to:i,type:s};return t.edge&&(n.attr=a({},t.edge)),n.attr=a(n.attr||{},o),n}function p(){for(N=D.NULL,k="";" "==E||" "==E||"\n"==E||"\r"==E;)o();do{var t=!1;if("#"==E){for(var e=O-1;" "==T.charAt(e)||" "==T.charAt(e);)e--;if("\n"==T.charAt(e)||""==T.charAt(e)){for(;""!=E&&"\n"!=E;)o();t=!0}}if("/"==E&&"/"==n()){for(;""!=E&&"\n"!=E;)o();t=!0}if("/"==E&&"*"==n()){for(;""!=E;){if("*"==E&&"/"==n()){o(),o();break}o()}t=!0}for(;" "==E||" "==E||"\n"==E||"\r"==E;)o()}while(t);if(""==E)return void(N=D.DELIMITER);var i=E+n();if(C[i])return N=D.DELIMITER,k=i,o(),void o();if(C[E])return N=D.DELIMITER,k=E,void o();if(r(E)||"-"==E){for(k+=E,o();r(E);)k+=E,o();return"false"==k?k=!1:"true"==k?k=!0:isNaN(Number(k))||(k=Number(k)),void(N=D.IDENTIFIER)}if('"'==E){for(o();""!=E&&('"'!=E||'"'==E&&'"'==n());)k+=E,'"'==E&&o(),o();if('"'!=E)throw x('End of string " expected');return o(),void(N=D.IDENTIFIER)}for(N=D.UNKNOWN;""!=E;)k+=E,o();throw new SyntaxError('Syntax error in part "'+w(k,30)+'"')}function u(){var t={};if(s(),p(),"strict"==k&&(t.strict=!0,p()),("graph"==k||"digraph"==k)&&(t.type=k,p()),N==D.IDENTIFIER&&(t.id=k,p()),"{"!=k)throw x("Angle bracket { expected");if(p(),m(t),"}"!=k)throw x("Angle bracket } expected");if(p(),""!==k)throw x("End of file expected");return p(),delete t.node,delete t.edge,delete t.graph,t}function m(t){for(;""!==k&&"}"!=k;)f(t),";"==k&&p()}function f(t){var e=g(t);if(e)return void b(t,e);var i=v(t);if(!i){if(N!=D.IDENTIFIER)throw x("Identifier expected");var s=k;if(p(),"="==k){if(p(),N!=D.IDENTIFIER)throw x("Identifier expected");t[s]=k,p()}else y(t,s)}}function g(t){var e=null;if("subgraph"==k&&(e={},e.type="subgraph",p(),N==D.IDENTIFIER&&(e.id=k,p())),"{"==k){if(p(),e||(e={}),e.parent=t,e.node=t.node,e.edge=t.edge,e.graph=t.graph,m(e),"}"!=k)throw x("Angle bracket } expected");p(),delete e.node,delete e.edge,delete e.graph,delete e.parent,t.subgraphs||(t.subgraphs=[]),t.subgraphs.push(e)}return e}function v(t){return"node"==k?(p(),t.node=_(),"node"):"edge"==k?(p(),t.edge=_(),"edge"):"graph"==k?(p(),t.graph=_(),"graph"):null}function y(t,e){var i={id:e},s=_();s&&(i.attr=s),d(t,i),b(t,e)}function b(t,e){for(;"->"==k||"--"==k;){var i,s=k;p();var o=g(t);if(o)i=o;else{if(N!=D.IDENTIFIER)throw x("Identifier or subgraph expected");i=k,d(t,{id:i}),p()}var n=_(),r=c(t,e,i,s,n);l(t,r),e=i}}function _(){for(var t=null;"["==k;){for(p(),t={};""!==k&&"]"!=k;){if(N!=D.IDENTIFIER)throw x("Attribute name expected");var e=k;if(p(),"="!=k)throw x("Equal sign = expected");if(p(),N!=D.IDENTIFIER)throw x("Attribute value expected");var i=k;h(t,e,i),p(),","==k&&p()}if("]"!=k)throw x("Bracket ] expected");p()}return t}function x(t){return new SyntaxError(t+', got "'+w(k,30)+'" (char '+O+")")}function w(t,e){return t.length<=e?t:t.substr(0,27)+"..."}function 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D={NULL:0,DELIMITER:1,IDENTIFIER:2,UNKNOWN:3},C={"{":!0,"}":!0,"[":!0,"]":!0,";":!0,"=":!0,",":!0,"->":!0,"--":!0},T="",O=0,E="",k="",N=D.NULL,I=/[a-zA-Z_0-9.:#]/;e.parseDOT=i,e.DOTToGraph=M},function(t,e){function i(t,e){var i=[],s=[];this.options={edges:{inheritColor:!0},nodes:{allowedToMove:!1,parseColor:!1}},void 0!==e&&(this.options.nodes.allowedToMove=e.allowedToMove|!1,this.options.nodes.parseColor=e.parseColor|!1,this.options.edges.inheritColor=e.inheritColor|!0);for(var o=t.edges,n=t.nodes,r=0;r=s&&(s=864e5),e=new Date(e.valueOf()-.05*s),i=new Date(i.valueOf()+.05*s)}return{start:e,end:i}},s.prototype.setWindow=function(t,e,i){var s=i&&void 0!==i.animate?i.animate:!0;if(1==arguments.length){var o=arguments[0];this.range.setRange(o.start,o.end,s)}else this.range.setRange(t,e,s)},s.prototype.moveTo=function(t,e){var i=this.range.end-this.range.start,s=r.convert(t,"Date").valueOf(),o=s-i/2,n=s+i/2,a=e&&void 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o=s.root.offsetHeight-s.root.clientHeight,n=s.root.offsetWidth-s.root.clientWidth;0===s.centerContainer.clientHeight&&(i.border.left=i.border.top,i.border.right=i.border.left),0===s.root.clientHeight&&(n=o),i.center.height=s.center.offsetHeight,i.left.height=s.left.offsetHeight,i.right.height=s.right.offsetHeight,i.top.height=s.top.clientHeight||-i.border.top,i.bottom.height=s.bottom.clientHeight||-i.border.bottom;var a=Math.max(i.left.height,i.center.height,i.right.height),d=i.top.height+a+i.bottom.height+o+i.border.top+i.border.bottom;s.root.style.height=r.option.asSize(e.height,d+"px"),i.root.height=s.root.offsetHeight,i.background.height=i.root.height-o;var l=i.root.height-i.top.height-i.bottom.height-o;i.centerContainer.height=l,i.leftContainer.height=l,i.rightContainer.height=i.leftContainer.height,i.root.width=s.root.offsetWidth,i.background.width=i.root.width-n,i.left.width=s.leftContainer.clientWidth||-i.border.left,i.leftContainer.width=i.left.width,i.right.width=s.rightContainer.clientWidth||-i.border.right,i.rightContainer.width=i.right.width;var c=i.root.width-i.left.width-i.right.width-n;i.center.width=c,i.centerContainer.width=c,i.top.width=c,i.bottom.width=c,s.background.style.height=i.background.height+"px",s.backgroundVertical.style.height=i.background.height+"px",s.backgroundHorizontal.style.height=i.centerContainer.height+"px",s.centerContainer.style.height=i.centerContainer.height+"px",s.leftContainer.style.height=i.leftContainer.height+"px",s.rightContainer.style.height=i.rightContainer.height+"px",s.background.style.width=i.background.width+"px",s.backgroundVertical.style.width=i.centerContainer.width+"px",s.backgroundHorizontal.style.width=i.background.width+"px",s.centerContainer.style.width=i.center.width+"px",s.top.style.width=i.top.width+"px",s.bottom.style.width=i.bottom.width+"px",s.background.style.left="0",s.background.style.top="0",s.backgroundVertical.style.left=i.left.width+i.border.left+"px",s.backgroundVertical.style.top="0",s.backgroundHorizontal.style.left="0",s.backgroundHorizontal.style.top=i.top.height+"px",s.centerContainer.style.left=i.left.width+"px",s.centerContainer.style.top=i.top.height+"px",s.leftContainer.style.left="0",s.leftContainer.style.top=i.top.height+"px",s.rightContainer.style.left=i.left.width+i.center.width+"px",s.rightContainer.style.top=i.top.height+"px",s.top.style.left=i.left.width+"px",s.top.style.top="0",s.bottom.style.left=i.left.width+"px",s.bottom.style.top=i.top.height+i.centerContainer.height+"px",this._updateScrollTop();var p=this.props.scrollTop;"bottom"==e.orientation&&(p+=Math.max(this.props.centerContainer.height-this.props.center.height-this.props.border.top-this.props.border.bottom,0)),s.center.style.left="0",s.center.style.top=p+"px",s.left.style.left="0",s.left.style.top=p+"px",s.right.style.left="0",s.right.style.top=p+"px";var u=0==this.props.scrollTop?"hidden":"",m=this.props.scrollTop==this.props.scrollTopMin?"hidden":"";if(s.shadowTop.style.visibility=u,s.shadowBottom.style.visibility=m,s.shadowTopLeft.style.visibility=u,s.shadowBottomLeft.style.visibility=m,s.shadowTopRight.style.visibility=u,s.shadowBottomRight.style.visibility=m,this.components.forEach(function(e){t=e.redraw()||t}),t){var f=3;this.redrawCount0&&(this.props.scrollTop=0),this.props.scrollTops;s++){var o=s%2===0?1.3*i:.5*i;this.lineTo(t+o*Math.sin(2*s*Math.PI/10),e-o*Math.cos(2*s*Math.PI/10))}this.closePath()},CanvasRenderingContext2D.prototype.roundRect=function(t,e,i,s,o){var n=Math.PI/180;0>i-2*o&&(o=i/2),0>s-2*o&&(o=s/2),this.beginPath(),this.moveTo(t+o,e),this.lineTo(t+i-o,e),this.arc(t+i-o,e+o,o,270*n,360*n,!1),this.lineTo(t+i,e+s-o),this.arc(t+i-o,e+s-o,o,0,90*n,!1),this.lineTo(t+o,e+s),this.arc(t+o,e+s-o,o,90*n,180*n,!1),this.lineTo(t,e+o),this.arc(t+o,e+o,o,180*n,270*n,!1)},CanvasRenderingContext2D.prototype.ellipse=function(t,e,i,s){var o=.5522848,n=i/2*o,r=s/2*o,a=t+i,h=e+s,d=t+i/2,l=e+s/2; +this.beginPath(),this.moveTo(t,l),this.bezierCurveTo(t,l-r,d-n,e,d,e),this.bezierCurveTo(d+n,e,a,l-r,a,l),this.bezierCurveTo(a,l+r,d+n,h,d,h),this.bezierCurveTo(d-n,h,t,l+r,t,l)},CanvasRenderingContext2D.prototype.database=function(t,e,i,s){var o=1/3,n=i,r=s*o,a=.5522848,h=n/2*a,d=r/2*a,l=t+n,c=e+r,p=t+n/2,u=e+r/2,m=e+(s-r/2),f=e+s;this.beginPath(),this.moveTo(l,u),this.bezierCurveTo(l,u+d,p+h,c,p,c),this.bezierCurveTo(p-h,c,t,u+d,t,u),this.bezierCurveTo(t,u-d,p-h,e,p,e),this.bezierCurveTo(p+h,e,l,u-d,l,u),this.lineTo(l,m),this.bezierCurveTo(l,m+d,p+h,f,p,f),this.bezierCurveTo(p-h,f,t,m+d,t,m),this.lineTo(t,u)},CanvasRenderingContext2D.prototype.arrow=function(t,e,i,s){var o=t-s*Math.cos(i),n=e-s*Math.sin(i),r=t-.9*s*Math.cos(i),a=e-.9*s*Math.sin(i),h=o+s/3*Math.cos(i+.5*Math.PI),d=n+s/3*Math.sin(i+.5*Math.PI),l=o+s/3*Math.cos(i-.5*Math.PI),c=n+s/3*Math.sin(i-.5*Math.PI);this.beginPath(),this.moveTo(t,e),this.lineTo(h,d),this.lineTo(r,a),this.lineTo(l,c),this.closePath()},CanvasRenderingContext2D.prototype.dashedLine=function(t,e,i,s,o){o||(o=[10,5]),0==p&&(p=.001);var n=o.length;this.moveTo(t,e);for(var r=i-t,a=s-e,h=a/r,d=Math.sqrt(r*r+a*a),l=0,c=!0;d>=.1;){var p=o[l++%n];p>d&&(p=d);var u=Math.sqrt(p*p/(1+h*h));0>r&&(u=-u),t+=u,e+=h*u,this[c?"lineTo":"moveTo"](t,e),d-=p,c=!c}})},function(t,e,i){function s(t,e){this.groupId=t,this.options=e}var o=i(2),n=i(53);s.prototype.getYRange=function(t){for(var e=t[0].y,i=t[0].y,s=0;st[s].y?t[s].y:e,i=i0){var r,a,h=Number(i.svg.style.height.replace("px",""));if(r=o.getSVGElement("path",i.svgElements,i.svg),r.setAttributeNS(null,"class",e.className),void 0!==e.style&&r.setAttributeNS(null,"style",e.style),a=1==e.options.catmullRom.enabled?s._catmullRom(t,e):s._linear(t),1==e.options.shaded.enabled){var d,l=o.getSVGElement("path",i.svgElements,i.svg);d="top"==e.options.shaded.orientation?"M"+t[0].x+",0 "+a+"L"+t[t.length-1].x+",0":"M"+t[0].x+","+h+" "+a+"L"+t[t.length-1].x+","+h,l.setAttributeNS(null,"class",e.className+" fill"),void 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g=d+(p[r].amount-p[r].resolved),v=d-(p[r].resolved+1);g0&&(n=Math.min(n,Math.abs(c[v].x-r))),a=s._getSafeDrawData(n,h,m),p[r].resolved+=1,"stack"==h.options.barChart.handleOverlap?(f=p[r].accumulated,p[r].accumulated+=h.zeroPosition-c[d].y):"sideBySide"==h.options.barChart.handleOverlap&&(a.width=a.width/p[r].amount,a.offset+=p[r].resolved*a.width-.5*a.width*(p[r].amount+1),"left"==h.options.barChart.align?a.offset-=.5*a.width:"right"==h.options.barChart.align&&(a.offset+=.5*a.width))}o.drawBar(c[d].x+a.offset,c[d].y-f,a.width,h.zeroPosition-c[d].y,h.className+" bar",i.svgElements,i.svg),1==h.options.drawPoints.enabled&&o.drawPoint(c[d].x+a.offset,c[d].y,h,i.svgElements,i.svg)}},s._getDataIntersections=function(t,e){for(var i,s=0;s0&&(i=Math.min(i,Math.abs(e[s-1].x-e[s].x))),0==i&&(void 0===t[e[s].x]&&(t[e[s].x]={amount:0,resolved:0,accumulated:0}),t[e[s].x].amount+=1)},s._getSafeDrawData=function(t,e,i){var s,o;return t0?(s=i>t?i:t,o=0,"left"==e.options.barChart.align?o-=.5*t:"right"==e.options.barChart.align&&(o+=.5*t)):(s=e.options.barChart.width,o=0,"left"==e.options.barChart.align?o-=.5*e.options.barChart.width:"right"==e.options.barChart.align&&(o+=.5*e.options.barChart.width)),{width:s,offset:o}},s.getStackedBarYRange=function(t,e,i,o,n){if(t.length>0){t.sort(function(t,e){return t.x==e.x?t.groupId-e.groupId:t.x-e.x});var r={};s._getDataIntersections(r,t),e[o]=s._getStackedBarYRange(r,t),e[o].yAxisOrientation=n,i.push(o)}},s._getStackedBarYRange=function(t,e){for(var i,s=e[0].y,o=e[0].y,n=0;ne[n].y?e[n].y:s,o=ot[r].accumulated?t[r].accumulated:s,o=ot[s].y?t[s].y:e,i=is;++s)i[s].apply(this,e)}return this},e.prototype.listeners=function(t){return this._callbacks=this._callbacks||{},this._callbacks[t]||[]},e.prototype.hasListeners=function(t){return!!this.listeners(t).length}},function(t,e,i){var s;(function(t,o){(function(n){function r(t,e,i){switch(arguments.length){case 2:return 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e=t.srcEvent,i=t.offsetDirection;if(this.manager.session.prevented)return void e.preventDefault();var s=this.actions,o=_(s,ni),n=_(s,ai),r=_(s,ri);return o||n&&i&Re||r&&i&Fe?this.preventSrc(e):void 0}},preventSrc:function(t){this.manager.session.prevented=!0,t.preventDefault()}};var hi=1,di=2,li=4,ci=8,pi=ci,ui=16,mi=32;K.prototype={defaults:{},set:function(t){return c(this.options,t),this.manager&&this.manager.touchAction.update(),this},recognizeWith:function(t){if(d(t,"recognizeWith",this))return this;var e=this.simultaneous;return t=te(t,this),e[t.id]||(e[t.id]=t,t.recognizeWith(this)),this},dropRecognizeWith:function(t){return d(t,"dropRecognizeWith",this)?this:(t=te(t,this),delete this.simultaneous[t.id],this)},requireFailure:function(t){if(d(t,"requireFailure",this))return this;var e=this.requireFail;return t=te(t,this),-1===w(e,t)&&(e.push(t),t.requireFailure(this)),this},dropRequireFailure:function(t){if(d(t,"dropRequireFailure",this))return this;t=te(t,this);var e=w(this.requireFail,t);return e>-1&&this.requireFail.splice(e,1),this},hasRequireFailures:function(){return this.requireFail.length>0},canRecognizeWith:function(t){return!!this.simultaneous[t.id]},emit:function(t){function e(e){i.manager.emit(i.options.event+(e?$(s):""),t)}var i=this,s=this.state;ci>s&&e(!0),e(),s>=ci&&e(!0)},tryEmit:function(t){return this.canEmit()?this.emit(t):void(this.state=mi)},canEmit:function(){for(var t=0;tn?Le:ze,i=n!=this.pX,s=Math.abs(t.deltaX)):(o=0===r?Ie:0>r?Pe:Ae,i=r!=this.pY,s=Math.abs(t.deltaY))),t.direction=o,i&&s>e.threshold&&o&e.direction},attrTest:function(t){return ee.prototype.attrTest.call(this,t)&&(this.state&di||!(this.state&di)&&this.directionTest(t))},emit:function(t){this.pX=t.deltaX,this.pY=t.deltaY;var e=J(t.direction);e&&this.manager.emit(this.options.event+e,t),this._super.emit.call(this,t)}}),u(se,ee,{defaults:{event:"pinch",threshold:0,pointers:2},getTouchAction:function(){return[ni]},attrTest:function(t){return this._super.attrTest.call(this,t)&&(Math.abs(t.scale-1)>this.options.threshold||this.state&di)},emit:function(t){if(this._super.emit.call(this,t),1!==t.scale){var e=t.scale<1?"in":"out";this.manager.emit(this.options.event+e,t)}}}),u(oe,K,{defaults:{event:"press",pointers:1,time:500,threshold:5},getTouchAction:function(){return[si]},process:function(t){var e=this.options,i=t.pointers.length===e.pointers,s=t.distancee.time;if(this._input=t,!s||!i||t.eventType&(ke|Ne)&&!o)this.reset();else if(t.eventType&Oe)this.reset(),this._timer=h(function(){this.state=pi,this.tryEmit() +},e.time,this);else if(t.eventType&ke)return pi;return mi},reset:function(){clearTimeout(this._timer)},emit:function(t){this.state===pi&&(t&&t.eventType&ke?this.manager.emit(this.options.event+"up",t):(this._input.timeStamp=ve(),this.manager.emit(this.options.event,this._input)))}}),u(ne,ee,{defaults:{event:"rotate",threshold:0,pointers:2},getTouchAction:function(){return[ni]},attrTest:function(t){return this._super.attrTest.call(this,t)&&(Math.abs(t.rotation)>this.options.threshold||this.state&di)}}),u(re,ee,{defaults:{event:"swipe",threshold:10,velocity:.65,direction:Re|Fe,pointers:1},getTouchAction:function(){return ie.prototype.getTouchAction.call(this)},attrTest:function(t){var e,i=this.options.direction;return i&(Re|Fe)?e=t.velocity:i&Re?e=t.velocityX:i&Fe&&(e=t.velocityY),this._super.attrTest.call(this,t)&&i&t.direction&&t.distance>this.options.threshold&&ge(e)>this.options.velocity&&t.eventType&ke},emit:function(t){var e=J(t.direction);e&&this.manager.emit(this.options.event+e,t),this.manager.emit(this.options.event,t)}}),u(ae,K,{defaults:{event:"tap",pointers:1,taps:1,interval:300,time:250,threshold:2,posThreshold:10},getTouchAction:function(){return[oi]},process:function(t){var e=this.options,i=t.pointers.length===e.pointers,s=t.distancet&&s>o;)o%3==0?(this.forceAggregateHubs(!0),this.normalizeClusterLevels()):this.increaseClusterLevel(),i=this.nodeIndices.length,o+=1;o>0&&1==e&&this.repositionNodes(),this._updateCalculationNodes()},e.openCluster=function(t){var e=this.moving;if(t.clusterSize>this.constants.clustering.sectorThreshold&&this._nodeInActiveArea(t)&&("default"!=this._sector()||1!=this.nodeIndices.length)){this._addSector(t);for(var i=0;this.nodeIndices.lengthi;)this.decreaseClusterLevel(),i+=1}else this._expandClusterNode(t,!1,!0),this._updateNodeIndexList(),this._updateDynamicEdges(),this._updateCalculationNodes(),this.updateLabels();this.moving!=e&&this.start()},e.updateClustersDefault=function(){1==this.constants.clustering.enabled&&this.updateClusters(0,!1,!1)},e.increaseClusterLevel=function(){this.updateClusters(-1,!1,!0)},e.decreaseClusterLevel=function(){this.updateClusters(1,!1,!0)},e.updateClusters=function(t,e,i,s){var o=this.moving,n=this.nodeIndices.length;this.previousScale>this.scale&&0==t&&this._collapseSector(),this.previousScale>this.scale||-1==t?this._formClusters(i):(this.previousScalethis.scale||-1==t)&&(this._aggregateHubs(i),this._updateNodeIndexList()),(this.previousScale>this.scale||-1==t)&&(this.handleChains(),this._updateNodeIndexList()),this.previousScale=this.scale,this._updateDynamicEdges(),this.updateLabels(),this.nodeIndices.lengththis.constants.clustering.chainThreshold&&this._reduceAmountOfChains(1-this.constants.clustering.chainThreshold/t)},e._aggregateHubs=function(t){this._getHubSize(),this._formClustersByHub(t,!1)},e.forceAggregateHubs=function(t){var e=this.moving,i=this.nodeIndices.length;this._aggregateHubs(!0),this._updateNodeIndexList(),this._updateDynamicEdges(),this.updateLabels(),this.nodeIndices.length!=i&&(this.clusterSession+=1),(0==t||void 0===t)&&this.moving!=e&&this.start()},e._openClustersBySize=function(){for(var t in this.nodes)if(this.nodes.hasOwnProperty(t)){var e=this.nodes[t];1==e.inView()&&(e.width*this.scale>this.constants.clustering.screenSizeThreshold*this.frame.canvas.clientWidth||e.height*this.scale>this.constants.clustering.screenSizeThreshold*this.frame.canvas.clientHeight)&&this.openCluster(e)}},e._openClusters=function(t,e){for(var i=0;i1&&(t.clusterSizei)){var r=n.from,a=n.to;n.to.options.mass>n.from.options.mass&&(r=n.to,a=n.from),1==a.dynamicEdgesLength?this._addToCluster(r,a,!1):1==r.dynamicEdgesLength&&this._addToCluster(a,r,!1)}}},e._forceClustersByZoom=function(){for(var t in this.nodes)if(this.nodes.hasOwnProperty(t)){var e=this.nodes[t];if(1==e.dynamicEdgesLength&&0!=e.dynamicEdges.length){var i=e.dynamicEdges[0],s=i.toId==e.id?this.nodes[i.fromId]:this.nodes[i.toId];e.id!=s.id&&(s.options.mass>e.options.mass?this._addToCluster(s,e,!0):this._addToCluster(e,s,!0))}}},e._clusterToSmallestNeighbour=function(t){for(var e=-1,i=null,s=0;so.clusterSessions.length&&(e=o.clusterSessions.length,i=o)}null!=o&&void 0!==this.nodes[o.id]&&this._addToCluster(o,t,!0)},e._formClustersByHub=function(t,e){for(var i in this.nodes)this.nodes.hasOwnProperty(i)&&this._formClusterFromHub(this.nodes[i],t,e)},e._formClusterFromHub=function(t,e,i,s){if(void 0===s&&(s=0),t.dynamicEdgesLength>=this.hubThreshold&&0==i||t.dynamicEdgesLength==this.hubThreshold&&1==i){for(var o,n,r,a=this.constants.clustering.clusterEdgeThreshold/this.scale,h=!1,d=[],l=t.dynamicEdges.length,c=0;l>c;c++)d.push(t.dynamicEdges[c].id);if(0==e)for(h=!1,c=0;l>c;c++){var p=this.edges[d[c]];if(void 0!==p&&p.connected&&p.toId!=p.fromId&&(o=p.to.x-p.from.x,n=p.to.y-p.from.y,r=Math.sqrt(o*o+n*n),a>r)){h=!0;break}}if(!e&&h||e)for(c=0;l>c;c++)if(p=this.edges[d[c]],void 0!==p){var u=this.nodes[p.fromId==t.id?p.toId:p.fromId];u.dynamicEdges.length<=this.hubThreshold+s&&u.id!=t.id&&this._addToCluster(t,u,e)}}},e._addToCluster=function(t,e,i){t.containedNodes[e.id]=e;for(var s=0;s1)for(var s=0;s1&&(e.label="[".concat(String(e.clusterSize),"]"))}for(t in this.nodes)this.nodes.hasOwnProperty(t)&&(e=this.nodes[t],1==e.clusterSize&&(e.label=void 0!==e.originalLabel?e.originalLabel:String(e.id)))},e.normalizeClusterLevels=function(){var t,e=0,i=1e9,s=0;for(t in this.nodes)this.nodes.hasOwnProperty(t)&&(s=this.nodes[t].clusterSessions.length,s>e&&(e=s),i>s&&(i=s));if(e-i>this.constants.clustering.clusterLevelDifference){var o=this.nodeIndices.length,n=e-this.constants.clustering.clusterLevelDifference;for(t in this.nodes)this.nodes.hasOwnProperty(t)&&this.nodes[t].clusterSessions.lengths&&(s=n.dynamicEdgesLength),t+=n.dynamicEdgesLength,e+=Math.pow(n.dynamicEdgesLength,2),i+=1}t/=i,e/=i;var r=e-Math.pow(t,2),a=Math.sqrt(r);this.hubThreshold=Math.floor(t+2*a),this.hubThreshold>s&&(this.hubThreshold=s)},e._reduceAmountOfChains=function(t){this.hubThreshold=2;var e=Math.floor(this.nodeIndices.length*t);for(var i in this.nodes)this.nodes.hasOwnProperty(i)&&2==this.nodes[i].dynamicEdgesLength&&this.nodes[i].dynamicEdges.length>=2&&e>0&&(this._formClusterFromHub(this.nodes[i],!0,!0,1),e-=1)},e._getChainFraction=function(){var t=0,e=0;for(var i in this.nodes)this.nodes.hasOwnProperty(i)&&(2==this.nodes[i].dynamicEdgesLength&&this.nodes[i].dynamicEdges.length>=2&&(t+=1),e+=1);return t/e}},function(t,e,i){var s=i(1),o=i(40);e._putDataInSector=function(){this.sectors.active[this._sector()].nodes=this.nodes,this.sectors.active[this._sector()].edges=this.edges,this.sectors.active[this._sector()].nodeIndices=this.nodeIndices},e._switchToSector=function(t,e){void 0===e||"active"==e?this._switchToActiveSector(t):this._switchToFrozenSector(t)},e._switchToActiveSector=function(t){this.nodeIndices=this.sectors.active[t].nodeIndices,this.nodes=this.sectors.active[t].nodes,this.edges=this.sectors.active[t].edges},e._switchToSupportSector=function(){this.nodeIndices=this.sectors.support.nodeIndices,this.nodes=this.sectors.support.nodes,this.edges=this.sectors.support.edges},e._switchToFrozenSector=function(t){this.nodeIndices=this.sectors.frozen[t].nodeIndices,this.nodes=this.sectors.frozen[t].nodes,this.edges=this.sectors.frozen[t].edges},e._loadLatestSector=function(){this._switchToSector(this._sector())},e._sector=function(){return this.activeSector[this.activeSector.length-1]},e._previousSector=function(){if(this.activeSector.length>1)return this.activeSector[this.activeSector.length-2];throw new TypeError("there are not enough sectors in the this.activeSector array.")},e._setActiveSector=function(t){this.activeSector.push(t)},e._forgetLastSector=function(){this.activeSector.pop()},e._createNewSector=function(t){this.sectors.active[t]={nodes:{},edges:{},nodeIndices:[],formationScale:this.scale,drawingNode:void 0},this.sectors.active[t].drawingNode=new o({id:t,color:{background:"#eaefef",border:"495c5e"}},{},{},this.constants),this.sectors.active[t].drawingNode.clusterSize=2},e._deleteActiveSector=function(t){delete this.sectors.active[t]},e._deleteFrozenSector=function(t){delete this.sectors.frozen[t]},e._freezeSector=function(t){this.sectors.frozen[t]=this.sectors.active[t],this._deleteActiveSector(t)},e._activateSector=function(t){this.sectors.active[t]=this.sectors.frozen[t],this._deleteFrozenSector(t)},e._mergeThisWithFrozen=function(t){for(var e in this.nodes)this.nodes.hasOwnProperty(e)&&(this.sectors.frozen[t].nodes[e]=this.nodes[e]);for(var i in this.edges)this.edges.hasOwnProperty(i)&&(this.sectors.frozen[t].edges[i]=this.edges[i]);for(var s=0;s1?this[t](o[0],o[1]):this[t](e))}return this._loadLatestSector(),i},e._doInSupportSector=function(t,e){var i=!1;if(void 0===e)this._switchToSupportSector(),i=this[t]();else{this._switchToSupportSector();var s=Array.prototype.splice.call(arguments,1);i=s.length>1?this[t](s[0],s[1]):this[t](e)}return this._loadLatestSector(),i},e._doInAllFrozenSectors=function(t,e){if(void 0===e)for(var i in this.sectors.frozen)this.sectors.frozen.hasOwnProperty(i)&&(this._switchToFrozenSector(i),this[t]());else for(var i in this.sectors.frozen)if(this.sectors.frozen.hasOwnProperty(i)){this._switchToFrozenSector(i);var s=Array.prototype.splice.call(arguments,1);s.length>1?this[t](s[0],s[1]):this[t](e)}this._loadLatestSector()},e._doInAllSectors=function(t,e){var i=Array.prototype.splice.call(arguments,1);void 0===e?(this._doInAllActiveSectors(t),this._doInAllFrozenSectors(t)):i.length>1?(this._doInAllActiveSectors(t,i[0],i[1]),this._doInAllFrozenSectors(t,i[0],i[1])):(this._doInAllActiveSectors(t,e),this._doInAllFrozenSectors(t,e))},e._clearNodeIndexList=function(){var t=this._sector();this.sectors.active[t].nodeIndices=[],this.nodeIndices=this.sectors.active[t].nodeIndices},e._drawSectorNodes=function(t,e){var i,s=1e9,o=-1e9,n=1e9,r=-1e9;for(var a in this.sectors[e])if(this.sectors[e].hasOwnProperty(a)&&void 0!==this.sectors[e][a].drawingNode){this._switchToSector(a,e),s=1e9,o=-1e9,n=1e9,r=-1e9;for(var h in this.nodes)this.nodes.hasOwnProperty(h)&&(i=this.nodes[h],i.resize(t),n>i.x-.5*i.width&&(n=i.x-.5*i.width),ri.y-.5*i.height&&(s=i.y-.5*i.height),o0?this.nodes[i[i.length-1]]:null},e._getEdgesOverlappingWith=function(t,e){var i=this.edges;for(var s in i)i.hasOwnProperty(s)&&i[s].isOverlappingWith(t)&&e.push(s)},e._getAllEdgesOverlappingWith=function(t){var e=[];return this._doInAllActiveSectors("_getEdgesOverlappingWith",t,e),e},e._getEdgeAt=function(t){var e=this._pointerToPositionObject(t),i=this._getAllEdgesOverlappingWith(e);return i.length>0?this.edges[i[i.length-1]]:null},e._addToSelection=function(t){t instanceof s?this.selectionObj.nodes[t.id]=t:this.selectionObj.edges[t.id]=t},e._addToHover=function(t){t instanceof s?this.hoverObj.nodes[t.id]=t:this.hoverObj.edges[t.id]=t},e._removeFromSelection=function(t){t instanceof s?delete this.selectionObj.nodes[t.id]:delete this.selectionObj.edges[t.id]},e._unselectAll=function(t){void 0===t&&(t=!1);for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&this.selectionObj.nodes[e].unselect();for(var i in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(i)&&this.selectionObj.edges[i].unselect();this.selectionObj={nodes:{},edges:{}},0==t&&this.emit("select",this.getSelection())},e._unselectClusters=function(t){void 0===t&&(t=!1);for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&this.selectionObj.nodes[e].clusterSize>1&&(this.selectionObj.nodes[e].unselect(),this._removeFromSelection(this.selectionObj.nodes[e]));0==t&&this.emit("select",this.getSelection())},e._getSelectedNodeCount=function(){var t=0;for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&(t+=1);return t},e._getSelectedNode=function(){for(var t in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(t))return this.selectionObj.nodes[t];return null},e._getSelectedEdge=function(){for(var t in this.selectionObj.edges)if(this.selectionObj.edges.hasOwnProperty(t))return this.selectionObj.edges[t];return null},e._getSelectedEdgeCount=function(){var t=0;for(var e in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(e)&&(t+=1);return t},e._getSelectedObjectCount=function(){var t=0;for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&(t+=1);for(var i in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(i)&&(t+=1);return t},e._selectionIsEmpty=function(){for(var t in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(t))return!1;for(var e in this.selectionObj.edges)if(this.selectionObj.edges.hasOwnProperty(e))return!1;return!0},e._clusterInSelection=function(){for(var t in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(t)&&this.selectionObj.nodes[t].clusterSize>1)return!0;return!1},e._selectConnectedEdges=function(t){for(var e=0;ei;i++){o=t[i];var n=this.nodes[o];if(!n)throw new RangeError('Node with id "'+o+'" not found');this._selectObject(n,!0,!0,e,!0)}this.redraw()},e.selectEdges=function(t){var e,i,s;if(!t||void 0==t.length)throw"Selection must be an array with ids";for(this._unselectAll(!0),e=0,i=t.length;i>e;e++){s=t[e];var o=this.edges[s];if(!o)throw new RangeError('Edge with id "'+s+'" not found');this._selectObject(o,!0,!0,!1,!0)}this.redraw()},e._updateSelection=function(){for(var t in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(t)&&(this.nodes.hasOwnProperty(t)||delete this.selectionObj.nodes[t]);for(var e in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(e)&&(this.edges.hasOwnProperty(e)||delete this.selectionObj.edges[e])}},function(t,e,i){var s=i(1),o=i(40),n=i(37);e._clearManipulatorBar=function(){this._recursiveDOMDelete(this.manipulationDiv),this.manipulationDOM={},this._manipulationReleaseOverload=function(){},delete this.sectors.support.nodes.targetNode,delete this.sectors.support.nodes.targetViaNode,this.controlNodesActive=!1,this.freezeSimulation=!1},e._restoreOverloadedFunctions=function(){for(var t in this.cachedFunctions)this.cachedFunctions.hasOwnProperty(t)&&(this[t]=this.cachedFunctions[t],delete this.cachedFunctions[t])},e._toggleEditMode=function(){this.editMode=!this.editMode;var t=this.manipulationDiv,e=this.closeDiv,i=this.editModeDiv;1==this.editMode?(t.style.display="block",e.style.display="block",i.style.display="none",e.onclick=this._toggleEditMode.bind(this)):(t.style.display="none",e.style.display="none",i.style.display="block",e.onclick=null),this._createManipulatorBar()},e._createManipulatorBar=function(){this.boundFunction&&this.off("select",this.boundFunction);var t=this.constants.locales[this.constants.locale];if(void 0!==this.edgeBeingEdited&&(this.edgeBeingEdited._disableControlNodes(),this.edgeBeingEdited=void 0,this.selectedControlNode=null,this.controlNodesActive=!1,this._redraw()),this._restoreOverloadedFunctions(),this.freezeSimulation=!1,this.blockConnectingEdgeSelection=!1,this.forceAppendSelection=!1,this.manipulationDOM={},1==this.editMode){for(;this.manipulationDiv.hasChildNodes();)this.manipulationDiv.removeChild(this.manipulationDiv.firstChild);this.manipulationDOM.addNodeSpan=document.createElement("span"),this.manipulationDOM.addNodeSpan.className="network-manipulationUI add",this.manipulationDOM.addNodeLabelSpan=document.createElement("span"),this.manipulationDOM.addNodeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.addNodeLabelSpan.innerHTML=t.addNode,this.manipulationDOM.addNodeSpan.appendChild(this.manipulationDOM.addNodeLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.addEdgeSpan=document.createElement("span"),this.manipulationDOM.addEdgeSpan.className="network-manipulationUI connect",this.manipulationDOM.addEdgeLabelSpan=document.createElement("span"),this.manipulationDOM.addEdgeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.addEdgeLabelSpan.innerHTML=t.addEdge,this.manipulationDOM.addEdgeSpan.appendChild(this.manipulationDOM.addEdgeLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.addNodeSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.addEdgeSpan),1==this._getSelectedNodeCount()&&this.triggerFunctions.edit?(this.manipulationDOM.seperatorLineDiv2=document.createElement("div"),this.manipulationDOM.seperatorLineDiv2.className="network-seperatorLine",this.manipulationDOM.editNodeSpan=document.createElement("span"),this.manipulationDOM.editNodeSpan.className="network-manipulationUI edit",this.manipulationDOM.editNodeLabelSpan=document.createElement("span"),this.manipulationDOM.editNodeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.editNodeLabelSpan.innerHTML=t.editNode,this.manipulationDOM.editNodeSpan.appendChild(this.manipulationDOM.editNodeLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv2),this.manipulationDiv.appendChild(this.manipulationDOM.editNodeSpan)):1==this._getSelectedEdgeCount()&&0==this._getSelectedNodeCount()&&(this.manipulationDOM.seperatorLineDiv3=document.createElement("div"),this.manipulationDOM.seperatorLineDiv3.className="network-seperatorLine",this.manipulationDOM.editEdgeSpan=document.createElement("span"),this.manipulationDOM.editEdgeSpan.className="network-manipulationUI edit",this.manipulationDOM.editEdgeLabelSpan=document.createElement("span"),this.manipulationDOM.editEdgeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.editEdgeLabelSpan.innerHTML=t.editEdge,this.manipulationDOM.editEdgeSpan.appendChild(this.manipulationDOM.editEdgeLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv3),this.manipulationDiv.appendChild(this.manipulationDOM.editEdgeSpan)),0==this._selectionIsEmpty()&&(this.manipulationDOM.seperatorLineDiv4=document.createElement("div"),this.manipulationDOM.seperatorLineDiv4.className="network-seperatorLine",this.manipulationDOM.deleteSpan=document.createElement("span"),this.manipulationDOM.deleteSpan.className="network-manipulationUI delete",this.manipulationDOM.deleteLabelSpan=document.createElement("span"),this.manipulationDOM.deleteLabelSpan.className="network-manipulationLabel",this.manipulationDOM.deleteLabelSpan.innerHTML=t.del,this.manipulationDOM.deleteSpan.appendChild(this.manipulationDOM.deleteLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv4),this.manipulationDiv.appendChild(this.manipulationDOM.deleteSpan)),this.manipulationDOM.addNodeSpan.onclick=this._createAddNodeToolbar.bind(this),this.manipulationDOM.addEdgeSpan.onclick=this._createAddEdgeToolbar.bind(this),1==this._getSelectedNodeCount()&&this.triggerFunctions.edit?this.manipulationDOM.editNodeSpan.onclick=this._editNode.bind(this):1==this._getSelectedEdgeCount()&&0==this._getSelectedNodeCount()&&(this.manipulationDOM.editEdgeSpan.onclick=this._createEditEdgeToolbar.bind(this)),0==this._selectionIsEmpty()&&(this.manipulationDOM.deleteSpan.onclick=this._deleteSelected.bind(this)),this.closeDiv.onclick=this._toggleEditMode.bind(this); +var e=this;this.boundFunction=e._createManipulatorBar,this.on("select",this.boundFunction)}else{for(;this.editModeDiv.hasChildNodes();)this.editModeDiv.removeChild(this.editModeDiv.firstChild);this.manipulationDOM.editModeSpan=document.createElement("span"),this.manipulationDOM.editModeSpan.className="network-manipulationUI edit editmode",this.manipulationDOM.editModeLabelSpan=document.createElement("span"),this.manipulationDOM.editModeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.editModeLabelSpan.innerHTML=t.edit,this.manipulationDOM.editModeSpan.appendChild(this.manipulationDOM.editModeLabelSpan),this.editModeDiv.appendChild(this.manipulationDOM.editModeSpan),this.manipulationDOM.editModeSpan.onclick=this._toggleEditMode.bind(this)}},e._createAddNodeToolbar=function(){this._clearManipulatorBar(),this.boundFunction&&this.off("select",this.boundFunction);var t=this.constants.locales[this.constants.locale];this.manipulationDOM={},this.manipulationDOM.backSpan=document.createElement("span"),this.manipulationDOM.backSpan.className="network-manipulationUI back",this.manipulationDOM.backLabelSpan=document.createElement("span"),this.manipulationDOM.backLabelSpan.className="network-manipulationLabel",this.manipulationDOM.backLabelSpan.innerHTML=t.back,this.manipulationDOM.backSpan.appendChild(this.manipulationDOM.backLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.descriptionSpan=document.createElement("span"),this.manipulationDOM.descriptionSpan.className="network-manipulationUI none",this.manipulationDOM.descriptionLabelSpan=document.createElement("span"),this.manipulationDOM.descriptionLabelSpan.className="network-manipulationLabel",this.manipulationDOM.descriptionLabelSpan.innerHTML=t.addDescription,this.manipulationDOM.descriptionSpan.appendChild(this.manipulationDOM.descriptionLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.backSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.descriptionSpan),this.manipulationDOM.backSpan.onclick=this._createManipulatorBar.bind(this);var e=this;this.boundFunction=e._addNode,this.on("select",this.boundFunction)},e._createAddEdgeToolbar=function(){this._clearManipulatorBar(),this._unselectAll(!0),this.freezeSimulation=!0,this.boundFunction&&this.off("select",this.boundFunction);var t=this.constants.locales[this.constants.locale];this._unselectAll(),this.forceAppendSelection=!1,this.blockConnectingEdgeSelection=!0,this.manipulationDOM={},this.manipulationDOM.backSpan=document.createElement("span"),this.manipulationDOM.backSpan.className="network-manipulationUI back",this.manipulationDOM.backLabelSpan=document.createElement("span"),this.manipulationDOM.backLabelSpan.className="network-manipulationLabel",this.manipulationDOM.backLabelSpan.innerHTML=t.back,this.manipulationDOM.backSpan.appendChild(this.manipulationDOM.backLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.descriptionSpan=document.createElement("span"),this.manipulationDOM.descriptionSpan.className="network-manipulationUI none",this.manipulationDOM.descriptionLabelSpan=document.createElement("span"),this.manipulationDOM.descriptionLabelSpan.className="network-manipulationLabel",this.manipulationDOM.descriptionLabelSpan.innerHTML=t.edgeDescription,this.manipulationDOM.descriptionSpan.appendChild(this.manipulationDOM.descriptionLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.backSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.descriptionSpan),this.manipulationDOM.backSpan.onclick=this._createManipulatorBar.bind(this);var e=this;this.boundFunction=e._handleConnect,this.on("select",this.boundFunction),this.cachedFunctions._handleTouch=this._handleTouch,this.cachedFunctions._manipulationReleaseOverload=this._manipulationReleaseOverload,this.cachedFunctions._handleDragStart=this._handleDragStart,this.cachedFunctions._handleDragEnd=this._handleDragEnd,this._handleTouch=this._handleConnect,this._manipulationReleaseOverload=function(){},this._handleDragStart=function(){},this._handleDragEnd=this._finishConnect,this._redraw()},e._createEditEdgeToolbar=function(){this._clearManipulatorBar(),this.controlNodesActive=!0,this.boundFunction&&this.off("select",this.boundFunction),this.edgeBeingEdited=this._getSelectedEdge(),this.edgeBeingEdited._enableControlNodes();var t=this.constants.locales[this.constants.locale];this.manipulationDOM={},this.manipulationDOM.backSpan=document.createElement("span"),this.manipulationDOM.backSpan.className="network-manipulationUI back",this.manipulationDOM.backLabelSpan=document.createElement("span"),this.manipulationDOM.backLabelSpan.className="network-manipulationLabel",this.manipulationDOM.backLabelSpan.innerHTML=t.back,this.manipulationDOM.backSpan.appendChild(this.manipulationDOM.backLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.descriptionSpan=document.createElement("span"),this.manipulationDOM.descriptionSpan.className="network-manipulationUI none",this.manipulationDOM.descriptionLabelSpan=document.createElement("span"),this.manipulationDOM.descriptionLabelSpan.className="network-manipulationLabel",this.manipulationDOM.descriptionLabelSpan.innerHTML=t.editEdgeDescription,this.manipulationDOM.descriptionSpan.appendChild(this.manipulationDOM.descriptionLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.backSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.descriptionSpan),this.manipulationDOM.backSpan.onclick=this._createManipulatorBar.bind(this),this.cachedFunctions._handleTouch=this._handleTouch,this.cachedFunctions._manipulationReleaseOverload=this._manipulationReleaseOverload,this.cachedFunctions._handleTap=this._handleTap,this.cachedFunctions._handleDragStart=this._handleDragStart,this.cachedFunctions._handleOnDrag=this._handleOnDrag,this._handleTouch=this._selectControlNode,this._handleTap=function(){},this._handleOnDrag=this._controlNodeDrag,this._handleDragStart=function(){},this._manipulationReleaseOverload=this._releaseControlNode,this._redraw()},e._selectControlNode=function(t){this.edgeBeingEdited.controlNodes.from.unselect(),this.edgeBeingEdited.controlNodes.to.unselect(),this.selectedControlNode=this.edgeBeingEdited._getSelectedControlNode(this._XconvertDOMtoCanvas(t.x),this._YconvertDOMtoCanvas(t.y)),null!==this.selectedControlNode&&(this.selectedControlNode.select(),this.freezeSimulation=!0),this._redraw()},e._controlNodeDrag=function(t){var e=this._getPointer(t.center);null!==this.selectedControlNode&&void 0!==this.selectedControlNode&&(this.selectedControlNode.x=this._XconvertDOMtoCanvas(e.x),this.selectedControlNode.y=this._YconvertDOMtoCanvas(e.y)),this._redraw()},e._releaseControlNode=function(t){var e=this._getNodeAt(t);null!==e?(1==this.edgeBeingEdited.controlNodes.from.selected&&(this.edgeBeingEdited._restoreControlNodes(),this._editEdge(e.id,this.edgeBeingEdited.to.id),this.edgeBeingEdited.controlNodes.from.unselect()),1==this.edgeBeingEdited.controlNodes.to.selected&&(this.edgeBeingEdited._restoreControlNodes(),this._editEdge(this.edgeBeingEdited.from.id,e.id),this.edgeBeingEdited.controlNodes.to.unselect())):this.edgeBeingEdited._restoreControlNodes(),this.freezeSimulation=!1,this._redraw()},e._handleConnect=function(t){if(0==this._getSelectedNodeCount()){var e=this._getNodeAt(t);if(null!=e)if(e.clusterSize>1)alert(this.constants.locales[this.constants.locale].createEdgeError);else{this._selectObject(e,!1);var i=this.sectors.support.nodes;i.targetNode=new o({id:"targetNode"},{},{},this.constants);var s=i.targetNode;s.x=e.x,s.y=e.y,this.edges.connectionEdge=new n({id:"connectionEdge",from:e.id,to:s.id},this,this.constants);var r=this.edges.connectionEdge;r.from=e,r.connected=!0,r.options.smoothCurves={enabled:!0,dynamic:!1,type:"continuous",roundness:.5},r.selected=!0,r.to=s,this.cachedFunctions._handleOnDrag=this._handleOnDrag,this._handleOnDrag=function(t){var e=this._getPointer(t.center),i=this.edges.connectionEdge;i.to.x=this._XconvertDOMtoCanvas(e.x),i.to.y=this._YconvertDOMtoCanvas(e.y)},this.moving=!0,this.start()}}},e._finishConnect=function(t){if(1==this._getSelectedNodeCount()){var e=this._getPointer(t.center);this._handleOnDrag=this.cachedFunctions._handleOnDrag,delete this.cachedFunctions._handleOnDrag;var i=this.edges.connectionEdge.fromId;delete this.edges.connectionEdge,delete this.sectors.support.nodes.targetNode,delete this.sectors.support.nodes.targetViaNode;var s=this._getNodeAt(e);null!=s&&(s.clusterSize>1?alert(this.constants.locales[this.constants.locale].createEdgeError):(this._createEdge(i,s.id),this._createManipulatorBar())),this._unselectAll()}},e._addNode=function(){if(this._selectionIsEmpty()&&1==this.editMode){var t=this._pointerToPositionObject(this.pointerPosition),e={id:s.randomUUID(),x:t.left,y:t.top,label:"new",allowedToMoveX:!0,allowedToMoveY:!0};if(this.triggerFunctions.add){if(2!=this.triggerFunctions.add.length)throw new Error("The function for add does not support two arguments (data,callback)");var i=this;this.triggerFunctions.add(e,function(t){i.nodesData.add(t),i._createManipulatorBar(),i.moving=!0,i.start()})}else this.nodesData.add(e),this._createManipulatorBar(),this.moving=!0,this.start()}},e._createEdge=function(t,e){if(1==this.editMode){var i={from:t,to:e};if(this.triggerFunctions.connect){if(2!=this.triggerFunctions.connect.length)throw new Error("The function for connect does not support two arguments (data,callback)");var s=this;this.triggerFunctions.connect(i,function(t){s.edgesData.add(t),s.moving=!0,s.start()})}else this.edgesData.add(i),this.moving=!0,this.start()}},e._editEdge=function(t,e){if(1==this.editMode){var i={id:this.edgeBeingEdited.id,from:t,to:e};if(this.triggerFunctions.editEdge){if(2!=this.triggerFunctions.editEdge.length)throw new Error("The function for edit does not support two arguments (data, callback)");var s=this;this.triggerFunctions.editEdge(i,function(t){s.edgesData.update(t),s.moving=!0,s.start()})}else this.edgesData.update(i),this.moving=!0,this.start()}},e._editNode=function(){if(!this.triggerFunctions.edit||1!=this.editMode)throw new Error("No edit function has been bound to this button");var t=this._getSelectedNode(),e={id:t.id,label:t.label,group:t.options.group,shape:t.options.shape,color:{background:t.options.color.background,border:t.options.color.border,highlight:{background:t.options.color.highlight.background,border:t.options.color.highlight.border}}};if(2!=this.triggerFunctions.edit.length)throw new Error("The function for edit does not support two arguments (data, callback)");var i=this;this.triggerFunctions.edit(e,function(t){i.nodesData.update(t),i._createManipulatorBar(),i.moving=!0,i.start()})},e._deleteSelected=function(){if(!this._selectionIsEmpty()&&1==this.editMode)if(this._clusterInSelection())alert(this.constants.locales[this.constants.locale].deleteClusterError);else{var t=this.getSelectedNodes(),e=this.getSelectedEdges();if(this.triggerFunctions.del){var i=this,s={nodes:t,edges:e};if(2!=this.triggerFunctions.del.length)throw new Error("The function for delete does not support two arguments (data, callback)");this.triggerFunctions.del(s,function(t){i.edgesData.remove(t.edges),i.nodesData.remove(t.nodes),i._unselectAll(),i.moving=!0,i.start()})}else this.edgesData.remove(e),this.nodesData.remove(t),this._unselectAll(),this.moving=!0,this.start()}}},function(t,e,i){var s=(i(1),i(47)),o=i(45);e._cleanNavigation=function(){if(0!=this.navigationHammers.existing.length){for(var t=0;t0){var t,e,i=0,s=!1,o=!1;for(e in this.nodes)this.nodes.hasOwnProperty(e)&&(t=this.nodes[e],-1!=t.level?s=!0:o=!0,is&&(n.xFixed=!1,n.x=i[n.level].minPos,r=!0):n.yFixed&&n.level>s&&(n.yFixed=!1,n.y=i[n.level].minPos,r=!0),1==r&&(i[n.level].minPos+=i[n.level].nodeSpacing,n.edges.length>1&&this._placeBranchNodes(n.edges,n.id,i,n.level))}},e._setLevel=function(t,e,i){for(var s=0;st)&&(o.level=t,o.edges.length>1&&this._setLevel(t+1,o.edges,o.id))}},e._setLevelDirected=function(t,e,i){this.nodes[i].hierarchyEnumerated=!0;for(var s,o,n=0;n1&&s.hierarchyEnumerated===!1&&this._setLevelDirected(s.level,s.edges,s.id)},e._restoreNodes=function(){for(var t in this.nodes)this.nodes.hasOwnProperty(t)&&(this.nodes[t].xFixed=!1,this.nodes[t].yFixed=!1)}},function(t,e,i){function s(){this.constants.smoothCurves.enabled=!this.constants.smoothCurves.enabled;var t=document.getElementById("graph_toggleSmooth");t.style.background=1==this.constants.smoothCurves.enabled?"#A4FF56":"#FF8532",this._configureSmoothCurves(!1)}function o(){for(var t in this.calculationNodes)this.calculationNodes.hasOwnProperty(t)&&(this.calculationNodes[t].vx=0,this.calculationNodes[t].vy=0,this.calculationNodes[t].fx=0,this.calculationNodes[t].fy=0);1==this.constants.hierarchicalLayout.enabled?(this._setupHierarchicalLayout(),a.call(this,"graph_H_nd",1,"physics_hierarchicalRepulsion_nodeDistance"),a.call(this,"graph_H_cg",1,"physics_centralGravity"),a.call(this,"graph_H_sc",1,"physics_springConstant"),a.call(this,"graph_H_sl",1,"physics_springLength"),a.call(this,"graph_H_damp",1,"physics_damping")):this.repositionNodes(),this.moving=!0,this.start()}function n(){var t="No options are required, default values used.",e=[],i=document.getElementById("graph_physicsMethod1"),s=document.getElementById("graph_physicsMethod2");if(1==i.checked){if(this.constants.physics.barnesHut.gravitationalConstant!=this.backupConstants.physics.barnesHut.gravitationalConstant&&e.push("gravitationalConstant: "+this.constants.physics.barnesHut.gravitationalConstant),this.constants.physics.centralGravity!=this.backupConstants.physics.barnesHut.centralGravity&&e.push("centralGravity: "+this.constants.physics.centralGravity),this.constants.physics.springLength!=this.backupConstants.physics.barnesHut.springLength&&e.push("springLength: "+this.constants.physics.springLength),this.constants.physics.springConstant!=this.backupConstants.physics.barnesHut.springConstant&&e.push("springConstant: "+this.constants.physics.springConstant),this.constants.physics.damping!=this.backupConstants.physics.barnesHut.damping&&e.push("damping: "+this.constants.physics.damping),0!=e.length){t="var options = {",t+="physics: {barnesHut: {";for(var o=0;othis.constants.clustering.clusterThreshold&&1==this.constants.clustering.enabled&&this.clusterToFit(this.constants.clustering.reduceToNodes,!1),this._calculateForces())},e._calculateForces=function(){this._calculateGravitationalForces(),this._calculateNodeForces(),this.constants.physics.springConstant>0&&(1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic?this._calculateSpringForcesWithSupport():1==this.constants.physics.hierarchicalRepulsion.enabled?this._calculateHierarchicalSpringForces():this._calculateSpringForces())},e._updateCalculationNodes=function(){if(1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic){this.calculationNodes={},this.calculationNodeIndices=[];for(var t in this.nodes)this.nodes.hasOwnProperty(t)&&(this.calculationNodes[t]=this.nodes[t]);var e=this.sectors.support.nodes;for(var i in e)e.hasOwnProperty(i)&&(this.edges.hasOwnProperty(e[i].parentEdgeId)?this.calculationNodes[i]=e[i]:e[i]._setForce(0,0));for(var s in this.calculationNodes)this.calculationNodes.hasOwnProperty(s)&&this.calculationNodeIndices.push(s)}else this.calculationNodes=this.nodes,this.calculationNodeIndices=this.nodeIndices},e._calculateGravitationalForces=function(){var t,e,i,s,o,n=this.calculationNodes,r=this.constants.physics.centralGravity,a=0;for(o=0;oSimulation Mode:Barnes HutRepulsionHierarchical
Options:
',this.containerElement.parentElement.insertBefore(this.physicsConfiguration,this.containerElement),this.optionsDiv=document.createElement("div"),this.optionsDiv.style.fontSize="14px",this.optionsDiv.style.fontFamily="verdana",this.containerElement.parentElement.insertBefore(this.optionsDiv,this.containerElement); +var e;e=document.getElementById("graph_BH_gc"),e.onchange=a.bind(this,"graph_BH_gc",-1,"physics_barnesHut_gravitationalConstant"),e=document.getElementById("graph_BH_cg"),e.onchange=a.bind(this,"graph_BH_cg",1,"physics_centralGravity"),e=document.getElementById("graph_BH_sc"),e.onchange=a.bind(this,"graph_BH_sc",1,"physics_springConstant"),e=document.getElementById("graph_BH_sl"),e.onchange=a.bind(this,"graph_BH_sl",1,"physics_springLength"),e=document.getElementById("graph_BH_damp"),e.onchange=a.bind(this,"graph_BH_damp",1,"physics_damping"),e=document.getElementById("graph_R_nd"),e.onchange=a.bind(this,"graph_R_nd",1,"physics_repulsion_nodeDistance"),e=document.getElementById("graph_R_cg"),e.onchange=a.bind(this,"graph_R_cg",1,"physics_centralGravity"),e=document.getElementById("graph_R_sc"),e.onchange=a.bind(this,"graph_R_sc",1,"physics_springConstant"),e=document.getElementById("graph_R_sl"),e.onchange=a.bind(this,"graph_R_sl",1,"physics_springLength"),e=document.getElementById("graph_R_damp"),e.onchange=a.bind(this,"graph_R_damp",1,"physics_damping"),e=document.getElementById("graph_H_nd"),e.onchange=a.bind(this,"graph_H_nd",1,"physics_hierarchicalRepulsion_nodeDistance"),e=document.getElementById("graph_H_cg"),e.onchange=a.bind(this,"graph_H_cg",1,"physics_centralGravity"),e=document.getElementById("graph_H_sc"),e.onchange=a.bind(this,"graph_H_sc",1,"physics_springConstant"),e=document.getElementById("graph_H_sl"),e.onchange=a.bind(this,"graph_H_sl",1,"physics_springLength"),e=document.getElementById("graph_H_damp"),e.onchange=a.bind(this,"graph_H_damp",1,"physics_damping"),e=document.getElementById("graph_H_direction"),e.onchange=a.bind(this,"graph_H_direction",t,"hierarchicalLayout_direction"),e=document.getElementById("graph_H_levsep"),e.onchange=a.bind(this,"graph_H_levsep",1,"hierarchicalLayout_levelSeparation"),e=document.getElementById("graph_H_nspac"),e.onchange=a.bind(this,"graph_H_nspac",1,"hierarchicalLayout_nodeSpacing");var i=document.getElementById("graph_physicsMethod1"),d=document.getElementById("graph_physicsMethod2"),l=document.getElementById("graph_physicsMethod3");d.checked=!0,this.constants.physics.barnesHut.enabled&&(i.checked=!0),this.constants.hierarchicalLayout.enabled&&(l.checked=!0);var c=document.getElementById("graph_toggleSmooth"),p=document.getElementById("graph_repositionNodes"),u=document.getElementById("graph_generateOptions");c.onclick=s.bind(this),p.onclick=o.bind(this),u.onclick=n.bind(this),c.style.background=1==this.constants.smoothCurves&&0==this.constants.dynamicSmoothCurves?"#A4FF56":"#FF8532",r.apply(this),i.onchange=r.bind(this),d.onchange=r.bind(this),l.onchange=r.bind(this)}},e._overWriteGraphConstants=function(t,e){var i=t.split("_");1==i.length?this.constants[i[0]]=e:2==i.length?this.constants[i[0]][i[1]]=e:3==i.length&&(this.constants[i[0]][i[1]][i[2]]=e)}},function(t){function e(t){throw new Error("Cannot find module '"+t+"'.")}e.keys=function(){return[]},e.resolve=e,t.exports=e,e.id=68},function(t,e){e._calculateNodeForces=function(){var t,e,i,s,o,n,r,a,h,d,l,c=this.calculationNodes,p=this.calculationNodeIndices,u=-2/3,m=4/3,f=this.constants.physics.repulsion.nodeDistance,g=f;for(d=0;di&&(r=.5*g>i?1:v*i+m,r*=0==n?1:1+n*this.constants.clustering.forceAmplification,r/=Math.max(i,.01*g),s=t*r,o=e*r,a.fx-=s,a.fy-=o,h.fx+=s,h.fy+=o)}}},function(t,e){e._calculateNodeForces=function(){var t,e,i,s,o,n,r,a,h,d,l=this.calculationNodes,c=this.calculationNodeIndices,p=this.constants.physics.hierarchicalRepulsion.nodeDistance;for(h=0;hi?-Math.pow(u*i,2)+Math.pow(u*p,2):0,0==i?i=.01:n/=i,s=t*n,o=e*n,r.fx-=s,r.fy-=o,a.fx+=s,a.fy+=o}},e._calculateHierarchicalSpringForces=function(){for(var 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a/core/src/main/resources/org/apache/spark/ui/static/webui.css +++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css @@ -145,7 +145,7 @@ pre { border: none; } -span.expand-additional-metrics { +span.expand-additional-metrics, span.expand-dag-viz { cursor: pointer; } diff --git a/core/src/main/scala/org/apache/spark/Aggregator.scala b/core/src/main/scala/org/apache/spark/Aggregator.scala index 3b684bbeceaf2..af9765d313e9e 100644 --- a/core/src/main/scala/org/apache/spark/Aggregator.scala +++ b/core/src/main/scala/org/apache/spark/Aggregator.scala @@ -88,10 +88,7 @@ case class Aggregator[K, V, C] ( combiners.iterator } else { val combiners = new ExternalAppendOnlyMap[K, C, C](identity, mergeCombiners, mergeCombiners) - while (iter.hasNext) { - val pair = iter.next() - combiners.insert(pair._1, pair._2) - } + combiners.insertAll(iter) // Update task metrics if context is not null // TODO: Make context non-optional in a future release Option(context).foreach { c => diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala index b986fa87dc2f4..66bda68088502 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala @@ -21,19 +21,29 @@ import java.util.concurrent.TimeUnit import scala.collection.mutable +import com.codahale.metrics.{Gauge, MetricRegistry} + import org.apache.spark.scheduler._ +import org.apache.spark.metrics.source.Source import org.apache.spark.util.{ThreadUtils, Clock, SystemClock, Utils} /** * An agent that dynamically allocates and removes executors based on the workload. * - * The add policy depends on whether there are backlogged tasks waiting to be scheduled. If - * the scheduler queue is not drained in N seconds, then new executors are added. If the queue - * persists for another M seconds, then more executors are added and so on. The number added - * in each round increases exponentially from the previous round until an upper bound on the - * number of executors has been reached. The upper bound is based both on a configured property - * and on the number of tasks pending: the policy will never increase the number of executor - * requests past the number needed to handle all pending tasks. + * The ExecutorAllocationManager maintains a moving target number of executors which is periodically + * synced to the cluster manager. The target starts at a configured initial value and changes with + * the number of pending and running tasks. + * + * Decreasing the target number of executors happens when the current target is more than needed to + * handle the current load. The target number of executors is always truncated to the number of + * executors that could run all current running and pending tasks at once. + * + * Increasing the target number of executors happens in response to backlogged tasks waiting to be + * scheduled. If the scheduler queue is not drained in N seconds, then new executors are added. If + * the queue persists for another M seconds, then more executors are added and so on. The number + * added in each round increases exponentially from the previous round until an upper bound has been + * reached. The upper bound is based both on a configured property and on the current number of + * running and pending tasks, as described above. * * The rationale for the exponential increase is twofold: (1) Executors should be added slowly * in the beginning in case the number of extra executors needed turns out to be small. Otherwise, @@ -105,8 +115,10 @@ private[spark] class ExecutorAllocationManager( // Number of executors to add in the next round private var numExecutorsToAdd = 1 - // Number of executors that have been requested but have not registered yet - private var numExecutorsPending = 0 + // The desired number of executors at this moment in time. If all our executors were to die, this + // is the number of executors we would immediately want from the cluster manager. + private var numExecutorsTarget = + conf.getInt("spark.dynamicAllocation.initialExecutors", minNumExecutors) // Executors that have been requested to be removed but have not been killed yet private val executorsPendingToRemove = new mutable.HashSet[String] @@ -135,6 +147,9 @@ private[spark] class ExecutorAllocationManager( private val executor = ThreadUtils.newDaemonSingleThreadScheduledExecutor("spark-dynamic-executor-allocation") + // Metric source for ExecutorAllocationManager to expose internal status to MetricsSystem. + val executorAllocationManagerSource = new ExecutorAllocationManagerSource + /** * Verify that the settings specified through the config are valid. * If not, throw an appropriate exception. @@ -199,13 +214,6 @@ private[spark] class ExecutorAllocationManager( executor.awaitTermination(10, TimeUnit.SECONDS) } - /** - * The number of executors we would have if the cluster manager were to fulfill all our existing - * requests. - */ - private def targetNumExecutors(): Int = - numExecutorsPending + executorIds.size - executorsPendingToRemove.size - /** * The maximum number of executors we would need under the current load to satisfy all running * and pending tasks, rounded up. @@ -227,7 +235,7 @@ private[spark] class ExecutorAllocationManager( private def schedule(): Unit = synchronized { val now = clock.getTimeMillis - addOrCancelExecutorRequests(now) + updateAndSyncNumExecutorsTarget(now) removeTimes.retain { case (executorId, expireTime) => val expired = now >= expireTime @@ -239,26 +247,28 @@ private[spark] class ExecutorAllocationManager( } /** + * Updates our target number of executors and syncs the result with the cluster manager. + * * Check to see whether our existing allocation and the requests we've made previously exceed our - * current needs. If so, let the cluster manager know so that it can cancel pending requests that - * are unneeded. + * current needs. If so, truncate our target and let the cluster manager know so that it can + * cancel pending requests that are unneeded. * * If not, and the add time has expired, see if we can request new executors and refresh the add * time. * * @return the delta in the target number of executors. */ - private def addOrCancelExecutorRequests(now: Long): Int = synchronized { - val currentTarget = targetNumExecutors + private def updateAndSyncNumExecutorsTarget(now: Long): Int = synchronized { val maxNeeded = maxNumExecutorsNeeded - if (maxNeeded < currentTarget) { + if (maxNeeded < numExecutorsTarget) { // The target number exceeds the number we actually need, so stop adding new - // executors and inform the cluster manager to cancel the extra pending requests. - val newTotalExecutors = math.max(maxNeeded, minNumExecutors) - client.requestTotalExecutors(newTotalExecutors) + // executors and inform the cluster manager to cancel the extra pending requests + val oldNumExecutorsTarget = numExecutorsTarget + numExecutorsTarget = math.max(maxNeeded, minNumExecutors) + client.requestTotalExecutors(numExecutorsTarget) numExecutorsToAdd = 1 - updateNumExecutorsPending(newTotalExecutors) + numExecutorsTarget - oldNumExecutorsTarget } else if (addTime != NOT_SET && now >= addTime) { val delta = addExecutors(maxNeeded) logDebug(s"Starting timer to add more executors (to " + @@ -281,21 +291,30 @@ private[spark] class ExecutorAllocationManager( */ private def addExecutors(maxNumExecutorsNeeded: Int): Int = { // Do not request more executors if it would put our target over the upper bound - val currentTarget = targetNumExecutors - if (currentTarget >= maxNumExecutors) { - logDebug(s"Not adding executors because there are already ${executorIds.size} " + - s"registered and $numExecutorsPending pending executor(s) (limit $maxNumExecutors)") + if (numExecutorsTarget >= maxNumExecutors) { + val numExecutorsPending = numExecutorsTarget - executorIds.size + logDebug(s"Not adding executors because there are already ${executorIds.size} registered " + + s"and ${numExecutorsPending} pending executor(s) (limit $maxNumExecutors)") numExecutorsToAdd = 1 return 0 } - val actualMaxNumExecutors = math.min(maxNumExecutors, maxNumExecutorsNeeded) - val newTotalExecutors = math.min(currentTarget + numExecutorsToAdd, actualMaxNumExecutors) - val addRequestAcknowledged = testing || client.requestTotalExecutors(newTotalExecutors) + val oldNumExecutorsTarget = numExecutorsTarget + // There's no point in wasting time ramping up to the number of executors we already have, so + // make sure our target is at least as much as our current allocation: + numExecutorsTarget = math.max(numExecutorsTarget, executorIds.size) + // Boost our target with the number to add for this round: + numExecutorsTarget += numExecutorsToAdd + // Ensure that our target doesn't exceed what we need at the present moment: + numExecutorsTarget = math.min(numExecutorsTarget, maxNumExecutorsNeeded) + // Ensure that our target fits within configured bounds: + numExecutorsTarget = math.max(math.min(numExecutorsTarget, maxNumExecutors), minNumExecutors) + + val addRequestAcknowledged = testing || client.requestTotalExecutors(numExecutorsTarget) if (addRequestAcknowledged) { - val delta = updateNumExecutorsPending(newTotalExecutors) + val delta = numExecutorsTarget - oldNumExecutorsTarget logInfo(s"Requesting $delta new executor(s) because tasks are backlogged" + - s" (new desired total will be $newTotalExecutors)") + s" (new desired total will be $numExecutorsTarget)") numExecutorsToAdd = if (delta == numExecutorsToAdd) { numExecutorsToAdd * 2 } else { @@ -304,23 +323,11 @@ private[spark] class ExecutorAllocationManager( delta } else { logWarning( - s"Unable to reach the cluster manager to request $newTotalExecutors total executors!") + s"Unable to reach the cluster manager to request $numExecutorsTarget total executors!") 0 } } - /** - * Given the new target number of executors, update the number of pending executor requests, - * and return the delta from the old number of pending requests. - */ - private def updateNumExecutorsPending(newTotalExecutors: Int): Int = { - val newNumExecutorsPending = - newTotalExecutors - executorIds.size + executorsPendingToRemove.size - val delta = newNumExecutorsPending - numExecutorsPending - numExecutorsPending = newNumExecutorsPending - delta - } - /** * Request the cluster manager to remove the given executor. * Return whether the request is received. @@ -372,10 +379,6 @@ private[spark] class ExecutorAllocationManager( // as idle again so as not to forget that it is a candidate for removal. (see SPARK-4951) executorIds.filter(listener.isExecutorIdle).foreach(onExecutorIdle) logInfo(s"New executor $executorId has registered (new total is ${executorIds.size})") - if (numExecutorsPending > 0) { - numExecutorsPending -= 1 - logDebug(s"Decremented number of pending executors ($numExecutorsPending left)") - } } else { logWarning(s"Duplicate executor $executorId has registered") } @@ -582,6 +585,29 @@ private[spark] class ExecutorAllocationManager( } } + /** + * Metric source for ExecutorAllocationManager to expose its internal executor allocation + * status to MetricsSystem. + * Note: These metrics heavily rely on the internal implementation of + * ExecutorAllocationManager, metrics or value of metrics will be changed when internal + * implementation is changed, so these metrics are not stable across Spark version. + */ + private[spark] class ExecutorAllocationManagerSource extends Source { + val sourceName = "ExecutorAllocationManager" + val metricRegistry = new MetricRegistry() + + private def registerGauge[T](name: String, value: => T, defaultValue: T): Unit = { + metricRegistry.register(MetricRegistry.name("executors", name), new Gauge[T] { + override def getValue: T = synchronized { Option(value).getOrElse(defaultValue) } + }) + } + + registerGauge("numberExecutorsToAdd", numExecutorsToAdd, 0) + registerGauge("numberExecutorsPendingToRemove", executorsPendingToRemove.size, 0) + registerGauge("numberAllExecutors", executorIds.size, 0) + registerGauge("numberTargetExecutors", numExecutorsTarget, 0) + registerGauge("numberMaxNeededExecutors", maxNumExecutorsNeeded(), 0) + } } private object ExecutorAllocationManager { diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index d65c94e410662..16072283edbe9 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -106,7 +106,7 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging */ protected def askTracker[T: ClassTag](message: Any): T = { try { - trackerEndpoint.askWithReply[T](message) + trackerEndpoint.askWithRetry[T](message) } catch { case e: Exception => logError("Error communicating with MapOutputTracker", e) diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala index 3653f724ba192..8aed1e20e0686 100644 --- a/core/src/main/scala/org/apache/spark/SecurityManager.scala +++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala @@ -150,8 +150,13 @@ import org.apache.spark.util.Utils * authorization. If not filter is in place the user is generally null and no authorization * can take place. * - * Connection encryption (SSL) configuration is organized hierarchically. The user can configure - * the default SSL settings which will be used for all the supported communication protocols unless + * When authentication is being used, encryption can also be enabled by setting the option + * spark.authenticate.enableSaslEncryption to true. This is only supported by communication + * channels that use the network-common library, and can be used as an alternative to SSL in those + * cases. + * + * SSL can be used for encryption for certain communication channels. The user can configure the + * default SSL settings which will be used for all the supported communication protocols unless * they are overwritten by protocol specific settings. This way the user can easily provide the * common settings for all the protocols without disabling the ability to configure each one * individually. @@ -412,6 +417,14 @@ private[spark] class SecurityManager(sparkConf: SparkConf) */ def isAuthenticationEnabled(): Boolean = authOn + /** + * Checks whether SASL encryption should be enabled. + * @return Whether to enable SASL encryption when connecting to services that support it. + */ + def isSaslEncryptionEnabled(): Boolean = { + sparkConf.getBoolean("spark.authenticate.enableSaslEncryption", false) + } + /** * Gets the user used for authenticating HTTP connections. * For now use a single hardcoded user. diff --git a/core/src/main/scala/org/apache/spark/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/SizeEstimator.scala new file mode 100644 index 0000000000000..54fc3a856adfa --- /dev/null +++ b/core/src/main/scala/org/apache/spark/SizeEstimator.scala @@ -0,0 +1,44 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark + +import org.apache.spark.annotation.DeveloperApi + +/** + * Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in + * memory-aware caches. + * + * Based on the following JavaWorld article: + * http://www.javaworld.com/javaworld/javaqa/2003-12/02-qa-1226-sizeof.html + */ +@DeveloperApi +object SizeEstimator { + /** + * :: DeveloperApi :: + * Estimate the number of bytes that the given object takes up on the JVM heap. The estimate + * includes space taken up by objects referenced by the given object, their references, and so on + * and so forth. + * + * This is useful for determining the amount of heap space a broadcast variable will occupy on + * each executor or the amount of space each object will take when caching objects in + * deserialized form. This is not the same as the serialized size of the object, which will + * typically be much smaller. + */ + @DeveloperApi + def estimate(obj: AnyRef): Long = org.apache.spark.util.SizeEstimator.estimate(obj) +} diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index d0cf2a8dd01cd..b5f040ceb15ca 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -217,6 +217,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli private var _heartbeatReceiver: RpcEndpointRef = _ @volatile private var _dagScheduler: DAGScheduler = _ private var _applicationId: String = _ + private var _applicationAttemptId: Option[String] = None private var _eventLogger: Option[EventLoggingListener] = None private var _executorAllocationManager: Option[ExecutorAllocationManager] = None private var _cleaner: Option[ContextCleaner] = None @@ -247,9 +248,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli private[spark] def eventLogDir: Option[URI] = _eventLogDir private[spark] def eventLogCodec: Option[String] = _eventLogCodec - // Generate the random name for a temp folder in Tachyon + // Generate the random name for a temp folder in external block store. // Add a timestamp as the suffix here to make it more safe - val tachyonFolderName = "spark-" + randomUUID.toString() + val externalBlockStoreFolderName = "spark-" + randomUUID.toString() + @deprecated("Use externalBlockStoreFolderName instead.", "1.4.0") + val tachyonFolderName = externalBlockStoreFolderName def isLocal: Boolean = (master == "local" || master.startsWith("local[")) @@ -313,6 +316,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } def applicationId: String = _applicationId + def applicationAttemptId: Option[String] = _applicationAttemptId def metricsSystem: MetricsSystem = if (_env != null) _env.metricsSystem else null @@ -343,6 +347,19 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli value } + /** Control our logLevel. This overrides any user-defined log settings. + * @param logLevel The desired log level as a string. + * Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN + */ + def setLogLevel(logLevel: String) { + val validLevels = Seq("ALL", "DEBUG", "ERROR", "FATAL", "INFO", "OFF", "TRACE", "WARN") + if (!validLevels.contains(logLevel)) { + throw new IllegalArgumentException( + s"Supplied level $logLevel did not match one of: ${validLevels.mkString(",")}") + } + Utils.setLogLevel(org.apache.log4j.Level.toLevel(logLevel)) + } + try { _conf = config.clone() _conf.validateSettings() @@ -386,19 +403,21 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } } - _conf.set("spark.tachyonStore.folderName", tachyonFolderName) + _conf.set("spark.externalBlockStore.folderName", externalBlockStoreFolderName) if (master == "yarn-client") System.setProperty("SPARK_YARN_MODE", "true") + // "_jobProgressListener" should be set up before creating SparkEnv because when creating + // "SparkEnv", some messages will be posted to "listenerBus" and we should not miss them. + _jobProgressListener = new JobProgressListener(_conf) + listenerBus.addListener(jobProgressListener) + // Create the Spark execution environment (cache, map output tracker, etc) _env = createSparkEnv(_conf, isLocal, listenerBus) SparkEnv.set(_env) _metadataCleaner = new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, _conf) - _jobProgressListener = new JobProgressListener(_conf) - listenerBus.addListener(jobProgressListener) - _statusTracker = new SparkStatusTracker(this) _progressBar = @@ -470,6 +489,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli _taskScheduler.start() _applicationId = _taskScheduler.applicationId() + _applicationAttemptId = taskScheduler.applicationAttemptId() _conf.set("spark.app.id", _applicationId) _env.blockManager.initialize(_applicationId) @@ -482,7 +502,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli _eventLogger = if (isEventLogEnabled) { val logger = - new EventLoggingListener(_applicationId, _eventLogDir.get, _conf, _hadoopConfiguration) + new EventLoggingListener(_applicationId, _applicationAttemptId, _eventLogDir.get, + _conf, _hadoopConfiguration) logger.start() listenerBus.addListener(logger) Some(logger) @@ -518,6 +539,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli _taskScheduler.postStartHook() _env.metricsSystem.registerSource(new DAGSchedulerSource(dagScheduler)) _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager)) + _executorAllocationManager.foreach { e => + _env.metricsSystem.registerSource(e.executorAllocationManagerSource) + } // Make sure the context is stopped if the user forgets about it. This avoids leaving // unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM @@ -555,7 +579,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli SparkEnv.executorActorSystemName, RpcAddress(host, port), ExecutorEndpoint.EXECUTOR_ENDPOINT_NAME) - Some(endpointRef.askWithReply[Array[ThreadStackTrace]](TriggerThreadDump)) + Some(endpointRef.askWithRetry[Array[ThreadStackTrace]](TriggerThreadDump)) } } catch { case e: Exception => @@ -640,6 +664,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, null) } + /** + * Execute a block of code in a scope such that all new RDDs created in this body will + * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}. + * + * Note: Return statements are NOT allowed in the given body. + */ + private def withScope[U](body: => U): U = RDDOperationScope.withScope[U](this)(body) + // Methods for creating RDDs /** Distribute a local Scala collection to form an RDD. @@ -650,7 +682,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @note avoid using `parallelize(Seq())` to create an empty `RDD`. Consider `emptyRDD` for an * RDD with no partitions, or `parallelize(Seq[T]())` for an RDD of `T` with empty partitions. */ - def parallelize[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { + def parallelize[T: ClassTag]( + seq: Seq[T], + numSlices: Int = defaultParallelism): RDD[T] = withScope { assertNotStopped() new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]()) } @@ -659,14 +693,16 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * * This method is identical to `parallelize`. */ - def makeRDD[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { + def makeRDD[T: ClassTag]( + seq: Seq[T], + numSlices: Int = defaultParallelism): RDD[T] = withScope { parallelize(seq, numSlices) } /** Distribute a local Scala collection to form an RDD, with one or more * location preferences (hostnames of Spark nodes) for each object. * Create a new partition for each collection item. */ - def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = { + def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = withScope { assertNotStopped() val indexToPrefs = seq.zipWithIndex.map(t => (t._2, t._1._2)).toMap new ParallelCollectionRDD[T](this, seq.map(_._1), seq.size, indexToPrefs) @@ -676,10 +712,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * Read a text file from HDFS, a local file system (available on all nodes), or any * Hadoop-supported file system URI, and return it as an RDD of Strings. */ - def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = { + def textFile( + path: String, + minPartitions: Int = defaultMinPartitions): RDD[String] = withScope { assertNotStopped() hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text], - minPartitions).map(pair => pair._2.toString).setName(path) + minPartitions).map(pair => pair._2.toString) } /** @@ -709,11 +747,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * * @param minPartitions A suggestion value of the minimal splitting number for input data. */ - def wholeTextFiles(path: String, minPartitions: Int = defaultMinPartitions): - RDD[(String, String)] = { + def wholeTextFiles( + path: String, + minPartitions: Int = defaultMinPartitions): RDD[(String, String)] = withScope { assertNotStopped() val job = new NewHadoopJob(hadoopConfiguration) - NewFileInputFormat.addInputPath(job, new Path(path)) + // Use setInputPaths so that wholeTextFiles aligns with hadoopFile/textFile in taking + // comma separated files as input. (see SPARK-7155) + NewFileInputFormat.setInputPaths(job, path) val updateConf = job.getConfiguration new WholeTextFileRDD( this, @@ -755,11 +796,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @note Small files are preferred; very large files may cause bad performance. */ @Experimental - def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions): - RDD[(String, PortableDataStream)] = { + def binaryFiles( + path: String, + minPartitions: Int = defaultMinPartitions): RDD[(String, PortableDataStream)] = withScope { assertNotStopped() val job = new NewHadoopJob(hadoopConfiguration) - NewFileInputFormat.addInputPath(job, new Path(path)) + // Use setInputPaths so that binaryFiles aligns with hadoopFile/textFile in taking + // comma separated files as input. (see SPARK-7155) + NewFileInputFormat.setInputPaths(job, path) val updateConf = job.getConfiguration new BinaryFileRDD( this, @@ -783,8 +827,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @return An RDD of data with values, represented as byte arrays */ @Experimental - def binaryRecords(path: String, recordLength: Int, conf: Configuration = hadoopConfiguration) - : RDD[Array[Byte]] = { + def binaryRecords( + path: String, + recordLength: Int, + conf: Configuration = hadoopConfiguration): RDD[Array[Byte]] = withScope { assertNotStopped() conf.setInt(FixedLengthBinaryInputFormat.RECORD_LENGTH_PROPERTY, recordLength) val br = newAPIHadoopFile[LongWritable, BytesWritable, FixedLengthBinaryInputFormat](path, @@ -825,8 +871,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], - minPartitions: Int = defaultMinPartitions - ): RDD[(K, V)] = { + minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope { assertNotStopped() // Add necessary security credentials to the JobConf before broadcasting it. SparkHadoopUtil.get.addCredentials(conf) @@ -846,8 +891,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], - minPartitions: Int = defaultMinPartitions - ): RDD[(K, V)] = { + minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope { assertNotStopped() // A Hadoop configuration can be about 10 KB, which is pretty big, so broadcast it. val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration)) @@ -878,7 +922,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli */ def hadoopFile[K, V, F <: InputFormat[K, V]] (path: String, minPartitions: Int) - (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = { + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope { hadoopFile(path, fm.runtimeClass.asInstanceOf[Class[F]], km.runtimeClass.asInstanceOf[Class[K]], @@ -901,13 +945,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * copy them using a `map` function. */ def hadoopFile[K, V, F <: InputFormat[K, V]](path: String) - (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope { hadoopFile[K, V, F](path, defaultMinPartitions) + } /** Get an RDD for a Hadoop file with an arbitrary new API InputFormat. */ def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]] (path: String) - (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = { + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope { newAPIHadoopFile( path, fm.runtimeClass.asInstanceOf[Class[F]], @@ -930,12 +975,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli fClass: Class[F], kClass: Class[K], vClass: Class[V], - conf: Configuration = hadoopConfiguration): RDD[(K, V)] = { + conf: Configuration = hadoopConfiguration): RDD[(K, V)] = withScope { assertNotStopped() // The call to new NewHadoopJob automatically adds security credentials to conf, // so we don't need to explicitly add them ourselves val job = new NewHadoopJob(conf) - NewFileInputFormat.addInputPath(job, new Path(path)) + // Use setInputPaths so that newAPIHadoopFile aligns with hadoopFile/textFile in taking + // comma separated files as input. (see SPARK-7155) + NewFileInputFormat.setInputPaths(job, path) val updatedConf = job.getConfiguration new NewHadoopRDD(this, fClass, kClass, vClass, updatedConf).setName(path) } @@ -962,7 +1009,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli conf: Configuration = hadoopConfiguration, fClass: Class[F], kClass: Class[K], - vClass: Class[V]): RDD[(K, V)] = { + vClass: Class[V]): RDD[(K, V)] = withScope { assertNotStopped() // Add necessary security credentials to the JobConf. Required to access secure HDFS. val jconf = new JobConf(conf) @@ -982,7 +1029,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli keyClass: Class[K], valueClass: Class[V], minPartitions: Int - ): RDD[(K, V)] = { + ): RDD[(K, V)] = withScope { assertNotStopped() val inputFormatClass = classOf[SequenceFileInputFormat[K, V]] hadoopFile(path, inputFormatClass, keyClass, valueClass, minPartitions) @@ -996,7 +1043,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first * copy them using a `map` function. * */ - def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V]): RDD[(K, V)] = { + def sequenceFile[K, V]( + path: String, + keyClass: Class[K], + valueClass: Class[V]): RDD[(K, V)] = withScope { assertNotStopped() sequenceFile(path, keyClass, valueClass, defaultMinPartitions) } @@ -1026,16 +1076,17 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli def sequenceFile[K, V] (path: String, minPartitions: Int = defaultMinPartitions) (implicit km: ClassTag[K], vm: ClassTag[V], - kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]) - : RDD[(K, V)] = { - assertNotStopped() - val kc = kcf() - val vc = vcf() - val format = classOf[SequenceFileInputFormat[Writable, Writable]] - val writables = hadoopFile(path, format, + kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]): RDD[(K, V)] = { + withScope { + assertNotStopped() + val kc = kcf() + val vc = vcf() + val format = classOf[SequenceFileInputFormat[Writable, Writable]] + val writables = hadoopFile(path, format, kc.writableClass(km).asInstanceOf[Class[Writable]], vc.writableClass(vm).asInstanceOf[Class[Writable]], minPartitions) - writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) } + writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) } + } } /** @@ -1048,21 +1099,18 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli */ def objectFile[T: ClassTag]( path: String, - minPartitions: Int = defaultMinPartitions - ): RDD[T] = { + minPartitions: Int = defaultMinPartitions): RDD[T] = withScope { assertNotStopped() sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minPartitions) .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes, Utils.getContextOrSparkClassLoader)) } - protected[spark] def checkpointFile[T: ClassTag]( - path: String - ): RDD[T] = { + protected[spark] def checkpointFile[T: ClassTag](path: String): RDD[T] = withScope { new CheckpointRDD[T](this, path) } /** Build the union of a list of RDDs. */ - def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = { + def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = withScope { val partitioners = rdds.flatMap(_.partitioner).toSet if (rdds.forall(_.partitioner.isDefined) && partitioners.size == 1) { new PartitionerAwareUnionRDD(this, rdds) @@ -1072,8 +1120,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } /** Build the union of a list of RDDs passed as variable-length arguments. */ - def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = + def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = withScope { union(Seq(first) ++ rest) + } /** Get an RDD that has no partitions or elements. */ def emptyRDD[T: ClassTag]: EmptyRDD[T] = new EmptyRDD[T](this) @@ -1632,7 +1681,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli partitions: Seq[Int], allowLocal: Boolean ): Array[U] = { - runJob(rdd, (context: TaskContext, iter: Iterator[T]) => func(iter), partitions, allowLocal) + val cleanedFunc = clean(func) + runJob(rdd, (ctx: TaskContext, it: Iterator[T]) => cleanedFunc(it), partitions, allowLocal) } /** @@ -1686,7 +1736,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli val callSite = getCallSite logInfo("Starting job: " + callSite.shortForm) val start = System.nanoTime - val result = dagScheduler.runApproximateJob(rdd, func, evaluator, callSite, timeout, + val cleanedFunc = clean(func) + val result = dagScheduler.runApproximateJob(rdd, cleanedFunc, evaluator, callSite, timeout, localProperties.get) logInfo( "Job finished: " + callSite.shortForm + ", took " + (System.nanoTime - start) / 1e9 + " s") @@ -1860,7 +1911,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // Note: this code assumes that the task scheduler has been initialized and has contacted // the cluster manager to get an application ID (in case the cluster manager provides one). listenerBus.post(SparkListenerApplicationStart(appName, Some(applicationId), - startTime, sparkUser)) + startTime, sparkUser, applicationAttemptId)) } /** Post the application end event */ @@ -2035,10 +2086,10 @@ object SparkContext extends Logging { } private[spark] val SPARK_JOB_DESCRIPTION = "spark.job.description" - private[spark] val SPARK_JOB_GROUP_ID = "spark.jobGroup.id" - private[spark] val SPARK_JOB_INTERRUPT_ON_CANCEL = "spark.job.interruptOnCancel" + private[spark] val RDD_SCOPE_KEY = "spark.rdd.scope" + private[spark] val RDD_SCOPE_NO_OVERRIDE_KEY = "spark.rdd.scope.noOverride" /** * Executor id for the driver. In earlier versions of Spark, this was ``, but this was diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 959aefabd8de4..0c4d28f786edd 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -40,6 +40,7 @@ import org.apache.spark.scheduler.OutputCommitCoordinator.OutputCommitCoordinato import org.apache.spark.serializer.Serializer import org.apache.spark.shuffle.{ShuffleMemoryManager, ShuffleManager} import org.apache.spark.storage._ +import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator} import org.apache.spark.util.{RpcUtils, Utils} /** @@ -69,6 +70,7 @@ class SparkEnv ( val sparkFilesDir: String, val metricsSystem: MetricsSystem, val shuffleMemoryManager: ShuffleMemoryManager, + val executorMemoryManager: ExecutorMemoryManager, val outputCommitCoordinator: OutputCommitCoordinator, val conf: SparkConf) extends Logging { @@ -382,6 +384,15 @@ object SparkEnv extends Logging { new OutputCommitCoordinatorEndpoint(rpcEnv, outputCommitCoordinator)) outputCommitCoordinator.coordinatorRef = Some(outputCommitCoordinatorRef) + val executorMemoryManager: ExecutorMemoryManager = { + val allocator = if (conf.getBoolean("spark.unsafe.offHeap", false)) { + MemoryAllocator.UNSAFE + } else { + MemoryAllocator.HEAP + } + new ExecutorMemoryManager(allocator) + } + val envInstance = new SparkEnv( executorId, rpcEnv, @@ -398,6 +409,7 @@ object SparkEnv extends Logging { sparkFilesDir, metricsSystem, shuffleMemoryManager, + executorMemoryManager, outputCommitCoordinator, conf) diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala index 7d7fe1a446313..d09e17dea0911 100644 --- a/core/src/main/scala/org/apache/spark/TaskContext.scala +++ b/core/src/main/scala/org/apache/spark/TaskContext.scala @@ -21,6 +21,7 @@ import java.io.Serializable import org.apache.spark.annotation.DeveloperApi import org.apache.spark.executor.TaskMetrics +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util.TaskCompletionListener @@ -133,4 +134,9 @@ abstract class TaskContext extends Serializable { /** ::DeveloperApi:: */ @DeveloperApi def taskMetrics(): TaskMetrics + + /** + * Returns the manager for this task's managed memory. + */ + private[spark] def taskMemoryManager(): TaskMemoryManager } diff --git a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala index 337c8e4ebebcd..b4d572cb52313 100644 --- a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala +++ b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala @@ -18,6 +18,7 @@ package org.apache.spark import org.apache.spark.executor.TaskMetrics +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util.{TaskCompletionListener, TaskCompletionListenerException} import scala.collection.mutable.ArrayBuffer @@ -27,6 +28,7 @@ private[spark] class TaskContextImpl( val partitionId: Int, override val taskAttemptId: Long, override val attemptNumber: Int, + override val taskMemoryManager: TaskMemoryManager, val runningLocally: Boolean = false, val taskMetrics: TaskMetrics = TaskMetrics.empty) extends TaskContext diff --git a/core/src/main/scala/org/apache/spark/annotation/Private.java b/core/src/main/scala/org/apache/spark/annotation/Private.java new file mode 100644 index 0000000000000..9082fcf0c84bc --- /dev/null +++ b/core/src/main/scala/org/apache/spark/annotation/Private.java @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.annotation; + +import java.lang.annotation.ElementType; +import java.lang.annotation.Retention; +import java.lang.annotation.RetentionPolicy; +import java.lang.annotation.Target; + +/** + * A class that is considered private to the internals of Spark -- there is a high-likelihood + * they will be changed in future versions of Spark. + * + * This should be used only when the standard Scala / Java means of protecting classes are + * insufficient. In particular, Java has no equivalent of private[spark], so we use this annotation + * in its place. + * + * NOTE: If there exists a Scaladoc comment that immediately precedes this annotation, the first + * line of the comment must be ":: Private ::" with no trailing blank line. This is because + * of the known issue that Scaladoc displays only either the annotation or the comment, whichever + * comes first. + */ +@Retention(RetentionPolicy.RUNTIME) +@Target({ElementType.TYPE, ElementType.FIELD, ElementType.METHOD, ElementType.PARAMETER, + ElementType.CONSTRUCTOR, ElementType.LOCAL_VARIABLE, ElementType.PACKAGE}) +public @interface Private {} diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala index 3be6783bba49d..02e49a853c5f7 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala @@ -755,6 +755,14 @@ class JavaSparkContext(val sc: SparkContext) */ def getLocalProperty(key: String): String = sc.getLocalProperty(key) + /** Control our logLevel. This overrides any user-defined log settings. + * @param logLevel The desired log level as a string. + * Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN + */ + def setLogLevel(logLevel: String) { + sc.setLogLevel(logLevel) + } + /** * Assigns a group ID to all the jobs started by this thread until the group ID is set to a * different value or cleared. diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala index 257491e90dd66..1f1debcf84ad4 100644 --- a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala +++ b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala @@ -56,16 +56,13 @@ private[spark] object SerDeUtil extends Logging { // {'\0', 0, 0, 0} /* Sentinel */ // }; // TODO: support Py_UNICODE with 2 bytes - // FIXME: unpickle array of float is wrong in Pyrolite, so we reverse the - // machine code for float/double here to workaround it. - // we should fix this after Pyrolite fix them val machineCodes: Map[Char, Int] = if (ByteOrder.nativeOrder().equals(ByteOrder.BIG_ENDIAN)) { Map('c' -> 1, 'B' -> 0, 'b' -> 1, 'H' -> 3, 'h' -> 5, 'I' -> 7, 'i' -> 9, - 'L' -> 11, 'l' -> 13, 'f' -> 14, 'd' -> 16, 'u' -> 21 + 'L' -> 11, 'l' -> 13, 'f' -> 15, 'd' -> 17, 'u' -> 21 ) } else { Map('c' -> 1, 'B' -> 0, 'b' -> 1, 'H' -> 2, 'h' -> 4, 'I' -> 6, 'i' -> 8, - 'L' -> 10, 'l' -> 12, 'f' -> 15, 'd' -> 17, 'u' -> 20 + 'L' -> 10, 'l' -> 12, 'f' -> 14, 'd' -> 16, 'u' -> 20 ) } override def construct(args: Array[Object]): Object = { diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala index c2c3e9a9e4827..848b62f9de71b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala @@ -17,6 +17,7 @@ package org.apache.spark.deploy +import scala.collection.mutable.HashSet import scala.concurrent._ import akka.actor._ @@ -31,21 +32,24 @@ import org.apache.spark.util.{ActorLogReceive, AkkaUtils, RpcUtils, Utils} /** * Proxy that relays messages to the driver. + * + * We currently don't support retry if submission fails. In HA mode, client will submit request to + * all masters and see which one could handle it. */ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends Actor with ActorLogReceive with Logging { - var masterActor: ActorSelection = _ + private val masterActors = driverArgs.masters.map { m => + context.actorSelection(Master.toAkkaUrl(m, AkkaUtils.protocol(context.system))) + } + private val lostMasters = new HashSet[Address] + private var activeMasterActor: ActorSelection = null + val timeout = RpcUtils.askTimeout(conf) override def preStart(): Unit = { - masterActor = context.actorSelection( - Master.toAkkaUrl(driverArgs.master, AkkaUtils.protocol(context.system))) - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) - println(s"Sending ${driverArgs.cmd} command to ${driverArgs.master}") - driverArgs.cmd match { case "launch" => // TODO: We could add an env variable here and intercept it in `sc.addJar` that would @@ -79,11 +83,17 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) driverArgs.supervise, command) - masterActor ! RequestSubmitDriver(driverDescription) + // This assumes only one Master is active at a time + for (masterActor <- masterActors) { + masterActor ! RequestSubmitDriver(driverDescription) + } case "kill" => val driverId = driverArgs.driverId - masterActor ! RequestKillDriver(driverId) + // This assumes only one Master is active at a time + for (masterActor <- masterActors) { + masterActor ! RequestKillDriver(driverId) + } } } @@ -92,10 +102,9 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) println("... waiting before polling master for driver state") Thread.sleep(5000) println("... polling master for driver state") - val statusFuture = (masterActor ? RequestDriverStatus(driverId))(timeout) + val statusFuture = (activeMasterActor ? RequestDriverStatus(driverId))(timeout) .mapTo[DriverStatusResponse] val statusResponse = Await.result(statusFuture, timeout) - statusResponse.found match { case false => println(s"ERROR: Cluster master did not recognize $driverId") @@ -122,20 +131,46 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) case SubmitDriverResponse(success, driverId, message) => println(message) - if (success) pollAndReportStatus(driverId.get) else System.exit(-1) + if (success) { + activeMasterActor = context.actorSelection(sender.path) + pollAndReportStatus(driverId.get) + } else if (!Utils.responseFromBackup(message)) { + System.exit(-1) + } + case KillDriverResponse(driverId, success, message) => println(message) - if (success) pollAndReportStatus(driverId) else System.exit(-1) + if (success) { + activeMasterActor = context.actorSelection(sender.path) + pollAndReportStatus(driverId) + } else if (!Utils.responseFromBackup(message)) { + System.exit(-1) + } case DisassociatedEvent(_, remoteAddress, _) => - println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.") - System.exit(-1) + if (!lostMasters.contains(remoteAddress)) { + println(s"Error connecting to master $remoteAddress.") + lostMasters += remoteAddress + // Note that this heuristic does not account for the fact that a Master can recover within + // the lifetime of this client. Thus, once a Master is lost it is lost to us forever. This + // is not currently a concern, however, because this client does not retry submissions. + if (lostMasters.size >= masterActors.size) { + println("No master is available, exiting.") + System.exit(-1) + } + } case AssociationErrorEvent(cause, _, remoteAddress, _, _) => - println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.") - println(s"Cause was: $cause") - System.exit(-1) + if (!lostMasters.contains(remoteAddress)) { + println(s"Error connecting to master ($remoteAddress).") + println(s"Cause was: $cause") + lostMasters += remoteAddress + if (lostMasters.size >= masterActors.size) { + println("No master is available, exiting.") + System.exit(-1) + } + } } } @@ -163,7 +198,9 @@ object Client { "driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf)) // Verify driverArgs.master is a valid url so that we can use it in ClientActor safely - Master.toAkkaUrl(driverArgs.master, AkkaUtils.protocol(actorSystem)) + for (m <- driverArgs.masters) { + Master.toAkkaUrl(m, AkkaUtils.protocol(actorSystem)) + } actorSystem.actorOf(Props(classOf[ClientActor], driverArgs, conf)) actorSystem.awaitTermination() diff --git a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala index 5cbac787dceeb..316e2d59f01b8 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala @@ -22,8 +22,7 @@ import java.net.{URI, URISyntaxException} import scala.collection.mutable.ListBuffer import org.apache.log4j.Level - -import org.apache.spark.util.{IntParam, MemoryParam} +import org.apache.spark.util.{IntParam, MemoryParam, Utils} /** * Command-line parser for the driver client. @@ -35,7 +34,7 @@ private[deploy] class ClientArguments(args: Array[String]) { var logLevel = Level.WARN // launch parameters - var master: String = "" + var masters: Array[String] = null var jarUrl: String = "" var mainClass: String = "" var supervise: Boolean = DEFAULT_SUPERVISE @@ -80,13 +79,13 @@ private[deploy] class ClientArguments(args: Array[String]) { } jarUrl = _jarUrl - master = _master + masters = Utils.parseStandaloneMasterUrls(_master) mainClass = _mainClass _driverOptions ++= tail case "kill" :: _master :: _driverId :: tail => cmd = "kill" - master = _master + masters = Utils.parseStandaloneMasterUrls(_master) driverId = _driverId case _ => diff --git a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala index cd16f992a3c0a..09973a0a2c998 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala @@ -19,10 +19,12 @@ package org.apache.spark.deploy import java.util.concurrent.CountDownLatch +import scala.collection.JavaConversions._ + import org.apache.spark.{Logging, SparkConf, SecurityManager} import org.apache.spark.network.TransportContext import org.apache.spark.network.netty.SparkTransportConf -import org.apache.spark.network.sasl.SaslRpcHandler +import org.apache.spark.network.sasl.SaslServerBootstrap import org.apache.spark.network.server.TransportServer import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler import org.apache.spark.util.Utils @@ -44,10 +46,7 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana private val transportConf = SparkTransportConf.fromSparkConf(sparkConf, numUsableCores = 0) private val blockHandler = new ExternalShuffleBlockHandler(transportConf) - private val transportContext: TransportContext = { - val handler = if (useSasl) new SaslRpcHandler(blockHandler, securityManager) else blockHandler - new TransportContext(transportConf, handler) - } + private val transportContext: TransportContext = new TransportContext(transportConf, blockHandler) private var server: TransportServer = _ @@ -62,7 +61,13 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana def start() { require(server == null, "Shuffle server already started") logInfo(s"Starting shuffle service on port $port with useSasl = $useSasl") - server = transportContext.createServer(port) + val bootstraps = + if (useSasl) { + Seq(new SaslServerBootstrap(transportConf, securityManager)) + } else { + Nil + } + server = transportContext.createServer(port, bootstraps) } def stop() { diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index cfaebf9ea5050..b563034457a91 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -17,12 +17,16 @@ package org.apache.spark.deploy +import java.io.{ByteArrayInputStream, DataInputStream} import java.lang.reflect.Method import java.security.PrivilegedExceptionAction +import java.util.{Arrays, Comparator} +import com.google.common.primitives.Longs import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path, PathFilter} import org.apache.hadoop.fs.FileSystem.Statistics +import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapreduce.JobContext import org.apache.hadoop.security.{Credentials, UserGroupInformation} @@ -32,6 +36,8 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.util.Utils import scala.collection.JavaConversions._ +import scala.concurrent.duration._ +import scala.language.postfixOps /** * :: DeveloperApi :: @@ -39,7 +45,8 @@ import scala.collection.JavaConversions._ */ @DeveloperApi class SparkHadoopUtil extends Logging { - val conf: Configuration = newConfiguration(new SparkConf()) + private val sparkConf = new SparkConf() + val conf: Configuration = newConfiguration(sparkConf) UserGroupInformation.setConfiguration(conf) /** @@ -201,6 +208,61 @@ class SparkHadoopUtil extends Logging { if (baseStatus.isDir) recurse(basePath) else Array(baseStatus) } + /** + * Lists all the files in a directory with the specified prefix, and does not end with the + * given suffix. The returned {{FileStatus}} instances are sorted by the modification times of + * the respective files. + */ + def listFilesSorted( + remoteFs: FileSystem, + dir: Path, + prefix: String, + exclusionSuffix: String): Array[FileStatus] = { + val fileStatuses = remoteFs.listStatus(dir, + new PathFilter { + override def accept(path: Path): Boolean = { + val name = path.getName + name.startsWith(prefix) && !name.endsWith(exclusionSuffix) + } + }) + Arrays.sort(fileStatuses, new Comparator[FileStatus] { + override def compare(o1: FileStatus, o2: FileStatus): Int = { + Longs.compare(o1.getModificationTime, o2.getModificationTime) + } + }) + fileStatuses + } + + /** + * How much time is remaining (in millis) from now to (fraction * renewal time for the token that + * is valid the latest)? + * This will return -ve (or 0) value if the fraction of validity has already expired. + */ + def getTimeFromNowToRenewal( + sparkConf: SparkConf, + fraction: Double, + credentials: Credentials): Long = { + val now = System.currentTimeMillis() + + val renewalInterval = + sparkConf.getLong("spark.yarn.token.renewal.interval", (24 hours).toMillis) + + credentials.getAllTokens.filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) + .map { t => + val identifier = new DelegationTokenIdentifier() + identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) + (identifier.getIssueDate + fraction * renewalInterval).toLong - now + }.foldLeft(0L)(math.max) + } + + + private[spark] def getSuffixForCredentialsPath(credentialsPath: Path): Int = { + val fileName = credentialsPath.getName + fileName.substring( + fileName.lastIndexOf(SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM) + 1).toInt + } + + private val HADOOP_CONF_PATTERN = "(\\$\\{hadoopconf-[^\\}\\$\\s]+\\})".r.unanchored /** @@ -231,6 +293,17 @@ class SparkHadoopUtil extends Logging { } } } + + /** + * Start a thread to periodically update the current user's credentials with new delegation + * tokens so that writes to HDFS do not fail. + */ + private[spark] def startExecutorDelegationTokenRenewer(conf: SparkConf) {} + + /** + * Stop the thread that does the delegation token updates. + */ + private[spark] def stopExecutorDelegationTokenRenewer() {} } object SparkHadoopUtil { @@ -251,6 +324,10 @@ object SparkHadoopUtil { } } + val SPARK_YARN_CREDS_TEMP_EXTENSION = ".tmp" + + val SPARK_YARN_CREDS_COUNTER_DELIM = "-" + def get: SparkHadoopUtil = { hadoop } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index b8ae4af18d1d1..8a0327984e195 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -118,8 +118,8 @@ object SparkSubmit { * Kill an existing submission using the REST protocol. Standalone and Mesos cluster mode only. */ private def kill(args: SparkSubmitArguments): Unit = { - new RestSubmissionClient() - .killSubmission(args.master, args.submissionToKill) + new RestSubmissionClient(args.master) + .killSubmission(args.submissionToKill) } /** @@ -127,8 +127,8 @@ object SparkSubmit { * Standalone and Mesos cluster mode only. */ private def requestStatus(args: SparkSubmitArguments): Unit = { - new RestSubmissionClient() - .requestSubmissionStatus(args.master, args.submissionToRequestStatusFor) + new RestSubmissionClient(args.master) + .requestSubmissionStatus(args.submissionToRequestStatusFor) } /** @@ -400,6 +400,10 @@ object SparkSubmit { OptionAssigner(args.archives, YARN, CLUSTER, clOption = "--archives"), OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"), + // Yarn client or cluster + OptionAssigner(args.principal, YARN, ALL_DEPLOY_MODES, clOption = "--principal"), + OptionAssigner(args.keytab, YARN, ALL_DEPLOY_MODES, clOption = "--keytab"), + // Other options OptionAssigner(args.executorCores, STANDALONE, ALL_DEPLOY_MODES, sysProp = "spark.executor.cores"), @@ -697,7 +701,7 @@ object SparkSubmit { } /** Provides utility functions to be used inside SparkSubmit. */ -private[deploy] object SparkSubmitUtils { +private[spark] object SparkSubmitUtils { // Exposed for testing var printStream = SparkSubmit.printStream diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index c621b8fc86f94..c0e4c771908b3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -63,6 +63,8 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S var action: SparkSubmitAction = null val sparkProperties: HashMap[String, String] = new HashMap[String, String]() var proxyUser: String = null + var principal: String = null + var keytab: String = null // Standalone cluster mode only var supervise: Boolean = false @@ -393,6 +395,12 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S case PROXY_USER => proxyUser = value + case PRINCIPAL => + principal = value + + case KEYTAB => + keytab = value + case HELP => printUsageAndExit(0) @@ -506,6 +514,13 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S | --num-executors NUM Number of executors to launch (Default: 2). | --archives ARCHIVES Comma separated list of archives to be extracted into the | working directory of each executor. + | --principal PRINCIPAL Principal to be used to login to KDC, while running on + | secure HDFS. + | --keytab KEYTAB The full path to the file that contains the keytab for the + | principal specified above. This keytab will be copied to + | the node running the Application Master via the Secure + | Distributed Cache, for renewing the login tickets and the + | delegation tokens periodically. """.stripMargin ) SparkSubmit.exitFn() diff --git a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala index ea6c85ee511d5..6a5011af17458 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala @@ -19,15 +19,19 @@ package org.apache.spark.deploy.history import org.apache.spark.ui.SparkUI -private[history] case class ApplicationHistoryInfo( - id: String, - name: String, +private[history] case class ApplicationAttemptInfo( + attemptId: Option[String], startTime: Long, endTime: Long, lastUpdated: Long, sparkUser: String, completed: Boolean = false) +private[history] case class ApplicationHistoryInfo( + id: String, + name: String, + attempts: List[ApplicationAttemptInfo]) + private[history] abstract class ApplicationHistoryProvider { /** @@ -41,9 +45,10 @@ private[history] abstract class ApplicationHistoryProvider { * Returns the Spark UI for a specific application. * * @param appId The application ID. + * @param attemptId The application attempt ID (or None if there is no attempt ID). * @return The application's UI, or None if application is not found. */ - def getAppUI(appId: String): Option[SparkUI] + def getAppUI(appId: String, attemptId: Option[String]): Option[SparkUI] /** * Called when the server is shutting down. diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index fb2cbbcccc54b..993763f3aa092 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -32,7 +32,7 @@ import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.io.CompressionCodec import org.apache.spark.scheduler._ import org.apache.spark.ui.SparkUI -import org.apache.spark.util.{ThreadUtils, Utils} +import org.apache.spark.util.{Clock, SystemClock, ThreadUtils, Utils} import org.apache.spark.{Logging, SecurityManager, SparkConf} /** @@ -40,8 +40,12 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf} * This provider checks for new finished applications in the background periodically and * renders the history application UI by parsing the associated event logs. */ -private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHistoryProvider - with Logging { +private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) + extends ApplicationHistoryProvider with Logging { + + def this(conf: SparkConf) = { + this(conf, new SystemClock()) + } import FsHistoryProvider._ @@ -75,8 +79,8 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis @volatile private var applications: mutable.LinkedHashMap[String, FsApplicationHistoryInfo] = new mutable.LinkedHashMap() - // List of applications to be deleted by event log cleaner. - private var appsToClean = new mutable.ListBuffer[FsApplicationHistoryInfo] + // List of application logs to be deleted by event log cleaner. + private var attemptsToClean = new mutable.ListBuffer[FsApplicationAttemptInfo] // Constants used to parse Spark 1.0.0 log directories. private[history] val LOG_PREFIX = "EVENT_LOG_" @@ -138,31 +142,33 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis override def getListing(): Iterable[FsApplicationHistoryInfo] = applications.values - override def getAppUI(appId: String): Option[SparkUI] = { + override def getAppUI(appId: String, attemptId: Option[String]): Option[SparkUI] = { try { - applications.get(appId).map { info => - val replayBus = new ReplayListenerBus() - val ui = { - val conf = this.conf.clone() - val appSecManager = new SecurityManager(conf) - SparkUI.createHistoryUI(conf, replayBus, appSecManager, appId, - s"${HistoryServer.UI_PATH_PREFIX}/$appId") - // Do not call ui.bind() to avoid creating a new server for each application - } + applications.get(appId).flatMap { appInfo => + appInfo.attempts.find(_.attemptId == attemptId).map { attempt => + val replayBus = new ReplayListenerBus() + val ui = { + val conf = this.conf.clone() + val appSecManager = new SecurityManager(conf) + SparkUI.createHistoryUI(conf, replayBus, appSecManager, appId, + HistoryServer.getAttemptURI(appId, attempt.attemptId)) + // Do not call ui.bind() to avoid creating a new server for each application + } - val appListener = new ApplicationEventListener() - replayBus.addListener(appListener) - val appInfo = replay(fs.getFileStatus(new Path(logDir, info.logPath)), replayBus) + val appListener = new ApplicationEventListener() + replayBus.addListener(appListener) + val appInfo = replay(fs.getFileStatus(new Path(logDir, attempt.logPath)), replayBus) - ui.setAppName(s"${appInfo.name} ($appId)") + ui.setAppName(s"${appInfo.name} ($appId)") - val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false) - ui.getSecurityManager.setAcls(uiAclsEnabled) - // make sure to set admin acls before view acls so they are properly picked up - ui.getSecurityManager.setAdminAcls(appListener.adminAcls.getOrElse("")) - ui.getSecurityManager.setViewAcls(appInfo.sparkUser, - appListener.viewAcls.getOrElse("")) - ui + val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false) + ui.getSecurityManager.setAcls(uiAclsEnabled) + // make sure to set admin acls before view acls so they are properly picked up + ui.getSecurityManager.setAdminAcls(appListener.adminAcls.getOrElse("")) + ui.getSecurityManager.setViewAcls(attempt.sparkUser, + appListener.viewAcls.getOrElse("")) + ui + } } } catch { case e: FileNotFoundException => None @@ -220,7 +226,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis */ private def mergeApplicationListing(logs: Seq[FileStatus]): Unit = { val bus = new ReplayListenerBus() - val newApps = logs.flatMap { fileStatus => + val newAttempts = logs.flatMap { fileStatus => try { val res = replay(fileStatus, bus) logInfo(s"Application log ${res.logPath} loaded successfully.") @@ -232,76 +238,104 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis e) None } - }.toSeq.sortWith(compareAppInfo) - - // When there are new logs, merge the new list with the existing one, maintaining - // the expected ordering (descending end time). Maintaining the order is important - // to avoid having to sort the list every time there is a request for the log list. - if (newApps.nonEmpty) { - val mergedApps = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() - def addIfAbsent(info: FsApplicationHistoryInfo): Unit = { - if (!mergedApps.contains(info.id) || - mergedApps(info.id).logPath.endsWith(EventLoggingListener.IN_PROGRESS) && - !info.logPath.endsWith(EventLoggingListener.IN_PROGRESS)) { - mergedApps += (info.id -> info) - } - } + } - val newIterator = newApps.iterator.buffered - val oldIterator = applications.values.iterator.buffered - while (newIterator.hasNext && oldIterator.hasNext) { - if (compareAppInfo(newIterator.head, oldIterator.head)) { - addIfAbsent(newIterator.next()) - } else { - addIfAbsent(oldIterator.next()) + if (newAttempts.isEmpty) { + return + } + + // Build a map containing all apps that contain new attempts. The app information in this map + // contains both the new app attempt, and those that were already loaded in the existing apps + // map. If an attempt has been updated, it replaces the old attempt in the list. + val newAppMap = new mutable.HashMap[String, FsApplicationHistoryInfo]() + newAttempts.foreach { attempt => + val appInfo = newAppMap.get(attempt.appId) + .orElse(applications.get(attempt.appId)) + .map { app => + val attempts = + app.attempts.filter(_.attemptId != attempt.attemptId).toList ++ List(attempt) + new FsApplicationHistoryInfo(attempt.appId, attempt.name, + attempts.sortWith(compareAttemptInfo)) } + .getOrElse(new FsApplicationHistoryInfo(attempt.appId, attempt.name, List(attempt))) + newAppMap(attempt.appId) = appInfo + } + + // Merge the new app list with the existing one, maintaining the expected ordering (descending + // end time). Maintaining the order is important to avoid having to sort the list every time + // there is a request for the log list. + val newApps = newAppMap.values.toSeq.sortWith(compareAppInfo) + val mergedApps = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() + def addIfAbsent(info: FsApplicationHistoryInfo): Unit = { + if (!mergedApps.contains(info.id)) { + mergedApps += (info.id -> info) } - newIterator.foreach(addIfAbsent) - oldIterator.foreach(addIfAbsent) + } - applications = mergedApps + val newIterator = newApps.iterator.buffered + val oldIterator = applications.values.iterator.buffered + while (newIterator.hasNext && oldIterator.hasNext) { + if (newAppMap.contains(oldIterator.head.id)) { + oldIterator.next() + } else if (compareAppInfo(newIterator.head, oldIterator.head)) { + addIfAbsent(newIterator.next()) + } else { + addIfAbsent(oldIterator.next()) + } } + newIterator.foreach(addIfAbsent) + oldIterator.foreach(addIfAbsent) + + applications = mergedApps } /** * Delete event logs from the log directory according to the clean policy defined by the user. */ - private def cleanLogs(): Unit = { + private[history] def cleanLogs(): Unit = { try { val maxAge = conf.getTimeAsSeconds("spark.history.fs.cleaner.maxAge", "7d") * 1000 - val now = System.currentTimeMillis() + val now = clock.getTimeMillis() val appsToRetain = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() + def shouldClean(attempt: FsApplicationAttemptInfo): Boolean = { + now - attempt.lastUpdated > maxAge && attempt.completed + } + // Scan all logs from the log directory. // Only completed applications older than the specified max age will be deleted. - applications.values.foreach { info => - if (now - info.lastUpdated <= maxAge || !info.completed) { - appsToRetain += (info.id -> info) - } else { - appsToClean += info + applications.values.foreach { app => + val (toClean, toRetain) = app.attempts.partition(shouldClean) + attemptsToClean ++= toClean + + if (toClean.isEmpty) { + appsToRetain += (app.id -> app) + } else if (toRetain.nonEmpty) { + appsToRetain += (app.id -> + new FsApplicationHistoryInfo(app.id, app.name, toRetain.toList)) } } applications = appsToRetain - val leftToClean = new mutable.ListBuffer[FsApplicationHistoryInfo] - appsToClean.foreach { info => + val leftToClean = new mutable.ListBuffer[FsApplicationAttemptInfo] + attemptsToClean.foreach { attempt => try { - val path = new Path(logDir, info.logPath) + val path = new Path(logDir, attempt.logPath) if (fs.exists(path)) { fs.delete(path, true) } } catch { case e: AccessControlException => - logInfo(s"No permission to delete ${info.logPath}, ignoring.") + logInfo(s"No permission to delete ${attempt.logPath}, ignoring.") case t: IOException => - logError(s"IOException in cleaning logs of ${info.logPath}", t) - leftToClean += info + logError(s"IOException in cleaning ${attempt.logPath}", t) + leftToClean += attempt } } - appsToClean = leftToClean + attemptsToClean = leftToClean } catch { case t: Exception => logError("Exception in cleaning logs", t) } @@ -315,14 +349,36 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis private def compareAppInfo( i1: FsApplicationHistoryInfo, i2: FsApplicationHistoryInfo): Boolean = { - if (i1.endTime != i2.endTime) i1.endTime >= i2.endTime else i1.startTime >= i2.startTime + val a1 = i1.attempts.head + val a2 = i2.attempts.head + if (a1.endTime != a2.endTime) a1.endTime >= a2.endTime else a1.startTime >= a2.startTime + } + + /** + * Comparison function that defines the sort order for application attempts within the same + * application. Order is: running attempts before complete attempts, running attempts sorted + * by start time, completed attempts sorted by end time. + * + * Normally applications should have a single running attempt; but failure to call sc.stop() + * may cause multiple running attempts to show up. + * + * @return Whether `a1` should precede `a2`. + */ + private def compareAttemptInfo( + a1: FsApplicationAttemptInfo, + a2: FsApplicationAttemptInfo): Boolean = { + if (a1.completed == a2.completed) { + if (a1.completed) a1.endTime >= a2.endTime else a1.startTime >= a2.startTime + } else { + !a1.completed + } } /** * Replays the events in the specified log file and returns information about the associated * application. */ - private def replay(eventLog: FileStatus, bus: ReplayListenerBus): FsApplicationHistoryInfo = { + private def replay(eventLog: FileStatus, bus: ReplayListenerBus): FsApplicationAttemptInfo = { val logPath = eventLog.getPath() logInfo(s"Replaying log path: $logPath") val logInput = @@ -336,10 +392,11 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis val appCompleted = isApplicationCompleted(eventLog) bus.addListener(appListener) bus.replay(logInput, logPath.toString, !appCompleted) - new FsApplicationHistoryInfo( + new FsApplicationAttemptInfo( logPath.getName(), - appListener.appId.getOrElse(logPath.getName()), appListener.appName.getOrElse(NOT_STARTED), + appListener.appId.getOrElse(logPath.getName()), + appListener.appAttemptId, appListener.startTime.getOrElse(-1L), appListener.endTime.getOrElse(-1L), getModificationTime(eventLog).get, @@ -425,13 +482,21 @@ private object FsHistoryProvider { val DEFAULT_LOG_DIR = "file:/tmp/spark-events" } -private class FsApplicationHistoryInfo( +private class FsApplicationAttemptInfo( val logPath: String, - id: String, - name: String, + val name: String, + val appId: String, + attemptId: Option[String], startTime: Long, endTime: Long, lastUpdated: Long, sparkUser: String, completed: Boolean = true) - extends ApplicationHistoryInfo(id, name, startTime, endTime, lastUpdated, sparkUser, completed) + extends ApplicationAttemptInfo( + attemptId, startTime, endTime, lastUpdated, sparkUser, completed) + +private class FsApplicationHistoryInfo( + id: String, + override val name: String, + override val attempts: List[FsApplicationAttemptInfo]) + extends ApplicationHistoryInfo(id, name, attempts) diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala index 3781b4e8c12bd..0830cc1ba1245 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala @@ -34,18 +34,28 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") val requestedIncomplete = Option(request.getParameter("showIncomplete")).getOrElse("false").toBoolean - val allApps = parent.getApplicationList().filter(_.completed != requestedIncomplete) - val actualFirst = if (requestedFirst < allApps.size) requestedFirst else 0 - val apps = allApps.slice(actualFirst, Math.min(actualFirst + pageSize, allApps.size)) + val allApps = parent.getApplicationList() + .filter(_.attempts.head.completed != requestedIncomplete) + val allAppsSize = allApps.size + + val actualFirst = if (requestedFirst < allAppsSize) requestedFirst else 0 + val appsToShow = allApps.slice(actualFirst, actualFirst + pageSize) val actualPage = (actualFirst / pageSize) + 1 - val last = Math.min(actualFirst + pageSize, allApps.size) - 1 - val pageCount = allApps.size / pageSize + (if (allApps.size % pageSize > 0) 1 else 0) + val last = Math.min(actualFirst + pageSize, allAppsSize) - 1 + val pageCount = allAppsSize / pageSize + (if (allAppsSize % pageSize > 0) 1 else 0) val secondPageFromLeft = 2 val secondPageFromRight = pageCount - 1 - val appTable = UIUtils.listingTable(appHeader, appRow, apps) + val hasMultipleAttempts = appsToShow.exists(_.attempts.size > 1) + val appTable = + if (hasMultipleAttempts) { + UIUtils.listingTable(appWithAttemptHeader, appWithAttemptRow, appsToShow) + } else { + UIUtils.listingTable(appHeader, appRow, appsToShow) + } + val providerConfig = parent.getProviderConfig() val content =
@@ -59,7 +69,7 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") // to the first and last page. If the current page +/- `plusOrMinus` is greater // than the 2nd page from the first page or less than the 2nd page from the last // page, `...` will be displayed. - if (allApps.size > 0) { + if (allAppsSize > 0) { val leftSideIndices = rangeIndices(actualPage - plusOrMinus until actualPage, 1 < _, requestedIncomplete) val rightSideIndices = @@ -67,7 +77,7 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") requestedIncomplete)

- Showing {actualFirst + 1}-{last + 1} of {allApps.size} + Showing {actualFirst + 1}-{last + 1} of {allAppsSize} {if (requestedIncomplete) "(Incomplete applications)"} { @@ -125,30 +135,85 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") "Spark User", "Last Updated") - private def rangeIndices(range: Seq[Int], condition: Int => Boolean, showIncomplete: Boolean): - Seq[Node] = { + private val appWithAttemptHeader = Seq( + "App ID", + "App Name", + "Attempt ID", + "Started", + "Completed", + "Duration", + "Spark User", + "Last Updated") + + private def rangeIndices( + range: Seq[Int], + condition: Int => Boolean, + showIncomplete: Boolean): Seq[Node] = { range.filter(condition).map(nextPage => {nextPage} ) } - private def appRow(info: ApplicationHistoryInfo): Seq[Node] = { - val uiAddress = HistoryServer.UI_PATH_PREFIX + s"/${info.id}" - val startTime = UIUtils.formatDate(info.startTime) - val endTime = if (info.endTime > 0) UIUtils.formatDate(info.endTime) else "-" + private def attemptRow( + renderAttemptIdColumn: Boolean, + info: ApplicationHistoryInfo, + attempt: ApplicationAttemptInfo, + isFirst: Boolean): Seq[Node] = { + val uiAddress = HistoryServer.getAttemptURI(info.id, attempt.attemptId) + val startTime = UIUtils.formatDate(attempt.startTime) + val endTime = if (attempt.endTime > 0) UIUtils.formatDate(attempt.endTime) else "-" val duration = - if (info.endTime > 0) UIUtils.formatDuration(info.endTime - info.startTime) else "-" - val lastUpdated = UIUtils.formatDate(info.lastUpdated) + if (attempt.endTime > 0) { + UIUtils.formatDuration(attempt.endTime - attempt.startTime) + } else { + "-" + } + val lastUpdated = UIUtils.formatDate(attempt.lastUpdated) - {info.id} - {info.name} - {startTime} - {endTime} - {duration} - {info.sparkUser} - {lastUpdated} + { + if (isFirst) { + if (info.attempts.size > 1 || renderAttemptIdColumn) { + + {info.id} + + {info.name} + } else { + {info.id} + {info.name} + } + } else { + Nil + } + } + { + if (renderAttemptIdColumn) { + if (info.attempts.size > 1 && attempt.attemptId.isDefined) { + + {attempt.attemptId.get} + } else { +   + } + } else { + Nil + } + } + {startTime} + {endTime} + + {duration} + {attempt.sparkUser} + {lastUpdated} } + private def appRow(info: ApplicationHistoryInfo): Seq[Node] = { + attemptRow(false, info, info.attempts.head, true) + } + + private def appWithAttemptRow(info: ApplicationHistoryInfo): Seq[Node] = { + attemptRow(true, info, info.attempts.head, true) ++ + info.attempts.drop(1).flatMap(attemptRow(true, info, _, false)) + } + private def makePageLink(linkPage: Int, showIncomplete: Boolean): String = { "/?" + Array( "page=" + linkPage, diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala index 56bef57e55392..754c8e9b6668b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala @@ -52,7 +52,11 @@ class HistoryServer( private val appLoader = new CacheLoader[String, SparkUI] { override def load(key: String): SparkUI = { - val ui = provider.getAppUI(key).getOrElse(throw new NoSuchElementException()) + val parts = key.split("/") + require(parts.length == 1 || parts.length == 2, s"Invalid app key $key") + val ui = provider + .getAppUI(parts(0), if (parts.length > 1) Some(parts(1)) else None) + .getOrElse(throw new NoSuchElementException()) attachSparkUI(ui) ui } @@ -69,6 +73,8 @@ class HistoryServer( private val loaderServlet = new HttpServlet { protected override def doGet(req: HttpServletRequest, res: HttpServletResponse): Unit = { + // Parse the URI created by getAttemptURI(). It contains an app ID and an optional + // attempt ID (separated by a slash). val parts = Option(req.getPathInfo()).getOrElse("").split("/") if (parts.length < 2) { res.sendError(HttpServletResponse.SC_BAD_REQUEST, @@ -76,18 +82,23 @@ class HistoryServer( return } - val appId = parts(1) + val appKey = + if (parts.length == 3) { + s"${parts(1)}/${parts(2)}" + } else { + parts(1) + } // Note we don't use the UI retrieved from the cache; the cache loader above will register // the app's UI, and all we need to do is redirect the user to the same URI that was // requested, and the proper data should be served at that point. try { - appCache.get(appId) + appCache.get(appKey) res.sendRedirect(res.encodeRedirectURL(req.getRequestURI())) } catch { case e: Exception => e.getCause() match { case nsee: NoSuchElementException => - val msg =
Application {appId} not found.
+ val msg =
Application {appKey} not found.
res.setStatus(HttpServletResponse.SC_NOT_FOUND) UIUtils.basicSparkPage(msg, "Not Found").foreach( n => res.getWriter().write(n.toString)) @@ -213,4 +224,9 @@ object HistoryServer extends Logging { } } + private[history] def getAttemptURI(appId: String, attemptId: Option[String]): String = { + val attemptSuffix = attemptId.map { id => s"/$id" }.getOrElse("") + s"${HistoryServer.UI_PATH_PREFIX}/${appId}${attemptSuffix}" + } + } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index 1c21c179562ac..0fac3cdcf55e7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -62,7 +62,7 @@ private[master] class Master( private val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf) private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs - + private val WORKER_TIMEOUT = conf.getLong("spark.worker.timeout", 60) * 1000 private val RETAINED_APPLICATIONS = conf.getInt("spark.deploy.retainedApplications", 200) private val RETAINED_DRIVERS = conf.getInt("spark.deploy.retainedDrivers", 200) @@ -86,7 +86,7 @@ private[master] class Master( private val drivers = new HashSet[DriverInfo] private val completedDrivers = new ArrayBuffer[DriverInfo] // Drivers currently spooled for scheduling - private val waitingDrivers = new ArrayBuffer[DriverInfo] + private val waitingDrivers = new ArrayBuffer[DriverInfo] private var nextDriverNumber = 0 Utils.checkHost(host, "Expected hostname") @@ -254,7 +254,8 @@ private[master] class Master( case RequestSubmitDriver(description) => { if (state != RecoveryState.ALIVE) { - val msg = s"Can only accept driver submissions in ALIVE state. Current state: $state." + val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " + + "Can only accept driver submissions in ALIVE state." sender ! SubmitDriverResponse(false, None, msg) } else { logInfo("Driver submitted " + description.command.mainClass) @@ -274,7 +275,8 @@ private[master] class Master( case RequestKillDriver(driverId) => { if (state != RecoveryState.ALIVE) { - val msg = s"Can only kill drivers in ALIVE state. Current state: $state." + val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " + + s"Can only kill drivers in ALIVE state." sender ! KillDriverResponse(driverId, success = false, msg) } else { logInfo("Asked to kill driver " + driverId) @@ -305,12 +307,18 @@ private[master] class Master( } case RequestDriverStatus(driverId) => { - (drivers ++ completedDrivers).find(_.id == driverId) match { - case Some(driver) => - sender ! DriverStatusResponse(found = true, Some(driver.state), - driver.worker.map(_.id), driver.worker.map(_.hostPort), driver.exception) - case None => - sender ! DriverStatusResponse(found = false, None, None, None, None) + if (state != RecoveryState.ALIVE) { + val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " + + "Can only request driver status in ALIVE state." + sender ! DriverStatusResponse(found = false, None, None, None, Some(new Exception(msg))) + } else { + (drivers ++ completedDrivers).find(_.id == driverId) match { + case Some(driver) => + sender ! DriverStatusResponse(found = true, Some(driver.state), + driver.worker.map(_.id), driver.worker.map(_.hostPort), driver.exception) + case None => + sender ! DriverStatusResponse(found = false, None, None, None, None) + } } } @@ -758,24 +766,24 @@ private[master] class Master( app.desc.appUiUrl = notFoundBasePath return false } - + val eventLogFilePrefix = EventLoggingListener.getLogPath( - eventLogDir, app.id, app.desc.eventLogCodec) + eventLogDir, app.id, None, app.desc.eventLogCodec) val fs = Utils.getHadoopFileSystem(eventLogDir, hadoopConf) - val inProgressExists = fs.exists(new Path(eventLogFilePrefix + + val inProgressExists = fs.exists(new Path(eventLogFilePrefix + EventLoggingListener.IN_PROGRESS)) - + if (inProgressExists) { // Event logging is enabled for this application, but the application is still in progress logWarning(s"Application $appName is still in progress, it may be terminated abnormally.") } - + val (eventLogFile, status) = if (inProgressExists) { (eventLogFilePrefix + EventLoggingListener.IN_PROGRESS, " (in progress)") } else { (eventLogFilePrefix, " (completed)") } - + val logInput = EventLoggingListener.openEventLog(new Path(eventLogFile), fs) val replayBus = new ReplayListenerBus() val ui = SparkUI.createHistoryUI(new SparkConf, replayBus, new SecurityManager(conf), @@ -859,8 +867,8 @@ private[master] class Master( } private def removeDriver( - driverId: String, - finalState: DriverState, + driverId: String, + finalState: DriverState, exception: Option[Exception]) { drivers.find(d => d.id == driverId) match { case Some(driver) => diff --git a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala index da5060778edeb..a03d460509e03 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala @@ -33,7 +33,7 @@ import scala.reflect.ClassTag * The implementation of this trait defines how name-object pairs are stored or retrieved. */ @DeveloperApi -trait PersistenceEngine { +abstract class PersistenceEngine { /** * Defines how the object is serialized and persisted. Implementation will diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala index aad9c87bdb987..dea0a65eeeaa6 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala @@ -44,9 +44,9 @@ class MasterWebUI(val master: Master, requestedPort: Int) attachPage(masterPage) attachHandler(createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR, "/static")) attachHandler(createRedirectHandler( - "/app/kill", "/", masterPage.handleAppKillRequest, httpMethod = "POST")) + "/app/kill", "/", masterPage.handleAppKillRequest, httpMethods = Set("POST"))) attachHandler(createRedirectHandler( - "/driver/kill", "/", masterPage.handleDriverKillRequest, httpMethod = "POST")) + "/driver/kill", "/", masterPage.handleDriverKillRequest, httpMethods = Set("POST"))) } /** Attach a reconstructed UI to this Master UI. Only valid after bind(). */ diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala new file mode 100644 index 0000000000000..be8560d10fc62 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala @@ -0,0 +1,180 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.mesos.ui + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import org.apache.spark.deploy.Command +import org.apache.spark.deploy.mesos.MesosDriverDescription +import org.apache.spark.scheduler.cluster.mesos.{MesosClusterSubmissionState, MesosClusterRetryState} +import org.apache.spark.ui.{UIUtils, WebUIPage} + + +private[ui] class DriverPage(parent: MesosClusterUI) extends WebUIPage("driver") { + + override def render(request: HttpServletRequest): Seq[Node] = { + val driverId = request.getParameter("id") + require(driverId != null && driverId.nonEmpty, "Missing id parameter") + + val state = parent.scheduler.getDriverState(driverId) + if (state.isEmpty) { + val content = +
+

Cannot find driver {driverId}

+
+ return UIUtils.basicSparkPage(content, s"Details for Job $driverId") + } + val driverState = state.get + val driverHeaders = Seq("Driver property", "Value") + val schedulerHeaders = Seq("Scheduler property", "Value") + val commandEnvHeaders = Seq("Command environment variable", "Value") + val launchedHeaders = Seq("Launched property", "Value") + val commandHeaders = Seq("Comamnd property", "Value") + val retryHeaders = Seq("Last failed status", "Next retry time", "Retry count") + val driverDescription = Iterable.apply(driverState.description) + val submissionState = Iterable.apply(driverState.submissionState) + val command = Iterable.apply(driverState.description.command) + val schedulerProperties = Iterable.apply(driverState.description.schedulerProperties) + val commandEnv = Iterable.apply(driverState.description.command.environment) + val driverTable = + UIUtils.listingTable(driverHeaders, driverRow, driverDescription) + val commandTable = + UIUtils.listingTable(commandHeaders, commandRow, command) + val commandEnvTable = + UIUtils.listingTable(commandEnvHeaders, propertiesRow, commandEnv) + val schedulerTable = + UIUtils.listingTable(schedulerHeaders, propertiesRow, schedulerProperties) + val launchedTable = + UIUtils.listingTable(launchedHeaders, launchedRow, submissionState) + val retryTable = + UIUtils.listingTable( + retryHeaders, retryRow, Iterable.apply(driverState.description.retryState)) + val content = +

Driver state information for driver id {driverId}

+ Back to Drivers +
+
+

Driver state: {driverState.state}

+

Driver properties

+ {driverTable} +

Driver command

+ {commandTable} +

Driver command environment

+ {commandEnvTable} +

Scheduler properties

+ {schedulerTable} +

Launched state

+ {launchedTable} +

Retry state

+ {retryTable} +
+
; + + UIUtils.basicSparkPage(content, s"Details for Job $driverId") + } + + private def launchedRow(submissionState: Option[MesosClusterSubmissionState]): Seq[Node] = { + submissionState.map { state => + + Mesos Slave ID + {state.slaveId.getValue} + + + Mesos Task ID + {state.taskId.getValue} + + + Launch Time + {state.startDate} + + + Finish Time + {state.finishDate.map(_.toString).getOrElse("")} + + + Last Task Status + {state.mesosTaskStatus.map(_.toString).getOrElse("")} + + }.getOrElse(Seq[Node]()) + } + + private def propertiesRow(properties: collection.Map[String, String]): Seq[Node] = { + properties.map { case (k, v) => + + {k}{v} + + }.toSeq + } + + private def commandRow(command: Command): Seq[Node] = { + + Main class{command.mainClass} + + + Arguments{command.arguments.mkString(" ")} + + + Class path entries{command.classPathEntries.mkString(" ")} + + + Java options{command.javaOpts.mkString((" "))} + + + Library path entries{command.libraryPathEntries.mkString((" "))} + + } + + private def driverRow(driver: MesosDriverDescription): Seq[Node] = { + + Name{driver.name} + + + Id{driver.submissionId} + + + Cores{driver.cores} + + + Memory{driver.mem} + + + Submitted{driver.submissionDate} + + + Supervise{driver.supervise} + + } + + private def retryRow(retryState: Option[MesosClusterRetryState]): Seq[Node] = { + retryState.map { state => + + + {state.lastFailureStatus} + + + {state.nextRetry} + + + {state.retries} + + + }.getOrElse(Seq[Node]()) + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala index 7b2005e0f1237..7419fa9699648 100644 --- a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala @@ -56,8 +56,9 @@ private[mesos] class MesosClusterPage(parent: MesosClusterUI) extends WebUIPage( } private def queuedRow(submission: MesosDriverDescription): Seq[Node] = { + val id = submission.submissionId - {submission.submissionId} + {id} {submission.submissionDate} {submission.command.mainClass} cpus: {submission.cores}, mem: {submission.mem} @@ -65,8 +66,9 @@ private[mesos] class MesosClusterPage(parent: MesosClusterUI) extends WebUIPage( } private def driverRow(state: MesosClusterSubmissionState): Seq[Node] = { + val id = state.driverDescription.submissionId - {state.driverDescription.submissionId} + {id} {state.driverDescription.submissionDate} {state.driverDescription.command.mainClass} cpus: {state.driverDescription.cores}, mem: {state.driverDescription.mem} @@ -77,8 +79,9 @@ private[mesos] class MesosClusterPage(parent: MesosClusterUI) extends WebUIPage( } private def retryRow(submission: MesosDriverDescription): Seq[Node] = { + val id = submission.submissionId - {submission.submissionId} + {id} {submission.submissionDate} {submission.command.mainClass} {submission.retryState.get.lastFailureStatus} diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala index 4865d46dbc4ab..3f693545a0349 100644 --- a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala @@ -39,6 +39,7 @@ private[spark] class MesosClusterUI( override def initialize() { attachPage(new MesosClusterPage(this)) + attachPage(new DriverPage(this)) attachHandler(createStaticHandler(MesosClusterUI.STATIC_RESOURCE_DIR, "/static")) } } diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala index 307cebfb4bd09..6078f50518ba4 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala @@ -18,9 +18,10 @@ package org.apache.spark.deploy.rest import java.io.{DataOutputStream, FileNotFoundException} -import java.net.{HttpURLConnection, SocketException, URL} +import java.net.{ConnectException, HttpURLConnection, SocketException, URL} import javax.servlet.http.HttpServletResponse +import scala.collection.mutable import scala.io.Source import com.fasterxml.jackson.core.JsonProcessingException @@ -51,57 +52,109 @@ import org.apache.spark.util.Utils * implementation of this client can use that information to retry using the version specified * by the server. */ -private[spark] class RestSubmissionClient extends Logging { +private[spark] class RestSubmissionClient(master: String) extends Logging { import RestSubmissionClient._ private val supportedMasterPrefixes = Seq("spark://", "mesos://") + private val masters: Array[String] = Utils.parseStandaloneMasterUrls(master) + + // Set of masters that lost contact with us, used to keep track of + // whether there are masters still alive for us to communicate with + private val lostMasters = new mutable.HashSet[String] + /** * Submit an application specified by the parameters in the provided request. * * If the submission was successful, poll the status of the submission and report * it to the user. Otherwise, report the error message provided by the server. */ - def createSubmission( - master: String, - request: CreateSubmissionRequest): SubmitRestProtocolResponse = { + def createSubmission(request: CreateSubmissionRequest): SubmitRestProtocolResponse = { logInfo(s"Submitting a request to launch an application in $master.") - validateMaster(master) - val url = getSubmitUrl(master) - val response = postJson(url, request.toJson) - response match { - case s: CreateSubmissionResponse => - reportSubmissionStatus(master, s) - handleRestResponse(s) - case unexpected => - handleUnexpectedRestResponse(unexpected) + var handled: Boolean = false + var response: SubmitRestProtocolResponse = null + for (m <- masters if !handled) { + validateMaster(m) + val url = getSubmitUrl(m) + try { + response = postJson(url, request.toJson) + response match { + case s: CreateSubmissionResponse => + if (s.success) { + reportSubmissionStatus(s) + handleRestResponse(s) + handled = true + } + case unexpected => + handleUnexpectedRestResponse(unexpected) + } + } catch { + case e: SubmitRestConnectionException => + if (handleConnectionException(m)) { + throw new SubmitRestConnectionException("Unable to connect to server", e) + } + } } response } /** Request that the server kill the specified submission. */ - def killSubmission(master: String, submissionId: String): SubmitRestProtocolResponse = { + def killSubmission(submissionId: String): SubmitRestProtocolResponse = { logInfo(s"Submitting a request to kill submission $submissionId in $master.") - validateMaster(master) - val response = post(getKillUrl(master, submissionId)) - response match { - case k: KillSubmissionResponse => handleRestResponse(k) - case unexpected => handleUnexpectedRestResponse(unexpected) + var handled: Boolean = false + var response: SubmitRestProtocolResponse = null + for (m <- masters if !handled) { + validateMaster(m) + val url = getKillUrl(m, submissionId) + try { + response = post(url) + response match { + case k: KillSubmissionResponse => + if (!Utils.responseFromBackup(k.message)) { + handleRestResponse(k) + handled = true + } + case unexpected => + handleUnexpectedRestResponse(unexpected) + } + } catch { + case e: SubmitRestConnectionException => + if (handleConnectionException(m)) { + throw new SubmitRestConnectionException("Unable to connect to server", e) + } + } } response } /** Request the status of a submission from the server. */ def requestSubmissionStatus( - master: String, submissionId: String, quiet: Boolean = false): SubmitRestProtocolResponse = { logInfo(s"Submitting a request for the status of submission $submissionId in $master.") - validateMaster(master) - val response = get(getStatusUrl(master, submissionId)) - response match { - case s: SubmissionStatusResponse => if (!quiet) { handleRestResponse(s) } - case unexpected => handleUnexpectedRestResponse(unexpected) + + var handled: Boolean = false + var response: SubmitRestProtocolResponse = null + for (m <- masters if !handled) { + validateMaster(m) + val url = getStatusUrl(m, submissionId) + try { + response = get(url) + response match { + case s: SubmissionStatusResponse if s.success => + if (!quiet) { + handleRestResponse(s) + } + handled = true + case unexpected => + handleUnexpectedRestResponse(unexpected) + } + } catch { + case e: SubmitRestConnectionException => + if (handleConnectionException(m)) { + throw new SubmitRestConnectionException("Unable to connect to server", e) + } + } } response } @@ -148,11 +201,16 @@ private[spark] class RestSubmissionClient extends Logging { conn.setRequestProperty("Content-Type", "application/json") conn.setRequestProperty("charset", "utf-8") conn.setDoOutput(true) - val out = new DataOutputStream(conn.getOutputStream) - Utils.tryWithSafeFinally { - out.write(json.getBytes(Charsets.UTF_8)) - } { - out.close() + try { + val out = new DataOutputStream(conn.getOutputStream) + Utils.tryWithSafeFinally { + out.write(json.getBytes(Charsets.UTF_8)) + } { + out.close() + } + } catch { + case e: ConnectException => + throw new SubmitRestConnectionException("Connect Exception when connect to server", e) } readResponse(conn) } @@ -191,11 +249,9 @@ private[spark] class RestSubmissionClient extends Logging { } } catch { case unreachable @ (_: FileNotFoundException | _: SocketException) => - throw new SubmitRestConnectionException( - s"Unable to connect to server ${connection.getURL}", unreachable) + throw new SubmitRestConnectionException("Unable to connect to server", unreachable) case malformed @ (_: JsonProcessingException | _: SubmitRestProtocolException) => - throw new SubmitRestProtocolException( - "Malformed response received from server", malformed) + throw new SubmitRestProtocolException("Malformed response received from server", malformed) } } @@ -241,13 +297,12 @@ private[spark] class RestSubmissionClient extends Logging { /** Report the status of a newly created submission. */ private def reportSubmissionStatus( - master: String, submitResponse: CreateSubmissionResponse): Unit = { if (submitResponse.success) { val submissionId = submitResponse.submissionId if (submissionId != null) { logInfo(s"Submission successfully created as $submissionId. Polling submission state...") - pollSubmissionStatus(master, submissionId) + pollSubmissionStatus(submissionId) } else { // should never happen logError("Application successfully submitted, but submission ID was not provided!") @@ -262,9 +317,9 @@ private[spark] class RestSubmissionClient extends Logging { * Poll the status of the specified submission and log it. * This retries up to a fixed number of times before giving up. */ - private def pollSubmissionStatus(master: String, submissionId: String): Unit = { + private def pollSubmissionStatus(submissionId: String): Unit = { (1 to REPORT_DRIVER_STATUS_MAX_TRIES).foreach { _ => - val response = requestSubmissionStatus(master, submissionId, quiet = true) + val response = requestSubmissionStatus(submissionId, quiet = true) val statusResponse = response match { case s: SubmissionStatusResponse => s case _ => return // unexpected type, let upstream caller handle it @@ -302,6 +357,21 @@ private[spark] class RestSubmissionClient extends Logging { private def handleUnexpectedRestResponse(unexpected: SubmitRestProtocolResponse): Unit = { logError(s"Error: Server responded with message of unexpected type ${unexpected.messageType}.") } + + /** + * When a connection exception is caught, return true if all masters are lost. + * Note that the heuristic used here does not take into account that masters + * can recover during the lifetime of this client. This assumption should be + * harmless because this client currently does not support retrying submission + * on failure yet (SPARK-6443). + */ + private def handleConnectionException(masterUrl: String): Boolean = { + if (!lostMasters.contains(masterUrl)) { + logWarning(s"Unable to connect to server ${masterUrl}.") + lostMasters += masterUrl + } + lostMasters.size >= masters.size + } } private[spark] object RestSubmissionClient { @@ -324,10 +394,10 @@ private[spark] object RestSubmissionClient { } val sparkProperties = conf.getAll.toMap val environmentVariables = env.filter { case (k, _) => k.startsWith("SPARK_") } - val client = new RestSubmissionClient + val client = new RestSubmissionClient(master) val submitRequest = client.constructSubmitRequest( appResource, mainClass, appArgs, sparkProperties, environmentVariables) - client.createSubmission(master, submitRequest) + client.createSubmission(submitRequest) } def main(args: Array[String]): Unit = { diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala b/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala index fd17a980c9319..8198296eeb341 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala @@ -53,7 +53,7 @@ private[spark] class MesosRestServer( new MesosStatusRequestServlet(scheduler, masterConf) } -private[deploy] class MesosSubmitRequestServlet( +private[mesos] class MesosSubmitRequestServlet( scheduler: MesosClusterScheduler, conf: SparkConf) extends SubmitRequestServlet { @@ -139,7 +139,7 @@ private[deploy] class MesosSubmitRequestServlet( } } -private[deploy] class MesosKillRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) +private[mesos] class MesosKillRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) extends KillRequestServlet { protected override def handleKill(submissionId: String): KillSubmissionResponse = { val k = scheduler.killDriver(submissionId) @@ -148,7 +148,7 @@ private[deploy] class MesosKillRequestServlet(scheduler: MesosClusterScheduler, } } -private[deploy] class MesosStatusRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) +private[mesos] class MesosStatusRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) extends StatusRequestServlet { protected override def handleStatus(submissionId: String): SubmissionStatusResponse = { val d = scheduler.getDriverStatus(submissionId) diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala index 88f9d880ac209..9678631da9f6f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala @@ -105,7 +105,7 @@ private[worker] class WorkerArguments(args: Array[String], conf: SparkConf) { if (masters != null) { // Two positional arguments were given printUsageAndExit(1) } - masters = value.stripPrefix("spark://").split(",").map("spark://" + _) + masters = Utils.parseStandaloneMasterUrls(value) parse(tail) case Nil => diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 8af46f3327adb..ed159dec4f998 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -20,6 +20,8 @@ package org.apache.spark.executor import java.net.URL import java.nio.ByteBuffer +import org.apache.hadoop.conf.Configuration + import scala.collection.mutable import scala.util.{Failure, Success} @@ -57,7 +59,7 @@ private[spark] class CoarseGrainedExecutorBackend( logInfo("Connecting to driver: " + driverUrl) rpcEnv.asyncSetupEndpointRefByURI(driverUrl).flatMap { ref => driver = Some(ref) - ref.sendWithReply[RegisteredExecutor.type]( + ref.ask[RegisteredExecutor.type]( RegisterExecutor(executorId, self, hostPort, cores, extractLogUrls)) } onComplete { case Success(msg) => Utils.tryLogNonFatalError { @@ -154,7 +156,7 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { executorConf, new SecurityManager(executorConf)) val driver = fetcher.setupEndpointRefByURI(driverUrl) - val props = driver.askWithReply[Seq[(String, String)]](RetrieveSparkProps) ++ + val props = driver.askWithRetry[Seq[(String, String)]](RetrieveSparkProps) ++ Seq[(String, String)](("spark.app.id", appId)) fetcher.shutdown() @@ -168,6 +170,12 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { driverConf.set(key, value) } } + if (driverConf.contains("spark.yarn.credentials.file")) { + logInfo("Will periodically update credentials from: " + + driverConf.get("spark.yarn.credentials.file")) + SparkHadoopUtil.get.startExecutorDelegationTokenRenewer(driverConf) + } + val env = SparkEnv.createExecutorEnv( driverConf, executorId, hostname, port, cores, isLocal = false) @@ -183,6 +191,7 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { env.rpcEnv.setupEndpoint("WorkerWatcher", new WorkerWatcher(env.rpcEnv, url)) } env.rpcEnv.awaitTermination() + SparkHadoopUtil.get.stopExecutorDelegationTokenRenewer() } } diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index f57e215c3f2ed..8f916e0502ecb 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -32,6 +32,7 @@ import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.scheduler.{DirectTaskResult, IndirectTaskResult, Task} import org.apache.spark.shuffle.FetchFailedException import org.apache.spark.storage.{StorageLevel, TaskResultBlockId} +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util._ /** @@ -178,6 +179,7 @@ private[spark] class Executor( } override def run(): Unit = { + val taskMemoryManager = new TaskMemoryManager(env.executorMemoryManager) val deserializeStartTime = System.currentTimeMillis() Thread.currentThread.setContextClassLoader(replClassLoader) val ser = env.closureSerializer.newInstance() @@ -190,6 +192,7 @@ private[spark] class Executor( val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask) updateDependencies(taskFiles, taskJars) task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader) + task.setTaskMemoryManager(taskMemoryManager) // If this task has been killed before we deserialized it, let's quit now. Otherwise, // continue executing the task. @@ -206,7 +209,21 @@ private[spark] class Executor( // Run the actual task and measure its runtime. taskStart = System.currentTimeMillis() - val value = task.run(taskAttemptId = taskId, attemptNumber = attemptNumber) + val value = try { + task.run(taskAttemptId = taskId, attemptNumber = attemptNumber) + } finally { + // Note: this memory freeing logic is duplicated in DAGScheduler.runLocallyWithinThread; + // when changing this, make sure to update both copies. + val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory() + if (freedMemory > 0) { + val errMsg = s"Managed memory leak detected; size = $freedMemory bytes, TID = $taskId" + if (conf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false)) { + throw new SparkException(errMsg) + } else { + logError(errMsg) + } + } + } val taskFinish = System.currentTimeMillis() // If the task has been killed, let's fail it. @@ -424,7 +441,7 @@ private[spark] class Executor( val message = Heartbeat(executorId, tasksMetrics.toArray, env.blockManager.blockManagerId) try { - val response = heartbeatReceiverRef.askWithReply[HeartbeatResponse](message) + val response = heartbeatReceiverRef.askWithRetry[HeartbeatResponse](message) if (response.reregisterBlockManager) { logWarning("Told to re-register on heartbeat") env.blockManager.reregister() diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala index 52862ae0ca5e4..ea36fb60bd540 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala @@ -33,11 +33,11 @@ object ExecutorExitCode { /** DiskStore failed to create a local temporary directory after many attempts. */ val DISK_STORE_FAILED_TO_CREATE_DIR = 53 - /** TachyonStore failed to initialize after many attempts. */ - val TACHYON_STORE_FAILED_TO_INITIALIZE = 54 + /** ExternalBlockStore failed to initialize after many attempts. */ + val EXTERNAL_BLOCK_STORE_FAILED_TO_INITIALIZE = 54 - /** TachyonStore failed to create a local temporary directory after many attempts. */ - val TACHYON_STORE_FAILED_TO_CREATE_DIR = 55 + /** ExternalBlockStore failed to create a local temporary directory after many attempts. */ + val EXTERNAL_BLOCK_STORE_FAILED_TO_CREATE_DIR = 55 def explainExitCode(exitCode: Int): String = { exitCode match { @@ -46,9 +46,11 @@ object ExecutorExitCode { case OOM => "OutOfMemoryError" case DISK_STORE_FAILED_TO_CREATE_DIR => "Failed to create local directory (bad spark.local.dir?)" - case TACHYON_STORE_FAILED_TO_INITIALIZE => "TachyonStore failed to initialize." - case TACHYON_STORE_FAILED_TO_CREATE_DIR => - "TachyonStore failed to create a local temporary directory." + // TODO: replace external block store with concrete implementation name + case EXTERNAL_BLOCK_STORE_FAILED_TO_INITIALIZE => "ExternalBlockStore failed to initialize." + // TODO: replace external block store with concrete implementation name + case EXTERNAL_BLOCK_STORE_FAILED_TO_CREATE_DIR => + "ExternalBlockStore failed to create a local temporary directory." case _ => "Unknown executor exit code (" + exitCode + ")" + ( if (exitCode > 128) { diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala index 3f0950dae1f24..6181c0ee9fa2b 100644 --- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala +++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala @@ -24,7 +24,7 @@ import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.network._ import org.apache.spark.network.buffer.ManagedBuffer import org.apache.spark.network.client.{TransportClientBootstrap, RpcResponseCallback, TransportClientFactory} -import org.apache.spark.network.sasl.{SaslRpcHandler, SaslClientBootstrap} +import org.apache.spark.network.sasl.{SaslClientBootstrap, SaslServerBootstrap} import org.apache.spark.network.server._ import org.apache.spark.network.shuffle.{RetryingBlockFetcher, BlockFetchingListener, OneForOneBlockFetcher} import org.apache.spark.network.shuffle.protocol.UploadBlock @@ -49,18 +49,18 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage private[this] var appId: String = _ override def init(blockDataManager: BlockDataManager): Unit = { - val (rpcHandler: RpcHandler, bootstrap: Option[TransportClientBootstrap]) = { - val nettyRpcHandler = new NettyBlockRpcServer(serializer, blockDataManager) - if (!authEnabled) { - (nettyRpcHandler, None) - } else { - (new SaslRpcHandler(nettyRpcHandler, securityManager), - Some(new SaslClientBootstrap(transportConf, conf.getAppId, securityManager))) - } + val rpcHandler = new NettyBlockRpcServer(serializer, blockDataManager) + var serverBootstrap: Option[TransportServerBootstrap] = None + var clientBootstrap: Option[TransportClientBootstrap] = None + if (authEnabled) { + serverBootstrap = Some(new SaslServerBootstrap(transportConf, securityManager)) + clientBootstrap = Some(new SaslClientBootstrap(transportConf, conf.getAppId, securityManager, + securityManager.isSaslEncryptionEnabled())) } transportContext = new TransportContext(transportConf, rpcHandler) - clientFactory = transportContext.createClientFactory(bootstrap.toList) - server = transportContext.createServer(conf.getInt("spark.blockManager.port", 0)) + clientFactory = transportContext.createClientFactory(clientBootstrap.toList) + server = transportContext.createServer(conf.getInt("spark.blockManager.port", 0), + serverBootstrap.toList) appId = conf.getAppId logInfo("Server created on " + server.getPort) } diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala index 16e905982cf64..497871ed6d5e5 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala @@ -656,7 +656,7 @@ private[nio] class ConnectionManager( connection.synchronized { if (connection.sparkSaslServer == null) { logDebug("Creating sasl Server") - connection.sparkSaslServer = new SparkSaslServer(conf.getAppId, securityManager) + connection.sparkSaslServer = new SparkSaslServer(conf.getAppId, securityManager, false) } } replyToken = connection.sparkSaslServer.response(securityMsg.getToken) @@ -800,7 +800,7 @@ private[nio] class ConnectionManager( if (!conn.isSaslComplete()) { conn.synchronized { if (conn.sparkSaslClient == null) { - conn.sparkSaslClient = new SparkSaslClient(conf.getAppId, securityManager) + conn.sparkSaslClient = new SparkSaslClient(conf.getAppId, securityManager, false) var firstResponse: Array[Byte] = null try { firstResponse = conn.sparkSaslClient.firstToken() diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala index 3406a7e97e368..ec185340c3a2d 100644 --- a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala @@ -33,7 +33,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Returns a future for counting the number of elements in the RDD. */ - def countAsync(): FutureAction[Long] = { + def countAsync(): FutureAction[Long] = self.withScope { val totalCount = new AtomicLong self.context.submitJob( self, @@ -53,7 +53,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Returns a future for retrieving all elements of this RDD. */ - def collectAsync(): FutureAction[Seq[T]] = { + def collectAsync(): FutureAction[Seq[T]] = self.withScope { val results = new Array[Array[T]](self.partitions.length) self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.length), (index, data) => results(index) = data, results.flatten.toSeq) @@ -62,7 +62,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Returns a future for retrieving the first num elements of the RDD. */ - def takeAsync(num: Int): FutureAction[Seq[T]] = { + def takeAsync(num: Int): FutureAction[Seq[T]] = self.withScope { val f = new ComplexFutureAction[Seq[T]] f.run { @@ -109,7 +109,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Applies a function f to all elements of this RDD. */ - def foreachAsync(f: T => Unit): FutureAction[Unit] = { + def foreachAsync(f: T => Unit): FutureAction[Unit] = self.withScope { val cleanF = self.context.clean(f) self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.length), (index, data) => Unit, Unit) @@ -118,7 +118,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Applies a function f to each partition of this RDD. */ - def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = { + def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = self.withScope { self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.length), (index, data) => Unit, Unit) } diff --git a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala index 71578d1210fde..922030263756b 100644 --- a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala @@ -31,7 +31,7 @@ private[spark] class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds: Array[BlockId]) extends RDD[T](sc, Nil) { - @transient lazy val locations_ = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get) + @transient lazy val _locations = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get) @volatile private var _isValid = true override def getPartitions: Array[Partition] = { @@ -54,7 +54,7 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds override def getPreferredLocations(split: Partition): Seq[String] = { assertValid() - locations_(split.asInstanceOf[BlockRDDPartition].blockId) + _locations(split.asInstanceOf[BlockRDDPartition].blockId) } /** @@ -79,14 +79,14 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds /** Check if this BlockRDD is valid. If not valid, exception is thrown. */ private[spark] def assertValid() { - if (!_isValid) { + if (!isValid) { throw new SparkException( "Attempted to use %s after its blocks have been removed!".format(toString)) } } protected def getBlockIdLocations(): Map[BlockId, Seq[String]] = { - locations_ + _locations } } diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala index 843a893235e56..926bce6f15a2a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala @@ -30,7 +30,7 @@ import org.apache.spark.util.StatCounter */ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { /** Add up the elements in this RDD. */ - def sum(): Double = { + def sum(): Double = self.withScope { self.fold(0.0)(_ + _) } @@ -38,37 +38,49 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * Return a [[org.apache.spark.util.StatCounter]] object that captures the mean, variance and * count of the RDD's elements in one operation. */ - def stats(): StatCounter = { + def stats(): StatCounter = self.withScope { self.mapPartitions(nums => Iterator(StatCounter(nums))).reduce((a, b) => a.merge(b)) } /** Compute the mean of this RDD's elements. */ - def mean(): Double = stats().mean + def mean(): Double = self.withScope { + stats().mean + } /** Compute the variance of this RDD's elements. */ - def variance(): Double = stats().variance + def variance(): Double = self.withScope { + stats().variance + } /** Compute the standard deviation of this RDD's elements. */ - def stdev(): Double = stats().stdev + def stdev(): Double = self.withScope { + stats().stdev + } /** * Compute the sample standard deviation of this RDD's elements (which corrects for bias in * estimating the standard deviation by dividing by N-1 instead of N). */ - def sampleStdev(): Double = stats().sampleStdev + def sampleStdev(): Double = self.withScope { + stats().sampleStdev + } /** * Compute the sample variance of this RDD's elements (which corrects for bias in * estimating the variance by dividing by N-1 instead of N). */ - def sampleVariance(): Double = stats().sampleVariance + def sampleVariance(): Double = self.withScope { + stats().sampleVariance + } /** * :: Experimental :: * Approximate operation to return the mean within a timeout. */ @Experimental - def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { + def meanApprox( + timeout: Long, + confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) val evaluator = new MeanEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) @@ -79,7 +91,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * Approximate operation to return the sum within a timeout. */ @Experimental - def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { + def sumApprox( + timeout: Long, + confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) val evaluator = new SumEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) @@ -93,7 +107,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * If the RDD contains infinity, NaN throws an exception * If the elements in RDD do not vary (max == min) always returns a single bucket. */ - def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = { + def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = self.withScope { // Scala's built-in range has issues. See #SI-8782 def customRange(min: Double, max: Double, steps: Int): IndexedSeq[Double] = { val span = max - min @@ -140,7 +154,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * the maximum value of the last position and all NaN entries will be counted * in that bucket. */ - def histogram(buckets: Array[Double], evenBuckets: Boolean = false): Array[Long] = { + def histogram( + buckets: Array[Double], + evenBuckets: Boolean = false): Array[Long] = self.withScope { if (buckets.length < 2) { throw new IllegalArgumentException("buckets array must have at least two elements") } diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala index f77abac42b623..2cefe63d44b20 100644 --- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala @@ -99,7 +99,7 @@ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSp */ @DeveloperApi class HadoopRDD[K, V]( - sc: SparkContext, + @transient sc: SparkContext, broadcastedConf: Broadcast[SerializableWritable[Configuration]], initLocalJobConfFuncOpt: Option[JobConf => Unit], inputFormatClass: Class[_ <: InputFormat[K, V]], @@ -108,6 +108,10 @@ class HadoopRDD[K, V]( minPartitions: Int) extends RDD[(K, V)](sc, Nil) with Logging { + if (initLocalJobConfFuncOpt.isDefined) { + sc.clean(initLocalJobConfFuncOpt.get) + } + def this( sc: SparkContext, conf: JobConf, diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala index 6afe50161dacd..d71bb63000904 100644 --- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala @@ -57,7 +57,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, */ // TODO: this currently doesn't work on P other than Tuple2! def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length) - : RDD[(K, V)] = + : RDD[(K, V)] = self.withScope { val part = new RangePartitioner(numPartitions, self, ascending) new ShuffledRDD[K, V, V](self, part) @@ -71,7 +71,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, * This is more efficient than calling `repartition` and then sorting within each partition * because it can push the sorting down into the shuffle machinery. */ - def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = { + def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = self.withScope { new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering) } @@ -81,7 +81,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, * performed efficiently by only scanning the partitions that might contain matching elements. * Otherwise, a standard `filter` is applied to all partitions. */ - def filterByRange(lower: K, upper: K): RDD[P] = { + def filterByRange(lower: K, upper: K): RDD[P] = self.withScope { def inRange(k: K): Boolean = ordering.gteq(k, lower) && ordering.lteq(k, upper) diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 05351ba4ff76b..a6d5d2c94e17f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -29,7 +29,7 @@ import scala.util.DynamicVariable import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus import org.apache.hadoop.conf.{Configurable, Configuration} -import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.fs.FileSystem import org.apache.hadoop.io.SequenceFile.CompressionType import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf, OutputFormat} @@ -75,7 +75,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) mergeCombiners: (C, C) => C, partitioner: Partitioner, mapSideCombine: Boolean = true, - serializer: Serializer = null): RDD[(K, C)] = { + serializer: Serializer = null): RDD[(K, C)] = self.withScope { require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0 if (keyClass.isArray) { if (mapSideCombine) { @@ -108,7 +108,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C, - numPartitions: Int): RDD[(K, C)] = { + numPartitions: Int): RDD[(K, C)] = self.withScope { combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions)) } @@ -122,7 +122,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * instead of creating a new U. */ def aggregateByKey[U: ClassTag](zeroValue: U, partitioner: Partitioner)(seqOp: (U, V) => U, - combOp: (U, U) => U): RDD[(K, U)] = { + combOp: (U, U) => U): RDD[(K, U)] = self.withScope { // Serialize the zero value to a byte array so that we can get a new clone of it on each key val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue) val zeroArray = new Array[Byte](zeroBuffer.limit) @@ -131,7 +131,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) lazy val cachedSerializer = SparkEnv.get.serializer.newInstance() val createZero = () => cachedSerializer.deserialize[U](ByteBuffer.wrap(zeroArray)) - combineByKey[U]((v: V) => seqOp(createZero(), v), seqOp, combOp, partitioner) + // We will clean the combiner closure later in `combineByKey` + val cleanedSeqOp = self.context.clean(seqOp) + combineByKey[U]((v: V) => cleanedSeqOp(createZero(), v), cleanedSeqOp, combOp, partitioner) } /** @@ -144,7 +146,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * instead of creating a new U. */ def aggregateByKey[U: ClassTag](zeroValue: U, numPartitions: Int)(seqOp: (U, V) => U, - combOp: (U, U) => U): RDD[(K, U)] = { + combOp: (U, U) => U): RDD[(K, U)] = self.withScope { aggregateByKey(zeroValue, new HashPartitioner(numPartitions))(seqOp, combOp) } @@ -158,7 +160,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * instead of creating a new U. */ def aggregateByKey[U: ClassTag](zeroValue: U)(seqOp: (U, V) => U, - combOp: (U, U) => U): RDD[(K, U)] = { + combOp: (U, U) => U): RDD[(K, U)] = self.withScope { aggregateByKey(zeroValue, defaultPartitioner(self))(seqOp, combOp) } @@ -167,7 +169,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * may be added to the result an arbitrary number of times, and must not change the result * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ - def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = { + def foldByKey( + zeroValue: V, + partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = self.withScope { // Serialize the zero value to a byte array so that we can get a new clone of it on each key val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue) val zeroArray = new Array[Byte](zeroBuffer.limit) @@ -177,7 +181,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) lazy val cachedSerializer = SparkEnv.get.serializer.newInstance() val createZero = () => cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray)) - combineByKey[V]((v: V) => func(createZero(), v), func, func, partitioner) + val cleanedFunc = self.context.clean(func) + combineByKey[V]((v: V) => cleanedFunc(createZero(), v), cleanedFunc, cleanedFunc, partitioner) } /** @@ -185,7 +190,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * may be added to the result an arbitrary number of times, and must not change the result * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ - def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = { + def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = self.withScope { foldByKey(zeroValue, new HashPartitioner(numPartitions))(func) } @@ -194,7 +199,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * may be added to the result an arbitrary number of times, and must not change the result * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ - def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = { + def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = self.withScope { foldByKey(zeroValue, defaultPartitioner(self))(func) } @@ -213,7 +218,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) */ def sampleByKey(withReplacement: Boolean, fractions: Map[K, Double], - seed: Long = Utils.random.nextLong): RDD[(K, V)] = { + seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope { require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.") @@ -242,9 +247,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * @return RDD containing the sampled subset */ @Experimental - def sampleByKeyExact(withReplacement: Boolean, + def sampleByKeyExact( + withReplacement: Boolean, fractions: Map[K, Double], - seed: Long = Utils.random.nextLong): RDD[(K, V)] = { + seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope { require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.") @@ -261,7 +267,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * the merging locally on each mapper before sending results to a reducer, similarly to a * "combiner" in MapReduce. */ - def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = { + def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope { combineByKey[V]((v: V) => v, func, func, partitioner) } @@ -270,7 +276,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * the merging locally on each mapper before sending results to a reducer, similarly to a * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions. */ - def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = { + def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = self.withScope { reduceByKey(new HashPartitioner(numPartitions), func) } @@ -280,7 +286,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/ * parallelism level. */ - def reduceByKey(func: (V, V) => V): RDD[(K, V)] = { + def reduceByKey(func: (V, V) => V): RDD[(K, V)] = self.withScope { reduceByKey(defaultPartitioner(self), func) } @@ -289,7 +295,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * immediately to the master as a Map. This will also perform the merging locally on each mapper * before sending results to a reducer, similarly to a "combiner" in MapReduce. */ - def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = { + def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = self.withScope { if (keyClass.isArray) { throw new SparkException("reduceByKeyLocally() does not support array keys") @@ -317,7 +323,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) /** Alias for reduceByKeyLocally */ @deprecated("Use reduceByKeyLocally", "1.0.0") - def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = reduceByKeyLocally(func) + def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = self.withScope { + reduceByKeyLocally(func) + } /** * Count the number of elements for each key, collecting the results to a local Map. @@ -327,7 +335,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * To handle very large results, consider using rdd.mapValues(_ => 1L).reduceByKey(_ + _), which * returns an RDD[T, Long] instead of a map. */ - def countByKey(): Map[K, Long] = self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap + def countByKey(): Map[K, Long] = self.withScope { + self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap + } /** * :: Experimental :: @@ -336,7 +346,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) */ @Experimental def countByKeyApprox(timeout: Long, confidence: Double = 0.95) - : PartialResult[Map[K, BoundedDouble]] = { + : PartialResult[Map[K, BoundedDouble]] = self.withScope { self.map(_._1).countByValueApprox(timeout, confidence) } @@ -360,7 +370,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * @param partitioner Partitioner to use for the resulting RDD. */ @Experimental - def countApproxDistinctByKey(p: Int, sp: Int, partitioner: Partitioner): RDD[(K, Long)] = { + def countApproxDistinctByKey( + p: Int, + sp: Int, + partitioner: Partitioner): RDD[(K, Long)] = self.withScope { require(p >= 4, s"p ($p) must be >= 4") require(sp <= 32, s"sp ($sp) must be <= 32") require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)") @@ -392,7 +405,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * It must be greater than 0.000017. * @param partitioner partitioner of the resulting RDD */ - def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = { + def countApproxDistinctByKey( + relativeSD: Double, + partitioner: Partitioner): RDD[(K, Long)] = self.withScope { require(relativeSD > 0.000017, s"accuracy ($relativeSD) must be greater than 0.000017") val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt assert(p <= 32) @@ -410,7 +425,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * It must be greater than 0.000017. * @param numPartitions number of partitions of the resulting RDD */ - def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): RDD[(K, Long)] = { + def countApproxDistinctByKey( + relativeSD: Double, + numPartitions: Int): RDD[(K, Long)] = self.withScope { countApproxDistinctByKey(relativeSD, new HashPartitioner(numPartitions)) } @@ -424,7 +441,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * @param relativeSD Relative accuracy. Smaller values create counters that require more space. * It must be greater than 0.000017. */ - def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = { + def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = self.withScope { countApproxDistinctByKey(relativeSD, defaultPartitioner(self)) } @@ -441,7 +458,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]]. */ - def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = { + def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = self.withScope { // groupByKey shouldn't use map side combine because map side combine does not // reduce the amount of data shuffled and requires all map side data be inserted // into a hash table, leading to more objects in the old gen. @@ -465,14 +482,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]]. */ - def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = { + def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = self.withScope { groupByKey(new HashPartitioner(numPartitions)) } /** * Return a copy of the RDD partitioned using the specified partitioner. */ - def partitionBy(partitioner: Partitioner): RDD[(K, V)] = { + def partitionBy(partitioner: Partitioner): RDD[(K, V)] = self.withScope { if (keyClass.isArray && partitioner.isInstanceOf[HashPartitioner]) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -488,7 +505,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and * (k, v2) is in `other`. Uses the given Partitioner to partition the output RDD. */ - def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = { + def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = self.withScope { this.cogroup(other, partitioner).flatMapValues( pair => for (v <- pair._1.iterator; w <- pair._2.iterator) yield (v, w) ) @@ -500,7 +517,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (v, None)) if no elements in `other` have key k. Uses the given Partitioner to * partition the output RDD. */ - def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = { + def leftOuterJoin[W]( + other: RDD[(K, W)], + partitioner: Partitioner): RDD[(K, (V, Option[W]))] = self.withScope { this.cogroup(other, partitioner).flatMapValues { pair => if (pair._2.isEmpty) { pair._1.iterator.map(v => (v, None)) @@ -517,7 +536,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * partition the output RDD. */ def rightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner) - : RDD[(K, (Option[V], W))] = { + : RDD[(K, (Option[V], W))] = self.withScope { this.cogroup(other, partitioner).flatMapValues { pair => if (pair._1.isEmpty) { pair._2.iterator.map(w => (None, w)) @@ -536,7 +555,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * in `this` have key k. Uses the given Partitioner to partition the output RDD. */ def fullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner) - : RDD[(K, (Option[V], Option[W]))] = { + : RDD[(K, (Option[V], Option[W]))] = self.withScope { this.cogroup(other, partitioner).flatMapValues { case (vs, Seq()) => vs.iterator.map(v => (Some(v), None)) case (Seq(), ws) => ws.iterator.map(w => (None, Some(w))) @@ -549,7 +568,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * existing partitioner/parallelism level. */ def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C) - : RDD[(K, C)] = { + : RDD[(K, C)] = self.withScope { combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self)) } @@ -563,7 +582,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ - def groupByKey(): RDD[(K, Iterable[V])] = { + def groupByKey(): RDD[(K, Iterable[V])] = self.withScope { groupByKey(defaultPartitioner(self)) } @@ -572,7 +591,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and * (k, v2) is in `other`. Performs a hash join across the cluster. */ - def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = { + def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = self.withScope { join(other, defaultPartitioner(self, other)) } @@ -581,7 +600,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and * (k, v2) is in `other`. Performs a hash join across the cluster. */ - def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))] = { + def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))] = self.withScope { join(other, new HashPartitioner(numPartitions)) } @@ -591,7 +610,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output * using the existing partitioner/parallelism level. */ - def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = { + def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = self.withScope { leftOuterJoin(other, defaultPartitioner(self, other)) } @@ -601,7 +620,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output * into `numPartitions` partitions. */ - def leftOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, Option[W]))] = { + def leftOuterJoin[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (V, Option[W]))] = self.withScope { leftOuterJoin(other, new HashPartitioner(numPartitions)) } @@ -611,7 +632,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting * RDD using the existing partitioner/parallelism level. */ - def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = { + def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = self.withScope { rightOuterJoin(other, defaultPartitioner(self, other)) } @@ -621,7 +642,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting * RDD into the given number of partitions. */ - def rightOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], W))] = { + def rightOuterJoin[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (Option[V], W))] = self.withScope { rightOuterJoin(other, new HashPartitioner(numPartitions)) } @@ -634,7 +657,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * in `this` have key k. Hash-partitions the resulting RDD using the existing partitioner/ * parallelism level. */ - def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = { + def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = self.withScope { fullOuterJoin(other, defaultPartitioner(self, other)) } @@ -646,7 +669,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements * in `this` have key k. Hash-partitions the resulting RDD into the given number of partitions. */ - def fullOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = { + def fullOuterJoin[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = self.withScope { fullOuterJoin(other, new HashPartitioner(numPartitions)) } @@ -656,7 +681,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Warning: this doesn't return a multimap (so if you have multiple values to the same key, only * one value per key is preserved in the map returned) */ - def collectAsMap(): Map[K, V] = { + def collectAsMap(): Map[K, V] = self.withScope { val data = self.collect() val map = new mutable.HashMap[K, V] map.sizeHint(data.length) @@ -668,7 +693,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Pass each value in the key-value pair RDD through a map function without changing the keys; * this also retains the original RDD's partitioning. */ - def mapValues[U](f: V => U): RDD[(K, U)] = { + def mapValues[U](f: V => U): RDD[(K, U)] = self.withScope { val cleanF = self.context.clean(f) new MapPartitionsRDD[(K, U), (K, V)](self, (context, pid, iter) => iter.map { case (k, v) => (k, cleanF(v)) }, @@ -679,7 +704,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Pass each value in the key-value pair RDD through a flatMap function without changing the * keys; this also retains the original RDD's partitioning. */ - def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)] = { + def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)] = self.withScope { val cleanF = self.context.clean(f) new MapPartitionsRDD[(K, U), (K, V)](self, (context, pid, iter) => iter.flatMap { case (k, v) => @@ -697,7 +722,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) other2: RDD[(K, W2)], other3: RDD[(K, W3)], partitioner: Partitioner) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -715,7 +740,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * list of values for that key in `this` as well as `other`. */ def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner) - : RDD[(K, (Iterable[V], Iterable[W]))] = { + : RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -730,7 +755,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -748,7 +773,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * for that key in `this`, `other1`, `other2` and `other3`. */ def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3)) } @@ -756,7 +781,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the * list of values for that key in `this` as well as `other`. */ - def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = { + def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { cogroup(other, defaultPartitioner(self, other)) } @@ -765,7 +790,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { cogroup(other1, other2, defaultPartitioner(self, other1, other2)) } @@ -773,7 +798,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the * list of values for that key in `this` as well as `other`. */ - def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = { + def cogroup[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { cogroup(other, new HashPartitioner(numPartitions)) } @@ -782,7 +809,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { cogroup(other1, other2, new HashPartitioner(numPartitions)) } @@ -795,24 +822,24 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) other2: RDD[(K, W2)], other3: RDD[(K, W3)], numPartitions: Int) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { cogroup(other1, other2, other3, new HashPartitioner(numPartitions)) } /** Alias for cogroup. */ - def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = { + def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { cogroup(other, defaultPartitioner(self, other)) } /** Alias for cogroup. */ def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { cogroup(other1, other2, defaultPartitioner(self, other1, other2)) } /** Alias for cogroup. */ def groupWith[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3)) } @@ -822,22 +849,27 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ - def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = + def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = self.withScope { subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.length))) + } /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ - def subtractByKey[W: ClassTag](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] = + def subtractByKey[W: ClassTag]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, V)] = self.withScope { subtractByKey(other, new HashPartitioner(numPartitions)) + } /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ - def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = + def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = self.withScope { new SubtractedRDD[K, V, W](self, other, p) + } /** * Return the list of values in the RDD for key `key`. This operation is done efficiently if the * RDD has a known partitioner by only searching the partition that the key maps to. */ - def lookup(key: K): Seq[V] = { + def lookup(key: K): Seq[V] = self.withScope { self.partitioner match { case Some(p) => val index = p.getPartition(key) @@ -859,7 +891,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class * supporting the key and value types K and V in this RDD. */ - def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) { + def saveAsHadoopFile[F <: OutputFormat[K, V]]( + path: String)(implicit fm: ClassTag[F]): Unit = self.withScope { saveAsHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } @@ -869,7 +902,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * supplied codec. */ def saveAsHadoopFile[F <: OutputFormat[K, V]]( - path: String, codec: Class[_ <: CompressionCodec]) (implicit fm: ClassTag[F]) { + path: String, + codec: Class[_ <: CompressionCodec])(implicit fm: ClassTag[F]): Unit = self.withScope { val runtimeClass = fm.runtimeClass saveAsHadoopFile(path, keyClass, valueClass, runtimeClass.asInstanceOf[Class[F]], codec) } @@ -878,7 +912,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat` * (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD. */ - def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) { + def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]]( + path: String)(implicit fm: ClassTag[F]): Unit = self.withScope { saveAsNewAPIHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } @@ -891,8 +926,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: NewOutputFormat[_, _]], - conf: Configuration = self.context.hadoopConfiguration) - { + conf: Configuration = self.context.hadoopConfiguration): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val job = new NewAPIHadoopJob(hadoopConf) @@ -912,7 +946,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], - codec: Class[_ <: CompressionCodec]) { + codec: Class[_ <: CompressionCodec]): Unit = self.withScope { saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, new JobConf(self.context.hadoopConfiguration), Some(codec)) } @@ -927,7 +961,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], conf: JobConf = new JobConf(self.context.hadoopConfiguration), - codec: Option[Class[_ <: CompressionCodec]] = None) { + codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf hadoopConf.setOutputKeyClass(keyClass) @@ -960,7 +994,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * output paths required (e.g. a table name to write to) in the same way as it would be * configured for a Hadoop MapReduce job. */ - def saveAsNewAPIHadoopDataset(conf: Configuration) { + def saveAsNewAPIHadoopDataset(conf: Configuration): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val job = new NewAPIHadoopJob(hadoopConf) @@ -1027,7 +1061,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * (e.g. a table name to write to) in the same way as it would be configured for a Hadoop * MapReduce job. */ - def saveAsHadoopDataset(conf: JobConf) { + def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val wrappedConf = new SerializableWritable(hadoopConf) diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index d80d94a588346..b3b60578c92e8 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -25,7 +25,7 @@ import scala.language.implicitConversions import scala.reflect.{classTag, ClassTag} import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus -import org.apache.hadoop.io.{Writable, BytesWritable, NullWritable, Text} +import org.apache.hadoop.io.{BytesWritable, NullWritable, Text} import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.mapred.TextOutputFormat @@ -277,12 +277,20 @@ abstract class RDD[T: ClassTag]( if (isCheckpointed) firstParent[T].iterator(split, context) else compute(split, context) } + /** + * Execute a block of code in a scope such that all new RDDs created in this body will + * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}. + * + * Note: Return statements are NOT allowed in the given body. + */ + private[spark] def withScope[U](body: => U): U = RDDOperationScope.withScope[U](sc)(body) + // Transformations (return a new RDD) /** * Return a new RDD by applying a function to all elements of this RDD. */ - def map[U: ClassTag](f: T => U): RDD[U] = { + def map[U: ClassTag](f: T => U): RDD[U] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.map(cleanF)) } @@ -291,7 +299,7 @@ abstract class RDD[T: ClassTag]( * Return a new RDD by first applying a function to all elements of this * RDD, and then flattening the results. */ - def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = { + def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.flatMap(cleanF)) } @@ -299,7 +307,7 @@ abstract class RDD[T: ClassTag]( /** * Return a new RDD containing only the elements that satisfy a predicate. */ - def filter(f: T => Boolean): RDD[T] = { + def filter(f: T => Boolean): RDD[T] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[T, T]( this, @@ -310,13 +318,16 @@ abstract class RDD[T: ClassTag]( /** * Return a new RDD containing the distinct elements in this RDD. */ - def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = + def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope { map(x => (x, null)).reduceByKey((x, y) => x, numPartitions).map(_._1) + } /** * Return a new RDD containing the distinct elements in this RDD. */ - def distinct(): RDD[T] = distinct(partitions.length) + def distinct(): RDD[T] = withScope { + distinct(partitions.length) + } /** * Return a new RDD that has exactly numPartitions partitions. @@ -327,7 +338,7 @@ abstract class RDD[T: ClassTag]( * If you are decreasing the number of partitions in this RDD, consider using `coalesce`, * which can avoid performing a shuffle. */ - def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = { + def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope { coalesce(numPartitions, shuffle = true) } @@ -352,7 +363,7 @@ abstract class RDD[T: ClassTag]( * data distributed using a hash partitioner. */ def coalesce(numPartitions: Int, shuffle: Boolean = false)(implicit ord: Ordering[T] = null) - : RDD[T] = { + : RDD[T] = withScope { if (shuffle) { /** Distributes elements evenly across output partitions, starting from a random partition. */ val distributePartition = (index: Int, items: Iterator[T]) => { @@ -377,16 +388,17 @@ abstract class RDD[T: ClassTag]( /** * Return a sampled subset of this RDD. - * + * * @param withReplacement can elements be sampled multiple times (replaced when sampled out) * @param fraction expected size of the sample as a fraction of this RDD's size * without replacement: probability that each element is chosen; fraction must be [0, 1] * with replacement: expected number of times each element is chosen; fraction must be >= 0 * @param seed seed for the random number generator */ - def sample(withReplacement: Boolean, + def sample( + withReplacement: Boolean, fraction: Double, - seed: Long = Utils.random.nextLong): RDD[T] = { + seed: Long = Utils.random.nextLong): RDD[T] = withScope { require(fraction >= 0.0, "Negative fraction value: " + fraction) if (withReplacement) { new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), true, seed) @@ -403,15 +415,32 @@ abstract class RDD[T: ClassTag]( * * @return split RDDs in an array */ - def randomSplit(weights: Array[Double], seed: Long = Utils.random.nextLong): Array[RDD[T]] = { + def randomSplit( + weights: Array[Double], + seed: Long = Utils.random.nextLong): Array[RDD[T]] = withScope { val sum = weights.sum val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _) normalizedCumWeights.sliding(2).map { x => - new PartitionwiseSampledRDD[T, T]( - this, new BernoulliCellSampler[T](x(0), x(1)), true, seed) + randomSampleWithRange(x(0), x(1), seed) }.toArray } + /** + * Internal method exposed for Random Splits in DataFrames. Samples an RDD given a probability + * range. + * @param lb lower bound to use for the Bernoulli sampler + * @param ub upper bound to use for the Bernoulli sampler + * @param seed the seed for the Random number generator + * @return A random sub-sample of the RDD without replacement. + */ + private[spark] def randomSampleWithRange(lb: Double, ub: Double, seed: Long): RDD[T] = { + this.mapPartitionsWithIndex { case (index, partition) => + val sampler = new BernoulliCellSampler[T](lb, ub) + sampler.setSeed(seed + index) + sampler.sample(partition) + } + } + /** * Return a fixed-size sampled subset of this RDD in an array * @@ -420,7 +449,9 @@ abstract class RDD[T: ClassTag]( * @param seed seed for the random number generator * @return sample of specified size in an array */ - def takeSample(withReplacement: Boolean, + // TODO: rewrite this without return statements so we can wrap it in a scope + def takeSample( + withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T] = { val numStDev = 10.0 @@ -468,7 +499,7 @@ abstract class RDD[T: ClassTag]( * Return the union of this RDD and another one. Any identical elements will appear multiple * times (use `.distinct()` to eliminate them). */ - def union(other: RDD[T]): RDD[T] = { + def union(other: RDD[T]): RDD[T] = withScope { if (partitioner.isDefined && other.partitioner == partitioner) { new PartitionerAwareUnionRDD(sc, Array(this, other)) } else { @@ -480,7 +511,9 @@ abstract class RDD[T: ClassTag]( * Return the union of this RDD and another one. Any identical elements will appear multiple * times (use `.distinct()` to eliminate them). */ - def ++(other: RDD[T]): RDD[T] = this.union(other) + def ++(other: RDD[T]): RDD[T] = withScope { + this.union(other) + } /** * Return this RDD sorted by the given key function. @@ -489,10 +522,11 @@ abstract class RDD[T: ClassTag]( f: (T) => K, ascending: Boolean = true, numPartitions: Int = this.partitions.length) - (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = + (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = withScope { this.keyBy[K](f) .sortByKey(ascending, numPartitions) .values + } /** * Return the intersection of this RDD and another one. The output will not contain any duplicate @@ -500,7 +534,7 @@ abstract class RDD[T: ClassTag]( * * Note that this method performs a shuffle internally. */ - def intersection(other: RDD[T]): RDD[T] = { + def intersection(other: RDD[T]): RDD[T] = withScope { this.map(v => (v, null)).cogroup(other.map(v => (v, null))) .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty } .keys @@ -514,8 +548,9 @@ abstract class RDD[T: ClassTag]( * * @param partitioner Partitioner to use for the resulting RDD */ - def intersection(other: RDD[T], partitioner: Partitioner)(implicit ord: Ordering[T] = null) - : RDD[T] = { + def intersection( + other: RDD[T], + partitioner: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope { this.map(v => (v, null)).cogroup(other.map(v => (v, null)), partitioner) .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty } .keys @@ -529,16 +564,14 @@ abstract class RDD[T: ClassTag]( * * @param numPartitions How many partitions to use in the resulting RDD */ - def intersection(other: RDD[T], numPartitions: Int): RDD[T] = { - this.map(v => (v, null)).cogroup(other.map(v => (v, null)), new HashPartitioner(numPartitions)) - .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty } - .keys + def intersection(other: RDD[T], numPartitions: Int): RDD[T] = withScope { + intersection(other, new HashPartitioner(numPartitions)) } /** * Return an RDD created by coalescing all elements within each partition into an array. */ - def glom(): RDD[Array[T]] = { + def glom(): RDD[Array[T]] = withScope { new MapPartitionsRDD[Array[T], T](this, (context, pid, iter) => Iterator(iter.toArray)) } @@ -546,7 +579,9 @@ abstract class RDD[T: ClassTag]( * Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of * elements (a, b) where a is in `this` and b is in `other`. */ - def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = new CartesianRDD(sc, this, other) + def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope { + new CartesianRDD(sc, this, other) + } /** * Return an RDD of grouped items. Each group consists of a key and a sequence of elements @@ -557,8 +592,9 @@ abstract class RDD[T: ClassTag]( * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ - def groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = + def groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope { groupBy[K](f, defaultPartitioner(this)) + } /** * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements @@ -569,8 +605,11 @@ abstract class RDD[T: ClassTag]( * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ - def groupBy[K](f: T => K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = + def groupBy[K]( + f: T => K, + numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope { groupBy(f, new HashPartitioner(numPartitions)) + } /** * Return an RDD of grouped items. Each group consists of a key and a sequence of elements @@ -582,7 +621,7 @@ abstract class RDD[T: ClassTag]( * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ def groupBy[K](f: T => K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null) - : RDD[(K, Iterable[T])] = { + : RDD[(K, Iterable[T])] = withScope { val cleanF = sc.clean(f) this.map(t => (cleanF(t), t)).groupByKey(p) } @@ -590,13 +629,16 @@ abstract class RDD[T: ClassTag]( /** * Return an RDD created by piping elements to a forked external process. */ - def pipe(command: String): RDD[String] = new PipedRDD(this, command) + def pipe(command: String): RDD[String] = withScope { + new PipedRDD(this, command) + } /** * Return an RDD created by piping elements to a forked external process. */ - def pipe(command: String, env: Map[String, String]): RDD[String] = + def pipe(command: String, env: Map[String, String]): RDD[String] = withScope { new PipedRDD(this, command, env) + } /** * Return an RDD created by piping elements to a forked external process. @@ -604,7 +646,7 @@ abstract class RDD[T: ClassTag]( * * @param command command to run in forked process. * @param env environment variables to set. - * @param printPipeContext Before piping elements, this function is called as an oppotunity + * @param printPipeContext Before piping elements, this function is called as an opportunity * to pipe context data. Print line function (like out.println) will be * passed as printPipeContext's parameter. * @param printRDDElement Use this function to customize how to pipe elements. This function @@ -622,7 +664,7 @@ abstract class RDD[T: ClassTag]( env: Map[String, String] = Map(), printPipeContext: (String => Unit) => Unit = null, printRDDElement: (T, String => Unit) => Unit = null, - separateWorkingDir: Boolean = false): RDD[String] = { + separateWorkingDir: Boolean = false): RDD[String] = withScope { new PipedRDD(this, command, env, if (printPipeContext ne null) sc.clean(printPipeContext) else null, if (printRDDElement ne null) sc.clean(printRDDElement) else null, @@ -636,9 +678,13 @@ abstract class RDD[T: ClassTag]( * should be `false` unless this is a pair RDD and the input function doesn't modify the keys. */ def mapPartitions[U: ClassTag]( - f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { - val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(iter) - new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) + f: Iterator[T] => Iterator[U], + preservesPartitioning: Boolean = false): RDD[U] = withScope { + val cleanedF = sc.clean(f) + new MapPartitionsRDD( + this, + (context: TaskContext, index: Int, iter: Iterator[T]) => cleanedF(iter), + preservesPartitioning) } /** @@ -649,9 +695,13 @@ abstract class RDD[T: ClassTag]( * should be `false` unless this is a pair RDD and the input function doesn't modify the keys. */ def mapPartitionsWithIndex[U: ClassTag]( - f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { - val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(index, iter) - new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) + f: (Int, Iterator[T]) => Iterator[U], + preservesPartitioning: Boolean = false): RDD[U] = withScope { + val cleanedF = sc.clean(f) + new MapPartitionsRDD( + this, + (context: TaskContext, index: Int, iter: Iterator[T]) => cleanedF(index, iter), + preservesPartitioning) } /** @@ -666,7 +716,7 @@ abstract class RDD[T: ClassTag]( @deprecated("use TaskContext.get", "1.2.0") def mapPartitionsWithContext[U: ClassTag]( f: (TaskContext, Iterator[T]) => Iterator[U], - preservesPartitioning: Boolean = false): RDD[U] = { + preservesPartitioning: Boolean = false): RDD[U] = withScope { val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(context, iter) new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) } @@ -677,7 +727,8 @@ abstract class RDD[T: ClassTag]( */ @deprecated("use mapPartitionsWithIndex", "0.7.0") def mapPartitionsWithSplit[U: ClassTag]( - f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + f: (Int, Iterator[T]) => Iterator[U], + preservesPartitioning: Boolean = false): RDD[U] = withScope { mapPartitionsWithIndex(f, preservesPartitioning) } @@ -689,7 +740,7 @@ abstract class RDD[T: ClassTag]( @deprecated("use mapPartitionsWithIndex", "1.0.0") def mapWith[A, U: ClassTag] (constructA: Int => A, preservesPartitioning: Boolean = false) - (f: (T, A) => U): RDD[U] = { + (f: (T, A) => U): RDD[U] = withScope { mapPartitionsWithIndex((index, iter) => { val a = constructA(index) iter.map(t => f(t, a)) @@ -704,7 +755,7 @@ abstract class RDD[T: ClassTag]( @deprecated("use mapPartitionsWithIndex and flatMap", "1.0.0") def flatMapWith[A, U: ClassTag] (constructA: Int => A, preservesPartitioning: Boolean = false) - (f: (T, A) => Seq[U]): RDD[U] = { + (f: (T, A) => Seq[U]): RDD[U] = withScope { mapPartitionsWithIndex((index, iter) => { val a = constructA(index) iter.flatMap(t => f(t, a)) @@ -717,11 +768,11 @@ abstract class RDD[T: ClassTag]( * partition with the index of that partition. */ @deprecated("use mapPartitionsWithIndex and foreach", "1.0.0") - def foreachWith[A](constructA: Int => A)(f: (T, A) => Unit) { + def foreachWith[A](constructA: Int => A)(f: (T, A) => Unit): Unit = withScope { mapPartitionsWithIndex { (index, iter) => val a = constructA(index) iter.map(t => {f(t, a); t}) - }.foreach(_ => {}) + } } /** @@ -730,7 +781,7 @@ abstract class RDD[T: ClassTag]( * partition with the index of that partition. */ @deprecated("use mapPartitionsWithIndex and filter", "1.0.0") - def filterWith[A](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = { + def filterWith[A](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = withScope { mapPartitionsWithIndex((index, iter) => { val a = constructA(index) iter.filter(t => p(t, a)) @@ -743,7 +794,7 @@ abstract class RDD[T: ClassTag]( * partitions* and the *same number of elements in each partition* (e.g. one was made through * a map on the other). */ - def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = { + def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope { zipPartitions(other, preservesPartitioning = false) { (thisIter, otherIter) => new Iterator[(T, U)] { def hasNext: Boolean = (thisIter.hasNext, otherIter.hasNext) match { @@ -765,33 +816,39 @@ abstract class RDD[T: ClassTag]( */ def zipPartitions[B: ClassTag, V: ClassTag] (rdd2: RDD[B], preservesPartitioning: Boolean) - (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = + (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope { new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, preservesPartitioning) + } def zipPartitions[B: ClassTag, V: ClassTag] (rdd2: RDD[B]) - (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, false) + (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope { + zipPartitions(rdd2, preservesPartitioning = false)(f) + } def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean) - (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = + (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope { new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, preservesPartitioning) + } def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C]) - (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, false) + (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope { + zipPartitions(rdd2, rdd3, preservesPartitioning = false)(f) + } def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean) - (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = + (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope { new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, preservesPartitioning) + } def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D]) - (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, false) + (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope { + zipPartitions(rdd2, rdd3, rdd4, preservesPartitioning = false)(f) + } // Actions (launch a job to return a value to the user program) @@ -799,7 +856,7 @@ abstract class RDD[T: ClassTag]( /** * Applies a function f to all elements of this RDD. */ - def foreach(f: T => Unit) { + def foreach(f: T => Unit): Unit = withScope { val cleanF = sc.clean(f) sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF)) } @@ -807,7 +864,7 @@ abstract class RDD[T: ClassTag]( /** * Applies a function f to each partition of this RDD. */ - def foreachPartition(f: Iterator[T] => Unit) { + def foreachPartition(f: Iterator[T] => Unit): Unit = withScope { val cleanF = sc.clean(f) sc.runJob(this, (iter: Iterator[T]) => cleanF(iter)) } @@ -815,7 +872,7 @@ abstract class RDD[T: ClassTag]( /** * Return an array that contains all of the elements in this RDD. */ - def collect(): Array[T] = { + def collect(): Array[T] = withScope { val results = sc.runJob(this, (iter: Iterator[T]) => iter.toArray) Array.concat(results: _*) } @@ -825,7 +882,7 @@ abstract class RDD[T: ClassTag]( * * The iterator will consume as much memory as the largest partition in this RDD. */ - def toLocalIterator: Iterator[T] = { + def toLocalIterator: Iterator[T] = withScope { def collectPartition(p: Int): Array[T] = { sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p), allowLocal = false).head } @@ -836,12 +893,14 @@ abstract class RDD[T: ClassTag]( * Return an array that contains all of the elements in this RDD. */ @deprecated("use collect", "1.0.0") - def toArray(): Array[T] = collect() + def toArray(): Array[T] = withScope { + collect() + } /** * Return an RDD that contains all matching values by applying `f`. */ - def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = { + def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = withScope { filter(f.isDefinedAt).map(f) } @@ -851,19 +910,23 @@ abstract class RDD[T: ClassTag]( * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ - def subtract(other: RDD[T]): RDD[T] = + def subtract(other: RDD[T]): RDD[T] = withScope { subtract(other, partitioner.getOrElse(new HashPartitioner(partitions.length))) + } /** * Return an RDD with the elements from `this` that are not in `other`. */ - def subtract(other: RDD[T], numPartitions: Int): RDD[T] = + def subtract(other: RDD[T], numPartitions: Int): RDD[T] = withScope { subtract(other, new HashPartitioner(numPartitions)) + } /** * Return an RDD with the elements from `this` that are not in `other`. */ - def subtract(other: RDD[T], p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = { + def subtract( + other: RDD[T], + p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope { if (partitioner == Some(p)) { // Our partitioner knows how to handle T (which, since we have a partitioner, is // really (K, V)) so make a new Partitioner that will de-tuple our fake tuples @@ -885,7 +948,7 @@ abstract class RDD[T: ClassTag]( * Reduces the elements of this RDD using the specified commutative and * associative binary operator. */ - def reduce(f: (T, T) => T): T = { + def reduce(f: (T, T) => T): T = withScope { val cleanF = sc.clean(f) val reducePartition: Iterator[T] => Option[T] = iter => { if (iter.hasNext) { @@ -914,7 +977,7 @@ abstract class RDD[T: ClassTag]( * @param depth suggested depth of the tree (default: 2) * @see [[org.apache.spark.rdd.RDD#reduce]] */ - def treeReduce(f: (T, T) => T, depth: Int = 2): T = { + def treeReduce(f: (T, T) => T, depth: Int = 2): T = withScope { require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.") val cleanF = context.clean(f) val reducePartition: Iterator[T] => Option[T] = iter => { @@ -946,7 +1009,7 @@ abstract class RDD[T: ClassTag]( * modify t1 and return it as its result value to avoid object allocation; however, it should not * modify t2. */ - def fold(zeroValue: T)(op: (T, T) => T): T = { + def fold(zeroValue: T)(op: (T, T) => T): T = withScope { // Clone the zero value since we will also be serializing it as part of tasks var jobResult = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance()) val cleanOp = sc.clean(op) @@ -964,7 +1027,7 @@ abstract class RDD[T: ClassTag]( * allowed to modify and return their first argument instead of creating a new U to avoid memory * allocation. */ - def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = { + def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = withScope { // Clone the zero value since we will also be serializing it as part of tasks var jobResult = Utils.clone(zeroValue, sc.env.serializer.newInstance()) val cleanSeqOp = sc.clean(seqOp) @@ -984,26 +1047,29 @@ abstract class RDD[T: ClassTag]( def treeAggregate[U: ClassTag](zeroValue: U)( seqOp: (U, T) => U, combOp: (U, U) => U, - depth: Int = 2): U = { + depth: Int = 2): U = withScope { require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.") if (partitions.length == 0) { - return Utils.clone(zeroValue, context.env.closureSerializer.newInstance()) - } - val cleanSeqOp = context.clean(seqOp) - val cleanCombOp = context.clean(combOp) - val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) - var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it))) - var numPartitions = partiallyAggregated.partitions.length - val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2) - // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation. - while (numPartitions > scale + numPartitions / scale) { - numPartitions /= scale - val curNumPartitions = numPartitions - partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { (i, iter) => - iter.map((i % curNumPartitions, _)) - }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values + Utils.clone(zeroValue, context.env.closureSerializer.newInstance()) + } else { + val cleanSeqOp = context.clean(seqOp) + val cleanCombOp = context.clean(combOp) + val aggregatePartition = + (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) + var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it))) + var numPartitions = partiallyAggregated.partitions.length + val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2) + // If creating an extra level doesn't help reduce + // the wall-clock time, we stop tree aggregation. + while (numPartitions > scale + numPartitions / scale) { + numPartitions /= scale + val curNumPartitions = numPartitions + partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { + (i, iter) => iter.map((i % curNumPartitions, _)) + }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values + } + partiallyAggregated.reduce(cleanCombOp) } - partiallyAggregated.reduce(cleanCombOp) } /** @@ -1017,7 +1083,9 @@ abstract class RDD[T: ClassTag]( * within a timeout, even if not all tasks have finished. */ @Experimental - def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { + def countApprox( + timeout: Long, + confidence: Double = 0.95): PartialResult[BoundedDouble] = withScope { val countElements: (TaskContext, Iterator[T]) => Long = { (ctx, iter) => var result = 0L while (iter.hasNext) { @@ -1038,7 +1106,7 @@ abstract class RDD[T: ClassTag]( * To handle very large results, consider using rdd.map(x => (x, 1L)).reduceByKey(_ + _), which * returns an RDD[T, Long] instead of a map. */ - def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = { + def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = withScope { map(value => (value, null)).countByKey() } @@ -1049,8 +1117,7 @@ abstract class RDD[T: ClassTag]( @Experimental def countByValueApprox(timeout: Long, confidence: Double = 0.95) (implicit ord: Ordering[T] = null) - : PartialResult[Map[T, BoundedDouble]] = - { + : PartialResult[Map[T, BoundedDouble]] = withScope { if (elementClassTag.runtimeClass.isArray) { throw new SparkException("countByValueApprox() does not support arrays") } @@ -1083,7 +1150,7 @@ abstract class RDD[T: ClassTag]( * If `sp` equals 0, the sparse representation is skipped. */ @Experimental - def countApproxDistinct(p: Int, sp: Int): Long = { + def countApproxDistinct(p: Int, sp: Int): Long = withScope { require(p >= 4, s"p ($p) must be at least 4") require(sp <= 32, s"sp ($sp) cannot be greater than 32") require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)") @@ -1109,7 +1176,7 @@ abstract class RDD[T: ClassTag]( * @param relativeSD Relative accuracy. Smaller values create counters that require more space. * It must be greater than 0.000017. */ - def countApproxDistinct(relativeSD: Double = 0.05): Long = { + def countApproxDistinct(relativeSD: Double = 0.05): Long = withScope { val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt countApproxDistinct(p, 0) } @@ -1127,7 +1194,9 @@ abstract class RDD[T: ClassTag]( * and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee * the same index assignments, you should sort the RDD with sortByKey() or save it to a file. */ - def zipWithIndex(): RDD[(T, Long)] = new ZippedWithIndexRDD(this) + def zipWithIndex(): RDD[(T, Long)] = withScope { + new ZippedWithIndexRDD(this) + } /** * Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, @@ -1139,7 +1208,7 @@ abstract class RDD[T: ClassTag]( * and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee * the same index assignments, you should sort the RDD with sortByKey() or save it to a file. */ - def zipWithUniqueId(): RDD[(T, Long)] = { + def zipWithUniqueId(): RDD[(T, Long)] = withScope { val n = this.partitions.length.toLong this.mapPartitionsWithIndex { case (k, iter) => iter.zipWithIndex.map { case (item, i) => @@ -1156,48 +1225,50 @@ abstract class RDD[T: ClassTag]( * @note due to complications in the internal implementation, this method will raise * an exception if called on an RDD of `Nothing` or `Null`. */ - def take(num: Int): Array[T] = { + def take(num: Int): Array[T] = withScope { if (num == 0) { - return new Array[T](0) - } - - val buf = new ArrayBuffer[T] - val totalParts = this.partitions.length - var partsScanned = 0 - while (buf.size < num && partsScanned < totalParts) { - // The number of partitions to try in this iteration. It is ok for this number to be - // greater than totalParts because we actually cap it at totalParts in runJob. - var numPartsToTry = 1 - if (partsScanned > 0) { - // If we didn't find any rows after the previous iteration, quadruple and retry. Otherwise, - // interpolate the number of partitions we need to try, but overestimate it by 50%. - // We also cap the estimation in the end. - if (buf.size == 0) { - numPartsToTry = partsScanned * 4 - } else { - // the left side of max is >=1 whenever partsScanned >= 2 - numPartsToTry = Math.max((1.5 * num * partsScanned / buf.size).toInt - partsScanned, 1) - numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) + new Array[T](0) + } else { + val buf = new ArrayBuffer[T] + val totalParts = this.partitions.length + var partsScanned = 0 + while (buf.size < num && partsScanned < totalParts) { + // The number of partitions to try in this iteration. It is ok for this number to be + // greater than totalParts because we actually cap it at totalParts in runJob. + var numPartsToTry = 1 + if (partsScanned > 0) { + // If we didn't find any rows after the previous iteration, quadruple and retry. + // Otherwise, interpolate the number of partitions we need to try, but overestimate + // it by 50%. We also cap the estimation in the end. + if (buf.size == 0) { + numPartsToTry = partsScanned * 4 + } else { + // the left side of max is >=1 whenever partsScanned >= 2 + numPartsToTry = Math.max((1.5 * num * partsScanned / buf.size).toInt - partsScanned, 1) + numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) + } } - } - val left = num - buf.size - val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts) - val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal = true) + val left = num - buf.size + val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts) + val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal = true) - res.foreach(buf ++= _.take(num - buf.size)) - partsScanned += numPartsToTry - } + res.foreach(buf ++= _.take(num - buf.size)) + partsScanned += numPartsToTry + } - buf.toArray + buf.toArray + } } /** * Return the first element in this RDD. */ - def first(): T = take(1) match { - case Array(t) => t - case _ => throw new UnsupportedOperationException("empty collection") + def first(): T = withScope { + take(1) match { + case Array(t) => t + case _ => throw new UnsupportedOperationException("empty collection") + } } /** @@ -1215,7 +1286,9 @@ abstract class RDD[T: ClassTag]( * @param ord the implicit ordering for T * @return an array of top elements */ - def top(num: Int)(implicit ord: Ordering[T]): Array[T] = takeOrdered(num)(ord.reverse) + def top(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope { + takeOrdered(num)(ord.reverse) + } /** * Returns the first k (smallest) elements from this RDD as defined by the specified @@ -1233,7 +1306,7 @@ abstract class RDD[T: ClassTag]( * @param ord the implicit ordering for T * @return an array of top elements */ - def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = { + def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope { if (num == 0) { Array.empty } else { @@ -1258,13 +1331,17 @@ abstract class RDD[T: ClassTag]( * Returns the max of this RDD as defined by the implicit Ordering[T]. * @return the maximum element of the RDD * */ - def max()(implicit ord: Ordering[T]): T = this.reduce(ord.max) + def max()(implicit ord: Ordering[T]): T = withScope { + this.reduce(ord.max) + } /** * Returns the min of this RDD as defined by the implicit Ordering[T]. * @return the minimum element of the RDD * */ - def min()(implicit ord: Ordering[T]): T = this.reduce(ord.min) + def min()(implicit ord: Ordering[T]): T = withScope { + this.reduce(ord.min) + } /** * @note due to complications in the internal implementation, this method will raise an @@ -1274,12 +1351,14 @@ abstract class RDD[T: ClassTag]( * @return true if and only if the RDD contains no elements at all. Note that an RDD * may be empty even when it has at least 1 partition. */ - def isEmpty(): Boolean = partitions.length == 0 || take(1).length == 0 + def isEmpty(): Boolean = withScope { + partitions.length == 0 || take(1).length == 0 + } /** * Save this RDD as a text file, using string representations of elements. */ - def saveAsTextFile(path: String) { + def saveAsTextFile(path: String): Unit = withScope { // https://issues.apache.org/jira/browse/SPARK-2075 // // NullWritable is a `Comparable` in Hadoop 1.+, so the compiler cannot find an implicit @@ -1306,7 +1385,7 @@ abstract class RDD[T: ClassTag]( /** * Save this RDD as a compressed text file, using string representations of elements. */ - def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) { + def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit = withScope { // https://issues.apache.org/jira/browse/SPARK-2075 val nullWritableClassTag = implicitly[ClassTag[NullWritable]] val textClassTag = implicitly[ClassTag[Text]] @@ -1324,7 +1403,7 @@ abstract class RDD[T: ClassTag]( /** * Save this RDD as a SequenceFile of serialized objects. */ - def saveAsObjectFile(path: String) { + def saveAsObjectFile(path: String): Unit = withScope { this.mapPartitions(iter => iter.grouped(10).map(_.toArray)) .map(x => (NullWritable.get(), new BytesWritable(Utils.serialize(x)))) .saveAsSequenceFile(path) @@ -1333,12 +1412,13 @@ abstract class RDD[T: ClassTag]( /** * Creates tuples of the elements in this RDD by applying `f`. */ - def keyBy[K](f: T => K): RDD[(K, T)] = { - map(x => (f(x), x)) + def keyBy[K](f: T => K): RDD[(K, T)] = withScope { + val cleanedF = sc.clean(f) + map(x => (cleanedF(x), x)) } /** A private method for tests, to look at the contents of each partition */ - private[spark] def collectPartitions(): Array[Array[T]] = { + private[spark] def collectPartitions(): Array[Array[T]] = withScope { sc.runJob(this, (iter: Iterator[T]) => iter.toArray) } @@ -1377,6 +1457,17 @@ abstract class RDD[T: ClassTag]( /** User code that created this RDD (e.g. `textFile`, `parallelize`). */ @transient private[spark] val creationSite = sc.getCallSite() + /** + * The scope associated with the operation that created this RDD. + * + * This is more flexible than the call site and can be defined hierarchically. For more + * detail, see the documentation of {{RDDOperationScope}}. This scope is not defined if the + * user instantiates this RDD himself without using any Spark operations. + */ + @transient private[spark] val scope: Option[RDDOperationScope] = { + Option(sc.getLocalProperty(SparkContext.RDD_SCOPE_KEY)).map(RDDOperationScope.fromJson) + } + private[spark] def getCreationSite: String = Option(creationSite).map(_.shortForm).getOrElse("") private[spark] def elementClassTag: ClassTag[T] = classTag[T] @@ -1455,14 +1546,14 @@ abstract class RDD[T: ClassTag]( /** A description of this RDD and its recursive dependencies for debugging. */ def toDebugString: String = { // Get a debug description of an rdd without its children - def debugSelf (rdd: RDD[_]): Seq[String] = { + def debugSelf(rdd: RDD[_]): Seq[String] = { import Utils.bytesToString val persistence = if (storageLevel != StorageLevel.NONE) storageLevel.description else "" val storageInfo = rdd.context.getRDDStorageInfo.filter(_.id == rdd.id).map(info => - " CachedPartitions: %d; MemorySize: %s; TachyonSize: %s; DiskSize: %s".format( + " CachedPartitions: %d; MemorySize: %s; ExternalBlockStoreSize: %s; DiskSize: %s".format( info.numCachedPartitions, bytesToString(info.memSize), - bytesToString(info.tachyonSize), bytesToString(info.diskSize))) + bytesToString(info.externalBlockStoreSize), bytesToString(info.diskSize))) s"$rdd [$persistence]" +: storageInfo } @@ -1512,10 +1603,11 @@ abstract class RDD[T: ClassTag]( case (desc: String, _) => s"$nextPrefix$desc" } ++ debugChildren(rdd, nextPrefix) } - def debugString(rdd: RDD[_], - prefix: String = "", - isShuffle: Boolean = true, - isLastChild: Boolean = false): Seq[String] = { + def debugString( + rdd: RDD[_], + prefix: String = "", + isShuffle: Boolean = true, + isLastChild: Boolean = false): Seq[String] = { if (isShuffle) { shuffleDebugString(rdd, prefix, isLastChild) } else { diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala new file mode 100644 index 0000000000000..537b56b49f866 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala @@ -0,0 +1,137 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rdd + +import java.util.concurrent.atomic.AtomicInteger + +import com.fasterxml.jackson.annotation.{JsonIgnore, JsonInclude, JsonPropertyOrder} +import com.fasterxml.jackson.annotation.JsonInclude.Include +import com.fasterxml.jackson.databind.ObjectMapper +import com.fasterxml.jackson.module.scala.DefaultScalaModule + +import org.apache.spark.SparkContext + +/** + * A general, named code block representing an operation that instantiates RDDs. + * + * All RDDs instantiated in the corresponding code block will store a pointer to this object. + * Examples include, but will not be limited to, existing RDD operations, such as textFile, + * reduceByKey, and treeAggregate. + * + * An operation scope may be nested in other scopes. For instance, a SQL query may enclose + * scopes associated with the public RDD APIs it uses under the hood. + * + * There is no particular relationship between an operation scope and a stage or a job. + * A scope may live inside one stage (e.g. map) or span across multiple jobs (e.g. take). + */ +@JsonInclude(Include.NON_NULL) +@JsonPropertyOrder(Array("id", "name", "parent")) +private[spark] class RDDOperationScope( + val name: String, + val parent: Option[RDDOperationScope] = None) { + + val id: Int = RDDOperationScope.nextScopeId() + + def toJson: String = { + RDDOperationScope.jsonMapper.writeValueAsString(this) + } + + /** + * Return a list of scopes that this scope is a part of, including this scope itself. + * The result is ordered from the outermost scope (eldest ancestor) to this scope. + */ + @JsonIgnore + def getAllScopes: Seq[RDDOperationScope] = { + parent.map(_.getAllScopes).getOrElse(Seq.empty) ++ Seq(this) + } + + override def equals(other: Any): Boolean = { + other match { + case s: RDDOperationScope => + id == s.id && name == s.name && parent == s.parent + case _ => false + } + } + + override def toString: String = toJson +} + +/** + * A collection of utility methods to construct a hierarchical representation of RDD scopes. + * An RDD scope tracks the series of operations that created a given RDD. + */ +private[spark] object RDDOperationScope { + private val jsonMapper = new ObjectMapper().registerModule(DefaultScalaModule) + private val scopeCounter = new AtomicInteger(0) + + def fromJson(s: String): RDDOperationScope = { + jsonMapper.readValue(s, classOf[RDDOperationScope]) + } + + /** Return a globally unique operation scope ID. */ + def nextScopeId(): Int = scopeCounter.getAndIncrement + + /** + * Execute the given body such that all RDDs created in this body will have the same scope. + * The name of the scope will be the name of the method that immediately encloses this one. + * + * Note: Return statements are NOT allowed in body. + */ + private[spark] def withScope[T]( + sc: SparkContext, + allowNesting: Boolean = false)(body: => T): T = { + val callerMethodName = Thread.currentThread.getStackTrace()(3).getMethodName + withScope[T](sc, callerMethodName, allowNesting)(body) + } + + /** + * Execute the given body such that all RDDs created in this body will have the same scope. + * + * If nesting is allowed, this concatenates the previous scope with the new one in a way that + * signifies the hierarchy. Otherwise, if nesting is not allowed, then any children calls to + * this method executed in the body will have no effect. + * + * Note: Return statements are NOT allowed in body. + */ + private[spark] def withScope[T]( + sc: SparkContext, + name: String, + allowNesting: Boolean = false)(body: => T): T = { + // Save the old scope to restore it later + val scopeKey = SparkContext.RDD_SCOPE_KEY + val noOverrideKey = SparkContext.RDD_SCOPE_NO_OVERRIDE_KEY + val oldScopeJson = sc.getLocalProperty(scopeKey) + val oldScope = Option(oldScopeJson).map(RDDOperationScope.fromJson) + val oldNoOverride = sc.getLocalProperty(noOverrideKey) + try { + // Set the scope only if the higher level caller allows us to do so + if (sc.getLocalProperty(noOverrideKey) == null) { + sc.setLocalProperty(scopeKey, new RDDOperationScope(name, oldScope).toJson) + } + // Optionally disallow the child body to override our scope + if (!allowNesting) { + sc.setLocalProperty(noOverrideKey, "true") + } + body + } finally { + // Remember to restore any state that was modified before exiting + sc.setLocalProperty(scopeKey, oldScopeJson) + sc.setLocalProperty(noOverrideKey, oldNoOverride) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala index 059f8963691f0..3dfcf67f0eb66 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala @@ -85,7 +85,9 @@ class SequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable : ClassTag * byte arrays to BytesWritable, and Strings to Text. The `path` can be on any Hadoop-supported * file system. */ - def saveAsSequenceFile(path: String, codec: Option[Class[_ <: CompressionCodec]] = None) { + def saveAsSequenceFile( + path: String, + codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope { def anyToWritable[U <% Writable](u: U): Writable = u // TODO We cannot force the return type of `anyToWritable` be same as keyWritableClass and diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcCallContext.scala b/core/src/main/scala/org/apache/spark/rpc/RpcCallContext.scala new file mode 100644 index 0000000000000..3e5b64265e919 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/RpcCallContext.scala @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +/** + * A callback that [[RpcEndpoint]] can use it to send back a message or failure. It's thread-safe + * and can be called in any thread. + */ +private[spark] trait RpcCallContext { + + /** + * Reply a message to the sender. If the sender is [[RpcEndpoint]], its [[RpcEndpoint.receive]] + * will be called. + */ + def reply(response: Any): Unit + + /** + * Report a failure to the sender. + */ + def sendFailure(e: Throwable): Unit + + /** + * The sender of this message. + */ + def sender: RpcEndpointRef +} diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala new file mode 100644 index 0000000000000..d2b2baef1d8c4 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala @@ -0,0 +1,148 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +import org.apache.spark.SparkException + +/** + * A factory class to create the [[RpcEnv]]. It must have an empty constructor so that it can be + * created using Reflection. + */ +private[spark] trait RpcEnvFactory { + + def create(config: RpcEnvConfig): RpcEnv +} + +/** + * A trait that requires RpcEnv thread-safely sending messages to it. + * + * Thread-safety means processing of one message happens before processing of the next message by + * the same [[ThreadSafeRpcEndpoint]]. In the other words, changes to internal fields of a + * [[ThreadSafeRpcEndpoint]] are visible when processing the next message, and fields in the + * [[ThreadSafeRpcEndpoint]] need not be volatile or equivalent. + * + * However, there is no guarantee that the same thread will be executing the same + * [[ThreadSafeRpcEndpoint]] for different messages. + */ +private[spark] trait ThreadSafeRpcEndpoint extends RpcEndpoint + + +/** + * An end point for the RPC that defines what functions to trigger given a message. + * + * It is guaranteed that `onStart`, `receive` and `onStop` will be called in sequence. + * + * The lift-cycle will be: + * + * constructor onStart receive* onStop + * + * Note: `receive` can be called concurrently. If you want `receive` is thread-safe, please use + * [[ThreadSafeRpcEndpoint]] + * + * If any error is thrown from one of [[RpcEndpoint]] methods except `onError`, `onError` will be + * invoked with the cause. If `onError` throws an error, [[RpcEnv]] will ignore it. + */ +private[spark] trait RpcEndpoint { + + /** + * The [[RpcEnv]] that this [[RpcEndpoint]] is registered to. + */ + val rpcEnv: RpcEnv + + /** + * The [[RpcEndpointRef]] of this [[RpcEndpoint]]. `self` will become valid when `onStart` is + * called. And `self` will become `null` when `onStop` is called. + * + * Note: Because before `onStart`, [[RpcEndpoint]] has not yet been registered and there is not + * valid [[RpcEndpointRef]] for it. So don't call `self` before `onStart` is called. + */ + final def self: RpcEndpointRef = { + require(rpcEnv != null, "rpcEnv has not been initialized") + rpcEnv.endpointRef(this) + } + + /** + * Process messages from [[RpcEndpointRef.send]] or [[RpcCallContext.reply)]]. If receiving a + * unmatched message, [[SparkException]] will be thrown and sent to `onError`. + */ + def receive: PartialFunction[Any, Unit] = { + case _ => throw new SparkException(self + " does not implement 'receive'") + } + + /** + * Process messages from [[RpcEndpointRef.ask]]. If receiving a unmatched message, + * [[SparkException]] will be thrown and sent to `onError`. + */ + def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case _ => context.sendFailure(new SparkException(self + " won't reply anything")) + } + + /** + * Invoked when any exception is thrown during handling messages. + */ + def onError(cause: Throwable): Unit = { + // By default, throw e and let RpcEnv handle it + throw cause + } + + /** + * Invoked before [[RpcEndpoint]] starts to handle any message. + */ + def onStart(): Unit = { + // By default, do nothing. + } + + /** + * Invoked when [[RpcEndpoint]] is stopping. + */ + def onStop(): Unit = { + // By default, do nothing. + } + + /** + * Invoked when `remoteAddress` is connected to the current node. + */ + def onConnected(remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * Invoked when `remoteAddress` is lost. + */ + def onDisconnected(remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * Invoked when some network error happens in the connection between the current node and + * `remoteAddress`. + */ + def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * A convenient method to stop [[RpcEndpoint]]. + */ + final def stop(): Unit = { + val _self = self + if (_self != null) { + rpcEnv.stop(_self) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala new file mode 100644 index 0000000000000..69181edb9ad44 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +import scala.concurrent.{Await, Future} +import scala.concurrent.duration.FiniteDuration +import scala.reflect.ClassTag + +import org.apache.spark.util.RpcUtils +import org.apache.spark.{SparkException, Logging, SparkConf} + +/** + * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe. + */ +private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) + extends Serializable with Logging { + + private[this] val maxRetries = RpcUtils.numRetries(conf) + private[this] val retryWaitMs = RpcUtils.retryWaitMs(conf) + private[this] val defaultAskTimeout = RpcUtils.askTimeout(conf) + + /** + * return the address for the [[RpcEndpointRef]] + */ + def address: RpcAddress + + def name: String + + /** + * Sends a one-way asynchronous message. Fire-and-forget semantics. + */ + def send(message: Any): Unit + + /** + * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a [[Future]] to + * receive the reply within the specified timeout. + * + * This method only sends the message once and never retries. + */ + def ask[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] + + /** + * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a [[Future]] to + * receive the reply within a default timeout. + * + * This method only sends the message once and never retries. + */ + def ask[T: ClassTag](message: Any): Future[T] = ask(message, defaultAskTimeout) + + /** + * Send a message to the corresponding [[RpcEndpoint]] and get its result within a default + * timeout, or throw a SparkException if this fails even after the default number of retries. + * The default `timeout` will be used in every trial of calling `sendWithReply`. Because this + * method retries, the message handling in the receiver side should be idempotent. + * + * Note: this is a blocking action which may cost a lot of time, so don't call it in an message + * loop of [[RpcEndpoint]]. + * + * @param message the message to send + * @tparam T type of the reply message + * @return the reply message from the corresponding [[RpcEndpoint]] + */ + def askWithRetry[T: ClassTag](message: Any): T = askWithRetry(message, defaultAskTimeout) + + /** + * Send a message to the corresponding [[RpcEndpoint.receive]] and get its result within a + * specified timeout, throw a SparkException if this fails even after the specified number of + * retries. `timeout` will be used in every trial of calling `sendWithReply`. Because this method + * retries, the message handling in the receiver side should be idempotent. + * + * Note: this is a blocking action which may cost a lot of time, so don't call it in an message + * loop of [[RpcEndpoint]]. + * + * @param message the message to send + * @param timeout the timeout duration + * @tparam T type of the reply message + * @return the reply message from the corresponding [[RpcEndpoint]] + */ + def askWithRetry[T: ClassTag](message: Any, timeout: FiniteDuration): T = { + // TODO: Consider removing multiple attempts + var attempts = 0 + var lastException: Exception = null + while (attempts < maxRetries) { + attempts += 1 + try { + val future = ask[T](message, timeout) + val result = Await.result(future, timeout) + if (result == null) { + throw new SparkException("Actor returned null") + } + return result + } catch { + case ie: InterruptedException => throw ie + case e: Exception => + lastException = e + logWarning(s"Error sending message [message = $message] in $attempts attempts", e) + } + Thread.sleep(retryWaitMs) + } + + throw new SparkException( + s"Error sending message [message = $message]", lastException) + } +} diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala index a5336b7563802..12b6b28d4d7ec 100644 --- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala @@ -20,13 +20,41 @@ package org.apache.spark.rpc import java.net.URI import scala.concurrent.{Await, Future} -import scala.concurrent.duration._ import scala.language.postfixOps -import scala.reflect.ClassTag -import org.apache.spark.{Logging, SparkException, SecurityManager, SparkConf} +import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.util.{RpcUtils, Utils} + +/** + * A RpcEnv implementation must have a [[RpcEnvFactory]] implementation with an empty constructor + * so that it can be created via Reflection. + */ +private[spark] object RpcEnv { + + private def getRpcEnvFactory(conf: SparkConf): RpcEnvFactory = { + // Add more RpcEnv implementations here + val rpcEnvNames = Map("akka" -> "org.apache.spark.rpc.akka.AkkaRpcEnvFactory") + val rpcEnvName = conf.get("spark.rpc", "akka") + val rpcEnvFactoryClassName = rpcEnvNames.getOrElse(rpcEnvName.toLowerCase, rpcEnvName) + Class.forName(rpcEnvFactoryClassName, true, Utils.getContextOrSparkClassLoader). + newInstance().asInstanceOf[RpcEnvFactory] + } + + def create( + name: String, + host: String, + port: Int, + conf: SparkConf, + securityManager: SecurityManager): RpcEnv = { + // Using Reflection to create the RpcEnv to avoid to depend on Akka directly + val config = RpcEnvConfig(conf, name, host, port, securityManager) + getRpcEnvFactory(conf).create(config) + } + +} + + /** * An RPC environment. [[RpcEndpoint]]s need to register itself with a name to [[RpcEnv]] to * receives messages. Then [[RpcEnv]] will process messages sent from [[RpcEndpointRef]] or remote @@ -112,6 +140,7 @@ private[spark] abstract class RpcEnv(conf: SparkConf) { def uriOf(systemName: String, address: RpcAddress, endpointName: String): String } + private[spark] case class RpcEnvConfig( conf: SparkConf, name: String, @@ -119,261 +148,9 @@ private[spark] case class RpcEnvConfig( port: Int, securityManager: SecurityManager) -/** - * A RpcEnv implementation must have a [[RpcEnvFactory]] implementation with an empty constructor - * so that it can be created via Reflection. - */ -private[spark] object RpcEnv { - - private def getRpcEnvFactory(conf: SparkConf): RpcEnvFactory = { - // Add more RpcEnv implementations here - val rpcEnvNames = Map("akka" -> "org.apache.spark.rpc.akka.AkkaRpcEnvFactory") - val rpcEnvName = conf.get("spark.rpc", "akka") - val rpcEnvFactoryClassName = rpcEnvNames.getOrElse(rpcEnvName.toLowerCase, rpcEnvName) - Class.forName(rpcEnvFactoryClassName, true, Utils.getContextOrSparkClassLoader). - newInstance().asInstanceOf[RpcEnvFactory] - } - - def create( - name: String, - host: String, - port: Int, - conf: SparkConf, - securityManager: SecurityManager): RpcEnv = { - // Using Reflection to create the RpcEnv to avoid to depend on Akka directly - val config = RpcEnvConfig(conf, name, host, port, securityManager) - getRpcEnvFactory(conf).create(config) - } - -} - -/** - * A factory class to create the [[RpcEnv]]. It must have an empty constructor so that it can be - * created using Reflection. - */ -private[spark] trait RpcEnvFactory { - - def create(config: RpcEnvConfig): RpcEnv -} /** - * An end point for the RPC that defines what functions to trigger given a message. - * - * It is guaranteed that `onStart`, `receive` and `onStop` will be called in sequence. - * - * The lift-cycle will be: - * - * constructor onStart receive* onStop - * - * Note: `receive` can be called concurrently. If you want `receive` is thread-safe, please use - * [[ThreadSafeRpcEndpoint]] - * - * If any error is thrown from one of [[RpcEndpoint]] methods except `onError`, `onError` will be - * invoked with the cause. If `onError` throws an error, [[RpcEnv]] will ignore it. - */ -private[spark] trait RpcEndpoint { - - /** - * The [[RpcEnv]] that this [[RpcEndpoint]] is registered to. - */ - val rpcEnv: RpcEnv - - /** - * The [[RpcEndpointRef]] of this [[RpcEndpoint]]. `self` will become valid when `onStart` is - * called. And `self` will become `null` when `onStop` is called. - * - * Note: Because before `onStart`, [[RpcEndpoint]] has not yet been registered and there is not - * valid [[RpcEndpointRef]] for it. So don't call `self` before `onStart` is called. - */ - final def self: RpcEndpointRef = { - require(rpcEnv != null, "rpcEnv has not been initialized") - rpcEnv.endpointRef(this) - } - - /** - * Process messages from [[RpcEndpointRef.send]] or [[RpcCallContext.reply)]]. If receiving a - * unmatched message, [[SparkException]] will be thrown and sent to `onError`. - */ - def receive: PartialFunction[Any, Unit] = { - case _ => throw new SparkException(self + " does not implement 'receive'") - } - - /** - * Process messages from [[RpcEndpointRef.sendWithReply]]. If receiving a unmatched message, - * [[SparkException]] will be thrown and sent to `onError`. - */ - def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { - case _ => context.sendFailure(new SparkException(self + " won't reply anything")) - } - - /** - * Call onError when any exception is thrown during handling messages. - * - * @param cause - */ - def onError(cause: Throwable): Unit = { - // By default, throw e and let RpcEnv handle it - throw cause - } - - /** - * Invoked before [[RpcEndpoint]] starts to handle any message. - */ - def onStart(): Unit = { - // By default, do nothing. - } - - /** - * Invoked when [[RpcEndpoint]] is stopping. - */ - def onStop(): Unit = { - // By default, do nothing. - } - - /** - * Invoked when `remoteAddress` is connected to the current node. - */ - def onConnected(remoteAddress: RpcAddress): Unit = { - // By default, do nothing. - } - - /** - * Invoked when `remoteAddress` is lost. - */ - def onDisconnected(remoteAddress: RpcAddress): Unit = { - // By default, do nothing. - } - - /** - * Invoked when some network error happens in the connection between the current node and - * `remoteAddress`. - */ - def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { - // By default, do nothing. - } - - /** - * A convenient method to stop [[RpcEndpoint]]. - */ - final def stop(): Unit = { - val _self = self - if (_self != null) { - rpcEnv.stop(_self) - } - } -} - -/** - * A trait that requires RpcEnv thread-safely sending messages to it. - * - * Thread-safety means processing of one message happens before processing of the next message by - * the same [[ThreadSafeRpcEndpoint]]. In the other words, changes to internal fields of a - * [[ThreadSafeRpcEndpoint]] are visible when processing the next message, and fields in the - * [[ThreadSafeRpcEndpoint]] need not be volatile or equivalent. - * - * However, there is no guarantee that the same thread will be executing the same - * [[ThreadSafeRpcEndpoint]] for different messages. - */ -trait ThreadSafeRpcEndpoint extends RpcEndpoint - -/** - * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe. - */ -private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) - extends Serializable with Logging { - - private[this] val maxRetries = RpcUtils.numRetries(conf) - private[this] val retryWaitMs = RpcUtils.retryWaitMs(conf) - private[this] val defaultAskTimeout = RpcUtils.askTimeout(conf) - - /** - * return the address for the [[RpcEndpointRef]] - */ - def address: RpcAddress - - def name: String - - /** - * Sends a one-way asynchronous message. Fire-and-forget semantics. - */ - def send(message: Any): Unit - - /** - * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a `Future` to - * receive the reply within a default timeout. - * - * This method only sends the message once and never retries. - */ - def sendWithReply[T: ClassTag](message: Any): Future[T] = - sendWithReply(message, defaultAskTimeout) - - /** - * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a `Future` to - * receive the reply within the specified timeout. - * - * This method only sends the message once and never retries. - */ - def sendWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] - - /** - * Send a message to the corresponding [[RpcEndpoint]] and get its result within a default - * timeout, or throw a SparkException if this fails even after the default number of retries. - * The default `timeout` will be used in every trial of calling `sendWithReply`. Because this - * method retries, the message handling in the receiver side should be idempotent. - * - * Note: this is a blocking action which may cost a lot of time, so don't call it in an message - * loop of [[RpcEndpoint]]. - * - * @param message the message to send - * @tparam T type of the reply message - * @return the reply message from the corresponding [[RpcEndpoint]] - */ - def askWithReply[T: ClassTag](message: Any): T = askWithReply(message, defaultAskTimeout) - - /** - * Send a message to the corresponding [[RpcEndpoint.receive]] and get its result within a - * specified timeout, throw a SparkException if this fails even after the specified number of - * retries. `timeout` will be used in every trial of calling `sendWithReply`. Because this method - * retries, the message handling in the receiver side should be idempotent. - * - * Note: this is a blocking action which may cost a lot of time, so don't call it in an message - * loop of [[RpcEndpoint]]. - * - * @param message the message to send - * @param timeout the timeout duration - * @tparam T type of the reply message - * @return the reply message from the corresponding [[RpcEndpoint]] - */ - def askWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): T = { - // TODO: Consider removing multiple attempts - var attempts = 0 - var lastException: Exception = null - while (attempts < maxRetries) { - attempts += 1 - try { - val future = sendWithReply[T](message, timeout) - val result = Await.result(future, timeout) - if (result == null) { - throw new SparkException("Actor returned null") - } - return result - } catch { - case ie: InterruptedException => throw ie - case e: Exception => - lastException = e - logWarning(s"Error sending message [message = $message] in $attempts attempts", e) - } - Thread.sleep(retryWaitMs) - } - - throw new SparkException( - s"Error sending message [message = $message]", lastException) - } - -} - -/** - * Represent a host with a port + * Represents a host and port. */ private[spark] case class RpcAddress(host: String, port: Int) { // TODO do we need to add the type of RpcEnv in the address? @@ -383,6 +160,7 @@ private[spark] case class RpcAddress(host: String, port: Int) { override val toString: String = hostPort } + private[spark] object RpcAddress { /** @@ -404,26 +182,3 @@ private[spark] object RpcAddress { RpcAddress(host, port) } } - -/** - * A callback that [[RpcEndpoint]] can use it to send back a message or failure. It's thread-safe - * and can be called in any thread. - */ -private[spark] trait RpcCallContext { - - /** - * Reply a message to the sender. If the sender is [[RpcEndpoint]], its [[RpcEndpoint.receive]] - * will be called. - */ - def reply(response: Any): Unit - - /** - * Report a failure to the sender. - */ - def sendFailure(e: Throwable): Unit - - /** - * The sender of this message. - */ - def sender: RpcEndpointRef -} diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala index 652e52f2b2e73..ba0d468f111ef 100644 --- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala @@ -293,7 +293,7 @@ private[akka] class AkkaRpcEndpointRef( actorRef ! AkkaMessage(message, false) } - override def sendWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] = { + override def ask[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] = { import scala.concurrent.ExecutionContext.Implicits.global actorRef.ask(AkkaMessage(message, true))(timeout).flatMap { case msg @ AkkaMessage(message, reply) => diff --git a/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala b/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala index 6d39a5e3fa64c..9f218c64cac2d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala @@ -26,6 +26,7 @@ package org.apache.spark.scheduler private[spark] class ApplicationEventListener extends SparkListener { var appName: Option[String] = None var appId: Option[String] = None + var appAttemptId: Option[String] = None var sparkUser: Option[String] = None var startTime: Option[Long] = None var endTime: Option[Long] = None @@ -35,6 +36,7 @@ private[spark] class ApplicationEventListener extends SparkListener { override def onApplicationStart(applicationStart: SparkListenerApplicationStart) { appName = Some(applicationStart.appName) appId = applicationStart.appId + appAttemptId = applicationStart.appAttemptId startTime = Some(applicationStart.time) sparkUser = Some(applicationStart.sparkUser) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 8c4bff4e83afc..5d812918a13d1 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -28,12 +28,15 @@ import scala.language.existentials import scala.language.postfixOps import scala.util.control.NonFatal +import org.apache.commons.lang3.SerializationUtils + import org.apache.spark._ import org.apache.spark.broadcast.Broadcast import org.apache.spark.executor.TaskMetrics import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult} import org.apache.spark.rdd.RDD import org.apache.spark.storage._ +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util._ import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat @@ -166,7 +169,7 @@ class DAGScheduler( taskMetrics: Array[(Long, Int, Int, TaskMetrics)], // (taskId, stageId, stateAttempt, metrics) blockManagerId: BlockManagerId): Boolean = { listenerBus.post(SparkListenerExecutorMetricsUpdate(execId, taskMetrics)) - blockManagerMaster.driverEndpoint.askWithReply[Boolean]( + blockManagerMaster.driverEndpoint.askWithRetry[Boolean]( BlockManagerHeartbeat(blockManagerId), 600 seconds) } @@ -509,7 +512,8 @@ class DAGScheduler( val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _] val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler) eventProcessLoop.post(JobSubmitted( - jobId, rdd, func2, partitions.toArray, allowLocal, callSite, waiter, properties)) + jobId, rdd, func2, partitions.toArray, allowLocal, callSite, waiter, + SerializationUtils.clone(properties))) waiter } @@ -546,7 +550,8 @@ class DAGScheduler( val partitions = (0 until rdd.partitions.size).toArray val jobId = nextJobId.getAndIncrement() eventProcessLoop.post(JobSubmitted( - jobId, rdd, func2, partitions, allowLocal = false, callSite, listener, properties)) + jobId, rdd, func2, partitions, allowLocal = false, callSite, listener, + SerializationUtils.clone(properties))) listener.awaitResult() // Will throw an exception if the job fails } @@ -643,8 +648,15 @@ class DAGScheduler( try { val rdd = job.finalStage.rdd val split = rdd.partitions(job.partitions(0)) - val taskContext = new TaskContextImpl(job.finalStage.id, job.partitions(0), taskAttemptId = 0, - attemptNumber = 0, runningLocally = true) + val taskMemoryManager = new TaskMemoryManager(env.executorMemoryManager) + val taskContext = + new TaskContextImpl( + job.finalStage.id, + job.partitions(0), + taskAttemptId = 0, + attemptNumber = 0, + taskMemoryManager = taskMemoryManager, + runningLocally = true) TaskContext.setTaskContext(taskContext) try { val result = job.func(taskContext, rdd.iterator(split, taskContext)) @@ -652,6 +664,16 @@ class DAGScheduler( } finally { taskContext.markTaskCompleted() TaskContext.unset() + // Note: this memory freeing logic is duplicated in Executor.run(); when changing this, + // make sure to update both copies. + val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory() + if (freedMemory > 0) { + if (sc.getConf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false)) { + throw new SparkException(s"Managed memory leak detected; size = $freedMemory bytes") + } else { + logError(s"Managed memory leak detected; size = $freedMemory bytes") + } + } } } catch { case e: Exception => @@ -686,8 +708,11 @@ class DAGScheduler( private[scheduler] def handleJobGroupCancelled(groupId: String) { // Cancel all jobs belonging to this job group. // First finds all active jobs with this group id, and then kill stages for them. - val activeInGroup = activeJobs.filter(activeJob => - Option(activeJob.properties).exists(_.get(SparkContext.SPARK_JOB_GROUP_ID) == groupId)) + val activeInGroup = activeJobs.filter { activeJob => + Option(activeJob.properties).exists { + _.getProperty(SparkContext.SPARK_JOB_GROUP_ID) == groupId + } + } val jobIds = activeInGroup.map(_.jobId) jobIds.foreach(handleJobCancellation(_, "part of cancelled job group %s".format(groupId))) submitWaitingStages() @@ -1374,6 +1399,7 @@ class DAGScheduler( def stop() { logInfo("Stopping DAGScheduler") + messageScheduler.shutdownNow() eventProcessLoop.stop() taskScheduler.stop() } diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala index 08e7727db2fde..529a5b2bf1a0d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala @@ -47,6 +47,7 @@ import org.apache.spark.util.{JsonProtocol, Utils} */ private[spark] class EventLoggingListener( appId: String, + appAttemptId : Option[String], logBaseDir: URI, sparkConf: SparkConf, hadoopConf: Configuration) @@ -54,8 +55,9 @@ private[spark] class EventLoggingListener( import EventLoggingListener._ - def this(appId: String, logBaseDir: URI, sparkConf: SparkConf) = - this(appId, logBaseDir, sparkConf, SparkHadoopUtil.get.newConfiguration(sparkConf)) + def this(appId: String, appAttemptId : Option[String], logBaseDir: URI, sparkConf: SparkConf) = + this(appId, appAttemptId, logBaseDir, sparkConf, + SparkHadoopUtil.get.newConfiguration(sparkConf)) private val shouldCompress = sparkConf.getBoolean("spark.eventLog.compress", false) private val shouldOverwrite = sparkConf.getBoolean("spark.eventLog.overwrite", false) @@ -89,7 +91,7 @@ private[spark] class EventLoggingListener( private[scheduler] val loggedEvents = new ArrayBuffer[JValue] // Visible for tests only. - private[scheduler] val logPath = getLogPath(logBaseDir, appId, compressionCodecName) + private[scheduler] val logPath = getLogPath(logBaseDir, appId, appAttemptId, compressionCodecName) /** * Creates the log file in the configured log directory. @@ -252,8 +254,12 @@ private[spark] object EventLoggingListener extends Logging { * we won't know which codec to use to decompress the metadata needed to open the file in * the first place. * + * The log file name will identify the compression codec used for the contents, if any. + * For example, app_123 for an uncompressed log, app_123.lzf for an LZF-compressed log. + * * @param logBaseDir Directory where the log file will be written. * @param appId A unique app ID. + * @param appAttemptId A unique attempt id of appId. May be the empty string. * @param compressionCodecName Name to identify the codec used to compress the contents * of the log, or None if compression is not enabled. * @return A path which consists of file-system-safe characters. @@ -261,11 +267,19 @@ private[spark] object EventLoggingListener extends Logging { def getLogPath( logBaseDir: URI, appId: String, + appAttemptId: Option[String], compressionCodecName: Option[String] = None): String = { - val sanitizedAppId = appId.replaceAll("[ :/]", "-").replaceAll("[.${}'\"]", "_").toLowerCase - // e.g. app_123, app_123.lzf - val logName = sanitizedAppId + compressionCodecName.map { "." + _ }.getOrElse("") - logBaseDir.toString.stripSuffix("/") + "/" + logName + val base = logBaseDir.toString.stripSuffix("/") + "/" + sanitize(appId) + val codec = compressionCodecName.map("." + _).getOrElse("") + if (appAttemptId.isDefined) { + base + "_" + sanitize(appAttemptId.get) + codec + } else { + base + codec + } + } + + private def sanitize(str: String): String = { + str.replaceAll("[ :/]", "-").replaceAll("[.${}'\"]", "_").toLowerCase } /** diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala index 7c184b1dcb308..0b1d47cff3746 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -85,7 +85,7 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { val msg = AskPermissionToCommitOutput(stage, partition, attempt) coordinatorRef match { case Some(endpointRef) => - endpointRef.askWithReply[Boolean](msg) + endpointRef.askWithRetry[Boolean](msg) case None => logError( "canCommit called after coordinator was stopped (is SparkEnv shutdown in progress)?") diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala index 992c477493d8e..646820520ea1b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala @@ -41,4 +41,12 @@ private[spark] trait SchedulerBackend { */ def applicationId(): String = appId + /** + * Get the attempt ID for this run, if the cluster manager supports multiple + * attempts. Applications run in client mode will not have attempt IDs. + * + * @return The application attempt id, if available. + */ + def applicationAttemptId(): Option[String] = None + } diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala index b711ff209af94..169d4fd3a94f0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala @@ -110,8 +110,8 @@ case class SparkListenerExecutorMetricsUpdate( extends SparkListenerEvent @DeveloperApi -case class SparkListenerApplicationStart(appName: String, appId: Option[String], time: Long, - sparkUser: String) extends SparkListenerEvent +case class SparkListenerApplicationStart(appName: String, appId: Option[String], + time: Long, sparkUser: String, appAttemptId: Option[String]) extends SparkListenerEvent @DeveloperApi case class SparkListenerApplicationEnd(time: Long) extends SparkListenerEvent diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala index c6dc3369ba5cc..e439d2a7e1229 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala @@ -33,6 +33,7 @@ class StageInfo( val name: String, val numTasks: Int, val rddInfos: Seq[RDDInfo], + val parentIds: Seq[Int], val details: String) { /** When this stage was submitted from the DAGScheduler to a TaskScheduler. */ var submissionTime: Option[Long] = None @@ -47,6 +48,18 @@ class StageInfo( failureReason = Some(reason) completionTime = Some(System.currentTimeMillis) } + + private[spark] def getStatusString: String = { + if (completionTime.isDefined) { + if (failureReason.isDefined) { + "failed" + } else { + "succeeded" + } + } else { + "running" + } + } } private[spark] object StageInfo { @@ -66,6 +79,7 @@ private[spark] object StageInfo { stage.name, numTasks.getOrElse(stage.numTasks), rddInfos, + stage.parents.map(_.id), stage.details) } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala index b09b19e2ac9e7..586d1e06204c1 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala @@ -25,6 +25,7 @@ import scala.collection.mutable.HashMap import org.apache.spark.{TaskContextImpl, TaskContext} import org.apache.spark.executor.TaskMetrics import org.apache.spark.serializer.SerializerInstance +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util.ByteBufferInputStream import org.apache.spark.util.Utils @@ -52,8 +53,13 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex * @return the result of the task */ final def run(taskAttemptId: Long, attemptNumber: Int): T = { - context = new TaskContextImpl(stageId = stageId, partitionId = partitionId, - taskAttemptId = taskAttemptId, attemptNumber = attemptNumber, runningLocally = false) + context = new TaskContextImpl( + stageId = stageId, + partitionId = partitionId, + taskAttemptId = taskAttemptId, + attemptNumber = attemptNumber, + taskMemoryManager = taskMemoryManager, + runningLocally = false) TaskContext.setTaskContext(context) context.taskMetrics.setHostname(Utils.localHostName()) taskThread = Thread.currentThread() @@ -68,6 +74,12 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex } } + private var taskMemoryManager: TaskMemoryManager = _ + + def setTaskMemoryManager(taskMemoryManager: TaskMemoryManager): Unit = { + this.taskMemoryManager = taskMemoryManager + } + def runTask(context: TaskContext): T def preferredLocations: Seq[TaskLocation] = Nil diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala index ed3418676e077..f25f3ed0d9037 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala @@ -73,9 +73,17 @@ private[spark] trait TaskScheduler { * @return An application ID */ def applicationId(): String = appId - + /** * Process a lost executor */ def executorLost(executorId: String, reason: ExecutorLossReason): Unit + + /** + * Get an application's attempt ID associated with the job. + * + * @return An application's Attempt ID + */ + def applicationAttemptId(): Option[String] + } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index 13a52d836f32f..b4b8a630694bb 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -513,6 +513,8 @@ private[spark] class TaskSchedulerImpl( override def applicationId(): String = backend.applicationId() + override def applicationAttemptId(): Option[String] = backend.applicationAttemptId() + } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 9656fb76858ea..f107148f3b8c6 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -68,6 +68,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) extends ThreadSafeRpcEndpoint with Logging { + override protected def log = CoarseGrainedSchedulerBackend.this.log private val addressToExecutorId = new HashMap[RpcAddress, String] @@ -112,6 +113,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp // Ignoring the task kill since the executor is not registered. logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.") } + } override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { @@ -122,7 +124,6 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp } else { logInfo("Registered executor: " + executorRef + " with ID " + executorId) context.reply(RegisteredExecutor) - addressToExecutorId(executorRef.address) = executorId totalCoreCount.addAndGet(cores) totalRegisteredExecutors.addAndGet(1) @@ -243,6 +244,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp properties += ((key, value)) } } + // TODO (prashant) send conf instead of properties driverEndpoint = rpcEnv.setupEndpoint( CoarseGrainedSchedulerBackend.ENDPOINT_NAME, new DriverEndpoint(rpcEnv, properties)) @@ -252,7 +254,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp try { if (driverEndpoint != null) { logInfo("Shutting down all executors") - driverEndpoint.askWithReply[Boolean](StopExecutors) + driverEndpoint.askWithRetry[Boolean](StopExecutors) } } catch { case e: Exception => @@ -264,7 +266,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp stopExecutors() try { if (driverEndpoint != null) { - driverEndpoint.askWithReply[Boolean](StopDriver) + driverEndpoint.askWithRetry[Boolean](StopDriver) } } catch { case e: Exception => @@ -287,7 +289,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp // Called by subclasses when notified of a lost worker def removeExecutor(executorId: String, reason: String) { try { - driverEndpoint.askWithReply[Boolean](RemoveExecutor(executorId, reason)) + driverEndpoint.askWithRetry[Boolean](RemoveExecutor(executorId, reason)) } catch { case e: Exception => throw new SparkException("Error notifying standalone scheduler's driver endpoint", e) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala index d987c7d563579..2a3a5d925d06f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala @@ -53,14 +53,14 @@ private[spark] abstract class YarnSchedulerBackend( * This includes executors already pending or running. */ override def doRequestTotalExecutors(requestedTotal: Int): Boolean = { - yarnSchedulerEndpoint.askWithReply[Boolean](RequestExecutors(requestedTotal)) + yarnSchedulerEndpoint.askWithRetry[Boolean](RequestExecutors(requestedTotal)) } /** * Request that the ApplicationMaster kill the specified executors. */ override def doKillExecutors(executorIds: Seq[String]): Boolean = { - yarnSchedulerEndpoint.askWithReply[Boolean](KillExecutors(executorIds)) + yarnSchedulerEndpoint.askWithRetry[Boolean](KillExecutors(executorIds)) } override def sufficientResourcesRegistered(): Boolean = { @@ -115,7 +115,7 @@ private[spark] abstract class YarnSchedulerBackend( amEndpoint match { case Some(am) => Future { - context.reply(am.askWithReply[Boolean](r)) + context.reply(am.askWithRetry[Boolean](r)) } onFailure { case NonFatal(e) => logError(s"Sending $r to AM was unsuccessful", e) @@ -130,7 +130,7 @@ private[spark] abstract class YarnSchedulerBackend( amEndpoint match { case Some(am) => Future { - context.reply(am.askWithReply[Boolean](k)) + context.reply(am.askWithRetry[Boolean](k)) } onFailure { case NonFatal(e) => logError(s"Sending $k to AM was unsuccessful", e) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index 3412301e64fd7..dc59545b43314 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -196,9 +196,14 @@ private[spark] class CoarseMesosSchedulerBackend( .addResources(createResource("cpus", cpusToUse)) .addResources(createResource("mem", MemoryUtils.calculateTotalMemory(sc))) - .build() + + sc.conf.getOption("spark.mesos.executor.docker.image").foreach { image => + MesosSchedulerBackendUtil + .setupContainerBuilderDockerInfo(image, sc.conf, task.getContainerBuilder()) + } + d.launchTasks( - Collections.singleton(offer.getId), Collections.singletonList(task), filters) + Collections.singleton(offer.getId), Collections.singletonList(task.build()), filters) } else { // Filter it out d.launchTasks( diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala index 0396e62be5309..06f0e2881c344 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala @@ -50,12 +50,13 @@ private[spark] class MesosClusterSubmissionState( val taskId: TaskID, val slaveId: SlaveID, var mesosTaskStatus: Option[TaskStatus], - var startDate: Date) + var startDate: Date, + var finishDate: Option[Date]) extends Serializable { def copy(): MesosClusterSubmissionState = { new MesosClusterSubmissionState( - driverDescription, taskId, slaveId, mesosTaskStatus, startDate) + driverDescription, taskId, slaveId, mesosTaskStatus, startDate, finishDate) } } @@ -95,6 +96,14 @@ private[spark] class MesosClusterSchedulerState( val finishedDrivers: Iterable[MesosClusterSubmissionState], val pendingRetryDrivers: Iterable[MesosDriverDescription]) +/** + * The full state of a Mesos driver, that is being used to display driver information on the UI. + */ +private[spark] class MesosDriverState( + val state: String, + val description: MesosDriverDescription, + val submissionState: Option[MesosClusterSubmissionState] = None) + /** * A Mesos scheduler that is responsible for launching submitted Spark drivers in cluster mode * as Mesos tasks in a Mesos cluster. @@ -233,6 +242,22 @@ private[spark] class MesosClusterScheduler( s } + /** + * Gets the driver state to be displayed on the Web UI. + */ + def getDriverState(submissionId: String): Option[MesosDriverState] = { + stateLock.synchronized { + queuedDrivers.find(_.submissionId.equals(submissionId)) + .map(d => new MesosDriverState("QUEUED", d)) + .orElse(launchedDrivers.get(submissionId) + .map(d => new MesosDriverState("RUNNING", d.driverDescription, Some(d)))) + .orElse(finishedDrivers.find(_.driverDescription.submissionId.equals(submissionId)) + .map(d => new MesosDriverState("FINISHED", d.driverDescription, Some(d)))) + .orElse(pendingRetryDrivers.find(_.submissionId.equals(submissionId)) + .map(d => new MesosDriverState("RETRYING", d))) + } + } + private def isQueueFull(): Boolean = launchedDrivers.size >= queuedCapacity /** @@ -439,7 +464,7 @@ private[spark] class MesosClusterScheduler( logTrace(s"Using offer ${offer.offer.getId.getValue} to launch driver " + submission.submissionId) val newState = new MesosClusterSubmissionState(submission, taskId, offer.offer.getSlaveId, - None, new Date()) + None, new Date(), None) launchedDrivers(submission.submissionId) = newState launchedDriversState.persist(submission.submissionId, newState) afterLaunchCallback(submission.submissionId) @@ -534,6 +559,7 @@ private[spark] class MesosClusterScheduler( // Check if the driver is supervise enabled and can be relaunched. if (state.driverDescription.supervise && shouldRelaunch(status.getState)) { removeFromLaunchedDrivers(taskId) + state.finishDate = Some(new Date()) val retryState: Option[MesosClusterRetryState] = state.driverDescription.retryState val (retries, waitTimeSec) = retryState .map { rs => (rs.retries + 1, Math.min(maxRetryWaitTime, rs.waitTime * 2)) } @@ -546,6 +572,7 @@ private[spark] class MesosClusterScheduler( pendingRetryDriversState.persist(taskId, newDriverDescription) } else if (TaskState.isFinished(TaskState.fromMesos(status.getState))) { removeFromLaunchedDrivers(taskId) + state.finishDate = Some(new Date()) if (finishedDrivers.size >= retainedDrivers) { val toRemove = math.max(retainedDrivers / 10, 1) finishedDrivers.trimStart(toRemove) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index 8346a2407489f..db0a080b3b0c0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -23,7 +23,7 @@ import java.util.{ArrayList => JArrayList, Collections, List => JList} import scala.collection.JavaConversions._ import scala.collection.mutable.{HashMap, HashSet} -import org.apache.mesos.Protos.{ExecutorInfo => MesosExecutorInfo, TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _} +import org.apache.mesos.Protos.{ExecutorInfo => MesosExecutorInfo, TaskInfo => MesosTaskInfo, _} import org.apache.mesos.protobuf.ByteString import org.apache.mesos.{Scheduler => MScheduler, _} import org.apache.spark.executor.MesosExecutorBackend @@ -56,7 +56,7 @@ private[spark] class MesosSchedulerBackend( // The listener bus to publish executor added/removed events. val listenerBus = sc.listenerBus - + private[mesos] val mesosExecutorCores = sc.conf.getDouble("spark.mesos.mesosExecutor.cores", 1) @volatile var appId: String = _ @@ -124,13 +124,19 @@ private[spark] class MesosSchedulerBackend( Value.Scalar.newBuilder() .setValue(MemoryUtils.calculateTotalMemory(sc)).build()) .build() - MesosExecutorInfo.newBuilder() + val executorInfo = MesosExecutorInfo.newBuilder() .setExecutorId(ExecutorID.newBuilder().setValue(execId).build()) .setCommand(command) .setData(ByteString.copyFrom(createExecArg())) .addResources(cpus) .addResources(memory) - .build() + + sc.conf.getOption("spark.mesos.executor.docker.image").foreach { image => + MesosSchedulerBackendUtil + .setupContainerBuilderDockerInfo(image, sc.conf, executorInfo.getContainerBuilder()) + } + + executorInfo.build() } /** diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala new file mode 100644 index 0000000000000..928c5cfed417a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala @@ -0,0 +1,142 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster.mesos + +import org.apache.mesos.Protos.{ContainerInfo, Volume} +import org.apache.mesos.Protos.ContainerInfo.DockerInfo + +import org.apache.spark.{Logging, SparkConf} + +/** + * A collection of utility functions which can be used by both the + * MesosSchedulerBackend and the CoarseMesosSchedulerBackend. + */ +private[mesos] object MesosSchedulerBackendUtil extends Logging { + /** + * Parse a comma-delimited list of volume specs, each of which + * takes the form [host-dir:]container-dir[:rw|:ro]. + */ + def parseVolumesSpec(volumes: String): List[Volume] = { + volumes.split(",").map(_.split(":")).flatMap { spec => + val vol: Volume.Builder = Volume + .newBuilder() + .setMode(Volume.Mode.RW) + spec match { + case Array(container_path) => + Some(vol.setContainerPath(container_path)) + case Array(container_path, "rw") => + Some(vol.setContainerPath(container_path)) + case Array(container_path, "ro") => + Some(vol.setContainerPath(container_path) + .setMode(Volume.Mode.RO)) + case Array(host_path, container_path) => + Some(vol.setContainerPath(container_path) + .setHostPath(host_path)) + case Array(host_path, container_path, "rw") => + Some(vol.setContainerPath(container_path) + .setHostPath(host_path)) + case Array(host_path, container_path, "ro") => + Some(vol.setContainerPath(container_path) + .setHostPath(host_path) + .setMode(Volume.Mode.RO)) + case spec => { + logWarning(s"Unable to parse volume specs: $volumes. " + + "Expected form: \"[host-dir:]container-dir[:rw|:ro](, ...)\"") + None + } + } + } + .map { _.build() } + .toList + } + + /** + * Parse a comma-delimited list of port mapping specs, each of which + * takes the form host_port:container_port[:udp|:tcp] + * + * Note: + * the docker form is [ip:]host_port:container_port, but the DockerInfo + * message has no field for 'ip', and instead has a 'protocol' field. + * Docker itself only appears to support TCP, so this alternative form + * anticipates the expansion of the docker form to allow for a protocol + * and leaves open the chance for mesos to begin to accept an 'ip' field + */ + def parsePortMappingsSpec(portmaps: String): List[DockerInfo.PortMapping] = { + portmaps.split(",").map(_.split(":")).flatMap { spec: Array[String] => + val portmap: DockerInfo.PortMapping.Builder = DockerInfo.PortMapping + .newBuilder() + .setProtocol("tcp") + spec match { + case Array(host_port, container_port) => + Some(portmap.setHostPort(host_port.toInt) + .setContainerPort(container_port.toInt)) + case Array(host_port, container_port, protocol) => + Some(portmap.setHostPort(host_port.toInt) + .setContainerPort(container_port.toInt) + .setProtocol(protocol)) + case spec => { + logWarning(s"Unable to parse port mapping specs: $portmaps. " + + "Expected form: \"host_port:container_port[:udp|:tcp](, ...)\"") + None + } + } + } + .map { _.build() } + .toList + } + + /** + * Construct a DockerInfo structure and insert it into a ContainerInfo + */ + def addDockerInfo( + container: ContainerInfo.Builder, + image: String, + volumes: Option[List[Volume]] = None, + network: Option[ContainerInfo.DockerInfo.Network] = None, + portmaps: Option[List[ContainerInfo.DockerInfo.PortMapping]] = None):Unit = { + + val docker = ContainerInfo.DockerInfo.newBuilder().setImage(image) + + network.foreach(docker.setNetwork) + portmaps.foreach(_.foreach(docker.addPortMappings)) + container.setType(ContainerInfo.Type.DOCKER) + container.setDocker(docker.build()) + volumes.foreach(_.foreach(container.addVolumes)) + } + + /** + * Setup a docker containerizer + */ + def setupContainerBuilderDockerInfo( + imageName: String, + conf: SparkConf, + builder: ContainerInfo.Builder): Unit = { + val volumes = conf + .getOption("spark.mesos.executor.docker.volumes") + .map(parseVolumesSpec) + val portmaps = conf + .getOption("spark.mesos.executor.docker.portmaps") + .map(parsePortMappingsSpec) + addDockerInfo( + builder, + imageName, + volumes = volumes, + portmaps = portmaps) + logDebug("setupContainerDockerInfo: using docker image: " + imageName) + } +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala index ac5b524517818..e64d06c4d3cfc 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -123,7 +123,7 @@ private[spark] class LocalBackend( } override def stop() { - localEndpoint.sendWithReply(StopExecutor) + localEndpoint.ask(StopExecutor) } override def reviveOffers() { diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index 754832b8a4ca7..f9f78852f032b 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -125,6 +125,13 @@ class KryoSerializer(conf: SparkConf) override def newInstance(): SerializerInstance = { new KryoSerializerInstance(this) } + + private[spark] override lazy val supportsRelocationOfSerializedObjects: Boolean = { + // If auto-reset is disabled, then Kryo may store references to duplicate occurrences of objects + // in the stream rather than writing those objects' serialized bytes, breaking relocation. See + // https://groups.google.com/d/msg/kryo-users/6ZUSyfjjtdo/FhGG1KHDXPgJ for more details. + newInstance().asInstanceOf[KryoSerializerInstance].getAutoReset() + } } private[spark] @@ -200,6 +207,16 @@ private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends Serializ override def deserializeStream(s: InputStream): DeserializationStream = { new KryoDeserializationStream(kryo, s) } + + /** + * Returns true if auto-reset is on. The only reason this would be false is if the user-supplied + * registrator explicitly turns auto-reset off. + */ + def getAutoReset(): Boolean = { + val field = classOf[Kryo].getDeclaredField("autoReset") + field.setAccessible(true) + field.get(kryo).asInstanceOf[Boolean] + } } /** diff --git a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala index ca6e971d227fb..6078c9d433ebf 100644 --- a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala @@ -23,7 +23,7 @@ import java.nio.ByteBuffer import scala.reflect.ClassTag import org.apache.spark.{SparkConf, SparkEnv} -import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.annotation.{DeveloperApi, Private} import org.apache.spark.util.{Utils, ByteBufferInputStream, NextIterator} /** @@ -63,6 +63,39 @@ abstract class Serializer { /** Creates a new [[SerializerInstance]]. */ def newInstance(): SerializerInstance + + /** + * :: Private :: + * Returns true if this serializer supports relocation of its serialized objects and false + * otherwise. This should return true if and only if reordering the bytes of serialized objects + * in serialization stream output is equivalent to having re-ordered those elements prior to + * serializing them. More specifically, the following should hold if a serializer supports + * relocation: + * + * {{{ + * serOut.open() + * position = 0 + * serOut.write(obj1) + * serOut.flush() + * position = # of bytes writen to stream so far + * obj1Bytes = output[0:position-1] + * serOut.write(obj2) + * serOut.flush() + * position2 = # of bytes written to stream so far + * obj2Bytes = output[position:position2-1] + * serIn.open([obj2bytes] concatenate [obj1bytes]) should return (obj2, obj1) + * }}} + * + * In general, this property should hold for serializers that are stateless and that do not + * write special metadata at the beginning or end of the serialization stream. + * + * This API is private to Spark; this method should not be overridden in third-party subclasses + * or called in user code and is subject to removal in future Spark releases. + * + * See SPARK-7311 for more details. + */ + @Private + private[spark] def supportsRelocationOfSerializedObjects: Boolean = false } @@ -101,7 +134,12 @@ abstract class SerializerInstance { */ @DeveloperApi abstract class SerializationStream { + /** The most general-purpose method to write an object. */ def writeObject[T: ClassTag](t: T): SerializationStream + /** Writes the object representing the key of a key-value pair. */ + def writeKey[T: ClassTag](key: T): SerializationStream = writeObject(key) + /** Writes the object representing the value of a key-value pair. */ + def writeValue[T: ClassTag](value: T): SerializationStream = writeObject(value) def flush(): Unit def close(): Unit @@ -120,7 +158,12 @@ abstract class SerializationStream { */ @DeveloperApi abstract class DeserializationStream { + /** The most general-purpose method to read an object. */ def readObject[T: ClassTag](): T + /** Reads the object representing the key of a key-value pair. */ + def readKey[T: ClassTag](): T = readObject[T]() + /** Reads the object representing the value of a key-value pair. */ + def readValue[T: ClassTag](): T = readObject[T]() def close(): Unit /** @@ -141,4 +184,25 @@ abstract class DeserializationStream { DeserializationStream.this.close() } } + + /** + * Read the elements of this stream through an iterator over key-value pairs. This can only be + * called once, as reading each element will consume data from the input source. + */ + def asKeyValueIterator: Iterator[(Any, Any)] = new NextIterator[(Any, Any)] { + override protected def getNext() = { + try { + (readKey[Any](), readValue[Any]()) + } catch { + case eof: EOFException => { + finished = true + null + } + } + } + + override protected def close() { + DeserializationStream.this.close() + } + } } diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala index 13c7115f88afa..e04c97fe61894 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala @@ -17,9 +17,12 @@ package org.apache.spark.shuffle +import org.apache.spark.annotation.DeveloperApi + /** * An opaque handle to a shuffle, used by a ShuffleManager to pass information about it to tasks. * * @param shuffleId ID of the shuffle */ -private[spark] abstract class ShuffleHandle(val shuffleId: Int) extends Serializable {} +@DeveloperApi +abstract class ShuffleHandle(val shuffleId: Int) extends Serializable {} diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala index 755f17d6aa15a..cd27c9e07a3cd 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala @@ -63,7 +63,7 @@ private[spark] class HashShuffleWriter[K, V]( for (elem <- iter) { val bucketId = dep.partitioner.getPartition(elem._1) - shuffle.writers(bucketId).write(elem) + shuffle.writers(bucketId).write(elem._1, elem._2) } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 55718e584c195..a46fecd2274ef 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -78,19 +78,11 @@ private[spark] class BlockManager( private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo] // Actual storage of where blocks are kept - private var tachyonInitialized = false + private var externalBlockStoreInitialized = false private[spark] val memoryStore = new MemoryStore(this, maxMemory) private[spark] val diskStore = new DiskStore(this, diskBlockManager) - private[spark] lazy val tachyonStore: TachyonStore = { - val storeDir = conf.get("spark.tachyonStore.baseDir", "/tmp_spark_tachyon") - val appFolderName = conf.get("spark.tachyonStore.folderName") - val tachyonStorePath = s"$storeDir/$appFolderName/${this.executorId}" - val tachyonMaster = conf.get("spark.tachyonStore.url", "tachyon://localhost:19998") - val tachyonBlockManager = - new TachyonBlockManager(this, tachyonStorePath, tachyonMaster) - tachyonInitialized = true - new TachyonStore(this, tachyonBlockManager) - } + private[spark] lazy val externalBlockStore: ExternalBlockStore = + new ExternalBlockStore(this, executorId) private[spark] val externalShuffleServiceEnabled = conf.getBoolean("spark.shuffle.service.enabled", false) @@ -119,7 +111,8 @@ private[spark] class BlockManager( // standard BlockTransferService to directly connect to other Executors. private[spark] val shuffleClient = if (externalShuffleServiceEnabled) { val transConf = SparkTransportConf.fromSparkConf(conf, numUsableCores) - new ExternalShuffleClient(transConf, securityManager, securityManager.isAuthenticationEnabled()) + new ExternalShuffleClient(transConf, securityManager, securityManager.isAuthenticationEnabled(), + securityManager.isSaslEncryptionEnabled()) } else { blockTransferService } @@ -320,13 +313,13 @@ private[spark] class BlockManager( /** * Get the BlockStatus for the block identified by the given ID, if it exists. - * NOTE: This is mainly for testing, and it doesn't fetch information from Tachyon. + * NOTE: This is mainly for testing, and it doesn't fetch information from external block store. */ def getStatus(blockId: BlockId): Option[BlockStatus] = { blockInfo.get(blockId).map { info => val memSize = if (memoryStore.contains(blockId)) memoryStore.getSize(blockId) else 0L val diskSize = if (diskStore.contains(blockId)) diskStore.getSize(blockId) else 0L - // Assume that block is not in Tachyon + // Assume that block is not in external block store BlockStatus(info.level, memSize, diskSize, 0L) } } @@ -376,10 +369,10 @@ private[spark] class BlockManager( if (info.tellMaster) { val storageLevel = status.storageLevel val inMemSize = Math.max(status.memSize, droppedMemorySize) - val inTachyonSize = status.tachyonSize + val inExternalBlockStoreSize = status.externalBlockStoreSize val onDiskSize = status.diskSize master.updateBlockInfo( - blockManagerId, blockId, storageLevel, inMemSize, onDiskSize, inTachyonSize) + blockManagerId, blockId, storageLevel, inMemSize, onDiskSize, inExternalBlockStoreSize) } else { true } @@ -397,15 +390,17 @@ private[spark] class BlockManager( BlockStatus(StorageLevel.NONE, 0L, 0L, 0L) case level => val inMem = level.useMemory && memoryStore.contains(blockId) - val inTachyon = level.useOffHeap && tachyonStore.contains(blockId) + val inExternalBlockStore = level.useOffHeap && externalBlockStore.contains(blockId) val onDisk = level.useDisk && diskStore.contains(blockId) val deserialized = if (inMem) level.deserialized else false - val replication = if (inMem || inTachyon || onDisk) level.replication else 1 - val storageLevel = StorageLevel(onDisk, inMem, inTachyon, deserialized, replication) + val replication = if (inMem || inExternalBlockStore || onDisk) level.replication else 1 + val storageLevel = + StorageLevel(onDisk, inMem, inExternalBlockStore, deserialized, replication) val memSize = if (inMem) memoryStore.getSize(blockId) else 0L - val tachyonSize = if (inTachyon) tachyonStore.getSize(blockId) else 0L + val externalBlockStoreSize = + if (inExternalBlockStore) externalBlockStore.getSize(blockId) else 0L val diskSize = if (onDisk) diskStore.getSize(blockId) else 0L - BlockStatus(storageLevel, memSize, diskSize, tachyonSize) + BlockStatus(storageLevel, memSize, diskSize, externalBlockStoreSize) } } } @@ -485,11 +480,11 @@ private[spark] class BlockManager( } } - // Look for the block in Tachyon + // Look for the block in external block store if (level.useOffHeap) { - logDebug(s"Getting block $blockId from tachyon") - if (tachyonStore.contains(blockId)) { - tachyonStore.getBytes(blockId) match { + logDebug(s"Getting block $blockId from ExternalBlockStore") + if (externalBlockStore.contains(blockId)) { + externalBlockStore.getBytes(blockId) match { case Some(bytes) => if (!asBlockResult) { return Some(bytes) @@ -498,7 +493,7 @@ private[spark] class BlockManager( dataDeserialize(blockId, bytes), DataReadMethod.Memory, info.size)) } case None => - logDebug(s"Block $blockId not found in tachyon") + logDebug(s"Block $blockId not found in externalBlockStore") } } } @@ -766,8 +761,8 @@ private[spark] class BlockManager( // We will drop it to disk later if the memory store can't hold it. (true, memoryStore) } else if (putLevel.useOffHeap) { - // Use tachyon for off-heap storage - (false, tachyonStore) + // Use external block store + (false, externalBlockStore) } else if (putLevel.useDisk) { // Don't get back the bytes from put unless we replicate them (putLevel.replication > 1, diskStore) @@ -802,7 +797,7 @@ private[spark] class BlockManager( val putBlockStatus = getCurrentBlockStatus(blockId, putBlockInfo) if (putBlockStatus.storageLevel != StorageLevel.NONE) { - // Now that the block is in either the memory, tachyon, or disk store, + // Now that the block is in either the memory, externalBlockStore, or disk store, // let other threads read it, and tell the master about it. marked = true putBlockInfo.markReady(size) @@ -1099,10 +1094,11 @@ private[spark] class BlockManager( // Removals are idempotent in disk store and memory store. At worst, we get a warning. val removedFromMemory = memoryStore.remove(blockId) val removedFromDisk = diskStore.remove(blockId) - val removedFromTachyon = if (tachyonInitialized) tachyonStore.remove(blockId) else false - if (!removedFromMemory && !removedFromDisk && !removedFromTachyon) { + val removedFromExternalBlockStore = + if (externalBlockStoreInitialized) externalBlockStore.remove(blockId) else false + if (!removedFromMemory && !removedFromDisk && !removedFromExternalBlockStore) { logWarning(s"Block $blockId could not be removed as it was not found in either " + - "the disk, memory, or tachyon store") + "the disk, memory, or external block store") } blockInfo.remove(blockId) if (tellMaster && info.tellMaster) { @@ -1136,7 +1132,7 @@ private[spark] class BlockManager( val level = info.level if (level.useMemory) { memoryStore.remove(id) } if (level.useDisk) { diskStore.remove(id) } - if (level.useOffHeap) { tachyonStore.remove(id) } + if (level.useOffHeap) { externalBlockStore.remove(id) } iterator.remove() logInfo(s"Dropped block $id") } @@ -1216,8 +1212,8 @@ private[spark] class BlockManager( blockInfo.clear() memoryStore.clear() diskStore.clear() - if (tachyonInitialized) { - tachyonStore.clear() + if (externalBlockStoreInitialized) { + externalBlockStore.clear() } metadataCleaner.cancel() broadcastCleaner.cancel() diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index c798843bd5d8a..a85e1c7632973 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -54,21 +54,22 @@ class BlockManagerMaster( storageLevel: StorageLevel, memSize: Long, diskSize: Long, - tachyonSize: Long): Boolean = { - val res = driverEndpoint.askWithReply[Boolean]( - UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize, tachyonSize)) + externalBlockStoreSize: Long): Boolean = { + val res = driverEndpoint.askWithRetry[Boolean]( + UpdateBlockInfo(blockManagerId, blockId, storageLevel, + memSize, diskSize, externalBlockStoreSize)) logDebug(s"Updated info of block $blockId") res } /** Get locations of the blockId from the driver */ def getLocations(blockId: BlockId): Seq[BlockManagerId] = { - driverEndpoint.askWithReply[Seq[BlockManagerId]](GetLocations(blockId)) + driverEndpoint.askWithRetry[Seq[BlockManagerId]](GetLocations(blockId)) } /** Get locations of multiple blockIds from the driver */ def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = { - driverEndpoint.askWithReply[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) + driverEndpoint.askWithRetry[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) } /** @@ -81,11 +82,11 @@ class BlockManagerMaster( /** Get ids of other nodes in the cluster from the driver */ def getPeers(blockManagerId: BlockManagerId): Seq[BlockManagerId] = { - driverEndpoint.askWithReply[Seq[BlockManagerId]](GetPeers(blockManagerId)) + driverEndpoint.askWithRetry[Seq[BlockManagerId]](GetPeers(blockManagerId)) } def getRpcHostPortForExecutor(executorId: String): Option[(String, Int)] = { - driverEndpoint.askWithReply[Option[(String, Int)]](GetRpcHostPortForExecutor(executorId)) + driverEndpoint.askWithRetry[Option[(String, Int)]](GetRpcHostPortForExecutor(executorId)) } /** @@ -93,12 +94,12 @@ class BlockManagerMaster( * blocks that the driver knows about. */ def removeBlock(blockId: BlockId) { - driverEndpoint.askWithReply[Boolean](RemoveBlock(blockId)) + driverEndpoint.askWithRetry[Boolean](RemoveBlock(blockId)) } /** Remove all blocks belonging to the given RDD. */ def removeRdd(rddId: Int, blocking: Boolean) { - val future = driverEndpoint.askWithReply[Future[Seq[Int]]](RemoveRdd(rddId)) + val future = driverEndpoint.askWithRetry[Future[Seq[Int]]](RemoveRdd(rddId)) future.onFailure { case e: Exception => logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}") @@ -110,7 +111,7 @@ class BlockManagerMaster( /** Remove all blocks belonging to the given shuffle. */ def removeShuffle(shuffleId: Int, blocking: Boolean) { - val future = driverEndpoint.askWithReply[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) + val future = driverEndpoint.askWithRetry[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) future.onFailure { case e: Exception => logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}") @@ -122,7 +123,7 @@ class BlockManagerMaster( /** Remove all blocks belonging to the given broadcast. */ def removeBroadcast(broadcastId: Long, removeFromMaster: Boolean, blocking: Boolean) { - val future = driverEndpoint.askWithReply[Future[Seq[Int]]]( + val future = driverEndpoint.askWithRetry[Future[Seq[Int]]]( RemoveBroadcast(broadcastId, removeFromMaster)) future.onFailure { case e: Exception => @@ -141,11 +142,11 @@ class BlockManagerMaster( * amount of remaining memory. */ def getMemoryStatus: Map[BlockManagerId, (Long, Long)] = { - driverEndpoint.askWithReply[Map[BlockManagerId, (Long, Long)]](GetMemoryStatus) + driverEndpoint.askWithRetry[Map[BlockManagerId, (Long, Long)]](GetMemoryStatus) } def getStorageStatus: Array[StorageStatus] = { - driverEndpoint.askWithReply[Array[StorageStatus]](GetStorageStatus) + driverEndpoint.askWithRetry[Array[StorageStatus]](GetStorageStatus) } /** @@ -166,7 +167,7 @@ class BlockManagerMaster( * master endpoint for a response to a prior message. */ val response = driverEndpoint. - askWithReply[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) + askWithRetry[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) val (blockManagerIds, futures) = response.unzip val result = Await.result(Future.sequence(futures), timeout) if (result == null) { @@ -190,7 +191,7 @@ class BlockManagerMaster( filter: BlockId => Boolean, askSlaves: Boolean): Seq[BlockId] = { val msg = GetMatchingBlockIds(filter, askSlaves) - val future = driverEndpoint.askWithReply[Future[Seq[BlockId]]](msg) + val future = driverEndpoint.askWithRetry[Future[Seq[BlockId]]](msg) Await.result(future, timeout) } @@ -205,7 +206,7 @@ class BlockManagerMaster( /** Send a one-way message to the master endpoint, to which we expect it to reply with true. */ private def tell(message: Any) { - if (!driverEndpoint.askWithReply[Boolean](message)) { + if (!driverEndpoint.askWithRetry[Boolean](message)) { throw new SparkException("BlockManagerMasterEndpoint returned false, expected true.") } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala index 4682167912ff0..3afb4c3c02e2d 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala @@ -60,9 +60,9 @@ class BlockManagerMasterEndpoint( context.reply(true) case UpdateBlockInfo( - blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize) => + blockManagerId, blockId, storageLevel, deserializedSize, size, externalBlockStoreSize) => context.reply(updateBlockInfo( - blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize)) + blockManagerId, blockId, storageLevel, deserializedSize, size, externalBlockStoreSize)) case GetLocations(blockId) => context.reply(getLocations(blockId)) @@ -132,7 +132,7 @@ class BlockManagerMasterEndpoint( val removeMsg = RemoveRdd(rddId) Future.sequence( blockManagerInfo.values.map { bm => - bm.slaveEndpoint.sendWithReply[Int](removeMsg) + bm.slaveEndpoint.ask[Int](removeMsg) }.toSeq ) } @@ -142,7 +142,7 @@ class BlockManagerMasterEndpoint( val removeMsg = RemoveShuffle(shuffleId) Future.sequence( blockManagerInfo.values.map { bm => - bm.slaveEndpoint.sendWithReply[Boolean](removeMsg) + bm.slaveEndpoint.ask[Boolean](removeMsg) }.toSeq ) } @@ -159,7 +159,7 @@ class BlockManagerMasterEndpoint( } Future.sequence( requiredBlockManagers.map { bm => - bm.slaveEndpoint.sendWithReply[Int](removeMsg) + bm.slaveEndpoint.ask[Int](removeMsg) }.toSeq ) } @@ -214,7 +214,7 @@ class BlockManagerMasterEndpoint( // Remove the block from the slave's BlockManager. // Doesn't actually wait for a confirmation and the message might get lost. // If message loss becomes frequent, we should add retry logic here. - blockManager.get.slaveEndpoint.sendWithReply[Boolean](RemoveBlock(blockId)) + blockManager.get.slaveEndpoint.ask[Boolean](RemoveBlock(blockId)) } } } @@ -253,7 +253,7 @@ class BlockManagerMasterEndpoint( blockManagerInfo.values.map { info => val blockStatusFuture = if (askSlaves) { - info.slaveEndpoint.sendWithReply[Option[BlockStatus]](getBlockStatus) + info.slaveEndpoint.ask[Option[BlockStatus]](getBlockStatus) } else { Future { info.getStatus(blockId) } } @@ -277,7 +277,7 @@ class BlockManagerMasterEndpoint( blockManagerInfo.values.map { info => val future = if (askSlaves) { - info.slaveEndpoint.sendWithReply[Seq[BlockId]](getMatchingBlockIds) + info.slaveEndpoint.ask[Seq[BlockId]](getMatchingBlockIds) } else { Future { info.blocks.keys.filter(filter).toSeq } } @@ -314,7 +314,7 @@ class BlockManagerMasterEndpoint( storageLevel: StorageLevel, memSize: Long, diskSize: Long, - tachyonSize: Long): Boolean = { + externalBlockStoreSize: Long): Boolean = { if (!blockManagerInfo.contains(blockManagerId)) { if (blockManagerId.isDriver && !isLocal) { @@ -332,7 +332,7 @@ class BlockManagerMasterEndpoint( } blockManagerInfo(blockManagerId).updateBlockInfo( - blockId, storageLevel, memSize, diskSize, tachyonSize) + blockId, storageLevel, memSize, diskSize, externalBlockStoreSize) var locations: mutable.HashSet[BlockManagerId] = null if (blockLocations.containsKey(blockId)) { @@ -396,8 +396,8 @@ case class BlockStatus( storageLevel: StorageLevel, memSize: Long, diskSize: Long, - tachyonSize: Long) { - def isCached: Boolean = memSize + diskSize + tachyonSize > 0 + externalBlockStoreSize: Long) { + def isCached: Boolean = memSize + diskSize + externalBlockStoreSize > 0 } @DeveloperApi @@ -429,7 +429,7 @@ private[spark] class BlockManagerInfo( storageLevel: StorageLevel, memSize: Long, diskSize: Long, - tachyonSize: Long) { + externalBlockStoreSize: Long) { updateLastSeenMs() @@ -445,9 +445,9 @@ private[spark] class BlockManagerInfo( } if (storageLevel.isValid) { - /* isValid means it is either stored in-memory, on-disk or on-Tachyon. + /* isValid means it is either stored in-memory, on-disk or on-externalBlockStore. * The memSize here indicates the data size in or dropped from memory, - * tachyonSize here indicates the data size in or dropped from Tachyon, + * externalBlockStoreSize here indicates the data size in or dropped from externalBlockStore, * and the diskSize here indicates the data size in or dropped to disk. * They can be both larger than 0, when a block is dropped from memory to disk. * Therefore, a safe way to set BlockStatus is to set its info in accurate modes. */ @@ -464,9 +464,9 @@ private[spark] class BlockManagerInfo( blockId, blockManagerId.hostPort, Utils.bytesToString(diskSize))) } if (storageLevel.useOffHeap) { - _blocks.put(blockId, BlockStatus(storageLevel, 0, 0, tachyonSize)) - logInfo("Added %s on tachyon on %s (size: %s)".format( - blockId, blockManagerId.hostPort, Utils.bytesToString(tachyonSize))) + _blocks.put(blockId, BlockStatus(storageLevel, 0, 0, externalBlockStoreSize)) + logInfo("Added %s on ExternalBlockStore on %s (size: %s)".format( + blockId, blockManagerId.hostPort, Utils.bytesToString(externalBlockStoreSize))) } } else if (_blocks.containsKey(blockId)) { // If isValid is not true, drop the block. @@ -482,8 +482,9 @@ private[spark] class BlockManagerInfo( blockId, blockManagerId.hostPort, Utils.bytesToString(blockStatus.diskSize))) } if (blockStatus.storageLevel.useOffHeap) { - logInfo("Removed %s on %s on tachyon (size: %s)".format( - blockId, blockManagerId.hostPort, Utils.bytesToString(blockStatus.tachyonSize))) + logInfo("Removed %s on %s on externalBlockStore (size: %s)".format( + blockId, blockManagerId.hostPort, + Utils.bytesToString(blockStatus.externalBlockStoreSize))) } } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala index f89d8d7493f7c..1683576067fe8 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala @@ -60,7 +60,7 @@ private[spark] object BlockManagerMessages { var storageLevel: StorageLevel, var memSize: Long, var diskSize: Long, - var tachyonSize: Long) + var externalBlockStoreSize: Long) extends ToBlockManagerMaster with Externalizable { @@ -72,7 +72,7 @@ private[spark] object BlockManagerMessages { storageLevel.writeExternal(out) out.writeLong(memSize) out.writeLong(diskSize) - out.writeLong(tachyonSize) + out.writeLong(externalBlockStoreSize) } override def readExternal(in: ObjectInput): Unit = Utils.tryOrIOException { @@ -81,7 +81,7 @@ private[spark] object BlockManagerMessages { storageLevel = StorageLevel(in) memSize = in.readLong() diskSize = in.readLong() - tachyonSize = in.readLong() + externalBlockStoreSize = in.readLong() } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala index 14833791f7a4d..8bc4e205bc3c6 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala @@ -33,7 +33,7 @@ import org.apache.spark.util.Utils * This interface does not support concurrent writes. Also, once the writer has * been opened, it cannot be reopened again. */ -private[spark] abstract class BlockObjectWriter(val blockId: BlockId) { +private[spark] abstract class BlockObjectWriter(val blockId: BlockId) extends OutputStream { def open(): BlockObjectWriter @@ -54,9 +54,14 @@ private[spark] abstract class BlockObjectWriter(val blockId: BlockId) { def revertPartialWritesAndClose() /** - * Writes an object. + * Writes a key-value pair. */ - def write(value: Any) + def write(key: Any, value: Any) + + /** + * Notify the writer that a record worth of bytes has been written with OutputStream#write. + */ + def recordWritten() /** * Returns the file segment of committed data that this Writer has written. @@ -203,12 +208,27 @@ private[spark] class DiskBlockObjectWriter( } } - override def write(value: Any) { + override def write(key: Any, value: Any) { + if (!initialized) { + open() + } + + objOut.writeKey(key) + objOut.writeValue(value) + recordWritten() + } + + override def write(b: Int): Unit = throw new UnsupportedOperationException() + + override def write(kvBytes: Array[Byte], offs: Int, len: Int): Unit = { if (!initialized) { open() } - objOut.writeObject(value) + bs.write(kvBytes, offs, len) + } + + override def recordWritten(): Unit = { numRecordsWritten += 1 writeMetrics.incShuffleRecordsWritten(1) @@ -238,7 +258,7 @@ private[spark] class DiskBlockObjectWriter( } // For testing - private[spark] def flush() { + private[spark] override def flush() { objOut.flush() bs.flush() } diff --git a/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala new file mode 100644 index 0000000000000..8964762df6af3 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala @@ -0,0 +1,102 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import java.nio.ByteBuffer + +/** + * An abstract class that the concrete external block manager has to inherit. + * The class has to have a no-argument constructor, and will be initialized by init, + * which is invoked by ExternalBlockStore. The main input parameter is blockId for all + * the methods, which is the unique identifier for Block in one Spark application. + * + * The underlying external block manager should avoid any name space conflicts among multiple + * Spark applications. For example, creating different directory for different applications + * by randomUUID + * + */ +private[spark] abstract class ExternalBlockManager { + + override def toString: String = {"External Block Store"} + + /** + * Initialize a concrete block manager implementation. Subclass should initialize its internal + * data structure, e.g, file system, in this function, which is invoked by ExternalBlockStore + * right after the class is constructed. The function should throw IOException on failure + * + * @throws java.io.IOException if there is any file system failure during the initialization. + */ + def init(blockManager: BlockManager, executorId: String): Unit + + /** + * Drop the block from underlying external block store, if it exists.. + * @return true on successfully removing the block + * false if the block could not be removed as it was not found + * + * @throws java.io.IOException if there is any file system failure in removing the block. + */ + def removeBlock(blockId: BlockId): Boolean + + /** + * Used by BlockManager to check the existence of the block in the underlying external + * block store. + * @return true if the block exists. + * false if the block does not exists. + * + * @throws java.io.IOException if there is any file system failure in checking + * the block existence. + */ + def blockExists(blockId: BlockId): Boolean + + /** + * Put the given block to the underlying external block store. Note that in normal case, + * putting a block should never fail unless something wrong happens to the underlying + * external block store, e.g., file system failure, etc. In this case, IOException + * should be thrown. + * + * @throws java.io.IOException if there is any file system failure in putting the block. + */ + def putBytes(blockId: BlockId, bytes: ByteBuffer): Unit + + /** + * Retrieve the block bytes. + * @return Some(ByteBuffer) if the block bytes is successfully retrieved + * None if the block does not exist in the external block store. + * + * @throws java.io.IOException if there is any file system failure in getting the block. + */ + def getBytes(blockId: BlockId): Option[ByteBuffer] + + /** + * Get the size of the block saved in the underlying external block store, + * which is saved before by putBytes. + * @return size of the block + * 0 if the block does not exist + * + * @throws java.io.IOException if there is any file system failure in getting the block size. + */ + def getSize(blockId: BlockId): Long + + /** + * Clean up any information persisted in the underlying external block store, + * e.g., the directory, files, etc,which is invoked by the shutdown hook of ExternalBlockStore + * during system shutdown. + * + */ + def shutdown() +} diff --git a/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala b/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala new file mode 100644 index 0000000000000..0bf770306ae9b --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala @@ -0,0 +1,181 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import java.nio.ByteBuffer +import org.apache.spark.Logging +import org.apache.spark.util.Utils +import scala.util.control.NonFatal + + +/** + * Stores BlockManager blocks on ExternalBlockStore. + * We capture any potential exception from underlying implementation + * and return with the expected failure value + */ +private[spark] class ExternalBlockStore(blockManager: BlockManager, executorId: String) + extends BlockStore(blockManager: BlockManager) with Logging { + + lazy val externalBlockManager: Option[ExternalBlockManager] = createBlkManager() + + logInfo("ExternalBlockStore started") + + override def getSize(blockId: BlockId): Long = { + try { + externalBlockManager.map(_.getSize(blockId)).getOrElse(0) + } catch { + case NonFatal(t) => + logError(s"error in getSize from $blockId", t) + 0L + } + } + + override def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel): PutResult = { + putIntoExternalBlockStore(blockId, bytes, returnValues = true) + } + + override def putArray( + blockId: BlockId, + values: Array[Any], + level: StorageLevel, + returnValues: Boolean): PutResult = { + putIterator(blockId, values.toIterator, level, returnValues) + } + + override def putIterator( + blockId: BlockId, + values: Iterator[Any], + level: StorageLevel, + returnValues: Boolean): PutResult = { + logDebug(s"Attempting to write values for block $blockId") + val bytes = blockManager.dataSerialize(blockId, values) + putIntoExternalBlockStore(blockId, bytes, returnValues) + } + + private def putIntoExternalBlockStore( + blockId: BlockId, + bytes: ByteBuffer, + returnValues: Boolean): PutResult = { + // So that we do not modify the input offsets ! + // duplicate does not copy buffer, so inexpensive + val byteBuffer = bytes.duplicate() + byteBuffer.rewind() + logDebug(s"Attempting to put block $blockId into ExtBlk store") + // we should never hit here if externalBlockManager is None. Handle it anyway for safety. + try { + val startTime = System.currentTimeMillis + if (externalBlockManager.isDefined) { + externalBlockManager.get.putBytes(blockId, bytes) + val finishTime = System.currentTimeMillis + logDebug("Block %s stored as %s file in ExternalBlockStore in %d ms".format( + blockId, Utils.bytesToString(byteBuffer.limit), finishTime - startTime)) + + if (returnValues) { + PutResult(bytes.limit(), Right(bytes.duplicate())) + } else { + PutResult(bytes.limit(), null) + } + } else { + logError(s"error in putBytes $blockId") + PutResult(bytes.limit(), null, Seq((blockId, BlockStatus.empty))) + } + } catch { + case NonFatal(t) => + logError(s"error in putBytes $blockId", t) + PutResult(bytes.limit(), null, Seq((blockId, BlockStatus.empty))) + } + } + + // We assume the block is removed even if exception thrown + override def remove(blockId: BlockId): Boolean = { + try { + externalBlockManager.map(_.removeBlock(blockId)).getOrElse(true) + } catch { + case NonFatal(t) => + logError(s"error in removing $blockId", t) + true + } + } + + override def getValues(blockId: BlockId): Option[Iterator[Any]] = { + getBytes(blockId).map(buffer => blockManager.dataDeserialize(blockId, buffer)) + } + + override def getBytes(blockId: BlockId): Option[ByteBuffer] = { + try { + externalBlockManager.flatMap(_.getBytes(blockId)) + } catch { + case NonFatal(t) => + logError(s"error in getBytes from $blockId", t) + None + } + } + + override def contains(blockId: BlockId): Boolean = { + try { + val ret = externalBlockManager.map(_.blockExists(blockId)).getOrElse(false) + if (!ret) { + logInfo(s"remove block $blockId") + blockManager.removeBlock(blockId, true) + } + ret + } catch { + case NonFatal(t) => + logError(s"error in getBytes from $blockId", t) + false + } + } + + private def addShutdownHook() { + Runtime.getRuntime.addShutdownHook(new Thread("ExternalBlockStore shutdown hook") { + override def run(): Unit = Utils.logUncaughtExceptions { + logDebug("Shutdown hook called") + externalBlockManager.map(_.shutdown()) + } + }) + } + + // Create concrete block manager and fall back to Tachyon by default for backward compatibility. + private def createBlkManager(): Option[ExternalBlockManager] = { + val clsName = blockManager.conf.getOption(ExternalBlockStore.BLOCK_MANAGER_NAME) + .getOrElse(ExternalBlockStore.DEFAULT_BLOCK_MANAGER_NAME) + + try { + val instance = Class.forName(clsName) + .newInstance() + .asInstanceOf[ExternalBlockManager] + instance.init(blockManager, executorId) + addShutdownHook(); + Some(instance) + } catch { + case NonFatal(t) => + logError("Cannot initialize external block store", t) + None + } + } +} + +private[spark] object ExternalBlockStore extends Logging { + val MAX_DIR_CREATION_ATTEMPTS = 10 + val SUB_DIRS_PER_DIR = "64" + val BASE_DIR = "spark.externalBlockStore.baseDir" + val FOLD_NAME = "spark.externalBlockStore.folderName" + val MASTER_URL = "spark.externalBlockStore.url" + val BLOCK_MANAGER_NAME = "spark.externalBlockStore.blockManager" + val DEFAULT_BLOCK_MANAGER_NAME = "org.apache.spark.storage.TachyonBlockManager" +} diff --git a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala index 034525b56f59c..96062626b5045 100644 --- a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala +++ b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala @@ -18,7 +18,7 @@ package org.apache.spark.storage import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.{RDDOperationScope, RDD} import org.apache.spark.util.Utils @DeveloperApi @@ -26,22 +26,25 @@ class RDDInfo( val id: Int, val name: String, val numPartitions: Int, - var storageLevel: StorageLevel) + var storageLevel: StorageLevel, + val parentIds: Seq[Int], + val scope: Option[RDDOperationScope] = None) extends Ordered[RDDInfo] { var numCachedPartitions = 0 var memSize = 0L var diskSize = 0L - var tachyonSize = 0L + var externalBlockStoreSize = 0L - def isCached: Boolean = (memSize + diskSize + tachyonSize > 0) && numCachedPartitions > 0 + def isCached: Boolean = + (memSize + diskSize + externalBlockStoreSize > 0) && numCachedPartitions > 0 override def toString: String = { import Utils.bytesToString ("RDD \"%s\" (%d) StorageLevel: %s; CachedPartitions: %d; TotalPartitions: %d; " + - "MemorySize: %s; TachyonSize: %s; DiskSize: %s").format( + "MemorySize: %s; ExternalBlockStoreSize: %s; DiskSize: %s").format( name, id, storageLevel.toString, numCachedPartitions, numPartitions, - bytesToString(memSize), bytesToString(tachyonSize), bytesToString(diskSize)) + bytesToString(memSize), bytesToString(externalBlockStoreSize), bytesToString(diskSize)) } override def compare(that: RDDInfo): Int = { @@ -51,7 +54,8 @@ class RDDInfo( private[spark] object RDDInfo { def fromRdd(rdd: RDD[_]): RDDInfo = { - val rddName = Option(rdd.name).getOrElse(rdd.id.toString) - new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel) + val rddName = Option(rdd.name).getOrElse(Utils.getFormattedClassName(rdd)) + val parentIds = rdd.dependencies.map(_.rdd.id) + new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel, parentIds, rdd.scope) } } diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala index f3379521d55e2..d0faab62c9e9e 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala @@ -17,14 +17,12 @@ package org.apache.spark.storage -import java.io.{InputStream, IOException} import java.util.concurrent.LinkedBlockingQueue import scala.collection.mutable.{ArrayBuffer, HashSet, Queue} -import scala.util.{Failure, Success, Try} +import scala.util.{Failure, Try} import org.apache.spark.{Logging, TaskContext} -import org.apache.spark.network.BlockTransferService import org.apache.spark.network.shuffle.{BlockFetchingListener, ShuffleClient} import org.apache.spark.network.buffer.ManagedBuffer import org.apache.spark.serializer.{SerializerInstance, Serializer} @@ -301,7 +299,7 @@ final class ShuffleBlockFetcherIterator( // the scheduler gets a FetchFailedException. Try(buf.createInputStream()).map { is0 => val is = blockManager.wrapForCompression(blockId, is0) - val iter = serializerInstance.deserializeStream(is).asIterator + val iter = serializerInstance.deserializeStream(is).asKeyValueIterator CompletionIterator[Any, Iterator[Any]](iter, { // Once the iterator is exhausted, release the buffer and set currentResult to null // so we don't release it again in cleanup. diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala index 134abea866218..703bce3e6b85b 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala @@ -26,9 +26,9 @@ import org.apache.spark.util.Utils /** * :: DeveloperApi :: * Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory, - * or Tachyon, whether to drop the RDD to disk if it falls out of memory or Tachyon , whether to - * keep the data in memory in a serialized format, and whether to replicate the RDD partitions on - * multiple nodes. + * or ExternalBlockStore, whether to drop the RDD to disk if it falls out of memory or + * ExternalBlockStore, whether to keep the data in memory in a serialized format, and whether + * to replicate the RDD partitions on multiple nodes. * * The [[org.apache.spark.storage.StorageLevel$]] singleton object contains some static constants * for commonly useful storage levels. To create your own storage level object, use the @@ -126,7 +126,7 @@ class StorageLevel private( var result = "" result += (if (useDisk) "Disk " else "") result += (if (useMemory) "Memory " else "") - result += (if (useOffHeap) "Tachyon " else "") + result += (if (useOffHeap) "ExternalBlockStore " else "") result += (if (deserialized) "Deserialized " else "Serialized ") result += s"${replication}x Replicated" result diff --git a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala index 2bd6b749be261..c4ac30092f807 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala @@ -199,33 +199,34 @@ class StorageStatus(val blockManagerId: BlockManagerId, val maxMem: Long) { val oldBlockStatus = getBlock(blockId).getOrElse(BlockStatus.empty) val changeInMem = newBlockStatus.memSize - oldBlockStatus.memSize val changeInDisk = newBlockStatus.diskSize - oldBlockStatus.diskSize - val changeInTachyon = newBlockStatus.tachyonSize - oldBlockStatus.tachyonSize + val changeInExternalBlockStore = + newBlockStatus.externalBlockStoreSize - oldBlockStatus.externalBlockStoreSize val level = newBlockStatus.storageLevel // Compute new info from old info - val (oldMem, oldDisk, oldTachyon) = blockId match { + val (oldMem, oldDisk, oldExternalBlockStore) = blockId match { case RDDBlockId(rddId, _) => _rddStorageInfo.get(rddId) - .map { case (mem, disk, tachyon, _) => (mem, disk, tachyon) } + .map { case (mem, disk, externalBlockStore, _) => (mem, disk, externalBlockStore) } .getOrElse((0L, 0L, 0L)) case _ => _nonRddStorageInfo } val newMem = math.max(oldMem + changeInMem, 0L) val newDisk = math.max(oldDisk + changeInDisk, 0L) - val newTachyon = math.max(oldTachyon + changeInTachyon, 0L) + val newExternalBlockStore = math.max(oldExternalBlockStore + changeInExternalBlockStore, 0L) // Set the correct info blockId match { case RDDBlockId(rddId, _) => // If this RDD is no longer persisted, remove it - if (newMem + newDisk + newTachyon == 0) { + if (newMem + newDisk + newExternalBlockStore == 0) { _rddStorageInfo.remove(rddId) } else { - _rddStorageInfo(rddId) = (newMem, newDisk, newTachyon, level) + _rddStorageInfo(rddId) = (newMem, newDisk, newExternalBlockStore, level) } case _ => - _nonRddStorageInfo = (newMem, newDisk, newTachyon) + _nonRddStorageInfo = (newMem, newDisk, newExternalBlockStore) } } @@ -247,13 +248,13 @@ private[spark] object StorageUtils { val numCachedPartitions = statuses.map(_.numRddBlocksById(rddId)).sum val memSize = statuses.map(_.memUsedByRdd(rddId)).sum val diskSize = statuses.map(_.diskUsedByRdd(rddId)).sum - val tachyonSize = statuses.map(_.offHeapUsedByRdd(rddId)).sum + val externalBlockStoreSize = statuses.map(_.offHeapUsedByRdd(rddId)).sum rddInfo.storageLevel = storageLevel rddInfo.numCachedPartitions = numCachedPartitions rddInfo.memSize = memSize rddInfo.diskSize = diskSize - rddInfo.tachyonSize = tachyonSize + rddInfo.externalBlockStoreSize = externalBlockStoreSize } } diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala index 583f1fdf0475b..bdc6276e41915 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala @@ -17,13 +17,16 @@ package org.apache.spark.storage +import java.io.IOException +import java.nio.ByteBuffer import java.text.SimpleDateFormat import java.util.{Date, Random} +import com.google.common.io.ByteStreams +import tachyon.client.{ReadType, WriteType, TachyonFS, TachyonFile} import tachyon.TachyonURI -import tachyon.client.{TachyonFile, TachyonFS} -import org.apache.spark.Logging +import org.apache.spark.{SparkException, SparkConf, Logging} import org.apache.spark.executor.ExecutorExitCode import org.apache.spark.util.Utils @@ -32,32 +35,94 @@ import org.apache.spark.util.Utils * Creates and maintains the logical mapping between logical blocks and tachyon fs locations. By * default, one block is mapped to one file with a name given by its BlockId. * - * @param rootDirs The directories to use for storing block files. Data will be hashed among these. */ -private[spark] class TachyonBlockManager( - blockManager: BlockManager, - rootDirs: String, - val master: String) - extends Logging { +private[spark] class TachyonBlockManager() extends ExternalBlockManager with Logging { - val client = if (master != null && master != "") TachyonFS.get(new TachyonURI(master)) else null - - if (client == null) { - logError("Failed to connect to the Tachyon as the master address is not configured") - System.exit(ExecutorExitCode.TACHYON_STORE_FAILED_TO_INITIALIZE) - } - - private val MAX_DIR_CREATION_ATTEMPTS = 10 - private val subDirsPerTachyonDir = - blockManager.conf.get("spark.tachyonStore.subDirectories", "64").toInt + var blockManager: BlockManager =_ + var rootDirs: String = _ + var master: String = _ + var client: tachyon.client.TachyonFS = _ + private var subDirsPerTachyonDir: Int = _ // Create one Tachyon directory for each path mentioned in spark.tachyonStore.folderName; // then, inside this directory, create multiple subdirectories that we will hash files into, // in order to avoid having really large inodes at the top level in Tachyon. - private val tachyonDirs: Array[TachyonFile] = createTachyonDirs() - private val subDirs = Array.fill(tachyonDirs.length)(new Array[TachyonFile](subDirsPerTachyonDir)) + private var tachyonDirs: Array[TachyonFile] = _ + private var subDirs: Array[Array[tachyon.client.TachyonFile]] = _ + + + override def init(blockManager: BlockManager, executorId: String): Unit = { + this.blockManager = blockManager + val storeDir = blockManager.conf.get(ExternalBlockStore.BASE_DIR, "/tmp_spark_tachyon") + val appFolderName = blockManager.conf.get(ExternalBlockStore.FOLD_NAME) + + rootDirs = s"$storeDir/$appFolderName/$executorId" + master = blockManager.conf.get(ExternalBlockStore.MASTER_URL, "tachyon://localhost:19998") + client = if (master != null && master != "") TachyonFS.get(new TachyonURI(master)) else null + // original implementation call System.exit, we change it to run without extblkstore support + if (client == null) { + logError("Failed to connect to the Tachyon as the master address is not configured") + throw new IOException("Failed to connect to the Tachyon as the master " + + "address is not configured") + } + subDirsPerTachyonDir = blockManager.conf.get("spark.externalBlockStore.subDirectories", + ExternalBlockStore.SUB_DIRS_PER_DIR).toInt + + // Create one Tachyon directory for each path mentioned in spark.tachyonStore.folderName; + // then, inside this directory, create multiple subdirectories that we will hash files into, + // in order to avoid having really large inodes at the top level in Tachyon. + tachyonDirs = createTachyonDirs() + subDirs = Array.fill(tachyonDirs.length)(new Array[TachyonFile](subDirsPerTachyonDir)) + tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir)) + } + + override def toString: String = {"ExternalBlockStore-Tachyon"} + + override def removeBlock(blockId: BlockId): Boolean = { + val file = getFile(blockId) + if (fileExists(file)) { + removeFile(file) + } else { + false + } + } + + override def blockExists(blockId: BlockId): Boolean = { + val file = getFile(blockId) + fileExists(file) + } - addShutdownHook() + override def putBytes(blockId: BlockId, bytes: ByteBuffer): Unit = { + val file = getFile(blockId) + val os = file.getOutStream(WriteType.TRY_CACHE) + os.write(bytes.array()) + os.close() + } + + override def getBytes(blockId: BlockId): Option[ByteBuffer] = { + val file = getFile(blockId) + if (file == null || file.getLocationHosts.size == 0) { + return None + } + val is = file.getInStream(ReadType.CACHE) + assert (is != null) + try { + val size = file.length + val bs = new Array[Byte](size.asInstanceOf[Int]) + ByteStreams.readFully(is, bs) + Some(ByteBuffer.wrap(bs)) + } catch { + case ioe: IOException => + logWarning(s"Failed to fetch the block $blockId from Tachyon", ioe) + None + } finally { + is.close() + } + } + + override def getSize(blockId: BlockId): Long = { + getFile(blockId.name).length + } def removeFile(file: TachyonFile): Boolean = { client.delete(new TachyonURI(file.getPath()), false) @@ -109,7 +174,7 @@ private[spark] class TachyonBlockManager( var tachyonDirId: String = null var tries = 0 val rand = new Random() - while (!foundLocalDir && tries < MAX_DIR_CREATION_ATTEMPTS) { + while (!foundLocalDir && tries < ExternalBlockStore.MAX_DIR_CREATION_ATTEMPTS) { tries += 1 try { tachyonDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536)) @@ -124,30 +189,27 @@ private[spark] class TachyonBlockManager( } } if (!foundLocalDir) { - logError("Failed " + MAX_DIR_CREATION_ATTEMPTS + " attempts to create tachyon dir in " + - rootDir) - System.exit(ExecutorExitCode.TACHYON_STORE_FAILED_TO_CREATE_DIR) + logError("Failed " + ExternalBlockStore.MAX_DIR_CREATION_ATTEMPTS + + " attempts to create tachyon dir in " + rootDir) + System.exit(ExecutorExitCode.EXTERNAL_BLOCK_STORE_FAILED_TO_CREATE_DIR) } logInfo("Created tachyon directory at " + tachyonDir) tachyonDir } } - private def addShutdownHook() { - tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir)) - Utils.addShutdownHook { () => - logDebug("Shutdown hook called") - tachyonDirs.foreach { tachyonDir => - try { - if (!Utils.hasRootAsShutdownDeleteDir(tachyonDir)) { - Utils.deleteRecursively(tachyonDir, client) - } - } catch { - case e: Exception => - logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) + override def shutdown() { + logDebug("Shutdown hook called") + tachyonDirs.foreach { tachyonDir => + try { + if (!Utils.hasRootAsShutdownDeleteDir(tachyonDir)) { + Utils.deleteRecursively(tachyonDir, client) } + } catch { + case e: Exception => + logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) } - client.close() } + client.close() } } diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala b/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala deleted file mode 100644 index 233d1e2b7c616..0000000000000 --- a/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala +++ /dev/null @@ -1,128 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.storage - -import java.io.IOException -import java.nio.ByteBuffer - -import com.google.common.io.ByteStreams -import tachyon.client.{ReadType, WriteType} - -import org.apache.spark.Logging -import org.apache.spark.util.Utils - -/** - * Stores BlockManager blocks on Tachyon. - */ -private[spark] class TachyonStore( - blockManager: BlockManager, - tachyonManager: TachyonBlockManager) - extends BlockStore(blockManager: BlockManager) with Logging { - - logInfo("TachyonStore started") - - override def getSize(blockId: BlockId): Long = { - tachyonManager.getFile(blockId.name).length - } - - override def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel): PutResult = { - putIntoTachyonStore(blockId, bytes, returnValues = true) - } - - override def putArray( - blockId: BlockId, - values: Array[Any], - level: StorageLevel, - returnValues: Boolean): PutResult = { - putIterator(blockId, values.toIterator, level, returnValues) - } - - override def putIterator( - blockId: BlockId, - values: Iterator[Any], - level: StorageLevel, - returnValues: Boolean): PutResult = { - logDebug(s"Attempting to write values for block $blockId") - val bytes = blockManager.dataSerialize(blockId, values) - putIntoTachyonStore(blockId, bytes, returnValues) - } - - private def putIntoTachyonStore( - blockId: BlockId, - bytes: ByteBuffer, - returnValues: Boolean): PutResult = { - // So that we do not modify the input offsets ! - // duplicate does not copy buffer, so inexpensive - val byteBuffer = bytes.duplicate() - byteBuffer.rewind() - logDebug(s"Attempting to put block $blockId into Tachyon") - val startTime = System.currentTimeMillis - val file = tachyonManager.getFile(blockId) - val os = file.getOutStream(WriteType.TRY_CACHE) - os.write(byteBuffer.array()) - os.close() - val finishTime = System.currentTimeMillis - logDebug("Block %s stored as %s file in Tachyon in %d ms".format( - blockId, Utils.bytesToString(byteBuffer.limit), finishTime - startTime)) - - if (returnValues) { - PutResult(bytes.limit(), Right(bytes.duplicate())) - } else { - PutResult(bytes.limit(), null) - } - } - - override def remove(blockId: BlockId): Boolean = { - val file = tachyonManager.getFile(blockId) - if (tachyonManager.fileExists(file)) { - tachyonManager.removeFile(file) - } else { - false - } - } - - override def getValues(blockId: BlockId): Option[Iterator[Any]] = { - getBytes(blockId).map(buffer => blockManager.dataDeserialize(blockId, buffer)) - } - - override def getBytes(blockId: BlockId): Option[ByteBuffer] = { - val file = tachyonManager.getFile(blockId) - if (file == null || file.getLocationHosts.size == 0) { - return None - } - val is = file.getInStream(ReadType.CACHE) - assert (is != null) - try { - val size = file.length - val bs = new Array[Byte](size.asInstanceOf[Int]) - ByteStreams.readFully(is, bs) - Some(ByteBuffer.wrap(bs)) - } catch { - case ioe: IOException => - logWarning(s"Failed to fetch the block $blockId from Tachyon", ioe) - None - } finally { - is.close() - } - } - - override def contains(blockId: BlockId): Boolean = { - val file = tachyonManager.getFile(blockId) - tachyonManager.fileExists(file) - } -} diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala index a091ca650c60c..dfd6fdb5e9993 100644 --- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala @@ -115,19 +115,21 @@ private[spark] object JettyUtils extends Logging { destPath: String, beforeRedirect: HttpServletRequest => Unit = x => (), basePath: String = "", - httpMethod: String = "GET"): ServletContextHandler = { + httpMethods: Set[String] = Set("GET")): ServletContextHandler = { val prefixedDestPath = attachPrefix(basePath, destPath) val servlet = new HttpServlet { override def doGet(request: HttpServletRequest, response: HttpServletResponse): Unit = { - httpMethod match { - case "GET" => doRequest(request, response) - case _ => response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + if (httpMethods.contains("GET")) { + doRequest(request, response) + } else { + response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) } } override def doPost(request: HttpServletRequest, response: HttpServletResponse): Unit = { - httpMethod match { - case "POST" => doRequest(request, response) - case _ => response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + if (httpMethods.contains("POST")) { + doRequest(request, response) + } else { + response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) } } private def doRequest(request: HttpServletRequest, response: HttpServletResponse): Unit = { diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index 580ab8b1325f8..a5271f0574e6c 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -25,6 +25,7 @@ import org.apache.spark.ui.env.{EnvironmentListener, EnvironmentTab} import org.apache.spark.ui.exec.{ExecutorsListener, ExecutorsTab} import org.apache.spark.ui.jobs.{JobsTab, JobProgressListener, StagesTab} import org.apache.spark.ui.storage.{StorageListener, StorageTab} +import org.apache.spark.ui.scope.RDDOperationGraphListener /** * Top level user interface for a Spark application. @@ -38,6 +39,7 @@ private[spark] class SparkUI private ( val executorsListener: ExecutorsListener, val jobProgressListener: JobProgressListener, val storageListener: StorageListener, + val operationGraphListener: RDDOperationGraphListener, var appName: String, val basePath: String) extends WebUI(securityManager, SparkUI.getUIPort(conf), conf, basePath, "SparkUI") @@ -55,8 +57,10 @@ private[spark] class SparkUI private ( attachTab(new ExecutorsTab(this)) attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static")) attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath)) + // This should be POST only, but, the YARN AM proxy won't proxy POSTs attachHandler(createRedirectHandler( - "/stages/stage/kill", "/stages", stagesTab.handleKillRequest, httpMethod = "POST")) + "/stages/stage/kill", "/stages", stagesTab.handleKillRequest, + httpMethods = Set("GET", "POST"))) } initialize() @@ -91,6 +95,9 @@ private[spark] abstract class SparkUITab(parent: SparkUI, prefix: String) private[spark] object SparkUI { val DEFAULT_PORT = 4040 val STATIC_RESOURCE_DIR = "org/apache/spark/ui/static" + val DEFAULT_POOL_NAME = "default" + val DEFAULT_RETAINED_STAGES = 1000 + val DEFAULT_RETAINED_JOBS = 1000 def getUIPort(conf: SparkConf): Int = { conf.getInt("spark.ui.port", SparkUI.DEFAULT_PORT) @@ -142,13 +149,16 @@ private[spark] object SparkUI { val storageStatusListener = new StorageStatusListener val executorsListener = new ExecutorsListener(storageStatusListener) val storageListener = new StorageListener(storageStatusListener) + val operationGraphListener = new RDDOperationGraphListener(conf) listenerBus.addListener(environmentListener) listenerBus.addListener(storageStatusListener) listenerBus.addListener(executorsListener) listenerBus.addListener(storageListener) + listenerBus.addListener(operationGraphListener) new SparkUI(sc, conf, securityManager, environmentListener, storageStatusListener, - executorsListener, _jobProgressListener, storageListener, appName, basePath) + executorsListener, _jobProgressListener, storageListener, operationGraphListener, + appName, basePath) } } diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala index f07864141a21c..e2d03f8342315 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala @@ -23,6 +23,7 @@ import java.util.{Locale, Date} import scala.xml.{Node, Text} import org.apache.spark.Logging +import org.apache.spark.ui.scope.RDDOperationGraph /** Utility functions for generating XML pages with spark content. */ private[spark] object UIUtils extends Logging { @@ -155,16 +156,26 @@ private[spark] object UIUtils extends Logging { def commonHeaderNodes: Seq[Node] = { - - + + + + + + + } + + def vizHeaderNodes: Seq[Node] = { + + + + + } /** Returns a spark page with correctly formatted headers */ @@ -173,7 +184,8 @@ private[spark] object UIUtils extends Logging { content: => Seq[Node], activeTab: SparkUITab, refreshInterval: Option[Int] = None, - helpText: Option[String] = None): Seq[Node] = { + helpText: Option[String] = None, + showVisualization: Boolean = false): Seq[Node] = { val appName = activeTab.appName val shortAppName = if (appName.length < 36) appName else appName.take(32) + "..." @@ -191,6 +203,7 @@ private[spark] object UIUtils extends Logging { {commonHeaderNodes} + {if (showVisualization) vizHeaderNodes else Seq.empty} {appName} - {title} @@ -315,4 +328,47 @@ private[spark] object UIUtils extends Logging {

} + + /** Return a "DAG visualization" DOM element that expands into a visualization for a stage. */ + def showDagVizForStage(stageId: Int, graph: Option[RDDOperationGraph]): Seq[Node] = { + showDagViz(graph.toSeq, forJob = false) + } + + /** Return a "DAG visualization" DOM element that expands into a visualization for a job. */ + def showDagVizForJob(jobId: Int, graphs: Seq[RDDOperationGraph]): Seq[Node] = { + showDagViz(graphs, forJob = true) + } + + /** + * Return a "DAG visualization" DOM element that expands into a visualization on the UI. + * + * This populates metadata necessary for generating the visualization on the front-end in + * a format that is expected by spark-dag-viz.js. Any changes in the format here must be + * reflected there. + */ + private def showDagViz(graphs: Seq[RDDOperationGraph], forJob: Boolean): Seq[Node] = { +
+ + + DAG visualization + +
+
+ { + graphs.map { g => + + } + } +
+
+ } } diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala index 69053fe44d7e4..0a08b000e2d03 100644 --- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala @@ -24,6 +24,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.scheduler._ import org.apache.spark.storage.{StorageStatus, StorageStatusListener} import org.apache.spark.ui.{SparkUI, SparkUITab} +import org.apache.spark.ui.jobs.UIData.ExecutorUIData private[ui] class ExecutorsTab(parent: SparkUI) extends SparkUITab(parent, "executors") { val listener = parent.executorsListener @@ -54,12 +55,22 @@ class ExecutorsListener(storageStatusListener: StorageStatusListener) extends Sp val executorToShuffleRead = HashMap[String, Long]() val executorToShuffleWrite = HashMap[String, Long]() val executorToLogUrls = HashMap[String, Map[String, String]]() + val executorIdToData = HashMap[String, ExecutorUIData]() def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded): Unit = synchronized { val eid = executorAdded.executorId executorToLogUrls(eid) = executorAdded.executorInfo.logUrlMap + executorIdToData(eid) = ExecutorUIData(executorAdded.time) + } + + override def onExecutorRemoved( + executorRemoved: SparkListenerExecutorRemoved): Unit = synchronized { + val eid = executorRemoved.executorId + val uiData = executorIdToData(eid) + uiData.finishTime = Some(executorRemoved.time) + uiData.finishReason = Some(executorRemoved.reason) } override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = synchronized { diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala index bd923d78a86ce..f6abf27db49dd 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala @@ -17,17 +17,183 @@ package org.apache.spark.ui.jobs -import scala.xml.{Node, NodeSeq} +import scala.collection.mutable.{HashMap, ListBuffer} +import scala.xml.{Node, NodeSeq, Unparsed} +import java.util.Date import javax.servlet.http.HttpServletRequest -import org.apache.spark.ui.{WebUIPage, UIUtils} -import org.apache.spark.ui.jobs.UIData.JobUIData +import org.apache.spark.ui.{UIUtils, WebUIPage} +import org.apache.spark.ui.jobs.UIData.{ExecutorUIData, JobUIData} +import org.apache.spark.JobExecutionStatus /** Page showing list of all ongoing and recently finished jobs */ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { - private val startTime: Option[Long] = parent.sc.map(_.startTime) - private val listener = parent.listener + private val JOBS_LEGEND = +
+ + Succeeded + + Failed + + Running +
.toString.filter(_ != '\n') + + private val EXECUTORS_LEGEND = +
+ + Added + + Removed +
.toString.filter(_ != '\n') + + private def getLastStageNameAndDescription(job: JobUIData): (String, String) = { + val lastStageInfo = Option(job.stageIds) + .filter(_.nonEmpty) + .flatMap { ids => parent.jobProgresslistener.stageIdToInfo.get(ids.max)} + val lastStageData = lastStageInfo.flatMap { s => + parent.jobProgresslistener.stageIdToData.get((s.stageId, s.attemptId)) + } + val name = lastStageInfo.map(_.name).getOrElse("(Unknown Stage Name)") + val description = lastStageData.flatMap(_.description).getOrElse("") + (name, description) + } + + private def makeJobEvent(jobUIDatas: Seq[JobUIData]): Seq[String] = { + jobUIDatas.filter { jobUIData => + jobUIData.status != JobExecutionStatus.UNKNOWN && jobUIData.submissionTime.isDefined + }.map { jobUIData => + val jobId = jobUIData.jobId + val status = jobUIData.status + val (jobName, jobDescription) = getLastStageNameAndDescription(jobUIData) + val displayJobDescription = if (jobDescription.isEmpty) jobName else jobDescription + val submissionTime = jobUIData.submissionTime.get + val completionTimeOpt = jobUIData.completionTime + val completionTime = completionTimeOpt.getOrElse(System.currentTimeMillis()) + val classNameByStatus = status match { + case JobExecutionStatus.SUCCEEDED => "succeeded" + case JobExecutionStatus.FAILED => "failed" + case JobExecutionStatus.RUNNING => "running" + } + + val jobEventJsonAsStr = + s""" + |{ + | 'className': 'job application-timeline-object ${classNameByStatus}', + | 'group': 'jobs', + | 'start': new Date(${submissionTime}), + | 'end': new Date(${completionTime}), + | 'content': '
Completion Time: ${UIUtils.formatDate(new Date(completionTime))}""" + } else { + "" + } + }">' + + | '${displayJobDescription} (Job ${jobId})
' + |} + """.stripMargin + jobEventJsonAsStr + } + } + + private def makeExecutorEvent(executorUIDatas: HashMap[String, ExecutorUIData]): Seq[String] = { + val events = ListBuffer[String]() + executorUIDatas.foreach { + case (executorId, event) => + val addedEvent = + s""" + |{ + | 'className': 'executor added', + | 'group': 'executors', + | 'start': new Date(${event.startTime}), + | 'content': '
Executor ${executorId} added
' + |} + """.stripMargin + events += addedEvent + + if (event.finishTime.isDefined) { + val removedEvent = + s""" + |{ + | 'className': 'executor removed', + | 'group': 'executors', + | 'start': new Date(${event.finishTime.get}), + | 'content': '
Reason: ${event.finishReason.get}""" + } else { + "" + } + }"' + + | 'data-html="true">Executor ${executorId} removed
' + |} + """.stripMargin + events += removedEvent + } + } + events.toSeq + } + + private def makeTimeline( + jobs: Seq[JobUIData], + executors: HashMap[String, ExecutorUIData], + startTime: Long): Seq[Node] = { + + val jobEventJsonAsStrSeq = makeJobEvent(jobs) + val executorEventJsonAsStrSeq = makeExecutorEvent(executors) + + val groupJsonArrayAsStr = + s""" + |[ + | { + | 'id': 'executors', + | 'content': '
Executors
${EXECUTORS_LEGEND}', + | }, + | { + | 'id': 'jobs', + | 'content': '
Jobs
${JOBS_LEGEND}', + | } + |] + """.stripMargin + + val eventArrayAsStr = + (jobEventJsonAsStrSeq ++ executorEventJsonAsStrSeq).mkString("[", ",", "]") + + + + Event timeline + ++ + ++ + + } private def jobsTable(jobs: Seq[JobUIData]): Seq[Node] = { val someJobHasJobGroup = jobs.exists(_.jobGroup.isDefined) @@ -42,15 +208,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { } def makeRow(job: JobUIData): Seq[Node] = { - val lastStageInfo = Option(job.stageIds) - .filter(_.nonEmpty) - .flatMap { ids => listener.stageIdToInfo.get(ids.max) } - val lastStageData = lastStageInfo.flatMap { s => - listener.stageIdToData.get((s.stageId, s.attemptId)) - } - - val lastStageName = lastStageInfo.map(_.name).getOrElse("(Unknown Stage Name)") - val lastStageDescription = lastStageData.flatMap(_.description).getOrElse("") + val (lastStageName, lastStageDescription) = getLastStageNameAndDescription(job) val duration: Option[Long] = { job.submissionTime.map { start => val end = job.completionTime.getOrElse(System.currentTimeMillis()) @@ -61,7 +219,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { val formattedSubmissionTime = job.submissionTime.map(UIUtils.formatDate).getOrElse("Unknown") val detailUrl = "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), job.jobId) - + {job.jobId} {job.jobGroup.map(id => s"($id)").getOrElse("")} @@ -95,11 +253,12 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { } def render(request: HttpServletRequest): Seq[Node] = { + val listener = parent.jobProgresslistener listener.synchronized { + val startTime = listener.startTime val activeJobs = listener.activeJobs.values.toSeq val completedJobs = listener.completedJobs.reverse.toSeq val failedJobs = listener.failedJobs.reverse.toSeq - val now = System.currentTimeMillis val activeJobsTable = jobsTable(activeJobs.sortBy(_.submissionTime.getOrElse(-1L)).reverse) @@ -115,11 +274,11 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { val summary: NodeSeq =
    - {if (startTime.isDefined) { + {if (parent.sc.isDefined) { // Total duration is not meaningful unless the UI is live
  • Total Duration: - {UIUtils.formatDuration(now - startTime.get)} + {UIUtils.formatDuration(System.currentTimeMillis() - startTime)}
    • }}
  • @@ -154,6 +313,10 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
var content = summary + val executorListener = parent.executorListener + content ++= makeTimeline(activeJobs ++ completedJobs ++ failedJobs, + executorListener.executorIdToData, startTime) + if (shouldShowActiveJobs) { content ++=

Active Jobs ({activeJobs.size})

++ activeJobsTable @@ -166,6 +329,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { content ++=

Failed Jobs ({failedJobs.size})

++ failedJobsTable } + val helpText = """A job is triggered by an action, like "count()" or "saveAsTextFile()".""" + " Click on a job's title to see information about the stages of tasks associated with" + " the job." diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala index 527f960af2dfc..236bc8ea92879 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala @@ -27,7 +27,7 @@ import org.apache.spark.ui.{WebUIPage, UIUtils} /** Page showing list of all ongoing and recently finished stages and pools */ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") { private val sc = parent.sc - private val listener = parent.listener + private val listener = parent.progressListener private def isFairScheduler = parent.isFairScheduler def render(request: HttpServletRequest): Seq[Node] = { @@ -42,18 +42,18 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") { val activeStagesTable = new StageTableBase(activeStages.sortBy(_.submissionTime).reverse, - parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler, + parent.basePath, parent.progressListener, isFairScheduler = parent.isFairScheduler, killEnabled = parent.killEnabled) val pendingStagesTable = new StageTableBase(pendingStages.sortBy(_.submissionTime).reverse, - parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler, + parent.basePath, parent.progressListener, isFairScheduler = parent.isFairScheduler, killEnabled = false) val completedStagesTable = new StageTableBase(completedStages.sortBy(_.submissionTime).reverse, parent.basePath, - parent.listener, isFairScheduler = parent.isFairScheduler, killEnabled = false) + parent.progressListener, isFairScheduler = parent.isFairScheduler, killEnabled = false) val failedStagesTable = new FailedStageTable(failedStages.sortBy(_.submissionTime).reverse, parent.basePath, - parent.listener, isFairScheduler = parent.isFairScheduler) + parent.progressListener, isFairScheduler = parent.isFairScheduler) // For now, pool information is only accessible in live UIs val pools = sc.map(_.getAllPools).getOrElse(Seq.empty[Schedulable]) diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala index 1f8536d1b7195..d5cdbfac104f8 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala @@ -26,7 +26,7 @@ import org.apache.spark.util.Utils /** Stage summary grouped by executors. */ private[ui] class ExecutorTable(stageId: Int, stageAttemptId: Int, parent: StagesTab) { - private val listener = parent.listener + private val listener = parent.progressListener def toNodeSeq: Seq[Node] = { listener.synchronized { diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala index 7541d3e9c72e7..96cc3d78d0f15 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala @@ -17,20 +17,168 @@ package org.apache.spark.ui.jobs -import scala.collection.mutable -import scala.xml.{NodeSeq, Node} +import java.util.Date + +import scala.collection.mutable.{Buffer, HashMap, ListBuffer} +import scala.xml.{NodeSeq, Node, Unparsed} import javax.servlet.http.HttpServletRequest import org.apache.spark.JobExecutionStatus import org.apache.spark.scheduler.StageInfo import org.apache.spark.ui.{UIUtils, WebUIPage} +import org.apache.spark.ui.jobs.UIData.ExecutorUIData /** Page showing statistics and stage list for a given job */ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") { - private val listener = parent.listener + + private val STAGES_LEGEND = +
+ + Completed + + Failed + + Active +
.toString.filter(_ != '\n') + + private val EXECUTORS_LEGEND = +
+ + Added + + Removed +
.toString.filter(_ != '\n') + + private def makeStageEvent(stageInfos: Seq[StageInfo]): Seq[String] = { + stageInfos.map { stage => + val stageId = stage.stageId + val attemptId = stage.attemptId + val name = stage.name + val status = stage.getStatusString + val submissionTime = stage.submissionTime.get + val completionTime = stage.completionTime.getOrElse(System.currentTimeMillis()) + + s""" + |{ + | 'className': 'stage job-timeline-object ${status}', + | 'group': 'stages', + | 'start': new Date(${submissionTime}), + | 'end': new Date(${completionTime}), + | 'content': '
Completion Time: ${UIUtils.formatDate(new Date(completionTime))}""" + } else { + "" + } + }">' + + | '${name} (Stage ${stageId}.${attemptId})
', + |} + """.stripMargin + } + } + + def makeExecutorEvent(executorUIDatas: HashMap[String, ExecutorUIData]): Seq[String] = { + val events = ListBuffer[String]() + executorUIDatas.foreach { + case (executorId, event) => + val addedEvent = + s""" + |{ + | 'className': 'executor added', + | 'group': 'executors', + | 'start': new Date(${event.startTime}), + | 'content': '
Executor ${executorId} added
' + |} + """.stripMargin + events += addedEvent + + if (event.finishTime.isDefined) { + val removedEvent = + s""" + |{ + | 'className': 'executor removed', + | 'group': 'executors', + | 'start': new Date(${event.finishTime.get}), + | 'content': '
Reason: ${event.finishReason.get}""" + } else { + "" + } + }"' + + | 'data-html="true">Executor ${executorId} removed
' + |} + """.stripMargin + events += removedEvent + } + } + events.toSeq + } + + private def makeTimeline( + stages: Seq[StageInfo], + executors: HashMap[String, ExecutorUIData], + appStartTime: Long): Seq[Node] = { + + val stageEventJsonAsStrSeq = makeStageEvent(stages) + val executorsJsonAsStrSeq = makeExecutorEvent(executors) + + val groupJsonArrayAsStr = + s""" + |[ + | { + | 'id': 'executors', + | 'content': '
Executors
${EXECUTORS_LEGEND}', + | }, + | { + | 'id': 'stages', + | 'content': '
Stages
${STAGES_LEGEND}', + | } + |] + """.stripMargin + + val eventArrayAsStr = + (stageEventJsonAsStrSeq ++ executorsJsonAsStrSeq).mkString("[", ",", "]") + + + + Event timeline + ++ + ++ + + } def render(request: HttpServletRequest): Seq[Node] = { + val listener = parent.jobProgresslistener + listener.synchronized { val parameterId = request.getParameter("id") require(parameterId != null && parameterId.nonEmpty, "Missing id parameter") @@ -51,14 +199,14 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") { // This could be empty if the JobProgressListener hasn't received information about the // stage or if the stage information has been garbage collected listener.stageIdToInfo.getOrElse(stageId, - new StageInfo(stageId, 0, "Unknown", 0, Seq.empty, "Unknown")) + new StageInfo(stageId, 0, "Unknown", 0, Seq.empty, Seq.empty, "Unknown")) } - val activeStages = mutable.Buffer[StageInfo]() - val completedStages = mutable.Buffer[StageInfo]() + val activeStages = Buffer[StageInfo]() + val completedStages = Buffer[StageInfo]() // If the job is completed, then any pending stages are displayed as "skipped": - val pendingOrSkippedStages = mutable.Buffer[StageInfo]() - val failedStages = mutable.Buffer[StageInfo]() + val pendingOrSkippedStages = Buffer[StageInfo]() + val failedStages = Buffer[StageInfo]() for (stage <- stages) { if (stage.submissionTime.isEmpty) { pendingOrSkippedStages += stage @@ -75,18 +223,18 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") { val activeStagesTable = new StageTableBase(activeStages.sortBy(_.submissionTime).reverse, - parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler, + parent.basePath, parent.jobProgresslistener, isFairScheduler = parent.isFairScheduler, killEnabled = parent.killEnabled) val pendingOrSkippedStagesTable = new StageTableBase(pendingOrSkippedStages.sortBy(_.stageId).reverse, - parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler, + parent.basePath, parent.jobProgresslistener, isFairScheduler = parent.isFairScheduler, killEnabled = false) val completedStagesTable = new StageTableBase(completedStages.sortBy(_.submissionTime).reverse, parent.basePath, - parent.listener, isFairScheduler = parent.isFairScheduler, killEnabled = false) + parent.jobProgresslistener, isFairScheduler = parent.isFairScheduler, killEnabled = false) val failedStagesTable = new FailedStageTable(failedStages.sortBy(_.submissionTime).reverse, parent.basePath, - parent.listener, isFairScheduler = parent.isFairScheduler) + parent.jobProgresslistener, isFairScheduler = parent.isFairScheduler) val shouldShowActiveStages = activeStages.nonEmpty val shouldShowPendingStages = !isComplete && pendingOrSkippedStages.nonEmpty @@ -154,6 +302,16 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") { var content = summary + val appStartTime = listener.startTime + val executorListener = parent.executorListener + val operationGraphListener = parent.operationGraphListener + + content ++= makeTimeline(activeStages ++ completedStages ++ failedStages, + executorListener.executorIdToData, appStartTime) + + content ++= UIUtils.showDagVizForJob( + jobId, operationGraphListener.getOperationGraphForJob(jobId)) + if (shouldShowActiveStages) { content ++=

Active Stages ({activeStages.size})

++ activeStagesTable.toNodeSeq @@ -174,7 +332,7 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") { content ++=

Failed Stages ({failedStages.size})

++ failedStagesTable.toNodeSeq } - UIUtils.headerSparkPage(s"Details for Job $jobId", content, parent) + UIUtils.headerSparkPage(s"Details for Job $jobId", content, parent, showVisualization = true) } } } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala index 625596885faa1..8f9aa9fdec819 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala @@ -25,6 +25,7 @@ import org.apache.spark.executor.TaskMetrics import org.apache.spark.scheduler._ import org.apache.spark.scheduler.SchedulingMode.SchedulingMode import org.apache.spark.storage.BlockManagerId +import org.apache.spark.ui.SparkUI import org.apache.spark.ui.jobs.UIData._ /** @@ -38,8 +39,6 @@ import org.apache.spark.ui.jobs.UIData._ @DeveloperApi class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { - import JobProgressListener._ - // Define a handful of type aliases so that data structures' types can serve as documentation. // These type aliases are public because they're used in the types of public fields: @@ -50,6 +49,9 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { type PoolName = String type ExecutorId = String + // Applicatin: + @volatile var startTime = -1L + // Jobs: val activeJobs = new HashMap[JobId, JobUIData] val completedJobs = ListBuffer[JobUIData]() @@ -75,6 +77,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { // Misc: val executorIdToBlockManagerId = HashMap[ExecutorId, BlockManagerId]() + def blockManagerIds: Seq[BlockManagerId] = executorIdToBlockManagerId.values.toSeq var schedulingMode: Option[SchedulingMode] = None @@ -82,8 +85,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { // To limit the total memory usage of JobProgressListener, we only track information for a fixed // number of non-active jobs and stages (there is no limit for active jobs and stages): - val retainedStages = conf.getInt("spark.ui.retainedStages", DEFAULT_RETAINED_STAGES) - val retainedJobs = conf.getInt("spark.ui.retainedJobs", DEFAULT_RETAINED_JOBS) + val retainedStages = conf.getInt("spark.ui.retainedStages", SparkUI.DEFAULT_RETAINED_STAGES) + val retainedJobs = conf.getInt("spark.ui.retainedJobs", SparkUI.DEFAULT_RETAINED_JOBS) // We can test for memory leaks by ensuring that collections that track non-active jobs and // stages do not grow without bound and that collections for active jobs/stages eventually become @@ -284,8 +287,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { activeStages(stage.stageId) = stage pendingStages.remove(stage.stageId) val poolName = Option(stageSubmitted.properties).map { - p => p.getProperty("spark.scheduler.pool", DEFAULT_POOL_NAME) - }.getOrElse(DEFAULT_POOL_NAME) + p => p.getProperty("spark.scheduler.pool", SparkUI.DEFAULT_POOL_NAME) + }.getOrElse(SparkUI.DEFAULT_POOL_NAME) stageIdToInfo(stage.stageId) = stage val stageData = stageIdToData.getOrElseUpdate((stage.stageId, stage.attemptId), new StageUIData) @@ -516,10 +519,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } } -} - -private object JobProgressListener { - val DEFAULT_POOL_NAME = "default" - val DEFAULT_RETAINED_STAGES = 1000 - val DEFAULT_RETAINED_JOBS = 1000 + override def onApplicationStart(appStarted: SparkListenerApplicationStart) { + startTime = appStarted.time + } } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala index 7ffcf291b5cc6..77ca60b000a9b 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala @@ -24,8 +24,12 @@ import org.apache.spark.ui.{SparkUI, SparkUITab} private[ui] class JobsTab(parent: SparkUI) extends SparkUITab(parent, "jobs") { val sc = parent.sc val killEnabled = parent.killEnabled - def isFairScheduler: Boolean = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) - val listener = parent.jobProgressListener + val jobProgresslistener = parent.jobProgressListener + val executorListener = parent.executorsListener + val operationGraphListener = parent.operationGraphListener + + def isFairScheduler: Boolean = + jobProgresslistener.schedulingMode.exists(_ == SchedulingMode.FAIR) attachPage(new AllJobsPage(this)) attachPage(new JobPage(this)) diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala index f47cdc935e539..d725b9d8565ac 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala @@ -27,7 +27,7 @@ import org.apache.spark.ui.{WebUIPage, UIUtils} /** Page showing specific pool details */ private[ui] class PoolPage(parent: StagesTab) extends WebUIPage("pool") { private val sc = parent.sc - private val listener = parent.listener + private val listener = parent.progressListener def render(request: HttpServletRequest): Seq[Node] = { listener.synchronized { @@ -40,7 +40,7 @@ private[ui] class PoolPage(parent: StagesTab) extends WebUIPage("pool") { case None => Seq[StageInfo]() } val activeStagesTable = new StageTableBase(activeStages.sortBy(_.submissionTime).reverse, - parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler, + parent.basePath, parent.progressListener, isFairScheduler = parent.isFairScheduler, killEnabled = parent.killEnabled) // For now, pool information is only accessible in live UIs diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala index df1899e7a9b84..9ba2af54dacf4 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala @@ -25,7 +25,7 @@ import org.apache.spark.ui.UIUtils /** Table showing list of pools */ private[ui] class PoolTable(pools: Seq[Schedulable], parent: StagesTab) { - private val listener = parent.listener + private val listener = parent.progressListener def toNodeSeq: Seq[Node] = { listener.synchronized { diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala index 797c9404bc449..579310070c76c 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala @@ -27,15 +27,17 @@ import org.apache.commons.lang3.StringEscapeUtils import org.apache.spark.executor.TaskMetrics import org.apache.spark.ui.{ToolTips, WebUIPage, UIUtils} import org.apache.spark.ui.jobs.UIData._ +import org.apache.spark.ui.scope.RDDOperationGraph import org.apache.spark.util.{Utils, Distribution} import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo} /** Page showing statistics and task list for a given stage */ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { - private val listener = parent.listener + private val progressListener = parent.progressListener + private val operationGraphListener = parent.operationGraphListener def render(request: HttpServletRequest): Seq[Node] = { - listener.synchronized { + progressListener.synchronized { val parameterId = request.getParameter("id") require(parameterId != null && parameterId.nonEmpty, "Missing id parameter") @@ -44,7 +46,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { val stageId = parameterId.toInt val stageAttemptId = parameterAttempt.toInt - val stageDataOption = listener.stageIdToData.get((stageId, stageAttemptId)) + val stageDataOption = progressListener.stageIdToData.get((stageId, stageAttemptId)) if (stageDataOption.isEmpty || stageDataOption.get.taskData.isEmpty) { val content = @@ -60,7 +62,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { val tasks = stageData.taskData.values.toSeq.sortBy(_.taskInfo.launchTime) val numCompleted = tasks.count(_.taskInfo.finished) - val accumulables = listener.stageIdToData((stageId, stageAttemptId)).accumulables + val accumulables = progressListener.stageIdToData((stageId, stageAttemptId)).accumulables val hasAccumulators = accumulables.size > 0 val summary = @@ -169,6 +171,9 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { + val dagViz = UIUtils.showDagVizForStage( + stageId, operationGraphListener.getOperationGraphForStage(stageId)) + val accumulableHeaders: Seq[String] = Seq("Accumulable", "Value") def accumulableRow(acc: AccumulableInfo): Elem = {acc.name}{acc.value} @@ -434,13 +439,15 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { val content = summary ++ showAdditionalMetrics ++ + dagViz ++

Summary Metrics for {numCompleted} Completed Tasks

++
{summaryTable.getOrElse("No tasks have reported metrics yet.")}
++

Aggregated Metrics by Executor

++ executorTable.toNodeSeq ++ maybeAccumulableTable ++

Tasks

++ taskTable - UIUtils.headerSparkPage("Details for Stage %d".format(stageId), content, parent) + UIUtils.headerSparkPage( + "Details for Stage %d".format(stageId), content, parent, showVisualization = true) } } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala index cb72890a0fd20..a33243d4252bf 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala @@ -76,15 +76,20 @@ private[ui] class StageTableBase( val basePathUri = UIUtils.prependBaseUri(basePath) val killLink = if (killEnabled) { - val killLinkUri = s"$basePathUri/stages/stage/kill/" val confirm = s"if (window.confirm('Are you sure you want to kill stage ${s.stageId} ?')) " + "{ this.parentNode.submit(); return true; } else { return false; }" + // SPARK-6846 this should be POST-only but YARN AM won't proxy POST + /* + val killLinkUri = s"$basePathUri/stages/stage/kill/"
(kill)
+ */ + val killLinkUri = s"$basePathUri/stages/stage/kill/?id=${s.stageId}&terminate=true" + (kill) } val nameLinkUri = s"$basePathUri/stages/stage?id=${s.stageId}&attempt=${s.attemptId}" @@ -174,7 +179,8 @@ private[ui] class StageTableBase( } /** Render an HTML row that represents a stage */ - private def renderStageRow(s: StageInfo): Seq[Node] = {stageRow(s)} + private def renderStageRow(s: StageInfo): Seq[Node] = + {stageRow(s)} } private[ui] class FailedStageTable( diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala index 1bd2d87e00796..55169956d8304 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala @@ -26,19 +26,20 @@ import org.apache.spark.ui.{SparkUI, SparkUITab} private[ui] class StagesTab(parent: SparkUI) extends SparkUITab(parent, "stages") { val sc = parent.sc val killEnabled = parent.killEnabled - val listener = parent.jobProgressListener + val progressListener = parent.jobProgressListener + val operationGraphListener = parent.operationGraphListener attachPage(new AllStagesPage(this)) attachPage(new StagePage(this)) attachPage(new PoolPage(this)) - def isFairScheduler: Boolean = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) + def isFairScheduler: Boolean = progressListener.schedulingMode.exists(_ == SchedulingMode.FAIR) def handleKillRequest(request: HttpServletRequest): Unit = { if (killEnabled && parent.securityManager.checkModifyPermissions(request.getRemoteUser)) { val killFlag = Option(request.getParameter("terminate")).getOrElse("false").toBoolean val stageId = Option(request.getParameter("id")).getOrElse("-1").toInt - if (stageId >= 0 && killFlag && listener.activeStages.contains(stageId)) { + if (stageId >= 0 && killFlag && progressListener.activeStages.contains(stageId)) { sc.get.cancelStage(stageId) } // Do a quick pause here to give Spark time to kill the stage so it shows up as diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala index 711a3697bda15..3d96113aa5fe9 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala @@ -24,7 +24,7 @@ import org.apache.spark.util.collection.OpenHashSet import scala.collection.mutable.HashMap -private[jobs] object UIData { +private[spark] object UIData { class ExecutorSummary { var taskTime : Long = 0 @@ -108,4 +108,9 @@ private[jobs] object UIData { var taskInfo: TaskInfo, var taskMetrics: Option[TaskMetrics] = None, var errorMessage: Option[String] = None) + + case class ExecutorUIData( + val startTime: Long, + var finishTime: Option[Long] = None, + var finishReason: Option[String] = None) } diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala new file mode 100644 index 0000000000000..edf005f7f325a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala @@ -0,0 +1,212 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ui.scope + +import scala.collection.mutable +import scala.collection.mutable.ListBuffer + +import org.apache.spark.Logging +import org.apache.spark.scheduler.StageInfo +import org.apache.spark.storage.StorageLevel + +/** + * A representation of a generic cluster graph used for storing information on RDD operations. + * + * Each graph is defined with a set of edges and a root cluster, which may contain children + * nodes and children clusters. Additionally, a graph may also have edges that enter or exit + * the graph from nodes that belong to adjacent graphs. + */ +private[ui] case class RDDOperationGraph( + edges: Seq[RDDOperationEdge], + outgoingEdges: Seq[RDDOperationEdge], + incomingEdges: Seq[RDDOperationEdge], + rootCluster: RDDOperationCluster) + +/** A node in an RDDOperationGraph. This represents an RDD. */ +private[ui] case class RDDOperationNode(id: Int, name: String, cached: Boolean) + +/** + * A directed edge connecting two nodes in an RDDOperationGraph. + * This represents an RDD dependency. + */ +private[ui] case class RDDOperationEdge(fromId: Int, toId: Int) + +/** + * A cluster that groups nodes together in an RDDOperationGraph. + * + * This represents any grouping of RDDs, including operation scopes (e.g. textFile, flatMap), + * stages, jobs, or any higher level construct. A cluster may be nested inside of other clusters. + */ +private[ui] class RDDOperationCluster(val id: String, val name: String) { + private val _childNodes = new ListBuffer[RDDOperationNode] + private val _childClusters = new ListBuffer[RDDOperationCluster] + + def childNodes: Seq[RDDOperationNode] = _childNodes.iterator.toSeq + def childClusters: Seq[RDDOperationCluster] = _childClusters.iterator.toSeq + def attachChildNode(childNode: RDDOperationNode): Unit = { _childNodes += childNode } + def attachChildCluster(childCluster: RDDOperationCluster): Unit = { + _childClusters += childCluster + } + + /** Return all the nodes container in this cluster, including ones nested in other clusters. */ + def getAllNodes: Seq[RDDOperationNode] = { + _childNodes ++ _childClusters.flatMap(_.childNodes) + } +} + +private[ui] object RDDOperationGraph extends Logging { + + /** + * Construct a RDDOperationGraph for a given stage. + * + * The root cluster represents the stage, and all children clusters represent RDD operations. + * Each node represents an RDD, and each edge represents a dependency between two RDDs pointing + * from the parent to the child. + * + * This does not currently merge common operation scopes across stages. This may be worth + * supporting in the future if we decide to group certain stages within the same job under + * a common scope (e.g. part of a SQL query). + */ + def makeOperationGraph(stage: StageInfo): RDDOperationGraph = { + val edges = new ListBuffer[RDDOperationEdge] + val nodes = new mutable.HashMap[Int, RDDOperationNode] + val clusters = new mutable.HashMap[String, RDDOperationCluster] // indexed by cluster ID + + // Root cluster is the stage cluster + val stageClusterId = s"stage_${stage.stageId}" + val stageClusterName = s"Stage ${stage.stageId}" + + { if (stage.attemptId == 0) "" else s" (attempt ${stage.attemptId})" } + val rootCluster = new RDDOperationCluster(stageClusterId, stageClusterName) + + // Find nodes, edges, and operation scopes that belong to this stage + stage.rddInfos.foreach { rdd => + edges ++= rdd.parentIds.map { parentId => RDDOperationEdge(parentId, rdd.id) } + + // TODO: differentiate between the intention to cache an RDD and whether it's actually cached + val node = nodes.getOrElseUpdate( + rdd.id, RDDOperationNode(rdd.id, rdd.name, rdd.storageLevel != StorageLevel.NONE)) + + if (rdd.scope == null) { + // This RDD has no encompassing scope, so we put it directly in the root cluster + // This should happen only if an RDD is instantiated outside of a public RDD API + rootCluster.attachChildNode(node) + } else { + // Otherwise, this RDD belongs to an inner cluster, + // which may be nested inside of other clusters + val rddScopes = rdd.scope.map { scope => scope.getAllScopes }.getOrElse(Seq.empty) + val rddClusters = rddScopes.map { scope => + val clusterId = scope.name + "_" + scope.id + val clusterName = scope.name + clusters.getOrElseUpdate(clusterId, new RDDOperationCluster(clusterId, clusterName)) + } + // Build the cluster hierarchy for this RDD + rddClusters.sliding(2).foreach { pc => + if (pc.size == 2) { + val parentCluster = pc(0) + val childCluster = pc(1) + parentCluster.attachChildCluster(childCluster) + } + } + // Attach the outermost cluster to the root cluster, and the RDD to the innermost cluster + rddClusters.headOption.foreach { cluster => rootCluster.attachChildCluster(cluster) } + rddClusters.lastOption.foreach { cluster => cluster.attachChildNode(node) } + } + } + + // Classify each edge as internal, outgoing or incoming + // This information is needed to reason about how stages relate to each other + val internalEdges = new ListBuffer[RDDOperationEdge] + val outgoingEdges = new ListBuffer[RDDOperationEdge] + val incomingEdges = new ListBuffer[RDDOperationEdge] + edges.foreach { case e: RDDOperationEdge => + val fromThisGraph = nodes.contains(e.fromId) + val toThisGraph = nodes.contains(e.toId) + (fromThisGraph, toThisGraph) match { + case (true, true) => internalEdges += e + case (true, false) => outgoingEdges += e + case (false, true) => incomingEdges += e + // should never happen + case _ => logWarning(s"Found an orphan edge in stage ${stage.stageId}: $e") + } + } + + RDDOperationGraph(internalEdges, outgoingEdges, incomingEdges, rootCluster) + } + + /** + * Generate the content of a dot file that describes the specified graph. + * + * Note that this only uses a minimal subset of features available to the DOT specification. + * Part of the styling must be done here because the rendering library must take certain + * attributes into account when arranging the graph elements. More style is added in the + * visualization later through post-processing in JavaScript. + * + * For the complete DOT specification, see http://www.graphviz.org/Documentation/dotguide.pdf. + */ + def makeDotFile(graph: RDDOperationGraph, forJob: Boolean): String = { + val dotFile = new StringBuilder + dotFile.append("digraph G {\n") + dotFile.append(makeDotSubgraph(graph.rootCluster, forJob, indent = " ")) + graph.edges.foreach { edge => + dotFile.append(s""" ${edge.fromId}->${edge.toId} [lineInterpolate="basis"];\n""") + } + dotFile.append("}") + val result = dotFile.toString() + logDebug(result) + result + } + + /** + * Return the dot representation of a node in an RDDOperationGraph. + * + * On the job page, is displayed as a small circle without labels. + * On the stage page, it is displayed as a box with an embedded label. + */ + private def makeDotNode(node: RDDOperationNode, forJob: Boolean): String = { + if (forJob) { + s"""${node.id} [label=" " shape="circle" padding="5" labelStyle="font-size: 0"]""" + } else { + s"""${node.id} [label="${node.name} (${node.id})" padding="5" labelStyle="font-size: 10"]""" + } + } + + /** Return the dot representation of a subgraph in an RDDOperationGraph. */ + private def makeDotSubgraph( + cluster: RDDOperationCluster, + forJob: Boolean, + indent: String): String = { + val subgraph = new StringBuilder + // TODO: move specific graph properties like these to spark-dag-viz.js + val paddingTop = if (forJob) 10 else 20 + subgraph.append(indent + s"subgraph cluster${cluster.id} {\n") + subgraph.append(indent + s""" label="${cluster.name}";\n""") + // If there are nested clusters, add some padding + // Do this for the stage page because we use bigger fonts there + if (cluster.childClusters.nonEmpty) { + subgraph.append(indent + s""" paddingTop="$paddingTop";\n""") + } + cluster.childNodes.foreach { node => + subgraph.append(indent + s" ${makeDotNode(node, forJob)};\n") + } + cluster.childClusters.foreach { cscope => + subgraph.append(makeDotSubgraph(cscope, forJob, indent + " ")) + } + subgraph.append(indent + "}\n") + subgraph.toString() + } +} diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraphListener.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraphListener.scala new file mode 100644 index 0000000000000..2884a49f31122 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraphListener.scala @@ -0,0 +1,68 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ui.scope + +import scala.collection.mutable + +import org.apache.spark.SparkConf +import org.apache.spark.scheduler._ +import org.apache.spark.ui.SparkUI + +/** + * A SparkListener that constructs a DAG of RDD operations. + */ +private[ui] class RDDOperationGraphListener(conf: SparkConf) extends SparkListener { + private val jobIdToStageIds = new mutable.HashMap[Int, Seq[Int]] + private val stageIdToGraph = new mutable.HashMap[Int, RDDOperationGraph] + private val stageIds = new mutable.ArrayBuffer[Int] + + // How many jobs or stages to retain graph metadata for + private val retainedStages = + conf.getInt("spark.ui.retainedStages", SparkUI.DEFAULT_RETAINED_STAGES) + + /** Return the graph metadata for the given stage, or None if no such information exists. */ + def getOperationGraphForJob(jobId: Int): Seq[RDDOperationGraph] = { + jobIdToStageIds.get(jobId) + .map { sids => sids.flatMap { sid => stageIdToGraph.get(sid) } } + .getOrElse { Seq.empty } + } + + /** Return the graph metadata for the given stage, or None if no such information exists. */ + def getOperationGraphForStage(stageId: Int): Option[RDDOperationGraph] = { + stageIdToGraph.get(stageId) + } + + /** On job start, construct a RDDOperationGraph for each stage in the job for display later. */ + override def onJobStart(jobStart: SparkListenerJobStart): Unit = synchronized { + val jobId = jobStart.jobId + val stageInfos = jobStart.stageInfos + + stageInfos.foreach { stageInfo => + stageIds += stageInfo.stageId + stageIdToGraph(stageInfo.stageId) = RDDOperationGraph.makeOperationGraph(stageInfo) + } + jobIdToStageIds(jobId) = stageInfos.map(_.stageId).sorted + + // Remove graph metadata for old stages + if (stageIds.size >= retainedStages) { + val toRemove = math.max(retainedStages / 10, 1) + stageIds.take(toRemove).foreach { id => stageIdToGraph.remove(id) } + stageIds.trimStart(toRemove) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala index 6ced6052d2b18..59dc6b547c1d8 100644 --- a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala +++ b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala @@ -42,7 +42,7 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") { "Cached Partitions", "Fraction Cached", "Size in Memory", - "Size in Tachyon", + "Size in ExternalBlockStore", "Size on Disk") /** Render an HTML row representing an RDD */ @@ -59,7 +59,7 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") { {rdd.numCachedPartitions} {"%.0f%%".format(rdd.numCachedPartitions * 100.0 / rdd.numPartitions)} {Utils.bytesToString(rdd.memSize)} - {Utils.bytesToString(rdd.tachyonSize)} + {Utils.bytesToString(rdd.externalBlockStoreSize)} {Utils.bytesToString(rdd.diskSize)} // scalastyle:on diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala index b725df3b44596..de3316d083a22 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -183,7 +183,9 @@ private[spark] object AkkaUtils extends Logging { lastException = e logWarning(s"Error sending message [message = $message] in $attempts attempts", e) } - Thread.sleep(retryInterval) + if (attempts < maxAttempts) { + Thread.sleep(retryInterval) + } } throw new SparkException( diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala index e3f52f6ff1e63..6fe32e469c732 100644 --- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala @@ -19,17 +19,20 @@ package org.apache.spark.util import java.io.{ByteArrayInputStream, ByteArrayOutputStream} -import scala.collection.mutable.Map -import scala.collection.mutable.Set +import scala.collection.mutable.{Map, Set} import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.{ClassReader, ClassVisitor, MethodVisitor, Type} import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._ import org.apache.spark.{Logging, SparkEnv, SparkException} +/** + * A cleaner that renders closures serializable if they can be done so safely. + */ private[spark] object ClosureCleaner extends Logging { + // Get an ASM class reader for a given class from the JAR that loaded it - private def getClassReader(cls: Class[_]): ClassReader = { + private[util] def getClassReader(cls: Class[_]): ClassReader = { // Copy data over, before delegating to ClassReader - else we can run out of open file handles. val className = cls.getName.replaceFirst("^.*\\.", "") + ".class" val resourceStream = cls.getResourceAsStream(className) @@ -55,10 +58,14 @@ private[spark] object ClosureCleaner extends Logging { private def getOuterClasses(obj: AnyRef): List[Class[_]] = { for (f <- obj.getClass.getDeclaredFields if f.getName == "$outer") { f.setAccessible(true) - if (isClosure(f.getType)) { - return f.getType :: getOuterClasses(f.get(obj)) - } else { - return f.getType :: Nil // Stop at the first $outer that is not a closure + val outer = f.get(obj) + // The outer pointer may be null if we have cleaned this closure before + if (outer != null) { + if (isClosure(f.getType)) { + return f.getType :: getOuterClasses(outer) + } else { + return f.getType :: Nil // Stop at the first $outer that is not a closure + } } } Nil @@ -68,16 +75,23 @@ private[spark] object ClosureCleaner extends Logging { private def getOuterObjects(obj: AnyRef): List[AnyRef] = { for (f <- obj.getClass.getDeclaredFields if f.getName == "$outer") { f.setAccessible(true) - if (isClosure(f.getType)) { - return f.get(obj) :: getOuterObjects(f.get(obj)) - } else { - return f.get(obj) :: Nil // Stop at the first $outer that is not a closure + val outer = f.get(obj) + // The outer pointer may be null if we have cleaned this closure before + if (outer != null) { + if (isClosure(f.getType)) { + return outer :: getOuterObjects(outer) + } else { + return outer :: Nil // Stop at the first $outer that is not a closure + } } } Nil } - private def getInnerClasses(obj: AnyRef): List[Class[_]] = { + /** + * Return a list of classes that represent closures enclosed in the given closure object. + */ + private def getInnerClosureClasses(obj: AnyRef): List[Class[_]] = { val seen = Set[Class[_]](obj.getClass) var stack = List[Class[_]](obj.getClass) while (!stack.isEmpty) { @@ -90,7 +104,7 @@ private[spark] object ClosureCleaner extends Logging { stack = cls :: stack } } - return (seen - obj.getClass).toList + (seen - obj.getClass).toList } private def createNullValue(cls: Class[_]): AnyRef = { @@ -101,21 +115,129 @@ private[spark] object ClosureCleaner extends Logging { } } - def clean(func: AnyRef, checkSerializable: Boolean = true) { + /** + * Clean the given closure in place. + * + * More specifically, this renders the given closure serializable as long as it does not + * explicitly reference unserializable objects. + * + * @param closure the closure to clean + * @param checkSerializable whether to verify that the closure is serializable after cleaning + * @param cleanTransitively whether to clean enclosing closures transitively + */ + def clean( + closure: AnyRef, + checkSerializable: Boolean = true, + cleanTransitively: Boolean = true): Unit = { + clean(closure, checkSerializable, cleanTransitively, Map.empty) + } + + /** + * Helper method to clean the given closure in place. + * + * The mechanism is to traverse the hierarchy of enclosing closures and null out any + * references along the way that are not actually used by the starting closure, but are + * nevertheless included in the compiled anonymous classes. Note that it is unsafe to + * simply mutate the enclosing closures in place, as other code paths may depend on them. + * Instead, we clone each enclosing closure and set the parent pointers accordingly. + * + * By default, closures are cleaned transitively. This means we detect whether enclosing + * objects are actually referenced by the starting one, either directly or transitively, + * and, if not, sever these closures from the hierarchy. In other words, in addition to + * nulling out unused field references, we also null out any parent pointers that refer + * to enclosing objects not actually needed by the starting closure. We determine + * transitivity by tracing through the tree of all methods ultimately invoked by the + * inner closure and record all the fields referenced in the process. + * + * For instance, transitive cleaning is necessary in the following scenario: + * + * class SomethingNotSerializable { + * def someValue = 1 + * def scope(name: String)(body: => Unit) = body + * def someMethod(): Unit = scope("one") { + * def x = someValue + * def y = 2 + * scope("two") { println(y + 1) } + * } + * } + * + * In this example, scope "two" is not serializable because it references scope "one", which + * references SomethingNotSerializable. Note that, however, the body of scope "two" does not + * actually depend on SomethingNotSerializable. This means we can safely null out the parent + * pointer of a cloned scope "one" and set it the parent of scope "two", such that scope "two" + * no longer references SomethingNotSerializable transitively. + * + * @param func the starting closure to clean + * @param checkSerializable whether to verify that the closure is serializable after cleaning + * @param cleanTransitively whether to clean enclosing closures transitively + * @param accessedFields a map from a class to a set of its fields that are accessed by + * the starting closure + */ + private def clean( + func: AnyRef, + checkSerializable: Boolean, + cleanTransitively: Boolean, + accessedFields: Map[Class[_], Set[String]]): Unit = { + + if (!isClosure(func.getClass)) { + logWarning("Expected a closure; got " + func.getClass.getName) + return + } + + // TODO: clean all inner closures first. This requires us to find the inner objects. // TODO: cache outerClasses / innerClasses / accessedFields + + if (func == null) { + return + } + + logDebug(s"+++ Cleaning closure $func (${func.getClass.getName}}) +++") + + // A list of classes that represents closures enclosed in the given one + val innerClasses = getInnerClosureClasses(func) + + // A list of enclosing objects and their respective classes, from innermost to outermost + // An outer object at a given index is of type outer class at the same index val outerClasses = getOuterClasses(func) - val innerClasses = getInnerClasses(func) val outerObjects = getOuterObjects(func) - val accessedFields = Map[Class[_], Set[String]]() - + // For logging purposes only + val declaredFields = func.getClass.getDeclaredFields + val declaredMethods = func.getClass.getDeclaredMethods + + logDebug(" + declared fields: " + declaredFields.size) + declaredFields.foreach { f => logDebug(" " + f) } + logDebug(" + declared methods: " + declaredMethods.size) + declaredMethods.foreach { m => logDebug(" " + m) } + logDebug(" + inner classes: " + innerClasses.size) + innerClasses.foreach { c => logDebug(" " + c.getName) } + logDebug(" + outer classes: " + outerClasses.size) + outerClasses.foreach { c => logDebug(" " + c.getName) } + logDebug(" + outer objects: " + outerObjects.size) + outerObjects.foreach { o => logDebug(" " + o) } + + // Fail fast if we detect return statements in closures getClassReader(func.getClass).accept(new ReturnStatementFinder(), 0) - - for (cls <- outerClasses) - accessedFields(cls) = Set[String]() - for (cls <- func.getClass :: innerClasses) - getClassReader(cls).accept(new FieldAccessFinder(accessedFields), 0) - // logInfo("accessedFields: " + accessedFields) + + // If accessed fields is not populated yet, we assume that + // the closure we are trying to clean is the starting one + if (accessedFields.isEmpty) { + logDebug(s" + populating accessed fields because this is the starting closure") + // Initialize accessed fields with the outer classes first + // This step is needed to associate the fields to the correct classes later + for (cls <- outerClasses) { + accessedFields(cls) = Set[String]() + } + // Populate accessed fields by visiting all fields and methods accessed by this and + // all of its inner closures. If transitive cleaning is enabled, this may recursively + // visits methods that belong to other classes in search of transitively referenced fields. + for (cls <- func.getClass :: innerClasses) { + getClassReader(cls).accept(new FieldAccessFinder(accessedFields, cleanTransitively), 0) + } + } + + logDebug(s" + fields accessed by starting closure: " + accessedFields.size) + accessedFields.foreach { f => logDebug(" " + f) } val inInterpreter = { try { @@ -126,34 +248,68 @@ private[spark] object ClosureCleaner extends Logging { } } + // List of outer (class, object) pairs, ordered from outermost to innermost + // Note that all outer objects but the outermost one (first one in this list) must be closures var outerPairs: List[(Class[_], AnyRef)] = (outerClasses zip outerObjects).reverse - var outer: AnyRef = null + var parent: AnyRef = null if (outerPairs.size > 0 && !isClosure(outerPairs.head._1)) { // The closure is ultimately nested inside a class; keep the object of that // class without cloning it since we don't want to clone the user's objects. - outer = outerPairs.head._2 + // Note that we still need to keep around the outermost object itself because + // we need it to clone its child closure later (see below). + logDebug(s" + outermost object is not a closure, so do not clone it: ${outerPairs.head}") + parent = outerPairs.head._2 // e.g. SparkContext outerPairs = outerPairs.tail + } else if (outerPairs.size > 0) { + logDebug(s" + outermost object is a closure, so we just keep it: ${outerPairs.head}") + } else { + logDebug(" + there are no enclosing objects!") } + // Clone the closure objects themselves, nulling out any fields that are not // used in the closure we're working on or any of its inner closures. for ((cls, obj) <- outerPairs) { - outer = instantiateClass(cls, outer, inInterpreter) + logDebug(s" + cloning the object $obj of class ${cls.getName}") + // We null out these unused references by cloning each object and then filling in all + // required fields from the original object. We need the parent here because the Java + // language specification requires the first constructor parameter of any closure to be + // its enclosing object. + val clone = instantiateClass(cls, parent, inInterpreter) for (fieldName <- accessedFields(cls)) { val field = cls.getDeclaredField(fieldName) field.setAccessible(true) val value = field.get(obj) - // logInfo("1: Setting " + fieldName + " on " + cls + " to " + value); - field.set(outer, value) + field.set(clone, value) } + // If transitive cleaning is enabled, we recursively clean any enclosing closure using + // the already populated accessed fields map of the starting closure + if (cleanTransitively && isClosure(clone.getClass)) { + logDebug(s" + cleaning cloned closure $clone recursively (${cls.getName})") + // No need to check serializable here for the outer closures because we're + // only interested in the serializability of the starting closure + clean(clone, checkSerializable = false, cleanTransitively, accessedFields) + } + parent = clone } - if (outer != null) { - // logInfo("2: Setting $outer on " + func.getClass + " to " + outer); + // Update the parent pointer ($outer) of this closure + if (parent != null) { val field = func.getClass.getDeclaredField("$outer") field.setAccessible(true) - field.set(func, outer) + // If the starting closure doesn't actually need our enclosing object, then just null it out + if (accessedFields.contains(func.getClass) && + !accessedFields(func.getClass).contains("$outer")) { + logDebug(s" + the starting closure doesn't actually need $parent, so we null it out") + field.set(func, null) + } else { + // Update this closure's parent pointer to point to our enclosing object, + // which could either be a cloned closure or the original user object + field.set(func, parent) + } } - + + logDebug(s" +++ closure $func (${func.getClass.getName}) is now cleaned +++") + if (checkSerializable) { ensureSerializable(func) } @@ -161,21 +317,25 @@ private[spark] object ClosureCleaner extends Logging { private def ensureSerializable(func: AnyRef) { try { - SparkEnv.get.closureSerializer.newInstance().serialize(func) + if (SparkEnv.get != null) { + SparkEnv.get.closureSerializer.newInstance().serialize(func) + } } catch { case ex: Exception => throw new SparkException("Task not serializable", ex) } } - private def instantiateClass(cls: Class[_], outer: AnyRef, inInterpreter: Boolean): AnyRef = { - // logInfo("Creating a " + cls + " with outer = " + outer) + private def instantiateClass( + cls: Class[_], + enclosingObject: AnyRef, + inInterpreter: Boolean): AnyRef = { if (!inInterpreter) { // This is a bona fide closure class, whose constructor has no effects // other than to set its fields, so use its constructor val cons = cls.getConstructors()(0) val params = cons.getParameterTypes.map(createNullValue).toArray - if (outer != null) { - params(0) = outer // First param is always outer object + if (enclosingObject != null) { + params(0) = enclosingObject // First param is always enclosing object } return cons.newInstance(params: _*).asInstanceOf[AnyRef] } else { @@ -184,26 +344,27 @@ private[spark] object ClosureCleaner extends Logging { val parentCtor = classOf[java.lang.Object].getDeclaredConstructor() val newCtor = rf.newConstructorForSerialization(cls, parentCtor) val obj = newCtor.newInstance().asInstanceOf[AnyRef] - if (outer != null) { - // logInfo("3: Setting $outer on " + cls + " to " + outer); + if (enclosingObject != null) { val field = cls.getDeclaredField("$outer") field.setAccessible(true) - field.set(obj, outer) + field.set(obj, enclosingObject) } obj } } } -private[spark] -class ReturnStatementFinder extends ClassVisitor(ASM4) { +private[spark] class ReturnStatementInClosureException + extends SparkException("Return statements aren't allowed in Spark closures") + +private class ReturnStatementFinder extends ClassVisitor(ASM4) { override def visitMethod(access: Int, name: String, desc: String, sig: String, exceptions: Array[String]): MethodVisitor = { if (name.contains("apply")) { new MethodVisitor(ASM4) { override def visitTypeInsn(op: Int, tp: String) { if (op == NEW && tp.contains("scala/runtime/NonLocalReturnControl")) { - throw new SparkException("Return statements aren't allowed in Spark closures") + throw new ReturnStatementInClosureException } } } @@ -213,26 +374,65 @@ class ReturnStatementFinder extends ClassVisitor(ASM4) { } } -private[spark] -class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends ClassVisitor(ASM4) { - override def visitMethod(access: Int, name: String, desc: String, - sig: String, exceptions: Array[String]): MethodVisitor = { +/** Helper class to identify a method. */ +private case class MethodIdentifier[T](cls: Class[T], name: String, desc: String) + +/** + * Find the fields accessed by a given class. + * + * The resulting fields are stored in the mutable map passed in through the constructor. + * This map is assumed to have its keys already populated with the classes of interest. + * + * @param fields the mutable map that stores the fields to return + * @param findTransitively if true, find fields indirectly referenced through method calls + * @param specificMethod if not empty, visit only this specific method + * @param visitedMethods a set of visited methods to avoid cycles + */ +private[util] class FieldAccessFinder( + fields: Map[Class[_], Set[String]], + findTransitively: Boolean, + specificMethod: Option[MethodIdentifier[_]] = None, + visitedMethods: Set[MethodIdentifier[_]] = Set.empty) + extends ClassVisitor(ASM4) { + + override def visitMethod( + access: Int, + name: String, + desc: String, + sig: String, + exceptions: Array[String]): MethodVisitor = { + + // If we are told to visit only a certain method and this is not the one, ignore it + if (specificMethod.isDefined && + (specificMethod.get.name != name || specificMethod.get.desc != desc)) { + return null + } + new MethodVisitor(ASM4) { override def visitFieldInsn(op: Int, owner: String, name: String, desc: String) { if (op == GETFIELD) { - for (cl <- output.keys if cl.getName == owner.replace('/', '.')) { - output(cl) += name + for (cl <- fields.keys if cl.getName == owner.replace('/', '.')) { + fields(cl) += name } } } - override def visitMethodInsn(op: Int, owner: String, name: String, - desc: String) { - // Check for calls a getter method for a variable in an interpreter wrapper object. - // This means that the corresponding field will be accessed, so we should save it. - if (op == INVOKEVIRTUAL && owner.endsWith("$iwC") && !name.endsWith("$outer")) { - for (cl <- output.keys if cl.getName == owner.replace('/', '.')) { - output(cl) += name + override def visitMethodInsn(op: Int, owner: String, name: String, desc: String) { + for (cl <- fields.keys if cl.getName == owner.replace('/', '.')) { + // Check for calls a getter method for a variable in an interpreter wrapper object. + // This means that the corresponding field will be accessed, so we should save it. + if (op == INVOKEVIRTUAL && owner.endsWith("$iwC") && !name.endsWith("$outer")) { + fields(cl) += name + } + // Optionally visit other methods to find fields that are transitively referenced + if (findTransitively) { + val m = MethodIdentifier(cl, name, desc) + if (!visitedMethods.contains(m)) { + // Keep track of visited methods to avoid potential infinite cycles + visitedMethods += m + ClosureCleaner.getClassReader(cl).accept( + new FieldAccessFinder(fields, findTransitively, Some(m), visitedMethods), 0) + } } } } @@ -240,9 +440,14 @@ class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends ClassVisitor } } -private[spark] class InnerClosureFinder(output: Set[Class[_]]) extends ClassVisitor(ASM4) { +private class InnerClosureFinder(output: Set[Class[_]]) extends ClassVisitor(ASM4) { var myName: String = null + // TODO: Recursively find inner closures that we indirectly reference, e.g. + // val closure1 = () = { () => 1 } + // val closure2 = () => { (1 to 5).map(closure1) } + // The second closure technically has two inner closures, but this finder only finds one + override def visit(version: Int, access: Int, name: String, sig: String, superName: String, interfaces: Array[String]) { myName = name diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala index 474f79fb756f6..3f162d1f6c3eb 100644 --- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala @@ -28,10 +28,11 @@ import org.json4s.DefaultFormats import org.json4s.JsonDSL._ import org.json4s.JsonAST._ +import org.apache.spark._ import org.apache.spark.executor._ +import org.apache.spark.rdd.RDDOperationScope import org.apache.spark.scheduler._ import org.apache.spark.storage._ -import org.apache.spark._ /** * Serializes SparkListener events to/from JSON. This protocol provides strong backwards- @@ -194,7 +195,8 @@ private[spark] object JsonProtocol { ("App Name" -> applicationStart.appName) ~ ("App ID" -> applicationStart.appId.map(JString(_)).getOrElse(JNothing)) ~ ("Timestamp" -> applicationStart.time) ~ - ("User" -> applicationStart.sparkUser) + ("User" -> applicationStart.sparkUser) ~ + ("App Attempt ID" -> applicationStart.appAttemptId.map(JString(_)).getOrElse(JNothing)) } def applicationEndToJson(applicationEnd: SparkListenerApplicationEnd): JValue = { @@ -227,6 +229,7 @@ private[spark] object JsonProtocol { def stageInfoToJson(stageInfo: StageInfo): JValue = { val rddInfo = JArray(stageInfo.rddInfos.map(rddInfoToJson).toList) + val parentIds = JArray(stageInfo.parentIds.map(JInt(_)).toList) val submissionTime = stageInfo.submissionTime.map(JInt(_)).getOrElse(JNothing) val completionTime = stageInfo.completionTime.map(JInt(_)).getOrElse(JNothing) val failureReason = stageInfo.failureReason.map(JString(_)).getOrElse(JNothing) @@ -235,6 +238,7 @@ private[spark] object JsonProtocol { ("Stage Name" -> stageInfo.name) ~ ("Number of Tasks" -> stageInfo.numTasks) ~ ("RDD Info" -> rddInfo) ~ + ("Parent IDs" -> parentIds) ~ ("Details" -> stageInfo.details) ~ ("Submission Time" -> submissionTime) ~ ("Completion Time" -> completionTime) ~ @@ -367,20 +371,23 @@ private[spark] object JsonProtocol { def rddInfoToJson(rddInfo: RDDInfo): JValue = { val storageLevel = storageLevelToJson(rddInfo.storageLevel) + val parentIds = JArray(rddInfo.parentIds.map(JInt(_)).toList) ("RDD ID" -> rddInfo.id) ~ ("Name" -> rddInfo.name) ~ + ("Scope" -> rddInfo.scope.map(_.toJson)) ~ + ("Parent IDs" -> parentIds) ~ ("Storage Level" -> storageLevel) ~ ("Number of Partitions" -> rddInfo.numPartitions) ~ ("Number of Cached Partitions" -> rddInfo.numCachedPartitions) ~ ("Memory Size" -> rddInfo.memSize) ~ - ("Tachyon Size" -> rddInfo.tachyonSize) ~ + ("ExternalBlockStore Size" -> rddInfo.externalBlockStoreSize) ~ ("Disk Size" -> rddInfo.diskSize) } def storageLevelToJson(storageLevel: StorageLevel): JValue = { ("Use Disk" -> storageLevel.useDisk) ~ ("Use Memory" -> storageLevel.useMemory) ~ - ("Use Tachyon" -> storageLevel.useOffHeap) ~ + ("Use ExternalBlockStore" -> storageLevel.useOffHeap) ~ ("Deserialized" -> storageLevel.deserialized) ~ ("Replication" -> storageLevel.replication) } @@ -389,7 +396,7 @@ private[spark] object JsonProtocol { val storageLevel = storageLevelToJson(blockStatus.storageLevel) ("Storage Level" -> storageLevel) ~ ("Memory Size" -> blockStatus.memSize) ~ - ("Tachyon Size" -> blockStatus.tachyonSize) ~ + ("ExternalBlockStore Size" -> blockStatus.externalBlockStoreSize) ~ ("Disk Size" -> blockStatus.diskSize) } @@ -518,7 +525,7 @@ private[spark] object JsonProtocol { // The "Stage Infos" field was added in Spark 1.2.0 val stageInfos = Utils.jsonOption(json \ "Stage Infos") .map(_.extract[Seq[JValue]].map(stageInfoFromJson)).getOrElse { - stageIds.map(id => new StageInfo(id, 0, "unknown", 0, Seq.empty, "unknown")) + stageIds.map(id => new StageInfo(id, 0, "unknown", 0, Seq.empty, Seq.empty, "unknown")) } SparkListenerJobStart(jobId, submissionTime, stageInfos, properties) } @@ -562,7 +569,8 @@ private[spark] object JsonProtocol { val appId = Utils.jsonOption(json \ "App ID").map(_.extract[String]) val time = (json \ "Timestamp").extract[Long] val sparkUser = (json \ "User").extract[String] - SparkListenerApplicationStart(appName, appId, time, sparkUser) + val appAttemptId = Utils.jsonOption(json \ "App Attempt ID").map(_.extract[String]) + SparkListenerApplicationStart(appName, appId, time, sparkUser, appAttemptId) } def applicationEndFromJson(json: JValue): SparkListenerApplicationEnd = { @@ -597,7 +605,10 @@ private[spark] object JsonProtocol { val attemptId = (json \ "Stage Attempt ID").extractOpt[Int].getOrElse(0) val stageName = (json \ "Stage Name").extract[String] val numTasks = (json \ "Number of Tasks").extract[Int] - val rddInfos = (json \ "RDD Info").extract[List[JValue]].map(rddInfoFromJson(_)) + val rddInfos = (json \ "RDD Info").extract[List[JValue]].map(rddInfoFromJson) + val parentIds = Utils.jsonOption(json \ "Parent IDs") + .map { l => l.extract[List[JValue]].map(_.extract[Int]) } + .getOrElse(Seq.empty) val details = (json \ "Details").extractOpt[String].getOrElse("") val submissionTime = Utils.jsonOption(json \ "Submission Time").map(_.extract[Long]) val completionTime = Utils.jsonOption(json \ "Completion Time").map(_.extract[Long]) @@ -607,7 +618,8 @@ private[spark] object JsonProtocol { case None => Seq[AccumulableInfo]() } - val stageInfo = new StageInfo(stageId, attemptId, stageName, numTasks, rddInfos, details) + val stageInfo = new StageInfo( + stageId, attemptId, stageName, numTasks, rddInfos, parentIds, details) stageInfo.submissionTime = submissionTime stageInfo.completionTime = completionTime stageInfo.failureReason = failureReason @@ -783,17 +795,25 @@ private[spark] object JsonProtocol { def rddInfoFromJson(json: JValue): RDDInfo = { val rddId = (json \ "RDD ID").extract[Int] val name = (json \ "Name").extract[String] + val scope = Utils.jsonOption(json \ "Scope") + .map(_.extract[String]) + .map(RDDOperationScope.fromJson) + val parentIds = Utils.jsonOption(json \ "Parent IDs") + .map { l => l.extract[List[JValue]].map(_.extract[Int]) } + .getOrElse(Seq.empty) val storageLevel = storageLevelFromJson(json \ "Storage Level") val numPartitions = (json \ "Number of Partitions").extract[Int] val numCachedPartitions = (json \ "Number of Cached Partitions").extract[Int] val memSize = (json \ "Memory Size").extract[Long] - val tachyonSize = (json \ "Tachyon Size").extract[Long] + // fallback to tachyon for backward compatibility + val externalBlockStoreSize = (json \ "ExternalBlockStore Size").toSome + .getOrElse(json \ "Tachyon Size").extract[Long] val diskSize = (json \ "Disk Size").extract[Long] - val rddInfo = new RDDInfo(rddId, name, numPartitions, storageLevel) + val rddInfo = new RDDInfo(rddId, name, numPartitions, storageLevel, parentIds, scope) rddInfo.numCachedPartitions = numCachedPartitions rddInfo.memSize = memSize - rddInfo.tachyonSize = tachyonSize + rddInfo.externalBlockStoreSize = externalBlockStoreSize rddInfo.diskSize = diskSize rddInfo } @@ -801,18 +821,22 @@ private[spark] object JsonProtocol { def storageLevelFromJson(json: JValue): StorageLevel = { val useDisk = (json \ "Use Disk").extract[Boolean] val useMemory = (json \ "Use Memory").extract[Boolean] - val useTachyon = (json \ "Use Tachyon").extract[Boolean] + // fallback to tachyon for backward compatability + val useExternalBlockStore = (json \ "Use ExternalBlockStore").toSome + .getOrElse(json \ "Use Tachyon").extract[Boolean] val deserialized = (json \ "Deserialized").extract[Boolean] val replication = (json \ "Replication").extract[Int] - StorageLevel(useDisk, useMemory, useTachyon, deserialized, replication) + StorageLevel(useDisk, useMemory, useExternalBlockStore, deserialized, replication) } def blockStatusFromJson(json: JValue): BlockStatus = { val storageLevel = storageLevelFromJson(json \ "Storage Level") val memorySize = (json \ "Memory Size").extract[Long] val diskSize = (json \ "Disk Size").extract[Long] - val tachyonSize = (json \ "Tachyon Size").extract[Long] - BlockStatus(storageLevel, memorySize, diskSize, tachyonSize) + // fallback to tachyon for backward compatability + val externalBlockStoreSize = (json \ "ExternalBlockStore Size").toSome + .getOrElse(json \ "Tachyon Size").extract[Long] + BlockStatus(storageLevel, memorySize, diskSize, externalBlockStoreSize) } def executorInfoFromJson(json: JValue): ExecutorInfo = { diff --git a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala index 4dd7ab9e0767b..d91c3294ddb8b 100644 --- a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala +++ b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala @@ -47,6 +47,11 @@ private[spark] object SizeEstimator extends Logging { private val FLOAT_SIZE = 4 private val DOUBLE_SIZE = 8 + // Fields can be primitive types, sizes are: 1, 2, 4, 8. Or fields can be pointers. The size of + // a pointer is 4 or 8 depending on the JVM (32-bit or 64-bit) and UseCompressedOops flag. + // The sizes should be in descending order, as we will use that information for fields placement. + private val fieldSizes = List(8, 4, 2, 1) + // Alignment boundary for objects // TODO: Is this arch dependent ? private val ALIGN_SIZE = 8 @@ -171,7 +176,7 @@ private[spark] object SizeEstimator extends Logging { // general all ClassLoaders and Classes will be shared between objects anyway. } else { val classInfo = getClassInfo(cls) - state.size += classInfo.shellSize + state.size += alignSize(classInfo.shellSize) for (field <- classInfo.pointerFields) { state.enqueue(field.get(obj)) } @@ -237,8 +242,8 @@ private[spark] object SizeEstimator extends Logging { } size } - - private def primitiveSize(cls: Class[_]): Long = { + + private def primitiveSize(cls: Class[_]): Int = { if (cls == classOf[Byte]) { BYTE_SIZE } else if (cls == classOf[Boolean]) { @@ -274,21 +279,50 @@ private[spark] object SizeEstimator extends Logging { val parent = getClassInfo(cls.getSuperclass) var shellSize = parent.shellSize var pointerFields = parent.pointerFields + val sizeCount = Array.fill(fieldSizes.max + 1)(0) + // iterate through the fields of this class and gather information. for (field <- cls.getDeclaredFields) { if (!Modifier.isStatic(field.getModifiers)) { val fieldClass = field.getType if (fieldClass.isPrimitive) { - shellSize += primitiveSize(fieldClass) + sizeCount(primitiveSize(fieldClass)) += 1 } else { field.setAccessible(true) // Enable future get()'s on this field - shellSize += pointerSize + sizeCount(pointerSize) += 1 pointerFields = field :: pointerFields } } } - shellSize = alignSize(shellSize) + // Based on the simulated field layout code in Aleksey Shipilev's report: + // http://cr.openjdk.java.net/~shade/papers/2013-shipilev-fieldlayout-latest.pdf + // The code is in Figure 9. + // The simplified idea of field layout consists of 4 parts (see more details in the report): + // + // 1. field alignment: HotSpot lays out the fields aligned by their size. + // 2. object alignment: HotSpot rounds instance size up to 8 bytes + // 3. consistent fields layouts throughout the hierarchy: This means we should layout + // superclass first. And we can use superclass's shellSize as a starting point to layout the + // other fields in this class. + // 4. class alignment: HotSpot rounds field blocks up to to HeapOopSize not 4 bytes, confirmed + // with Aleksey. see https://bugs.openjdk.java.net/browse/CODETOOLS-7901322 + // + // The real world field layout is much more complicated. There are three kinds of fields + // order in Java 8. And we don't consider the @contended annotation introduced by Java 8. + // see the HotSpot classloader code, layout_fields method for more details. + // hg.openjdk.java.net/jdk8/jdk8/hotspot/file/tip/src/share/vm/classfile/classFileParser.cpp + var alignedSize = shellSize + for (size <- fieldSizes if sizeCount(size) > 0) { + val count = sizeCount(size) + // If there are internal gaps, smaller field can fit in. + alignedSize = math.max(alignedSize, alignSizeUp(shellSize, size) + size * count) + shellSize += size * count + } + + // Should choose a larger size to be new shellSize and clearly alignedSize >= shellSize, and + // round up the instance filed blocks + shellSize = alignSizeUp(alignedSize, pointerSize) // Create and cache a new ClassInfo val newInfo = new ClassInfo(shellSize, pointerFields) @@ -296,8 +330,15 @@ private[spark] object SizeEstimator extends Logging { newInfo } - private def alignSize(size: Long): Long = { - val rem = size % ALIGN_SIZE - if (rem == 0) size else (size + ALIGN_SIZE - rem) - } + private def alignSize(size: Long): Long = alignSizeUp(size, ALIGN_SIZE) + + /** + * Compute aligned size. The alignSize must be 2^n, otherwise the result will be wrong. + * When alignSize = 2^n, alignSize - 1 = 2^n - 1. The binary representation of (alignSize - 1) + * will only have n trailing 1s(0b00...001..1). ~(alignSize - 1) will be 0b11..110..0. Hence, + * (size + alignSize - 1) & ~(alignSize - 1) will set the last n bits to zeros, which leads to + * multiple of alignSize. + */ + private def alignSizeUp(size: Long, alignSize: Int): Long = + (size + alignSize - 1) & ~(alignSize - 1) } diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 4c028c06a5138..be4db02ab86d0 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -1299,16 +1299,18 @@ private[spark] object Utils extends Logging { } /** Default filtering function for finding call sites using `getCallSite`. */ - private def coreExclusionFunction(className: String): Boolean = { - // A regular expression to match classes of the "core" Spark API that we want to skip when - // finding the call site of a method. + private def sparkInternalExclusionFunction(className: String): Boolean = { + // A regular expression to match classes of the internal Spark API's + // that we want to skip when finding the call site of a method. val SPARK_CORE_CLASS_REGEX = """^org\.apache\.spark(\.api\.java)?(\.util)?(\.rdd)?(\.broadcast)?\.[A-Z]""".r + val SPARK_SQL_CLASS_REGEX = """^org\.apache\.spark\.sql.*""".r val SCALA_CORE_CLASS_PREFIX = "scala" - val isSparkCoreClass = SPARK_CORE_CLASS_REGEX.findFirstIn(className).isDefined + val isSparkClass = SPARK_CORE_CLASS_REGEX.findFirstIn(className).isDefined || + SPARK_SQL_CLASS_REGEX.findFirstIn(className).isDefined val isScalaClass = className.startsWith(SCALA_CORE_CLASS_PREFIX) // If the class is a Spark internal class or a Scala class, then exclude. - isSparkCoreClass || isScalaClass + isSparkClass || isScalaClass } /** @@ -1318,7 +1320,7 @@ private[spark] object Utils extends Logging { * * @param skipClass Function that is used to exclude non-user-code classes. */ - def getCallSite(skipClass: String => Boolean = coreExclusionFunction): CallSite = { + def getCallSite(skipClass: String => Boolean = sparkInternalExclusionFunction): CallSite = { // Keep crawling up the stack trace until we find the first function not inside of the spark // package. We track the last (shallowest) contiguous Spark method. This might be an RDD // transformation, a SparkContext function (such as parallelize), or anything else that leads @@ -1357,9 +1359,17 @@ private[spark] object Utils extends Logging { } val callStackDepth = System.getProperty("spark.callstack.depth", "20").toInt - CallSite( - shortForm = s"$lastSparkMethod at $firstUserFile:$firstUserLine", - longForm = callStack.take(callStackDepth).mkString("\n")) + val shortForm = + if (firstUserFile == "HiveSessionImpl.java") { + // To be more user friendly, show a nicer string for queries submitted from the JDBC + // server. + "Spark JDBC Server Query" + } else { + s"$lastSparkMethod at $firstUserFile:$firstUserLine" + } + val longForm = callStack.take(callStackDepth).mkString("\n") + + CallSite(shortForm, longForm) } /** Return a string containing part of a file from byte 'start' to 'end'. */ @@ -2012,6 +2022,13 @@ private[spark] object Utils extends Logging { } } + /** + * configure a new log4j level + */ + def setLogLevel(l: org.apache.log4j.Level) { + org.apache.log4j.Logger.getRootLogger().setLevel(l) + } + /** * config a log4j properties used for testsuite */ @@ -2142,6 +2159,22 @@ private[spark] object Utils extends Logging { .getOrElse(UserGroupInformation.getCurrentUser().getShortUserName()) } + /** + * Split the comma delimited string of master URLs into a list. + * For instance, "spark://abc,def" becomes [spark://abc, spark://def]. + */ + def parseStandaloneMasterUrls(masterUrls: String): Array[String] = { + masterUrls.stripPrefix("spark://").split(",").map("spark://" + _) + } + + /** An identifier that backup masters use in their responses. */ + val BACKUP_STANDALONE_MASTER_PREFIX = "Current state is not alive" + + /** Return true if the response message is sent from a backup Master on standby. */ + def responseFromBackup(msg: String): Boolean = { + msg.startsWith(BACKUP_STANDALONE_MASTER_PREFIX) + } + /** * Adds a shutdown hook with default priority. * diff --git a/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala new file mode 100644 index 0000000000000..a60bffe611f14 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala @@ -0,0 +1,144 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.io.OutputStream + +import scala.collection.mutable.ArrayBuffer + +/** + * A logical byte buffer that wraps a list of byte arrays. All the byte arrays have equal size. The + * advantage of this over a standard ArrayBuffer is that it can grow without claiming large amounts + * of memory and needing to copy the full contents. The disadvantage is that the contents don't + * occupy a contiguous segment of memory. + */ +private[spark] class ChainedBuffer(chunkSize: Int) { + private val chunkSizeLog2 = (math.log(chunkSize) / math.log(2)).toInt + assert(math.pow(2, chunkSizeLog2).toInt == chunkSize, + s"ChainedBuffer chunk size $chunkSize must be a power of two") + private val chunks: ArrayBuffer[Array[Byte]] = new ArrayBuffer[Array[Byte]]() + private var _size: Int = _ + + /** + * Feed bytes from this buffer into a BlockObjectWriter. + * + * @param pos Offset in the buffer to read from. + * @param os OutputStream to read into. + * @param len Number of bytes to read. + */ + def read(pos: Int, os: OutputStream, len: Int): Unit = { + if (pos + len > _size) { + throw new IndexOutOfBoundsException( + s"Read of $len bytes at position $pos would go past size ${_size} of buffer") + } + var chunkIndex = pos >> chunkSizeLog2 + var posInChunk = pos - (chunkIndex << chunkSizeLog2) + var written = 0 + while (written < len) { + val toRead = math.min(len - written, chunkSize - posInChunk) + os.write(chunks(chunkIndex), posInChunk, toRead) + written += toRead + chunkIndex += 1 + posInChunk = 0 + } + } + + /** + * Read bytes from this buffer into a byte array. + * + * @param pos Offset in the buffer to read from. + * @param bytes Byte array to read into. + * @param offs Offset in the byte array to read to. + * @param len Number of bytes to read. + */ + def read(pos: Int, bytes: Array[Byte], offs: Int, len: Int): Unit = { + if (pos + len > _size) { + throw new IndexOutOfBoundsException( + s"Read of $len bytes at position $pos would go past size of buffer") + } + var chunkIndex = pos >> chunkSizeLog2 + var posInChunk = pos - (chunkIndex << chunkSizeLog2) + var written = 0 + while (written < len) { + val toRead = math.min(len - written, chunkSize - posInChunk) + System.arraycopy(chunks(chunkIndex), posInChunk, bytes, offs + written, toRead) + written += toRead + chunkIndex += 1 + posInChunk = 0 + } + } + + /** + * Write bytes from a byte array into this buffer. + * + * @param pos Offset in the buffer to write to. + * @param bytes Byte array to write from. + * @param offs Offset in the byte array to write from. + * @param len Number of bytes to write. + */ + def write(pos: Int, bytes: Array[Byte], offs: Int, len: Int): Unit = { + if (pos > _size) { + throw new IndexOutOfBoundsException( + s"Write at position $pos starts after end of buffer ${_size}") + } + // Grow if needed + val endChunkIndex = (pos + len - 1) >> chunkSizeLog2 + while (endChunkIndex >= chunks.length) { + chunks += new Array[Byte](chunkSize) + } + + var chunkIndex = pos >> chunkSizeLog2 + var posInChunk = pos - (chunkIndex << chunkSizeLog2) + var written = 0 + while (written < len) { + val toWrite = math.min(len - written, chunkSize - posInChunk) + System.arraycopy(bytes, offs + written, chunks(chunkIndex), posInChunk, toWrite) + written += toWrite + chunkIndex += 1 + posInChunk = 0 + } + + _size = math.max(_size, pos + len) + } + + /** + * Total size of buffer that can be written to without allocating additional memory. + */ + def capacity: Int = chunks.size * chunkSize + + /** + * Size of the logical buffer. + */ + def size: Int = _size +} + +/** + * Output stream that writes to a ChainedBuffer. + */ +private[spark] class ChainedBufferOutputStream(chainedBuffer: ChainedBuffer) extends OutputStream { + private var pos = 0 + + override def write(b: Int): Unit = { + throw new UnsupportedOperationException() + } + + override def write(bytes: Array[Byte], offs: Int, len: Int): Unit = { + chainedBuffer.write(pos, bytes, offs, len) + pos += len + } +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala index f912049563906..b850973145077 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala @@ -174,7 +174,7 @@ class ExternalAppendOnlyMap[K, V, C]( val it = currentMap.destructiveSortedIterator(keyComparator) while (it.hasNext) { val kv = it.next() - writer.write(kv) + writer.write(kv._1, kv._2) objectsWritten += 1 if (objectsWritten == serializerBatchSize) { @@ -435,7 +435,9 @@ class ExternalAppendOnlyMap[K, V, C]( */ private def readNextItem(): (K, C) = { try { - val item = deserializeStream.readObject().asInstanceOf[(K, C)] + val k = deserializeStream.readKey().asInstanceOf[K] + val c = deserializeStream.readValue().asInstanceOf[C] + val item = (k, c) objectsRead += 1 if (objectsRead == serializerBatchSize) { objectsRead = 0 diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index ef3cac622505e..7d5cf7b61e56a 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -26,7 +26,7 @@ import scala.collection.mutable import com.google.common.io.ByteStreams import org.apache.spark._ -import org.apache.spark.serializer.{DeserializationStream, Serializer} +import org.apache.spark.serializer._ import org.apache.spark.executor.ShuffleWriteMetrics import org.apache.spark.storage.{BlockObjectWriter, BlockId} @@ -66,10 +66,11 @@ import org.apache.spark.storage.{BlockObjectWriter, BlockId} * * At a high level, this class works internally as follows: * - * - We repeatedly fill up buffers of in-memory data, using either a SizeTrackingAppendOnlyMap if - * we want to combine by key, or an simple SizeTrackingBuffer if we don't. Inside these buffers, - * we sort elements of type ((Int, K), C) where the Int is the partition ID. This is done to - * avoid calling the partitioner multiple times on the same key (e.g. for RangePartitioner). + * - We repeatedly fill up buffers of in-memory data, using either a PartitionedAppendOnlyMap if + * we want to combine by key, or a PartitionedSerializedPairBuffer or PartitionedPairBuffer if we + * don't. Inside these buffers, we sort elements by partition ID and then possibly also by key. + * To avoid calling the partitioner multiple times with each key, we store the partition ID + * alongside each record. * * - When each buffer reaches our memory limit, we spill it to a file. This file is sorted first * by partition ID and possibly second by key or by hash code of the key, if we want to do @@ -96,7 +97,7 @@ private[spark] class ExternalSorter[K, V, C]( partitioner: Option[Partitioner] = None, ordering: Option[Ordering[K]] = None, serializer: Option[Serializer] = None) - extends Logging with Spillable[SizeTrackingPairCollection[(Int, K), C]] { + extends Logging with Spillable[WritablePartitionedPairCollection[K, C]] { private val numPartitions = partitioner.map(_.numPartitions).getOrElse(1) private val shouldPartition = numPartitions > 1 @@ -126,11 +127,21 @@ private[spark] class ExternalSorter[K, V, C]( if (shouldPartition) partitioner.get.getPartition(key) else 0 } + private val metaInitialRecords = 256 + private val kvChunkSize = conf.getInt("spark.shuffle.sort.kvChunkSize", 1 << 22) // 4 MB + private val useSerializedPairBuffer = + !ordering.isDefined && conf.getBoolean("spark.shuffle.sort.serializeMapOutputs", true) && + ser.supportsRelocationOfSerializedObjects + // Data structures to store in-memory objects before we spill. Depending on whether we have an // Aggregator set, we either put objects into an AppendOnlyMap where we combine them, or we // store them in an array buffer. - private var map = new SizeTrackingAppendOnlyMap[(Int, K), C] - private var buffer = new SizeTrackingPairBuffer[(Int, K), C] + private var map = new PartitionedAppendOnlyMap[K, C] + private var buffer = if (useSerializedPairBuffer) { + new PartitionedSerializedPairBuffer[K, C](metaInitialRecords, kvChunkSize, serInstance) + } else { + new PartitionedPairBuffer[K, C] + } // Total spilling statistics private var _diskBytesSpilled = 0L @@ -163,33 +174,6 @@ private[spark] class ExternalSorter[K, V, C]( } }) - // A comparator for (Int, K) pairs that orders them by only their partition ID - private val partitionComparator: Comparator[(Int, K)] = new Comparator[(Int, K)] { - override def compare(a: (Int, K), b: (Int, K)): Int = { - a._1 - b._1 - } - } - - // A comparator that orders (Int, K) pairs by partition ID and then possibly by key - private val partitionKeyComparator: Comparator[(Int, K)] = { - if (ordering.isDefined || aggregator.isDefined) { - // Sort by partition ID then key comparator - new Comparator[(Int, K)] { - override def compare(a: (Int, K), b: (Int, K)): Int = { - val partitionDiff = a._1 - b._1 - if (partitionDiff != 0) { - partitionDiff - } else { - keyComparator.compare(a._2, b._2) - } - } - } - } else { - // Just sort it by partition ID - partitionComparator - } - } - // Information about a spilled file. Includes sizes in bytes of "batches" written by the // serializer as we periodically reset its stream, as well as number of elements in each // partition, used to efficiently keep track of partitions when merging. @@ -221,16 +205,18 @@ private[spark] class ExternalSorter[K, V, C]( } else if (bypassMergeSort) { // SPARK-4479: Also bypass buffering if merge sort is bypassed to avoid defensive copies if (records.hasNext) { - spillToPartitionFiles(records.map { kv => - ((getPartition(kv._1), kv._1), kv._2.asInstanceOf[C]) - }) + spillToPartitionFiles( + WritablePartitionedIterator.fromIterator(records.map { kv => + ((getPartition(kv._1), kv._1), kv._2.asInstanceOf[C]) + }) + ) } } else { // Stick values into our buffer while (records.hasNext) { addElementsRead() val kv = records.next() - buffer.insert((getPartition(kv._1), kv._1), kv._2.asInstanceOf[C]) + buffer.insert(getPartition(kv._1), kv._1, kv._2.asInstanceOf[C]) maybeSpillCollection(usingMap = false) } } @@ -248,11 +234,15 @@ private[spark] class ExternalSorter[K, V, C]( if (usingMap) { if (maybeSpill(map, map.estimateSize())) { - map = new SizeTrackingAppendOnlyMap[(Int, K), C] + map = new PartitionedAppendOnlyMap[K, C] } } else { if (maybeSpill(buffer, buffer.estimateSize())) { - buffer = new SizeTrackingPairBuffer[(Int, K), C] + buffer = if (useSerializedPairBuffer) { + new PartitionedSerializedPairBuffer[K, C](metaInitialRecords, kvChunkSize, serInstance) + } else { + new PartitionedPairBuffer[K, C] + } } } } @@ -260,7 +250,7 @@ private[spark] class ExternalSorter[K, V, C]( /** * Spill the current in-memory collection to disk, adding a new file to spills, and clear it. */ - override protected[this] def spill(collection: SizeTrackingPairCollection[(Int, K), C]): Unit = { + override protected[this] def spill(collection: WritablePartitionedPairCollection[K, C]): Unit = { if (bypassMergeSort) { spillToPartitionFiles(collection) } else { @@ -277,7 +267,7 @@ private[spark] class ExternalSorter[K, V, C]( * * @param collection whichever collection we're using (map or buffer) */ - private def spillToMergeableFile(collection: SizeTrackingPairCollection[(Int, K), C]): Unit = { + private def spillToMergeableFile(collection: WritablePartitionedPairCollection[K, C]): Unit = { assert(!bypassMergeSort) // Because these files may be read during shuffle, their compression must be controlled by @@ -308,14 +298,10 @@ private[spark] class ExternalSorter[K, V, C]( var success = false try { - val it = collection.destructiveSortedIterator(partitionKeyComparator) + val it = collection.destructiveSortedWritablePartitionedIterator(comparator) while (it.hasNext) { - val elem = it.next() - val partitionId = elem._1._1 - val key = elem._1._2 - val value = elem._2 - writer.write(key) - writer.write(value) + val partitionId = it.nextPartition() + it.writeNext(writer) elementsPerPartition(partitionId) += 1 objectsWritten += 1 @@ -357,11 +343,11 @@ private[spark] class ExternalSorter[K, V, C]( * * @param collection whichever collection we're using (map or buffer) */ - private def spillToPartitionFiles(collection: SizeTrackingPairCollection[(Int, K), C]): Unit = { - spillToPartitionFiles(collection.iterator) + private def spillToPartitionFiles(collection: WritablePartitionedPairCollection[K, C]): Unit = { + spillToPartitionFiles(collection.writablePartitionedIterator()) } - private def spillToPartitionFiles(iterator: Iterator[((Int, K), C)]): Unit = { + private def spillToPartitionFiles(iterator: WritablePartitionedIterator): Unit = { assert(bypassMergeSort) // Create our file writers if we haven't done so yet @@ -385,11 +371,8 @@ private[spark] class ExternalSorter[K, V, C]( // No need to sort stuff, just write each element out while (iterator.hasNext) { - val elem = iterator.next() - val partitionId = elem._1._1 - val key = elem._1._2 - val value = elem._2 - partitionWriters(partitionId).write((key, value)) + val partitionId = iterator.nextPartition() + iterator.writeNext(partitionWriters(partitionId)) } } @@ -527,7 +510,8 @@ private[spark] class ExternalSorter[K, V, C]( val k = elem._1 var c = elem._2 while (sorted.hasNext && sorted.head._1 == k) { - c = mergeCombiners(c, sorted.head._2) + val pair = sorted.next() + c = mergeCombiners(c, pair._2) } (k, c) } @@ -617,8 +601,8 @@ private[spark] class ExternalSorter[K, V, C]( if (finished || deserializeStream == null) { return null } - val k = deserializeStream.readObject().asInstanceOf[K] - val c = deserializeStream.readObject().asInstanceOf[C] + val k = deserializeStream.readKey().asInstanceOf[K] + val c = deserializeStream.readValue().asInstanceOf[C] lastPartitionId = partitionId // Start reading the next batch if we're done with this one indexInBatch += 1 @@ -694,27 +678,27 @@ private[spark] class ExternalSorter[K, V, C]( */ def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = { val usingMap = aggregator.isDefined - val collection: SizeTrackingPairCollection[(Int, K), C] = if (usingMap) map else buffer + val collection: WritablePartitionedPairCollection[K, C] = if (usingMap) map else buffer if (spills.isEmpty && partitionWriters == null) { // Special case: if we have only in-memory data, we don't need to merge streams, and perhaps // we don't even need to sort by anything other than partition ID if (!ordering.isDefined) { // The user hasn't requested sorted keys, so only sort by partition ID, not key - groupByPartition(collection.destructiveSortedIterator(partitionComparator)) + groupByPartition(collection.partitionedDestructiveSortedIterator(None)) } else { // We do need to sort by both partition ID and key - groupByPartition(collection.destructiveSortedIterator(partitionKeyComparator)) + groupByPartition(collection.partitionedDestructiveSortedIterator(Some(keyComparator))) } } else if (bypassMergeSort) { // Read data from each partition file and merge it together with the data in memory; // note that there's no ordering or aggregator in this case -- we just partition objects - val collIter = groupByPartition(collection.destructiveSortedIterator(partitionComparator)) + val collIter = groupByPartition(collection.partitionedDestructiveSortedIterator(None)) collIter.map { case (partitionId, values) => (partitionId, values ++ readPartitionFile(partitionWriters(partitionId))) } } else { // Merge spilled and in-memory data - merge(spills, collection.destructiveSortedIterator(partitionKeyComparator)) + merge(spills, collection.partitionedDestructiveSortedIterator(comparator)) } } @@ -761,15 +745,29 @@ private[spark] class ExternalSorter[K, V, C]( context.taskMetrics.shuffleWriteMetrics.foreach( _.incShuffleWriteTime(System.nanoTime - writeStartTime)) } + } else if (spills.isEmpty && partitionWriters == null) { + // Case where we only have in-memory data + val collection = if (aggregator.isDefined) map else buffer + val it = collection.destructiveSortedWritablePartitionedIterator(comparator) + while (it.hasNext) { + val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize, + context.taskMetrics.shuffleWriteMetrics.get) + val partitionId = it.nextPartition() + while (it.hasNext && it.nextPartition() == partitionId) { + it.writeNext(writer) + } + writer.commitAndClose() + val segment = writer.fileSegment() + lengths(partitionId) = segment.length + } } else { - // Either we're not bypassing merge-sort or we have only in-memory data; get an iterator by - // partition and just write everything directly. + // Not bypassing merge-sort; get an iterator by partition and just write everything directly. for ((id, elements) <- this.partitionedIterator) { if (elements.hasNext) { val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize, context.taskMetrics.shuffleWriteMetrics.get) for (elem <- elements) { - writer.write(elem) + writer.write(elem._1, elem._2) } writer.commitAndClose() val segment = writer.fileSegment() @@ -798,7 +796,7 @@ private[spark] class ExternalSorter[K, V, C]( if (writer.isOpen) { writer.commitAndClose() } - blockManager.diskStore.getValues(writer.blockId, ser).get.asInstanceOf[Iterator[Product2[K, C]]] + new PairIterator[K, C](blockManager.diskStore.getValues(writer.blockId, ser).get) } def stop(): Unit = { @@ -828,6 +826,14 @@ private[spark] class ExternalSorter[K, V, C]( (0 until numPartitions).iterator.map(p => (p, new IteratorForPartition(p, buffered))) } + private def comparator: Option[Comparator[K]] = { + if (ordering.isDefined || aggregator.isDefined) { + Some(keyComparator) + } else { + None + } + } + /** * An iterator that reads only the elements for a given partition ID from an underlying buffered * stream, assuming this partition is the next one to be read. Used to make it easier to return diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairCollection.scala b/core/src/main/scala/org/apache/spark/util/collection/PairIterator.scala similarity index 55% rename from core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairCollection.scala rename to core/src/main/scala/org/apache/spark/util/collection/PairIterator.scala index faa4e2b12ddb6..d75959f480756 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairCollection.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/PairIterator.scala @@ -17,18 +17,8 @@ package org.apache.spark.util.collection -import java.util.Comparator +private[spark] class PairIterator[K, V](iter: Iterator[Any]) extends Iterator[(K, V)] { + def hasNext: Boolean = iter.hasNext -/** - * A common interface for our size-tracking collections of key-value pairs, which are used in - * external operations. These all support estimating the size and obtaining a memory-efficient - * sorted iterator. - */ -// TODO: should extend Iterable[Product2[K, V]] instead of (K, V) -private[spark] trait SizeTrackingPairCollection[K, V] extends Iterable[(K, V)] { - /** Estimate the collection's current memory usage in bytes. */ - def estimateSize(): Long - - /** Iterate through the data in a given key order. This may destroy the underlying collection. */ - def destructiveSortedIterator(keyComparator: Comparator[K]): Iterator[(K, V)] + def next(): (K, V) = (iter.next().asInstanceOf[K], iter.next().asInstanceOf[V]) } diff --git a/core/src/main/scala/org/apache/spark/util/collection/PartitionedAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/PartitionedAppendOnlyMap.scala new file mode 100644 index 0000000000000..e2e2f1faae9d1 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/PartitionedAppendOnlyMap.scala @@ -0,0 +1,44 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.util.Comparator + +import org.apache.spark.util.collection.WritablePartitionedPairCollection._ + +/** + * Implementation of WritablePartitionedPairCollection that wraps a map in which the keys are tuples + * of (partition ID, K) + */ +private[spark] class PartitionedAppendOnlyMap[K, V] + extends SizeTrackingAppendOnlyMap[(Int, K), V] with WritablePartitionedPairCollection[K, V] { + + def partitionedDestructiveSortedIterator(keyComparator: Option[Comparator[K]]) + : Iterator[((Int, K), V)] = { + val comparator = keyComparator.map(partitionKeyComparator).getOrElse(partitionComparator) + destructiveSortedIterator(comparator) + } + + def writablePartitionedIterator(): WritablePartitionedIterator = { + WritablePartitionedIterator.fromIterator(super.iterator) + } + + def insert(partition: Int, key: K, value: V): Unit = { + update((partition, key), value) + } +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala similarity index 66% rename from core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairBuffer.scala rename to core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala index 9e9c16c5a2962..e8332e1a87eac 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairBuffer.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala @@ -19,11 +19,15 @@ package org.apache.spark.util.collection import java.util.Comparator +import org.apache.spark.storage.BlockObjectWriter +import org.apache.spark.util.collection.WritablePartitionedPairCollection._ + /** - * Append-only buffer of key-value pairs that keeps track of its estimated size in bytes. + * Append-only buffer of key-value pairs, each with a corresponding partition ID, that keeps track + * of its estimated size in bytes. */ -private[spark] class SizeTrackingPairBuffer[K, V](initialCapacity: Int = 64) - extends SizeTracker with SizeTrackingPairCollection[K, V] +private[spark] class PartitionedPairBuffer[K, V](initialCapacity: Int = 64) + extends WritablePartitionedPairCollection[K, V] with SizeTracker { require(initialCapacity <= (1 << 29), "Can't make capacity bigger than 2^29 elements") require(initialCapacity >= 1, "Invalid initial capacity") @@ -35,35 +39,16 @@ private[spark] class SizeTrackingPairBuffer[K, V](initialCapacity: Int = 64) private var data = new Array[AnyRef](2 * initialCapacity) /** Add an element into the buffer */ - def insert(key: K, value: V): Unit = { + def insert(partition: Int, key: K, value: V): Unit = { if (curSize == capacity) { growArray() } - data(2 * curSize) = key.asInstanceOf[AnyRef] + data(2 * curSize) = (partition, key.asInstanceOf[AnyRef]) data(2 * curSize + 1) = value.asInstanceOf[AnyRef] curSize += 1 afterUpdate() } - /** Total number of elements in buffer */ - override def size: Int = curSize - - /** Iterate over the elements of the buffer */ - override def iterator: Iterator[(K, V)] = new Iterator[(K, V)] { - var pos = 0 - - override def hasNext: Boolean = pos < curSize - - override def next(): (K, V) = { - if (!hasNext) { - throw new NoSuchElementException - } - val pair = (data(2 * pos).asInstanceOf[K], data(2 * pos + 1).asInstanceOf[V]) - pos += 1 - pair - } - } - /** Double the size of the array because we've reached capacity */ private def growArray(): Unit = { if (capacity == (1 << 29)) { @@ -79,8 +64,29 @@ private[spark] class SizeTrackingPairBuffer[K, V](initialCapacity: Int = 64) } /** Iterate through the data in a given order. For this class this is not really destructive. */ - override def destructiveSortedIterator(keyComparator: Comparator[K]): Iterator[(K, V)] = { - new Sorter(new KVArraySortDataFormat[K, AnyRef]).sort(data, 0, curSize, keyComparator) + override def partitionedDestructiveSortedIterator(keyComparator: Option[Comparator[K]]) + : Iterator[((Int, K), V)] = { + val comparator = keyComparator.map(partitionKeyComparator).getOrElse(partitionComparator) + new Sorter(new KVArraySortDataFormat[(Int, K), AnyRef]).sort(data, 0, curSize, comparator) iterator } + + override def writablePartitionedIterator(): WritablePartitionedIterator = { + WritablePartitionedIterator.fromIterator(iterator) + } + + private def iterator(): Iterator[((Int, K), V)] = new Iterator[((Int, K), V)] { + var pos = 0 + + override def hasNext: Boolean = pos < curSize + + override def next(): ((Int, K), V) = { + if (!hasNext) { + throw new NoSuchElementException + } + val pair = (data(2 * pos).asInstanceOf[(Int, K)], data(2 * pos + 1).asInstanceOf[V]) + pos += 1 + pair + } + } } diff --git a/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala new file mode 100644 index 0000000000000..ac9ea6393628f --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala @@ -0,0 +1,254 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.io.InputStream +import java.nio.IntBuffer +import java.util.Comparator + +import org.apache.spark.SparkEnv +import org.apache.spark.serializer.{JavaSerializerInstance, SerializerInstance} +import org.apache.spark.storage.BlockObjectWriter +import org.apache.spark.util.collection.PartitionedSerializedPairBuffer._ + +/** + * Append-only buffer of key-value pairs, each with a corresponding partition ID, that serializes + * its records upon insert and stores them as raw bytes. + * + * We use two data-structures to store the contents. The serialized records are stored in a + * ChainedBuffer that can expand gracefully as records are added. This buffer is accompanied by a + * metadata buffer that stores pointers into the data buffer as well as the partition ID of each + * record. Each entry in the metadata buffer takes up a fixed amount of space. + * + * Sorting the collection means swapping entries in the metadata buffer - the record buffer need not + * be modified at all. Storing the partition IDs in the metadata buffer means that comparisons can + * happen without following any pointers, which should minimize cache misses. + * + * Currently, only sorting by partition is supported. + * + * @param metaInitialRecords The initial number of entries in the metadata buffer. + * @param kvBlockSize The size of each byte buffer in the ChainedBuffer used to store the records. + * @param serializerInstance the serializer used for serializing inserted records. + */ +private[spark] class PartitionedSerializedPairBuffer[K, V]( + metaInitialRecords: Int, + kvBlockSize: Int, + serializerInstance: SerializerInstance) + extends WritablePartitionedPairCollection[K, V] with SizeTracker { + + if (serializerInstance.isInstanceOf[JavaSerializerInstance]) { + throw new IllegalArgumentException("PartitionedSerializedPairBuffer does not support" + + " Java-serialized objects.") + } + + private var metaBuffer = IntBuffer.allocate(metaInitialRecords * RECORD_SIZE) + + private val kvBuffer: ChainedBuffer = new ChainedBuffer(kvBlockSize) + private val kvOutputStream = new ChainedBufferOutputStream(kvBuffer) + private val kvSerializationStream = serializerInstance.serializeStream(kvOutputStream) + + def insert(partition: Int, key: K, value: V): Unit = { + if (metaBuffer.position == metaBuffer.capacity) { + growMetaBuffer() + } + + val keyStart = kvBuffer.size + if (keyStart < 0) { + throw new Exception(s"Can't grow buffer beyond ${1 << 31} bytes") + } + kvSerializationStream.writeKey[Any](key) + kvSerializationStream.flush() + val valueStart = kvBuffer.size + kvSerializationStream.writeValue[Any](value) + kvSerializationStream.flush() + val valueEnd = kvBuffer.size + + metaBuffer.put(keyStart) + metaBuffer.put(valueStart) + metaBuffer.put(valueEnd) + metaBuffer.put(partition) + } + + /** Double the size of the array because we've reached capacity */ + private def growMetaBuffer(): Unit = { + if (metaBuffer.capacity.toLong * 2 > Int.MaxValue) { + // Doubling the capacity would create an array bigger than Int.MaxValue, so don't + throw new Exception(s"Can't grow buffer beyond ${Int.MaxValue} bytes") + } + val newMetaBuffer = IntBuffer.allocate(metaBuffer.capacity * 2) + newMetaBuffer.put(metaBuffer.array) + metaBuffer = newMetaBuffer + } + + /** Iterate through the data in a given order. For this class this is not really destructive. */ + override def partitionedDestructiveSortedIterator(keyComparator: Option[Comparator[K]]) + : Iterator[((Int, K), V)] = { + sort(keyComparator) + val is = orderedInputStream + val deserStream = serializerInstance.deserializeStream(is) + new Iterator[((Int, K), V)] { + var metaBufferPos = 0 + def hasNext: Boolean = metaBufferPos < metaBuffer.position + def next(): ((Int, K), V) = { + val key = deserStream.readKey[Any]().asInstanceOf[K] + val value = deserStream.readValue[Any]().asInstanceOf[V] + val partition = metaBuffer.get(metaBufferPos + PARTITION) + metaBufferPos += RECORD_SIZE + ((partition, key), value) + } + } + } + + override def estimateSize: Long = metaBuffer.capacity * 4 + kvBuffer.capacity + + override def destructiveSortedWritablePartitionedIterator(keyComparator: Option[Comparator[K]]) + : WritablePartitionedIterator = { + sort(keyComparator) + writablePartitionedIterator + } + + override def writablePartitionedIterator(): WritablePartitionedIterator = { + new WritablePartitionedIterator { + // current position in the meta buffer in ints + var pos = 0 + + def writeNext(writer: BlockObjectWriter): Unit = { + val keyStart = metaBuffer.get(pos + KEY_START) + val valueEnd = metaBuffer.get(pos + VAL_END) + pos += RECORD_SIZE + kvBuffer.read(keyStart, writer, valueEnd - keyStart) + writer.recordWritten() + } + def nextPartition(): Int = metaBuffer.get(pos + PARTITION) + def hasNext(): Boolean = pos < metaBuffer.position + } + } + + // Visible for testing + def orderedInputStream: OrderedInputStream = { + new OrderedInputStream(metaBuffer, kvBuffer) + } + + private def sort(keyComparator: Option[Comparator[K]]): Unit = { + val comparator = if (keyComparator.isEmpty) { + new Comparator[Int]() { + def compare(partition1: Int, partition2: Int): Int = { + partition1 - partition2 + } + } + } else { + throw new UnsupportedOperationException() + } + + val sorter = new Sorter(new SerializedSortDataFormat) + sorter.sort(metaBuffer, 0, metaBuffer.position / RECORD_SIZE, comparator) + } +} + +private[spark] class OrderedInputStream(metaBuffer: IntBuffer, kvBuffer: ChainedBuffer) + extends InputStream { + + private var metaBufferPos = 0 + private var kvBufferPos = + if (metaBuffer.position > 0) metaBuffer.get(metaBufferPos + KEY_START) else 0 + + override def read(bytes: Array[Byte]): Int = read(bytes, 0, bytes.length) + + override def read(bytes: Array[Byte], offs: Int, len: Int): Int = { + if (metaBufferPos >= metaBuffer.position) { + return -1 + } + val bytesRemainingInRecord = metaBuffer.get(metaBufferPos + VAL_END) - kvBufferPos + val toRead = math.min(bytesRemainingInRecord, len) + kvBuffer.read(kvBufferPos, bytes, offs, toRead) + if (toRead == bytesRemainingInRecord) { + metaBufferPos += RECORD_SIZE + if (metaBufferPos < metaBuffer.position) { + kvBufferPos = metaBuffer.get(metaBufferPos + KEY_START) + } + } else { + kvBufferPos += toRead + } + toRead + } + + override def read(): Int = { + throw new UnsupportedOperationException() + } +} + +private[spark] class SerializedSortDataFormat extends SortDataFormat[Int, IntBuffer] { + + private val META_BUFFER_TMP = new Array[Int](RECORD_SIZE) + + /** Return the sort key for the element at the given index. */ + override protected def getKey(metaBuffer: IntBuffer, pos: Int): Int = { + metaBuffer.get(pos * RECORD_SIZE + PARTITION) + } + + /** Swap two elements. */ + override def swap(metaBuffer: IntBuffer, pos0: Int, pos1: Int): Unit = { + val iOff = pos0 * RECORD_SIZE + val jOff = pos1 * RECORD_SIZE + System.arraycopy(metaBuffer.array, iOff, META_BUFFER_TMP, 0, RECORD_SIZE) + System.arraycopy(metaBuffer.array, jOff, metaBuffer.array, iOff, RECORD_SIZE) + System.arraycopy(META_BUFFER_TMP, 0, metaBuffer.array, jOff, RECORD_SIZE) + } + + /** Copy a single element from src(srcPos) to dst(dstPos). */ + override def copyElement( + src: IntBuffer, + srcPos: Int, + dst: IntBuffer, + dstPos: Int): Unit = { + val srcOff = srcPos * RECORD_SIZE + val dstOff = dstPos * RECORD_SIZE + System.arraycopy(src.array, srcOff, dst.array, dstOff, RECORD_SIZE) + } + + /** + * Copy a range of elements starting at src(srcPos) to dst, starting at dstPos. + * Overlapping ranges are allowed. + */ + override def copyRange( + src: IntBuffer, + srcPos: Int, + dst: IntBuffer, + dstPos: Int, + length: Int): Unit = { + val srcOff = srcPos * RECORD_SIZE + val dstOff = dstPos * RECORD_SIZE + System.arraycopy(src.array, srcOff, dst.array, dstOff, RECORD_SIZE * length) + } + + /** + * Allocates a Buffer that can hold up to 'length' elements. + * All elements of the buffer should be considered invalid until data is explicitly copied in. + */ + override def allocate(length: Int): IntBuffer = { + IntBuffer.allocate(length * RECORD_SIZE) + } +} + +private[spark] object PartitionedSerializedPairBuffer { + val KEY_START = 0 + val VAL_START = 1 + val VAL_END = 2 + val PARTITION = 3 + val RECORD_SIZE = Seq(KEY_START, VAL_START, VAL_END, PARTITION).size // num ints of metadata +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala index eb4de413867a0..722f78bd15986 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala @@ -21,7 +21,7 @@ package org.apache.spark.util.collection * An append-only map that keeps track of its estimated size in bytes. */ private[spark] class SizeTrackingAppendOnlyMap[K, V] - extends AppendOnlyMap[K, V] with SizeTracker with SizeTrackingPairCollection[K, V] + extends AppendOnlyMap[K, V] with SizeTracker { override def update(key: K, value: V): Unit = { super.update(key, value) diff --git a/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala b/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala new file mode 100644 index 0000000000000..f26d1618c9200 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala @@ -0,0 +1,113 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.util.Comparator + +import org.apache.spark.storage.BlockObjectWriter + +/** + * A common interface for size-tracking collections of key-value pairs that + * - Have an associated partition for each key-value pair. + * - Support a memory-efficient sorted iterator + * - Support a WritablePartitionedIterator for writing the contents directly as bytes. + */ +private[spark] trait WritablePartitionedPairCollection[K, V] { + /** + * Insert a key-value pair with a partition into the collection + */ + def insert(partition: Int, key: K, value: V): Unit + + /** + * Iterate through the data in order of partition ID and then the given comparator. This may + * destroy the underlying collection. + */ + def partitionedDestructiveSortedIterator(keyComparator: Option[Comparator[K]]) + : Iterator[((Int, K), V)] + + /** + * Iterate through the data and write out the elements instead of returning them. Records are + * returned in order of their partition ID and then the given comparator. + * This may destroy the underlying collection. + */ + def destructiveSortedWritablePartitionedIterator(keyComparator: Option[Comparator[K]]) + : WritablePartitionedIterator = { + WritablePartitionedIterator.fromIterator(partitionedDestructiveSortedIterator(keyComparator)) + } + + /** + * Iterate through the data and write out the elements instead of returning them. + */ + def writablePartitionedIterator(): WritablePartitionedIterator +} + +private[spark] object WritablePartitionedPairCollection { + /** + * A comparator for (Int, K) pairs that orders them by only their partition ID. + */ + def partitionComparator[K]: Comparator[(Int, K)] = new Comparator[(Int, K)] { + override def compare(a: (Int, K), b: (Int, K)): Int = { + a._1 - b._1 + } + } + + /** + * A comparator for (Int, K) pairs that orders them both by their partition ID and a key ordering. + */ + def partitionKeyComparator[K](keyComparator: Comparator[K]): Comparator[(Int, K)] = { + new Comparator[(Int, K)] { + override def compare(a: (Int, K), b: (Int, K)): Int = { + val partitionDiff = a._1 - b._1 + if (partitionDiff != 0) { + partitionDiff + } else { + keyComparator.compare(a._2, b._2) + } + } + } + } +} + +/** + * Iterator that writes elements to a BlockObjectWriter instead of returning them. Each element + * has an associated partition. + */ +private[spark] trait WritablePartitionedIterator { + def writeNext(writer: BlockObjectWriter): Unit + + def hasNext(): Boolean + + def nextPartition(): Int +} + +private[spark] object WritablePartitionedIterator { + def fromIterator(it: Iterator[((Int, _), _)]): WritablePartitionedIterator = { + new WritablePartitionedIterator { + var cur = if (it.hasNext) it.next() else null + + def writeNext(writer: BlockObjectWriter): Unit = { + writer.write(cur._1._2, cur._2) + cur = if (it.hasNext) it.next() else null + } + + def hasNext(): Boolean = cur != null + + def nextPartition(): Int = cur._1._1 + } + } +} diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java index 8a4f2a08fe701..c2089b0e56a1f 100644 --- a/core/src/test/java/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java @@ -157,11 +157,11 @@ public void sample() { public void randomSplit() { List ints = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10); JavaRDD rdd = sc.parallelize(ints); - JavaRDD[] splits = rdd.randomSplit(new double[] { 0.4, 0.6, 1.0 }, 11); + JavaRDD[] splits = rdd.randomSplit(new double[] { 0.4, 0.6, 1.0 }, 31); Assert.assertEquals(3, splits.length); - Assert.assertEquals(2, splits[0].count()); - Assert.assertEquals(3, splits[1].count()); - Assert.assertEquals(5, splits[2].count()); + Assert.assertEquals(1, splits[0].count()); + Assert.assertEquals(2, splits[1].count()); + Assert.assertEquals(7, splits[2].count()); } @Test @@ -1009,7 +1009,7 @@ public void persist() { @Test public void iterator() { JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2); - TaskContext context = new TaskContextImpl(0, 0, 0L, 0, false, new TaskMetrics()); + TaskContext context = new TaskContextImpl(0, 0, 0L, 0, null, false, new TaskMetrics()); Assert.assertEquals(1, rdd.iterator(rdd.partitions().get(0), context).next().intValue()); } diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala index 70529d9216591..668ddf9f5f0a9 100644 --- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala @@ -65,7 +65,7 @@ class CacheManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAf // in blockManager.put is a losing battle. You have been warned. blockManager = sc.env.blockManager cacheManager = sc.env.cacheManager - val context = new TaskContextImpl(0, 0, 0, 0) + val context = new TaskContextImpl(0, 0, 0, 0, null) val computeValue = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) val getValue = blockManager.get(RDDBlockId(rdd.id, split.index)) assert(computeValue.toList === List(1, 2, 3, 4)) @@ -77,7 +77,7 @@ class CacheManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAf val result = new BlockResult(Array(5, 6, 7).iterator, DataReadMethod.Memory, 12) when(blockManager.get(RDDBlockId(0, 0))).thenReturn(Some(result)) - val context = new TaskContextImpl(0, 0, 0, 0) + val context = new TaskContextImpl(0, 0, 0, 0, null) val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) assert(value.toList === List(5, 6, 7)) } @@ -86,14 +86,14 @@ class CacheManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAf // Local computation should not persist the resulting value, so don't expect a put(). when(blockManager.get(RDDBlockId(0, 0))).thenReturn(None) - val context = new TaskContextImpl(0, 0, 0, 0, true) + val context = new TaskContextImpl(0, 0, 0, 0, null, true) val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) assert(value.toList === List(1, 2, 3, 4)) } test("verify task metrics updated correctly") { cacheManager = sc.env.cacheManager - val context = new TaskContextImpl(0, 0, 0, 0) + val context = new TaskContextImpl(0, 0, 0, 0, null) cacheManager.getOrCompute(rdd3, split, context, StorageLevel.MEMORY_ONLY) assert(context.taskMetrics.updatedBlocks.getOrElse(Seq()).size === 2) } diff --git a/core/src/test/scala/org/apache/spark/DriverSuite.scala b/core/src/test/scala/org/apache/spark/DriverSuite.scala index 9bd5dfec8703a..c42dfbc82ada4 100644 --- a/core/src/test/scala/org/apache/spark/DriverSuite.scala +++ b/core/src/test/scala/org/apache/spark/DriverSuite.scala @@ -28,7 +28,7 @@ import org.apache.spark.util.Utils class DriverSuite extends FunSuite with Timeouts { - test("driver should exit after finishing without cleanup (SPARK-530)") { + ignore("driver should exit after finishing without cleanup (SPARK-530)") { val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!")) val masters = Table("master", "local", "local-cluster[2,1,512]") forAll(masters) { (master: String) => diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala index 22acc270b983e..84f787ee3715d 100644 --- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala @@ -78,7 +78,7 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit test("starting state") { sc = createSparkContext() val manager = sc.executorAllocationManager.get - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 1) assert(executorsPendingToRemove(manager).isEmpty) assert(executorIds(manager).isEmpty) assert(addTime(manager) === ExecutorAllocationManager.NOT_SET) @@ -91,108 +91,108 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(0, 1000))) // Keep adding until the limit is reached - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 1) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 1) + assert(numExecutorsTarget(manager) === 2) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 3) + assert(numExecutorsTarget(manager) === 4) assert(numExecutorsToAdd(manager) === 4) assert(addExecutors(manager) === 4) - assert(numExecutorsPending(manager) === 7) + assert(numExecutorsTarget(manager) === 8) assert(numExecutorsToAdd(manager) === 8) - assert(addExecutors(manager) === 3) // reached the limit of 10 - assert(numExecutorsPending(manager) === 10) + assert(addExecutors(manager) === 2) // reached the limit of 10 + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 10) + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) // Register previously requested executors onExecutorAdded(manager, "first") - assert(numExecutorsPending(manager) === 9) + assert(numExecutorsTarget(manager) === 10) onExecutorAdded(manager, "second") onExecutorAdded(manager, "third") onExecutorAdded(manager, "fourth") - assert(numExecutorsPending(manager) === 6) + assert(numExecutorsTarget(manager) === 10) onExecutorAdded(manager, "first") // duplicates should not count onExecutorAdded(manager, "second") - assert(numExecutorsPending(manager) === 6) + assert(numExecutorsTarget(manager) === 10) // Try adding again // This should still fail because the number pending + running is still at the limit assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 6) + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 6) + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) } test("add executors capped by num pending tasks") { - sc = createSparkContext(1, 10) + sc = createSparkContext(0, 10) val manager = sc.executorAllocationManager.get sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(0, 5))) // Verify that we're capped at number of tasks in the stage - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 1) + assert(numExecutorsTarget(manager) === 1) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 3) + assert(numExecutorsTarget(manager) === 3) assert(numExecutorsToAdd(manager) === 4) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 5) + assert(numExecutorsTarget(manager) === 5) assert(numExecutorsToAdd(manager) === 1) - // Verify that running a task reduces the cap + // Verify that running a task doesn't affect the target sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(1, 3))) sc.listenerBus.postToAll(SparkListenerExecutorAdded( 0L, "executor-1", new ExecutorInfo("host1", 1, Map.empty))) sc.listenerBus.postToAll(SparkListenerTaskStart(1, 0, createTaskInfo(0, 0, "executor-1"))) - assert(numExecutorsPending(manager) === 4) + assert(numExecutorsTarget(manager) === 5) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 5) + assert(numExecutorsTarget(manager) === 6) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 7) + assert(numExecutorsTarget(manager) === 8) assert(numExecutorsToAdd(manager) === 4) assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 7) + assert(numExecutorsTarget(manager) === 8) assert(numExecutorsToAdd(manager) === 1) - // Verify that re-running a task doesn't reduce the cap further + // Verify that re-running a task doesn't blow things up sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(2, 3))) sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, createTaskInfo(0, 0, "executor-1"))) sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, createTaskInfo(1, 0, "executor-1"))) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 8) + assert(numExecutorsTarget(manager) === 9) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 9) + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) // Verify that running a task once we're at our limit doesn't blow things up sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, createTaskInfo(0, 1, "executor-1"))) assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 9) + assert(numExecutorsTarget(manager) === 10) } test("cancel pending executors when no longer needed") { - sc = createSparkContext(1, 10) + sc = createSparkContext(0, 10) val manager = sc.executorAllocationManager.get sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(2, 5))) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 1) + assert(numExecutorsTarget(manager) === 1) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 3) + assert(numExecutorsTarget(manager) === 3) val task1Info = createTaskInfo(0, 0, "executor-1") sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, task1Info)) @@ -266,7 +266,6 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit // Add a few executors assert(addExecutors(manager) === 1) assert(addExecutors(manager) === 2) - assert(addExecutors(manager) === 4) onExecutorAdded(manager, "1") onExecutorAdded(manager, "2") onExecutorAdded(manager, "3") @@ -274,55 +273,57 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit onExecutorAdded(manager, "5") onExecutorAdded(manager, "6") onExecutorAdded(manager, "7") - assert(executorIds(manager).size === 7) + onExecutorAdded(manager, "8") + assert(executorIds(manager).size === 8) // Remove until limit assert(removeExecutor(manager, "1")) assert(removeExecutor(manager, "2")) - assert(!removeExecutor(manager, "3")) // lower limit reached - assert(!removeExecutor(manager, "4")) + assert(removeExecutor(manager, "3")) + assert(!removeExecutor(manager, "4")) // lower limit reached + assert(!removeExecutor(manager, "5")) onExecutorRemoved(manager, "1") onExecutorRemoved(manager, "2") + onExecutorRemoved(manager, "3") assert(executorIds(manager).size === 5) // Add until limit - assert(addExecutors(manager) === 5) // upper limit reached + assert(addExecutors(manager) === 2) // upper limit reached assert(addExecutors(manager) === 0) - assert(!removeExecutor(manager, "3")) // still at lower limit - assert(!removeExecutor(manager, "4")) - onExecutorAdded(manager, "8") + assert(!removeExecutor(manager, "4")) // still at lower limit + assert(!removeExecutor(manager, "5")) onExecutorAdded(manager, "9") onExecutorAdded(manager, "10") onExecutorAdded(manager, "11") onExecutorAdded(manager, "12") + onExecutorAdded(manager, "13") assert(executorIds(manager).size === 10) // Remove succeeds again, now that we are no longer at the lower limit - assert(removeExecutor(manager, "3")) assert(removeExecutor(manager, "4")) assert(removeExecutor(manager, "5")) assert(removeExecutor(manager, "6")) + assert(removeExecutor(manager, "7")) assert(executorIds(manager).size === 10) - assert(addExecutors(manager) === 1) - onExecutorRemoved(manager, "3") + assert(addExecutors(manager) === 0) onExecutorRemoved(manager, "4") + onExecutorRemoved(manager, "5") assert(executorIds(manager).size === 8) - // Add succeeds again, now that we are no longer at the upper limit - // Number of executors added restarts at 1 - assert(addExecutors(manager) === 2) - assert(addExecutors(manager) === 1) // upper limit reached + // Number of executors pending restarts at 1 + assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 0) assert(executorIds(manager).size === 8) - onExecutorRemoved(manager, "5") onExecutorRemoved(manager, "6") - onExecutorAdded(manager, "13") + onExecutorRemoved(manager, "7") onExecutorAdded(manager, "14") + onExecutorAdded(manager, "15") assert(executorIds(manager).size === 8) assert(addExecutors(manager) === 0) // still at upper limit - onExecutorAdded(manager, "15") onExecutorAdded(manager, "16") + onExecutorAdded(manager, "17") assert(executorIds(manager).size === 10) + assert(numExecutorsTarget(manager) === 10) } test("starting/canceling add timer") { @@ -405,33 +406,33 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit } test("mock polling loop with no events") { - sc = createSparkContext(1, 20) + sc = createSparkContext(0, 20) val manager = sc.executorAllocationManager.get val clock = new ManualClock(2020L) manager.setClock(clock) // No events - we should not be adding or removing - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) schedule(manager) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) clock.advance(100L) schedule(manager) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) clock.advance(1000L) schedule(manager) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) clock.advance(10000L) schedule(manager) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) } test("mock polling loop add behavior") { - sc = createSparkContext(1, 20) + sc = createSparkContext(0, 20) val clock = new ManualClock(2020L) val manager = sc.executorAllocationManager.get manager.setClock(clock) @@ -441,43 +442,43 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit onSchedulerBacklogged(manager) clock.advance(schedulerBacklogTimeout * 1000 / 2) schedule(manager) - assert(numExecutorsPending(manager) === 0) // timer not exceeded yet + assert(numExecutorsTarget(manager) === 0) // timer not exceeded yet clock.advance(schedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 1) // first timer exceeded + assert(numExecutorsTarget(manager) === 1) // first timer exceeded clock.advance(sustainedSchedulerBacklogTimeout * 1000 / 2) schedule(manager) - assert(numExecutorsPending(manager) === 1) // second timer not exceeded yet + assert(numExecutorsTarget(manager) === 1) // second timer not exceeded yet clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 1 + 2) // second timer exceeded + assert(numExecutorsTarget(manager) === 1 + 2) // second timer exceeded clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 1 + 2 + 4) // third timer exceeded + assert(numExecutorsTarget(manager) === 1 + 2 + 4) // third timer exceeded // Scheduler queue drained onSchedulerQueueEmpty(manager) clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7) // timer is canceled + assert(numExecutorsTarget(manager) === 7) // timer is canceled clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7) + assert(numExecutorsTarget(manager) === 7) // Scheduler queue backlogged again onSchedulerBacklogged(manager) clock.advance(schedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7 + 1) // timer restarted + assert(numExecutorsTarget(manager) === 7 + 1) // timer restarted clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7 + 1 + 2) + assert(numExecutorsTarget(manager) === 7 + 1 + 2) clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7 + 1 + 2 + 4) + assert(numExecutorsTarget(manager) === 7 + 1 + 2 + 4) clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 20) // limit reached + assert(numExecutorsTarget(manager) === 20) // limit reached } test("mock polling loop remove behavior") { @@ -671,6 +672,31 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit assert(!removeTimes(manager).contains("executor-1")) } + test("avoid ramp up when target < running executors") { + sc = createSparkContext(0, 100000) + val manager = sc.executorAllocationManager.get + val stage1 = createStageInfo(0, 1000) + sc.listenerBus.postToAll(SparkListenerStageSubmitted(stage1)) + + assert(addExecutors(manager) === 1) + assert(addExecutors(manager) === 2) + assert(addExecutors(manager) === 4) + assert(addExecutors(manager) === 8) + assert(numExecutorsTarget(manager) === 15) + (0 until 15).foreach { i => + onExecutorAdded(manager, s"executor-$i") + } + assert(executorIds(manager).size === 15) + sc.listenerBus.postToAll(SparkListenerStageCompleted(stage1)) + + adjustRequestedExecutors(manager) + assert(numExecutorsTarget(manager) === 0) + + sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(1, 1000))) + addExecutors(manager) + assert(numExecutorsTarget(manager) === 16) + } + private def createSparkContext(minExecutors: Int = 1, maxExecutors: Int = 5): SparkContext = { val conf = new SparkConf() .setMaster("local") @@ -701,7 +727,7 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { private val executorIdleTimeout = 3L private def createStageInfo(stageId: Int, numTasks: Int): StageInfo = { - new StageInfo(stageId, 0, "name", numTasks, Seq.empty, "no details") + new StageInfo(stageId, 0, "name", numTasks, Seq.empty, Seq.empty, "no details") } private def createTaskInfo(taskId: Int, taskIndex: Int, executorId: String): TaskInfo = { @@ -713,7 +739,7 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { * ------------------------------------------------------- */ private val _numExecutorsToAdd = PrivateMethod[Int]('numExecutorsToAdd) - private val _numExecutorsPending = PrivateMethod[Int]('numExecutorsPending) + private val _numExecutorsTarget = PrivateMethod[Int]('numExecutorsTarget) private val _maxNumExecutorsNeeded = PrivateMethod[Int]('maxNumExecutorsNeeded) private val _executorsPendingToRemove = PrivateMethod[collection.Set[String]]('executorsPendingToRemove) @@ -722,7 +748,8 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { private val _removeTimes = PrivateMethod[collection.Map[String, Long]]('removeTimes) private val _schedule = PrivateMethod[Unit]('schedule) private val _addExecutors = PrivateMethod[Int]('addExecutors) - private val _addOrCancelExecutorRequests = PrivateMethod[Int]('addOrCancelExecutorRequests) + private val _updateAndSyncNumExecutorsTarget = + PrivateMethod[Int]('updateAndSyncNumExecutorsTarget) private val _removeExecutor = PrivateMethod[Boolean]('removeExecutor) private val _onExecutorAdded = PrivateMethod[Unit]('onExecutorAdded) private val _onExecutorRemoved = PrivateMethod[Unit]('onExecutorRemoved) @@ -735,8 +762,8 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { manager invokePrivate _numExecutorsToAdd() } - private def numExecutorsPending(manager: ExecutorAllocationManager): Int = { - manager invokePrivate _numExecutorsPending() + private def numExecutorsTarget(manager: ExecutorAllocationManager): Int = { + manager invokePrivate _numExecutorsTarget() } private def executorsPendingToRemove( @@ -766,7 +793,7 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { } private def adjustRequestedExecutors(manager: ExecutorAllocationManager): Int = { - manager invokePrivate _addOrCancelExecutorRequests(0L) + manager invokePrivate _updateAndSyncNumExecutorsTarget(0L) } private def removeExecutor(manager: ExecutorAllocationManager, id: String): Boolean = { diff --git a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala index 0fd570e5297d9..b789912e9ebef 100644 --- a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala +++ b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala @@ -48,7 +48,7 @@ class HeartbeatReceiverSuite extends FunSuite with LocalSparkContext { val metrics = new TaskMetrics val blockManagerId = BlockManagerId("executor-1", "localhost", 12345) - val response = receiverRef.askWithReply[HeartbeatResponse]( + val response = receiverRef.askWithRetry[HeartbeatResponse]( Heartbeat("executor-1", Array(1L -> metrics), blockManagerId)) verify(scheduler).executorHeartbeatReceived( @@ -71,7 +71,7 @@ class HeartbeatReceiverSuite extends FunSuite with LocalSparkContext { val metrics = new TaskMetrics val blockManagerId = BlockManagerId("executor-1", "localhost", 12345) - val response = receiverRef.askWithReply[HeartbeatResponse]( + val response = receiverRef.askWithRetry[HeartbeatResponse]( Heartbeat("executor-1", Array(1L -> metrics), blockManagerId)) verify(scheduler).executorHeartbeatReceived( diff --git a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala index 4d3e09793faff..ae17fc60e4a43 100644 --- a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala +++ b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala @@ -141,6 +141,41 @@ class JobCancellationSuite extends FunSuite with Matchers with BeforeAndAfter assert(jobB.get() === 100) } + test("inherited job group (SPARK-6629)") { + sc = new SparkContext("local[2]", "test") + + // Add a listener to release the semaphore once any tasks are launched. + val sem = new Semaphore(0) + sc.addSparkListener(new SparkListener { + override def onTaskStart(taskStart: SparkListenerTaskStart) { + sem.release() + } + }) + + sc.setJobGroup("jobA", "this is a job to be cancelled") + @volatile var exception: Exception = null + val jobA = new Thread() { + // The job group should be inherited by this thread + override def run(): Unit = { + exception = intercept[SparkException] { + sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.count() + } + } + } + jobA.start() + + // Block until both tasks of job A have started and cancel job A. + sem.acquire(2) + sc.cancelJobGroup("jobA") + jobA.join(10000) + assert(!jobA.isAlive) + assert(exception.getMessage contains "cancel") + + // Once A is cancelled, job B should finish fairly quickly. + val jobB = sc.parallelize(1 to 100, 2).countAsync() + assert(jobB.get() === 100) + } + test("job group with interruption") { sc = new SparkContext("local[2]", "test") diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala index 728558a424780..9049db7755358 100644 --- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala @@ -25,7 +25,9 @@ import com.google.common.io.Files import org.scalatest.FunSuite -import org.apache.hadoop.io.BytesWritable +import org.apache.hadoop.io.{BytesWritable, LongWritable, Text} +import org.apache.hadoop.mapred.TextInputFormat +import org.apache.hadoop.mapreduce.lib.input.{TextInputFormat => NewTextInputFormat} import org.apache.spark.util.Utils import scala.concurrent.Await @@ -213,4 +215,63 @@ class SparkContextSuite extends FunSuite with LocalSparkContext { sc.stop() } } + + test("Comma separated paths for newAPIHadoopFile/wholeTextFiles/binaryFiles (SPARK-7155)") { + // Regression test for SPARK-7155 + // dir1 and dir2 are used for wholeTextFiles and binaryFiles + val dir1 = Utils.createTempDir() + val dir2 = Utils.createTempDir() + + val dirpath1=dir1.getAbsolutePath + val dirpath2=dir2.getAbsolutePath + + // file1 and file2 are placed inside dir1, they are also used for + // textFile, hadoopFile, and newAPIHadoopFile + // file3, file4 and file5 are placed inside dir2, they are used for + // textFile, hadoopFile, and newAPIHadoopFile as well + val file1 = new File(dir1, "part-00000") + val file2 = new File(dir1, "part-00001") + val file3 = new File(dir2, "part-00000") + val file4 = new File(dir2, "part-00001") + val file5 = new File(dir2, "part-00002") + + val filepath1=file1.getAbsolutePath + val filepath2=file2.getAbsolutePath + val filepath3=file3.getAbsolutePath + val filepath4=file4.getAbsolutePath + val filepath5=file5.getAbsolutePath + + + try { + // Create 5 text files. + Files.write("someline1 in file1\nsomeline2 in file1\nsomeline3 in file1", file1, UTF_8) + Files.write("someline1 in file2\nsomeline2 in file2", file2, UTF_8) + Files.write("someline1 in file3", file3, UTF_8) + Files.write("someline1 in file4\nsomeline2 in file4", file4, UTF_8) + Files.write("someline1 in file2\nsomeline2 in file5", file5, UTF_8) + + sc = new SparkContext(new SparkConf().setAppName("test").setMaster("local")) + + // Test textFile, hadoopFile, and newAPIHadoopFile for file1 and file2 + assert(sc.textFile(filepath1 + "," + filepath2).count() == 5L) + assert(sc.hadoopFile(filepath1 + "," + filepath2, + classOf[TextInputFormat], classOf[LongWritable], classOf[Text]).count() == 5L) + assert(sc.newAPIHadoopFile(filepath1 + "," + filepath2, + classOf[NewTextInputFormat], classOf[LongWritable], classOf[Text]).count() == 5L) + + // Test textFile, hadoopFile, and newAPIHadoopFile for file3, file4, and file5 + assert(sc.textFile(filepath3 + "," + filepath4 + "," + filepath5).count() == 5L) + assert(sc.hadoopFile(filepath3 + "," + filepath4 + "," + filepath5, + classOf[TextInputFormat], classOf[LongWritable], classOf[Text]).count() == 5L) + assert(sc.newAPIHadoopFile(filepath3 + "," + filepath4 + "," + filepath5, + classOf[NewTextInputFormat], classOf[LongWritable], classOf[Text]).count() == 5L) + + // Test wholeTextFiles, and binaryFiles for dir1 and dir2 + assert(sc.wholeTextFiles(dirpath1 + "," + dirpath2).count() == 5L) + assert(sc.binaryFiles(dirpath1 + "," + dirpath2).count() == 5L) + + } finally { + sc.stop() + } + } } diff --git a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala index b5383d553add1..10917c866cc7d 100644 --- a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala +++ b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala @@ -17,10 +17,11 @@ package org.apache.spark -import java.util.concurrent.Semaphore +import java.util.concurrent.{TimeUnit, Semaphore} import java.util.concurrent.atomic.AtomicBoolean import java.util.concurrent.atomic.AtomicInteger +import org.apache.spark.scheduler._ import org.scalatest.FunSuite /** @@ -189,4 +190,47 @@ class ThreadingSuite extends FunSuite with LocalSparkContext { assert(sc.getLocalProperty("test") === "parent") assert(sc.getLocalProperty("Foo") === null) } + + test("mutations to local properties should not affect submitted jobs (SPARK-6629)") { + val jobStarted = new Semaphore(0) + val jobEnded = new Semaphore(0) + @volatile var jobResult: JobResult = null + + sc = new SparkContext("local", "test") + sc.setJobGroup("originalJobGroupId", "description") + sc.addSparkListener(new SparkListener { + override def onJobStart(jobStart: SparkListenerJobStart): Unit = { + jobStarted.release() + } + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = { + jobResult = jobEnd.jobResult + jobEnded.release() + } + }) + + // Create a new thread which will inherit the current thread's properties + val thread = new Thread() { + override def run(): Unit = { + assert(sc.getLocalProperty(SparkContext.SPARK_JOB_GROUP_ID) === "originalJobGroupId") + // Sleeps for a total of 10 seconds, but allows cancellation to interrupt the task + try { + sc.parallelize(1 to 100).foreach { x => + Thread.sleep(100) + } + } catch { + case s: SparkException => // ignored so that we don't print noise in test logs + } + } + } + thread.start() + // Wait for the job to start, then mutate the original properties, which should have been + // inherited by the running job but hopefully defensively copied or snapshotted: + jobStarted.tryAcquire(10, TimeUnit.SECONDS) + sc.setJobGroup("modifiedJobGroupId", "description") + // Canceling the original job group should cancel the running job. In other words, the + // modification of the properties object should not affect the properties of running jobs + sc.cancelJobGroup("originalJobGroupId") + jobEnded.tryAcquire(10, TimeUnit.SECONDS) + assert(jobResult.isInstanceOf[JobFailed]) + } } diff --git a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala index c8fdfa693912e..06e5f1cf6b96f 100644 --- a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala +++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala @@ -17,9 +17,11 @@ package org.apache.spark.broadcast +import scala.concurrent.duration._ import scala.util.Random import org.scalatest.{Assertions, FunSuite} +import org.scalatest.concurrent.Eventually._ import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkException, SparkEnv} import org.apache.spark.io.SnappyCompressionCodec @@ -307,7 +309,17 @@ class BroadcastSuite extends FunSuite with LocalSparkContext { removeFromDriver: Boolean) { sc = if (distributed) { - new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", broadcastConf) + val _sc = + new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", broadcastConf) + // Wait until all salves are up + eventually(timeout(10.seconds), interval(10.milliseconds)) { + _sc.jobProgressListener.synchronized { + val numBlockManagers = _sc.jobProgressListener.blockManagerIds.size + assert(numBlockManagers == numSlaves + 1, + s"Expect ${numSlaves + 1} block managers, but was ${numBlockManagers}") + } + } + _sc } else { new SparkContext("local", "test", broadcastConf) } diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala index c4e6f06146b0a..61c95419aedcf 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -320,7 +320,7 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties runSparkSubmit(args) } - test("includes jars passed in through --jars") { + ignore("includes jars passed in through --jars") { val unusedJar = TestUtils.createJarWithClasses(Seq.empty) val jar1 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassA")) val jar2 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassB")) @@ -334,7 +334,7 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties runSparkSubmit(args) } - test("includes jars passed in through --packages") { + ignore("includes jars passed in through --packages") { val unusedJar = TestUtils.createJarWithClasses(Seq.empty) val packagesString = "com.databricks:spark-csv_2.10:0.1,com.databricks:spark-avro_2.10:0.1" val args = Seq( diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala index 1b2b699cb11e6..da9578478bed9 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala @@ -104,7 +104,7 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { assert(jarPath.indexOf(ivyPath) >= 0, "should use non-default ivy path") } - test("search for artifact at other repositories") { + ignore("search for artifact at other repositories") { val path = SparkSubmitUtils.resolveMavenCoordinates("com.agimatec:agimatec-validation:0.9.3", Option("https://oss.sonatype.org/content/repositories/agimatec/"), None, true) assert(path.indexOf("agimatec-validation") >= 0, "should find package. If it doesn't, check" + @@ -117,7 +117,7 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { } } - test("neglects Spark and Spark's dependencies") { + ignore("neglects Spark and Spark's dependencies") { val components = Seq("bagel_", "catalyst_", "core_", "graphx_", "hive_", "mllib_", "repl_", "sql_", "streaming_", "yarn_", "network-common_", "network-shuffle_", "network-yarn_") diff --git a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala index 9e367a0d9af0d..a0a0afa48833e 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.deploy.history import java.io.{BufferedOutputStream, File, FileOutputStream, OutputStreamWriter} import java.net.URI +import java.util.concurrent.TimeUnit import scala.io.Source @@ -30,7 +31,7 @@ import org.scalatest.Matchers import org.apache.spark.{Logging, SparkConf} import org.apache.spark.io._ import org.apache.spark.scheduler._ -import org.apache.spark.util.{JsonProtocol, Utils} +import org.apache.spark.util.{JsonProtocol, ManualClock, Utils} class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers with Logging { @@ -47,10 +48,11 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers /** Create a fake log file using the new log format used in Spark 1.3+ */ private def newLogFile( appId: String, + appAttemptId: Option[String], inProgress: Boolean, codec: Option[String] = None): File = { val ip = if (inProgress) EventLoggingListener.IN_PROGRESS else "" - val logUri = EventLoggingListener.getLogPath(testDir.toURI, appId) + val logUri = EventLoggingListener.getLogPath(testDir.toURI, appId, appAttemptId) val logPath = new URI(logUri).getPath + ip new File(logPath) } @@ -59,22 +61,23 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers val provider = new FsHistoryProvider(createTestConf()) // Write a new-style application log. - val newAppComplete = newLogFile("new1", inProgress = false) + val newAppComplete = newLogFile("new1", None, inProgress = false) writeFile(newAppComplete, true, None, - SparkListenerApplicationStart("new-app-complete", None, 1L, "test"), + SparkListenerApplicationStart("new-app-complete", None, 1L, "test", None), SparkListenerApplicationEnd(5L) ) // Write a new-style application log. - val newAppCompressedComplete = newLogFile("new1compressed", inProgress = false, Some("lzf")) + val newAppCompressedComplete = newLogFile("new1compressed", None, inProgress = false, + Some("lzf")) writeFile(newAppCompressedComplete, true, None, - SparkListenerApplicationStart("new-app-compressed-complete", None, 1L, "test"), + SparkListenerApplicationStart("new-app-compressed-complete", None, 1L, "test", None), SparkListenerApplicationEnd(4L)) // Write an unfinished app, new-style. - val newAppIncomplete = newLogFile("new2", inProgress = true) + val newAppIncomplete = newLogFile("new2", None, inProgress = true) writeFile(newAppIncomplete, true, None, - SparkListenerApplicationStart("new-app-incomplete", None, 1L, "test") + SparkListenerApplicationStart("new-app-incomplete", None, 1L, "test", None) ) // Write an old-style application log. @@ -82,7 +85,7 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers oldAppComplete.mkdir() createEmptyFile(new File(oldAppComplete, provider.SPARK_VERSION_PREFIX + "1.0")) writeFile(new File(oldAppComplete, provider.LOG_PREFIX + "1"), false, None, - SparkListenerApplicationStart("old-app-complete", None, 2L, "test"), + SparkListenerApplicationStart("old-app-complete", None, 2L, "test", None), SparkListenerApplicationEnd(3L) ) createEmptyFile(new File(oldAppComplete, provider.APPLICATION_COMPLETE)) @@ -96,33 +99,45 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers oldAppIncomplete.mkdir() createEmptyFile(new File(oldAppIncomplete, provider.SPARK_VERSION_PREFIX + "1.0")) writeFile(new File(oldAppIncomplete, provider.LOG_PREFIX + "1"), false, None, - SparkListenerApplicationStart("old-app-incomplete", None, 2L, "test") + SparkListenerApplicationStart("old-app-incomplete", None, 2L, "test", None) ) // Force a reload of data from the log directory, and check that both logs are loaded. // Take the opportunity to check that the offset checks work as expected. - provider.checkForLogs() + updateAndCheck(provider) { list => + list.size should be (5) + list.count(_.attempts.head.completed) should be (3) + + def makeAppInfo( + id: String, + name: String, + start: Long, + end: Long, + lastMod: Long, + user: String, + completed: Boolean): ApplicationHistoryInfo = { + ApplicationHistoryInfo(id, name, + List(ApplicationAttemptInfo(None, start, end, lastMod, user, completed))) + } - val list = provider.getListing().toSeq - list should not be (null) - list.size should be (5) - list.count(_.completed) should be (3) - - list(0) should be (ApplicationHistoryInfo(newAppComplete.getName(), "new-app-complete", 1L, 5L, - newAppComplete.lastModified(), "test", true)) - list(1) should be (ApplicationHistoryInfo(newAppCompressedComplete.getName(), - "new-app-compressed-complete", 1L, 4L, newAppCompressedComplete.lastModified(), "test", true)) - list(2) should be (ApplicationHistoryInfo(oldAppComplete.getName(), "old-app-complete", 2L, 3L, - oldAppComplete.lastModified(), "test", true)) - list(3) should be (ApplicationHistoryInfo(oldAppIncomplete.getName(), "old-app-incomplete", 2L, - -1L, oldAppIncomplete.lastModified(), "test", false)) - list(4) should be (ApplicationHistoryInfo(newAppIncomplete.getName(), "new-app-incomplete", 1L, - -1L, newAppIncomplete.lastModified(), "test", false)) - - // Make sure the UI can be rendered. - list.foreach { case info => - val appUi = provider.getAppUI(info.id) - appUi should not be null + list(0) should be (makeAppInfo(newAppComplete.getName(), "new-app-complete", 1L, 5L, + newAppComplete.lastModified(), "test", true)) + list(1) should be (makeAppInfo(newAppCompressedComplete.getName(), + "new-app-compressed-complete", 1L, 4L, newAppCompressedComplete.lastModified(), "test", + true)) + list(2) should be (makeAppInfo(oldAppComplete.getName(), "old-app-complete", 2L, 3L, + oldAppComplete.lastModified(), "test", true)) + list(3) should be (makeAppInfo(oldAppIncomplete.getName(), "old-app-incomplete", 2L, -1L, + oldAppIncomplete.lastModified(), "test", false)) + list(4) should be (makeAppInfo(newAppIncomplete.getName(), "new-app-incomplete", 1L, -1L, + newAppIncomplete.lastModified(), "test", false)) + + // Make sure the UI can be rendered. + list.foreach { case info => + val appUi = provider.getAppUI(info.id, None) + appUi should not be null + appUi should not be None + } } } @@ -138,7 +153,7 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers logDir.mkdir() createEmptyFile(new File(logDir, provider.SPARK_VERSION_PREFIX + "1.0")) writeFile(new File(logDir, provider.LOG_PREFIX + "1"), false, Option(codec), - SparkListenerApplicationStart("app2", None, 2L, "test"), + SparkListenerApplicationStart("app2", None, 2L, "test", None), SparkListenerApplicationEnd(3L) ) createEmptyFile(new File(logDir, provider.COMPRESSION_CODEC_PREFIX + codecName)) @@ -159,52 +174,52 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers } test("SPARK-3697: ignore directories that cannot be read.") { - val logFile1 = newLogFile("new1", inProgress = false) + val logFile1 = newLogFile("new1", None, inProgress = false) writeFile(logFile1, true, None, - SparkListenerApplicationStart("app1-1", None, 1L, "test"), + SparkListenerApplicationStart("app1-1", None, 1L, "test", None), SparkListenerApplicationEnd(2L) ) - val logFile2 = newLogFile("new2", inProgress = false) + val logFile2 = newLogFile("new2", None, inProgress = false) writeFile(logFile2, true, None, - SparkListenerApplicationStart("app1-2", None, 1L, "test"), + SparkListenerApplicationStart("app1-2", None, 1L, "test", None), SparkListenerApplicationEnd(2L) ) logFile2.setReadable(false, false) val provider = new FsHistoryProvider(createTestConf()) - provider.checkForLogs() - - val list = provider.getListing().toSeq - list should not be (null) - list.size should be (1) + updateAndCheck(provider) { list => + list.size should be (1) + } } test("history file is renamed from inprogress to completed") { val provider = new FsHistoryProvider(createTestConf()) - val logFile1 = newLogFile("app1", inProgress = true) + val logFile1 = newLogFile("app1", None, inProgress = true) writeFile(logFile1, true, None, - SparkListenerApplicationStart("app1", Some("app1"), 1L, "test"), + SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", None), SparkListenerApplicationEnd(2L) ) - provider.checkForLogs() - val appListBeforeRename = provider.getListing() - appListBeforeRename.size should be (1) - appListBeforeRename.head.logPath should endWith(EventLoggingListener.IN_PROGRESS) + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.head.asInstanceOf[FsApplicationAttemptInfo].logPath should + endWith(EventLoggingListener.IN_PROGRESS) + } - logFile1.renameTo(newLogFile("app1", inProgress = false)) - provider.checkForLogs() - val appListAfterRename = provider.getListing() - appListAfterRename.size should be (1) - appListAfterRename.head.logPath should not endWith(EventLoggingListener.IN_PROGRESS) + logFile1.renameTo(newLogFile("app1", None, inProgress = false)) + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.head.asInstanceOf[FsApplicationAttemptInfo].logPath should not + endWith(EventLoggingListener.IN_PROGRESS) + } } test("SPARK-5582: empty log directory") { val provider = new FsHistoryProvider(createTestConf()) - val logFile1 = newLogFile("app1", inProgress = true) + val logFile1 = newLogFile("app1", None, inProgress = true) writeFile(logFile1, true, None, - SparkListenerApplicationStart("app1", Some("app1"), 1L, "test"), + SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", None), SparkListenerApplicationEnd(2L)) val oldLog = new File(testDir, "old1") @@ -215,6 +230,126 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers appListAfterRename.size should be (1) } + test("apps with multiple attempts") { + val provider = new FsHistoryProvider(createTestConf()) + + val attempt1 = newLogFile("app1", Some("attempt1"), inProgress = false) + writeFile(attempt1, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", Some("attempt1")), + SparkListenerApplicationEnd(2L) + ) + + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.size should be (1) + } + + val attempt2 = newLogFile("app1", Some("attempt2"), inProgress = true) + writeFile(attempt2, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2")) + ) + + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.size should be (2) + list.head.attempts.head.attemptId should be (Some("attempt2")) + } + + val completedAttempt2 = newLogFile("app1", Some("attempt2"), inProgress = false) + attempt2.delete() + writeFile(attempt2, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2")), + SparkListenerApplicationEnd(4L) + ) + + updateAndCheck(provider) { list => + list should not be (null) + list.size should be (1) + list.head.attempts.size should be (2) + list.head.attempts.head.attemptId should be (Some("attempt2")) + } + + val app2Attempt1 = newLogFile("app2", Some("attempt1"), inProgress = false) + writeFile(attempt2, true, None, + SparkListenerApplicationStart("app2", Some("app2"), 5L, "test", Some("attempt1")), + SparkListenerApplicationEnd(6L) + ) + + updateAndCheck(provider) { list => + list.size should be (2) + list.head.attempts.size should be (1) + list.last.attempts.size should be (2) + list.head.attempts.head.attemptId should be (Some("attempt1")) + + list.foreach { case app => + app.attempts.foreach { attempt => + val appUi = provider.getAppUI(app.id, attempt.attemptId) + appUi should not be null + } + } + + } + } + + test("log cleaner") { + val maxAge = TimeUnit.SECONDS.toMillis(10) + val clock = new ManualClock(maxAge / 2) + val provider = new FsHistoryProvider( + createTestConf().set("spark.history.fs.cleaner.maxAge", s"${maxAge}ms"), clock) + + val log1 = newLogFile("app1", Some("attempt1"), inProgress = false) + writeFile(log1, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", Some("attempt1")), + SparkListenerApplicationEnd(2L) + ) + log1.setLastModified(0L) + + val log2 = newLogFile("app1", Some("attempt2"), inProgress = false) + writeFile(log2, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2")), + SparkListenerApplicationEnd(4L) + ) + log2.setLastModified(clock.getTimeMillis()) + + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.size should be (2) + } + + // Move the clock forward so log1 exceeds the max age. + clock.advance(maxAge) + + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.size should be (1) + list.head.attempts.head.attemptId should be (Some("attempt2")) + } + assert(!log1.exists()) + + // Do the same for the other log. + clock.advance(maxAge) + + updateAndCheck(provider) { list => + list.size should be (0) + } + assert(!log2.exists()) + } + + /** + * Asks the provider to check for logs and calls a function to perform checks on the updated + * app list. Example: + * + * updateAndCheck(provider) { list => + * // asserts + * } + */ + private def updateAndCheck(provider: FsHistoryProvider) + (checkFn: Seq[ApplicationHistoryInfo] => Unit): Unit = { + provider.checkForLogs() + provider.cleanLogs() + checkFn(provider.getListing().toSeq) + } + private def writeFile(file: File, isNewFormat: Boolean, codec: Option[CompressionCodec], events: SparkListenerEvent*) = { val fstream = new FileOutputStream(file) diff --git a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala index 20de46fdab909..71ba9c18257b8 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala @@ -36,7 +36,8 @@ class HistoryServerSuite extends FunSuite with Matchers with MockitoSugar { val request = mock[HttpServletRequest] val ui = mock[SparkUI] val link = "/history/app1" - val info = new ApplicationHistoryInfo("app1", "app1", 0, 2, 1, "xxx", true) + val info = new ApplicationHistoryInfo("app1", "app1", + List(ApplicationAttemptInfo(None, 0, 2, 1, "xxx", true))) when(historyServer.getApplicationList()).thenReturn(Seq(info)) when(ui.basePath).thenReturn(link) when(historyServer.getProviderConfig()).thenReturn(Map[String, String]()) diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala index 0a318a27ac212..f4d548d9e7720 100644 --- a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala @@ -39,7 +39,6 @@ import org.apache.spark.deploy.master.DriverState._ * Tests for the REST application submission protocol used in standalone cluster mode. */ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { - private val client = new RestSubmissionClient private var actorSystem: Option[ActorSystem] = None private var server: Option[RestSubmissionServer] = None @@ -52,7 +51,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val appArgs = Array("one", "two", "three") val sparkProperties = Map("spark.app.name" -> "pi") val environmentVariables = Map("SPARK_ONE" -> "UN", "SPARK_TWO" -> "DEUX") - val request = client.constructSubmitRequest( + val request = new RestSubmissionClient("spark://host:port").constructSubmitRequest( "my-app-resource", "my-main-class", appArgs, sparkProperties, environmentVariables) assert(request.action === Utils.getFormattedClassName(request)) assert(request.clientSparkVersion === SPARK_VERSION) @@ -71,7 +70,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val request = constructSubmitRequest(masterUrl, appArgs) assert(request.appArgs === appArgs) assert(request.sparkProperties("spark.master") === masterUrl) - val response = client.createSubmission(masterUrl, request) + val response = new RestSubmissionClient(masterUrl).createSubmission(request) val submitResponse = getSubmitResponse(response) assert(submitResponse.action === Utils.getFormattedClassName(submitResponse)) assert(submitResponse.serverSparkVersion === SPARK_VERSION) @@ -102,7 +101,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val submissionId = "my-lyft-driver" val killMessage = "your driver is killed" val masterUrl = startDummyServer(killMessage = killMessage) - val response = client.killSubmission(masterUrl, submissionId) + val response = new RestSubmissionClient(masterUrl).killSubmission(submissionId) val killResponse = getKillResponse(response) assert(killResponse.action === Utils.getFormattedClassName(killResponse)) assert(killResponse.serverSparkVersion === SPARK_VERSION) @@ -116,7 +115,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val submissionState = KILLED val submissionException = new Exception("there was an irresponsible mix of alcohol and cars") val masterUrl = startDummyServer(state = submissionState, exception = Some(submissionException)) - val response = client.requestSubmissionStatus(masterUrl, submissionId) + val response = new RestSubmissionClient(masterUrl).requestSubmissionStatus(submissionId) val statusResponse = getStatusResponse(response) assert(statusResponse.action === Utils.getFormattedClassName(statusResponse)) assert(statusResponse.serverSparkVersion === SPARK_VERSION) @@ -129,13 +128,14 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("create then kill") { val masterUrl = startSmartServer() val request = constructSubmitRequest(masterUrl) - val response1 = client.createSubmission(masterUrl, request) + val client = new RestSubmissionClient(masterUrl) + val response1 = client.createSubmission(request) val submitResponse = getSubmitResponse(response1) assert(submitResponse.success) assert(submitResponse.submissionId != null) // kill submission that was just created val submissionId = submitResponse.submissionId - val response2 = client.killSubmission(masterUrl, submissionId) + val response2 = client.killSubmission(submissionId) val killResponse = getKillResponse(response2) assert(killResponse.success) assert(killResponse.submissionId === submissionId) @@ -144,13 +144,14 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("create then request status") { val masterUrl = startSmartServer() val request = constructSubmitRequest(masterUrl) - val response1 = client.createSubmission(masterUrl, request) + val client = new RestSubmissionClient(masterUrl) + val response1 = client.createSubmission(request) val submitResponse = getSubmitResponse(response1) assert(submitResponse.success) assert(submitResponse.submissionId != null) // request status of submission that was just created val submissionId = submitResponse.submissionId - val response2 = client.requestSubmissionStatus(masterUrl, submissionId) + val response2 = client.requestSubmissionStatus(submissionId) val statusResponse = getStatusResponse(response2) assert(statusResponse.success) assert(statusResponse.submissionId === submissionId) @@ -160,8 +161,9 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("create then kill then request status") { val masterUrl = startSmartServer() val request = constructSubmitRequest(masterUrl) - val response1 = client.createSubmission(masterUrl, request) - val response2 = client.createSubmission(masterUrl, request) + val client = new RestSubmissionClient(masterUrl) + val response1 = client.createSubmission(request) + val response2 = client.createSubmission(request) val submitResponse1 = getSubmitResponse(response1) val submitResponse2 = getSubmitResponse(response2) assert(submitResponse1.success) @@ -171,13 +173,13 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val submissionId1 = submitResponse1.submissionId val submissionId2 = submitResponse2.submissionId // kill only submission 1, but not submission 2 - val response3 = client.killSubmission(masterUrl, submissionId1) + val response3 = client.killSubmission(submissionId1) val killResponse = getKillResponse(response3) assert(killResponse.success) assert(killResponse.submissionId === submissionId1) // request status for both submissions: 1 should be KILLED but 2 should be RUNNING still - val response4 = client.requestSubmissionStatus(masterUrl, submissionId1) - val response5 = client.requestSubmissionStatus(masterUrl, submissionId2) + val response4 = client.requestSubmissionStatus(submissionId1) + val response5 = client.requestSubmissionStatus(submissionId2) val statusResponse1 = getStatusResponse(response4) val statusResponse2 = getStatusResponse(response5) assert(statusResponse1.submissionId === submissionId1) @@ -189,13 +191,14 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("kill or request status before create") { val masterUrl = startSmartServer() val doesNotExist = "does-not-exist" + val client = new RestSubmissionClient(masterUrl) // kill a non-existent submission - val response1 = client.killSubmission(masterUrl, doesNotExist) + val response1 = client.killSubmission(doesNotExist) val killResponse = getKillResponse(response1) assert(!killResponse.success) assert(killResponse.submissionId === doesNotExist) // request status for a non-existent submission - val response2 = client.requestSubmissionStatus(masterUrl, doesNotExist) + val response2 = client.requestSubmissionStatus(doesNotExist) val statusResponse = getStatusResponse(response2) assert(!statusResponse.success) assert(statusResponse.submissionId === doesNotExist) @@ -339,6 +342,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("client handles faulty server") { val masterUrl = startFaultyServer() + val client = new RestSubmissionClient(masterUrl) val httpUrl = masterUrl.replace("spark://", "http://") val v = RestSubmissionServer.PROTOCOL_VERSION val submitRequestPath = s"$httpUrl/$v/submissions/create" @@ -425,7 +429,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { mainJar) ++ appArgs val args = new SparkSubmitArguments(commandLineArgs) val (_, _, sparkProperties, _) = SparkSubmit.prepareSubmitEnvironment(args) - client.constructSubmitRequest( + new RestSubmissionClient("spark://host:port").constructSubmitRequest( mainJar, mainClass, appArgs, sparkProperties.toMap, Map.empty) } @@ -492,7 +496,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { method: String, body: String = ""): (SubmitRestProtocolResponse, Int) = { val conn = sendHttpRequest(url, method, body) - (client.readResponse(conn), conn.getResponseCode) + (new RestSubmissionClient("spark://host:port").readResponse(conn), conn.getResponseCode) } } diff --git a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala index aea76c1adcc09..85eb2a1d07ba4 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala @@ -176,7 +176,7 @@ class PipedRDDSuite extends FunSuite with SharedSparkContext { } val hadoopPart1 = generateFakeHadoopPartition() val pipedRdd = new PipedRDD(nums, "printenv " + varName) - val tContext = new TaskContextImpl(0, 0, 0, 0) + val tContext = new TaskContextImpl(0, 0, 0, 0, null) val rddIter = pipedRdd.compute(hadoopPart1, tContext) val arr = rddIter.toArray assert(arr(0) == "/some/path") diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala new file mode 100644 index 0000000000000..d75ecbf1f0b4d --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala @@ -0,0 +1,133 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rdd + +import org.scalatest.{BeforeAndAfter, FunSuite} + +import org.apache.spark.{TaskContext, Partition, SparkContext} + +/** + * + */ +class RDDOperationScopeSuite extends FunSuite with BeforeAndAfter { + private var sc: SparkContext = null + private val scope1 = new RDDOperationScope("scope1") + private val scope2 = new RDDOperationScope("scope2", parent = Some(scope1)) + private val scope3 = new RDDOperationScope("scope3", parent = Some(scope2)) + + before { + sc = new SparkContext("local", "test") + } + + after { + sc.stop() + } + + test("getAllScopes") { + assert(scope1.getAllScopes === Seq(scope1)) + assert(scope2.getAllScopes === Seq(scope1, scope2)) + assert(scope3.getAllScopes === Seq(scope1, scope2, scope3)) + } + + test("json de/serialization") { + val scope1Json = scope1.toJson + val scope2Json = scope2.toJson + val scope3Json = scope3.toJson + assert(scope1Json === s"""{"id":${scope1.id},"name":"scope1"}""") + assert(scope2Json === s"""{"id":${scope2.id},"name":"scope2","parent":$scope1Json}""") + assert(scope3Json === s"""{"id":${scope3.id},"name":"scope3","parent":$scope2Json}""") + assert(RDDOperationScope.fromJson(scope1Json) === scope1) + assert(RDDOperationScope.fromJson(scope2Json) === scope2) + assert(RDDOperationScope.fromJson(scope3Json) === scope3) + } + + test("withScope") { + val rdd0: MyCoolRDD = new MyCoolRDD(sc) + var rdd1: MyCoolRDD = null + var rdd2: MyCoolRDD = null + var rdd3: MyCoolRDD = null + RDDOperationScope.withScope(sc, "scope1", allowNesting = false) { + rdd1 = new MyCoolRDD(sc) + RDDOperationScope.withScope(sc, "scope2", allowNesting = false) { + rdd2 = new MyCoolRDD(sc) + RDDOperationScope.withScope(sc, "scope3", allowNesting = false) { + rdd3 = new MyCoolRDD(sc) + } + } + } + assert(rdd0.scope.isEmpty) + assert(rdd1.scope.isDefined) + assert(rdd2.scope.isDefined) + assert(rdd3.scope.isDefined) + assert(rdd1.scope.get.getAllScopes.map(_.name) === Seq("scope1")) + assert(rdd2.scope.get.getAllScopes.map(_.name) === Seq("scope1")) + assert(rdd3.scope.get.getAllScopes.map(_.name) === Seq("scope1")) + } + + test("withScope with partial nesting") { + val rdd0: MyCoolRDD = new MyCoolRDD(sc) + var rdd1: MyCoolRDD = null + var rdd2: MyCoolRDD = null + var rdd3: MyCoolRDD = null + RDDOperationScope.withScope(sc, "scope1", allowNesting = true) { // allow nesting here + rdd1 = new MyCoolRDD(sc) + RDDOperationScope.withScope(sc, "scope2", allowNesting = false) { // stop nesting here + rdd2 = new MyCoolRDD(sc) + RDDOperationScope.withScope(sc, "scope3", allowNesting = false) { + rdd3 = new MyCoolRDD(sc) + } + } + } + assert(rdd0.scope.isEmpty) + assert(rdd1.scope.isDefined) + assert(rdd2.scope.isDefined) + assert(rdd3.scope.isDefined) + assert(rdd1.scope.get.getAllScopes.map(_.name) === Seq("scope1")) + assert(rdd2.scope.get.getAllScopes.map(_.name) === Seq("scope1", "scope2")) + assert(rdd3.scope.get.getAllScopes.map(_.name) === Seq("scope1", "scope2")) + } + + test("withScope with multiple layers of nesting") { + val rdd0: MyCoolRDD = new MyCoolRDD(sc) + var rdd1: MyCoolRDD = null + var rdd2: MyCoolRDD = null + var rdd3: MyCoolRDD = null + RDDOperationScope.withScope(sc, "scope1", allowNesting = true) { + rdd1 = new MyCoolRDD(sc) + RDDOperationScope.withScope(sc, "scope2", allowNesting = true) { + rdd2 = new MyCoolRDD(sc) + RDDOperationScope.withScope(sc, "scope3", allowNesting = true) { + rdd3 = new MyCoolRDD(sc) + } + } + } + assert(rdd0.scope.isEmpty) + assert(rdd1.scope.isDefined) + assert(rdd2.scope.isDefined) + assert(rdd3.scope.isDefined) + assert(rdd1.scope.get.getAllScopes.map(_.name) === Seq("scope1")) + assert(rdd2.scope.get.getAllScopes.map(_.name) === Seq("scope1", "scope2")) + assert(rdd3.scope.get.getAllScopes.map(_.name) === Seq("scope1", "scope2", "scope3")) + } + +} + +private class MyCoolRDD(sc: SparkContext) extends RDD[Int](sc, Nil) { + override def getPartitions: Array[Partition] = Array.empty + override def compute(p: Partition, context: TaskContext): Iterator[Int] = { Nil.toIterator } +} diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala index 44c88b00c442a..ae3339d80f9c6 100644 --- a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala +++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala @@ -100,8 +100,8 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } val rpcEndpointRef = env.setupEndpoint("send-ref", endpoint) - val newRpcEndpointRef = rpcEndpointRef.askWithReply[RpcEndpointRef]("Hello") - val reply = newRpcEndpointRef.askWithReply[String]("Echo") + val newRpcEndpointRef = rpcEndpointRef.askWithRetry[RpcEndpointRef]("Hello") + val reply = newRpcEndpointRef.askWithRetry[String]("Echo") assert("Echo" === reply) } @@ -115,7 +115,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } } }) - val reply = rpcEndpointRef.askWithReply[String]("hello") + val reply = rpcEndpointRef.askWithRetry[String]("hello") assert("hello" === reply) } @@ -134,7 +134,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { // Use anotherEnv to find out the RpcEndpointRef val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-remotely") try { - val reply = rpcEndpointRef.askWithReply[String]("hello") + val reply = rpcEndpointRef.askWithRetry[String]("hello") assert("hello" === reply) } finally { anotherEnv.shutdown() @@ -162,7 +162,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-timeout") try { val e = intercept[Exception] { - rpcEndpointRef.askWithReply[String]("hello", 1 millis) + rpcEndpointRef.askWithRetry[String]("hello", 1 millis) } assert(e.isInstanceOf[TimeoutException] || e.getCause.isInstanceOf[TimeoutException]) } finally { @@ -399,7 +399,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } }) - val f = endpointRef.sendWithReply[String]("Hi") + val f = endpointRef.ask[String]("Hi") val ack = Await.result(f, 5 seconds) assert("ack" === ack) @@ -419,7 +419,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { // Use anotherEnv to find out the RpcEndpointRef val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "sendWithReply-remotely") try { - val f = rpcEndpointRef.sendWithReply[String]("hello") + val f = rpcEndpointRef.ask[String]("hello") val ack = Await.result(f, 5 seconds) assert("ack" === ack) } finally { @@ -437,7 +437,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } }) - val f = endpointRef.sendWithReply[String]("Hi") + val f = endpointRef.ask[String]("Hi") val e = intercept[SparkException] { Await.result(f, 5 seconds) } @@ -460,7 +460,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val rpcEndpointRef = anotherEnv.setupEndpointRef( "local", env.address, "sendWithReply-remotely-error") try { - val f = rpcEndpointRef.sendWithReply[String]("hello") + val f = rpcEndpointRef.ask[String]("hello") val e = intercept[SparkException] { Await.result(f, 5 seconds) } @@ -529,7 +529,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val rpcEndpointRef = anotherEnv.setupEndpointRef( "local", env.address, "sendWithReply-unserializable-error") try { - val f = rpcEndpointRef.sendWithReply[String]("hello") + val f = rpcEndpointRef.ask[String]("hello") intercept[TimeoutException] { Await.result(f, 1 seconds) } diff --git a/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala index 58214c0637235..a33a83db7bc9e 100644 --- a/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala +++ b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala @@ -40,7 +40,7 @@ class AkkaRpcEnvSuite extends RpcEnvSuite { RpcEnvConfig(conf, "test", "localhost", 12346, new SecurityManager(conf))) try { val newRef = newRpcEnv.setupEndpointRef("local", ref.address, "test_endpoint") - assert("akka.tcp://local@localhost:12345/user/test_endpoint" === + assert(s"akka.tcp://local@${env.address}/user/test_endpoint" === newRef.asInstanceOf[AkkaRpcEndpointRef].actorRef.path.toString) } finally { newRpcEnv.shutdown() diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala index 3c52a8c4460c6..6a8ae29aae675 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -95,6 +95,7 @@ class DAGSchedulerSuite override def setDAGScheduler(dagScheduler: DAGScheduler) = {} override def defaultParallelism() = 2 override def executorLost(executorId: String, reason: ExecutorLossReason): Unit = {} + override def applicationAttemptId(): Option[String] = None } /** Length of time to wait while draining listener events. */ @@ -173,6 +174,10 @@ class DAGSchedulerSuite dagEventProcessLoopTester = new DAGSchedulerEventProcessLoopTester(scheduler) } + after { + scheduler.stop() + } + override def afterAll() { super.afterAll() } @@ -260,8 +265,9 @@ class DAGSchedulerSuite override def taskSucceeded(partition: Int, value: Any) = numResults += 1 override def jobFailed(exception: Exception) = throw exception } - submit(new MyRDD(sc, 0, Nil), Array(), listener = fakeListener) + val jobId = submit(new MyRDD(sc, 0, Nil), Array(), listener = fakeListener) assert(numResults === 0) + cancel(jobId) } test("run trivial job") { @@ -404,6 +410,7 @@ class DAGSchedulerSuite taskMetrics: Array[(Long, TaskMetrics)], blockManagerId: BlockManagerId): Boolean = true override def executorLost(executorId: String, reason: ExecutorLossReason): Unit = {} + override def applicationAttemptId(): Option[String] = None } val noKillScheduler = new DAGScheduler( sc, diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala index 6d25edb7d20dc..b52a8d11d147d 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala @@ -61,7 +61,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef test("Verify log file exist") { // Verify logging directory exists val conf = getLoggingConf(testDirPath) - val eventLogger = new EventLoggingListener("test", testDirPath.toUri(), conf) + val eventLogger = new EventLoggingListener("test", None, testDirPath.toUri(), conf) eventLogger.start() val logPath = new Path(eventLogger.logPath + EventLoggingListener.IN_PROGRESS) @@ -95,7 +95,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef } test("Log overwriting") { - val logUri = EventLoggingListener.getLogPath(testDir.toURI, "test") + val logUri = EventLoggingListener.getLogPath(testDir.toURI, "test", None) val logPath = new URI(logUri).getPath // Create file before writing the event log new FileOutputStream(new File(logPath)).close() @@ -108,18 +108,18 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef test("Event log name") { // without compression assert(s"file:/base-dir/app1" === EventLoggingListener.getLogPath( - Utils.resolveURI("/base-dir"), "app1")) + Utils.resolveURI("/base-dir"), "app1", None)) // with compression assert(s"file:/base-dir/app1.lzf" === - EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), "app1", Some("lzf"))) + EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), "app1", None, Some("lzf"))) // illegal characters in app ID assert(s"file:/base-dir/a-fine-mind_dollar_bills__1" === EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), - "a fine:mind$dollar{bills}.1")) + "a fine:mind$dollar{bills}.1", None)) // illegal characters in app ID with compression assert(s"file:/base-dir/a-fine-mind_dollar_bills__1.lz4" === EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), - "a fine:mind$dollar{bills}.1", Some("lz4"))) + "a fine:mind$dollar{bills}.1", None, Some("lz4"))) } /* ----------------- * @@ -140,10 +140,10 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef val conf = getLoggingConf(testDirPath, compressionCodec) extraConf.foreach { case (k, v) => conf.set(k, v) } val logName = compressionCodec.map("test-" + _).getOrElse("test") - val eventLogger = new EventLoggingListener(logName, testDirPath.toUri(), conf) + val eventLogger = new EventLoggingListener(logName, None, testDirPath.toUri(), conf) val listenerBus = new LiveListenerBus val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", None, - 125L, "Mickey") + 125L, "Mickey", None) val applicationEnd = SparkListenerApplicationEnd(1000L) // A comprehensive test on JSON de/serialization of all events is in JsonProtocolSuite @@ -186,7 +186,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef val eventLogPath = eventLogger.logPath val expectedLogDir = testDir.toURI() assert(eventLogPath === EventLoggingListener.getLogPath( - expectedLogDir, sc.applicationId, compressionCodec.map(CompressionCodec.getShortName))) + expectedLogDir, sc.applicationId, None, compressionCodec.map(CompressionCodec.getShortName))) // Begin listening for events that trigger asserts val eventExistenceListener = new EventExistenceListener(eventLogger) diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala index 6de6d2fec622a..dabe4574b6456 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala @@ -50,7 +50,7 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter { val fstream = fileSystem.create(logFilePath) val writer = new PrintWriter(fstream) val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", None, - 125L, "Mickey") + 125L, "Mickey", None) val applicationEnd = SparkListenerApplicationEnd(1000L) writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationStart)))) writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationEnd)))) @@ -146,7 +146,7 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter { * log the events. */ private class EventMonster(conf: SparkConf) - extends EventLoggingListener("test", new URI("testdir"), conf) { + extends EventLoggingListener("test", None, new URI("testdir"), conf) { override def start() { } diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala index 057e226916027..83ae8701243e5 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala @@ -51,7 +51,7 @@ class TaskContextSuite extends FunSuite with BeforeAndAfter with LocalSparkConte } test("all TaskCompletionListeners should be called even if some fail") { - val context = new TaskContextImpl(0, 0, 0, 0) + val context = new TaskContextImpl(0, 0, 0, 0, null) val listener = mock(classOf[TaskCompletionListener]) context.addTaskCompletionListener(_ => throw new Exception("blah")) context.addTaskCompletionListener(listener) diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala index cdd7be0fbe5dd..ab863f3d8d672 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala @@ -73,6 +73,52 @@ class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with Mo s"cd test-app-1*; ./bin/spark-class ${classOf[MesosExecutorBackend].getName}") } + test("spark docker properties correctly populate the DockerInfo message") { + val taskScheduler = mock[TaskSchedulerImpl] + + val conf = new SparkConf() + .set("spark.mesos.executor.docker.image", "spark/mock") + .set("spark.mesos.executor.docker.volumes", "/a,/b:/b,/c:/c:rw,/d:ro,/e:/e:ro") + .set("spark.mesos.executor.docker.portmaps", "80:8080,53:53:tcp") + + val listenerBus = mock[LiveListenerBus] + listenerBus.post( + SparkListenerExecutorAdded(anyLong, "s1", new ExecutorInfo("host1", 2, Map.empty))) + + val sc = mock[SparkContext] + when(sc.executorMemory).thenReturn(100) + when(sc.getSparkHome()).thenReturn(Option("/spark-home")) + when(sc.executorEnvs).thenReturn(new mutable.HashMap[String, String]) + when(sc.conf).thenReturn(conf) + when(sc.listenerBus).thenReturn(listenerBus) + + val backend = new MesosSchedulerBackend(taskScheduler, sc, "master") + + val execInfo = backend.createExecutorInfo("mockExecutor") + assert(execInfo.getContainer.getDocker.getImage.equals("spark/mock")) + val portmaps = execInfo.getContainer.getDocker.getPortMappingsList + assert(portmaps.get(0).getHostPort.equals(80)) + assert(portmaps.get(0).getContainerPort.equals(8080)) + assert(portmaps.get(0).getProtocol.equals("tcp")) + assert(portmaps.get(1).getHostPort.equals(53)) + assert(portmaps.get(1).getContainerPort.equals(53)) + assert(portmaps.get(1).getProtocol.equals("tcp")) + val volumes = execInfo.getContainer.getVolumesList + assert(volumes.get(0).getContainerPath.equals("/a")) + assert(volumes.get(0).getMode.equals(Volume.Mode.RW)) + assert(volumes.get(1).getContainerPath.equals("/b")) + assert(volumes.get(1).getHostPath.equals("/b")) + assert(volumes.get(1).getMode.equals(Volume.Mode.RW)) + assert(volumes.get(2).getContainerPath.equals("/c")) + assert(volumes.get(2).getHostPath.equals("/c")) + assert(volumes.get(2).getMode.equals(Volume.Mode.RW)) + assert(volumes.get(3).getContainerPath.equals("/d")) + assert(volumes.get(3).getMode.equals(Volume.Mode.RO)) + assert(volumes.get(4).getContainerPath.equals("/e")) + assert(volumes.get(4).getHostPath.equals("/e")) + assert(volumes.get(4).getMode.equals(Volume.Mode.RO)) + } + test("mesos resource offers result in launching tasks") { def createOffer(id: Int, mem: Int, cpu: Int): Offer = { val builder = Offer.newBuilder() diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala index 1b13559e77cb8..778a7eee73b23 100644 --- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala @@ -280,6 +280,15 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { val thrown = intercept[SparkException](ser.serialize(largeObject)) assert(thrown.getMessage.contains(kryoBufferMaxProperty)) } + + test("getAutoReset") { + val ser = new KryoSerializer(new SparkConf).newInstance().asInstanceOf[KryoSerializerInstance] + assert(ser.getAutoReset) + val conf = new SparkConf().set("spark.kryo.registrator", + classOf[RegistratorWithoutAutoReset].getName) + val ser2 = new KryoSerializer(conf).newInstance().asInstanceOf[KryoSerializerInstance] + assert(!ser2.getAutoReset) + } } @@ -313,4 +322,10 @@ object KryoTest { k.register(classOf[java.util.HashMap[_, _]]) } } + + class RegistratorWithoutAutoReset extends KryoRegistrator { + override def registerClasses(k: Kryo) { + k.setAutoReset(false) + } + } } diff --git a/core/src/test/scala/org/apache/spark/serializer/SerializerPropertiesSuite.scala b/core/src/test/scala/org/apache/spark/serializer/SerializerPropertiesSuite.scala new file mode 100644 index 0000000000000..bb34033fe9e7e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/serializer/SerializerPropertiesSuite.scala @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.serializer + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream} + +import scala.util.Random + +import org.scalatest.{Assertions, FunSuite} + +import org.apache.spark.SparkConf +import org.apache.spark.serializer.KryoTest.RegistratorWithoutAutoReset + +/** + * Tests to ensure that [[Serializer]] implementations obey the API contracts for methods that + * describe properties of the serialized stream, such as + * [[Serializer.supportsRelocationOfSerializedObjects]]. + */ +class SerializerPropertiesSuite extends FunSuite { + + import SerializerPropertiesSuite._ + + test("JavaSerializer does not support relocation") { + // Per a comment on the SPARK-4550 JIRA ticket, Java serialization appears to write out the + // full class name the first time an object is written to an output stream, but subsequent + // references to the class write a more compact identifier; this prevents relocation. + val ser = new JavaSerializer(new SparkConf()) + testSupportsRelocationOfSerializedObjects(ser, generateRandomItem) + } + + test("KryoSerializer supports relocation when auto-reset is enabled") { + val ser = new KryoSerializer(new SparkConf) + assert(ser.newInstance().asInstanceOf[KryoSerializerInstance].getAutoReset()) + testSupportsRelocationOfSerializedObjects(ser, generateRandomItem) + } + + test("KryoSerializer does not support relocation when auto-reset is disabled") { + val conf = new SparkConf().set("spark.kryo.registrator", + classOf[RegistratorWithoutAutoReset].getName) + val ser = new KryoSerializer(conf) + assert(!ser.newInstance().asInstanceOf[KryoSerializerInstance].getAutoReset()) + testSupportsRelocationOfSerializedObjects(ser, generateRandomItem) + } + +} + +object SerializerPropertiesSuite extends Assertions { + + def generateRandomItem(rand: Random): Any = { + val randomFunctions: Seq[() => Any] = Seq( + () => rand.nextInt(), + () => rand.nextString(rand.nextInt(10)), + () => rand.nextDouble(), + () => rand.nextBoolean(), + () => (rand.nextInt(), rand.nextString(rand.nextInt(10))), + () => MyCaseClass(rand.nextInt(), rand.nextString(rand.nextInt(10))), + () => { + val x = MyCaseClass(rand.nextInt(), rand.nextString(rand.nextInt(10))) + (x, x) + } + ) + randomFunctions(rand.nextInt(randomFunctions.size)).apply() + } + + def testSupportsRelocationOfSerializedObjects( + serializer: Serializer, + generateRandomItem: Random => Any): Unit = { + if (!serializer.supportsRelocationOfSerializedObjects) { + return + } + val NUM_TRIALS = 5 + val rand = new Random(42) + for (_ <- 1 to NUM_TRIALS) { + val items = { + // Make sure that we have duplicate occurrences of the same object in the stream: + val randomItems = Seq.fill(10)(generateRandomItem(rand)) + randomItems ++ randomItems.take(5) + } + val baos = new ByteArrayOutputStream() + val serStream = serializer.newInstance().serializeStream(baos) + def serializeItem(item: Any): Array[Byte] = { + val itemStartOffset = baos.toByteArray.length + serStream.writeObject(item) + serStream.flush() + val itemEndOffset = baos.toByteArray.length + baos.toByteArray.slice(itemStartOffset, itemEndOffset).clone() + } + val itemsAndSerializedItems: Seq[(Any, Array[Byte])] = { + val serItems = items.map { + item => (item, serializeItem(item)) + } + serStream.close() + rand.shuffle(serItems) + } + val reorderedSerializedData: Array[Byte] = itemsAndSerializedItems.flatMap(_._2).toArray + val deserializedItemsStream = serializer.newInstance().deserializeStream( + new ByteArrayInputStream(reorderedSerializedData)) + assert(deserializedItemsStream.asIterator.toSeq === itemsAndSerializedItems.map(_._1)) + deserializedItemsStream.close() + } + } +} + +private case class MyCaseClass(foo: Int, bar: String) diff --git a/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala b/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala index 963264cef3a71..86fcf447287f7 100644 --- a/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala +++ b/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala @@ -24,7 +24,7 @@ import scala.reflect.ClassTag /** - * A serializer implementation that always return a single element in a deserialization stream. + * A serializer implementation that always returns two elements in a deserialization stream. */ class TestSerializer extends Serializer { override def newInstance(): TestSerializerInstance = new TestSerializerInstance @@ -51,7 +51,7 @@ class TestDeserializationStream extends DeserializationStream { override def readObject[T: ClassTag](): T = { count += 1 - if (count == 2) { + if (count == 3) { throw new EOFException } new Object().asInstanceOf[T] diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala index 7d76435cd75e7..84384bb48999a 100644 --- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala @@ -59,8 +59,8 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { val shuffle1 = shuffleBlockManager.forMapTask(1, 1, 1, new JavaSerializer(conf), new ShuffleWriteMetrics) for (writer <- shuffle1.writers) { - writer.write("test1") - writer.write("test2") + writer.write("test1", "value") + writer.write("test2", "value") } for (writer <- shuffle1.writers) { writer.commitAndClose() @@ -73,8 +73,8 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { new ShuffleWriteMetrics) for (writer <- shuffle2.writers) { - writer.write("test3") - writer.write("test4") + writer.write("test3", "value") + writer.write("test4", "vlue") } for (writer <- shuffle2.writers) { writer.commitAndClose() @@ -91,8 +91,8 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { val shuffle3 = shuffleBlockManager.forMapTask(1, 3, 1, new JavaSerializer(testConf), new ShuffleWriteMetrics) for (writer <- shuffle3.writers) { - writer.write("test3") - writer.write("test4") + writer.write("test3", "value") + writer.write("test4", "value") } for (writer <- shuffle3.writers) { writer.commitAndClose() diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index 6957bc72e9903..151955ef7f435 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -356,7 +356,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach master.removeExecutor(store.blockManagerId.executorId) assert(master.getLocations("a1").size == 0, "a1 was not removed from master") - val reregister = !master.driverEndpoint.askWithReply[Boolean]( + val reregister = !master.driverEndpoint.askWithRetry[Boolean]( BlockManagerHeartbeat(store.blockManagerId)) assert(reregister == true) } @@ -526,6 +526,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach test("tachyon storage") { // TODO Make the spark.test.tachyon.enable true after using tachyon 0.5.0 testing jar. val tachyonUnitTestEnabled = conf.getBoolean("spark.test.tachyon.enable", false) + conf.set(ExternalBlockStore.BLOCK_MANAGER_NAME, ExternalBlockStore.DEFAULT_BLOCK_MANAGER_NAME) if (tachyonUnitTestEnabled) { store = makeBlockManager(1200) val a1 = new Array[Byte](400) diff --git a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala index 003a728cb84a0..43ef469c1fd48 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala @@ -32,7 +32,7 @@ class BlockObjectWriterSuite extends FunSuite { val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, new JavaSerializer(new SparkConf()).newInstance(), 1024, os => os, true, writeMetrics) - writer.write(Long.box(20)) + writer.write(Long.box(20), Long.box(30)) // Record metrics update on every write assert(writeMetrics.shuffleRecordsWritten === 1) // Metrics don't update on every write @@ -40,7 +40,7 @@ class BlockObjectWriterSuite extends FunSuite { // After 32 writes, metrics should update for (i <- 0 until 32) { writer.flush() - writer.write(Long.box(i)) + writer.write(Long.box(i), Long.box(i)) } assert(writeMetrics.shuffleBytesWritten > 0) assert(writeMetrics.shuffleRecordsWritten === 33) @@ -54,7 +54,7 @@ class BlockObjectWriterSuite extends FunSuite { val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, new JavaSerializer(new SparkConf()).newInstance(), 1024, os => os, true, writeMetrics) - writer.write(Long.box(20)) + writer.write(Long.box(20), Long.box(30)) // Record metrics update on every write assert(writeMetrics.shuffleRecordsWritten === 1) // Metrics don't update on every write @@ -62,7 +62,7 @@ class BlockObjectWriterSuite extends FunSuite { // After 32 writes, metrics should update for (i <- 0 until 32) { writer.flush() - writer.write(Long.box(i)) + writer.write(Long.box(i), Long.box(i)) } assert(writeMetrics.shuffleBytesWritten > 0) assert(writeMetrics.shuffleRecordsWritten === 33) diff --git a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala index 37b593b2c5f79..2080c432d77db 100644 --- a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala @@ -89,7 +89,7 @@ class ShuffleBlockFetcherIteratorSuite extends FunSuite { ) val iterator = new ShuffleBlockFetcherIterator( - new TaskContextImpl(0, 0, 0, 0), + new TaskContextImpl(0, 0, 0, 0, null), transfer, blockManager, blocksByAddress, @@ -154,7 +154,7 @@ class ShuffleBlockFetcherIteratorSuite extends FunSuite { val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])]( (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq)) - val taskContext = new TaskContextImpl(0, 0, 0, 0) + val taskContext = new TaskContextImpl(0, 0, 0, 0, null) val iterator = new ShuffleBlockFetcherIterator( taskContext, transfer, @@ -217,7 +217,7 @@ class ShuffleBlockFetcherIteratorSuite extends FunSuite { val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])]( (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq)) - val taskContext = new TaskContextImpl(0, 0, 0, 0) + val taskContext = new TaskContextImpl(0, 0, 0, 0, null) val iterator = new ShuffleBlockFetcherIterator( taskContext, transfer, diff --git a/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala b/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala index ef5c55f91c39a..17193ddbfd894 100644 --- a/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala @@ -240,11 +240,11 @@ class StorageSuite extends FunSuite { assert(status.rddBlocksById(1000).size === status.numRddBlocksById(1000)) } - test("storage status memUsed, diskUsed, tachyonUsed") { + test("storage status memUsed, diskUsed, externalBlockStoreUsed") { val status = storageStatus2 def actualMemUsed: Long = status.blocks.values.map(_.memSize).sum def actualDiskUsed: Long = status.blocks.values.map(_.diskSize).sum - def actualOffHeapUsed: Long = status.blocks.values.map(_.tachyonSize).sum + def actualOffHeapUsed: Long = status.blocks.values.map(_.externalBlockStoreSize).sum assert(status.memUsed === actualMemUsed) assert(status.diskUsed === actualDiskUsed) assert(status.offHeapUsed === actualOffHeapUsed) @@ -287,8 +287,8 @@ class StorageSuite extends FunSuite { // For testing StorageUtils.updateRddInfo private def stockRDDInfos: Seq[RDDInfo] = { - val info0 = new RDDInfo(0, "0", 10, memAndDisk) - val info1 = new RDDInfo(1, "1", 3, memAndDisk) + val info0 = new RDDInfo(0, "0", 10, memAndDisk, Seq(3)) + val info1 = new RDDInfo(1, "1", 3, memAndDisk, Seq(4)) Seq(info0, info1) } @@ -300,12 +300,12 @@ class StorageSuite extends FunSuite { assert(rddInfos(0).numCachedPartitions === 5) assert(rddInfos(0).memSize === 5L) assert(rddInfos(0).diskSize === 10L) - assert(rddInfos(0).tachyonSize === 0L) + assert(rddInfos(0).externalBlockStoreSize === 0L) assert(rddInfos(1).storageLevel === memAndDisk) assert(rddInfos(1).numCachedPartitions === 3) assert(rddInfos(1).memSize === 3L) assert(rddInfos(1).diskSize === 6L) - assert(rddInfos(1).tachyonSize === 0L) + assert(rddInfos(1).externalBlockStoreSize === 0L) } test("StorageUtils.getRddBlockLocations") { diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala index eb9db550fd74c..d53d7f3ba5ae7 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala @@ -350,7 +350,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before } } - test("kill stage is POST only") { + test("kill stage POST/GET response is correct") { def getResponseCode(url: URL, method: String): Int = { val connection = url.openConnection().asInstanceOf[HttpURLConnection] connection.setRequestMethod(method) @@ -365,7 +365,8 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before eventually(timeout(5 seconds), interval(50 milliseconds)) { val url = new URL( sc.ui.get.appUIAddress.stripSuffix("/") + "/stages/stage/kill/?id=0&terminate=true") - getResponseCode(url, "GET") should be (405) + // SPARK-6846: should be POST only but YARN AM doesn't proxy POST + getResponseCode(url, "GET") should be (200) getResponseCode(url, "POST") should be (200) } } diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala index 21d8267114133..967dd0821ebd0 100644 --- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala @@ -34,12 +34,12 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc val jobCompletionTime = 1421191296660L private def createStageStartEvent(stageId: Int) = { - val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, "") + val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, null, "") SparkListenerStageSubmitted(stageInfo) } private def createStageEndEvent(stageId: Int, failed: Boolean = false) = { - val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, "") + val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, null, "") if (failed) { stageInfo.failureReason = Some("Failed!") } @@ -51,7 +51,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc stageIds: Seq[Int], jobGroup: Option[String] = None): SparkListenerJobStart = { val stageInfos = stageIds.map { stageId => - new StageInfo(stageId, 0, stageId.toString, 0, null, "") + new StageInfo(stageId, 0, stageId.toString, 0, null, null, "") } val properties: Option[Properties] = jobGroup.map { groupId => val props = new Properties() diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala index 3744e479d2f05..7b38e6d9473e1 100644 --- a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala @@ -35,10 +35,10 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { private val none = StorageLevel.NONE private val taskInfo = new TaskInfo(0, 0, 0, 0, "big", "dog", TaskLocality.ANY, false) private val taskInfo1 = new TaskInfo(1, 1, 1, 1, "big", "cat", TaskLocality.ANY, false) - private def rddInfo0 = new RDDInfo(0, "freedom", 100, memOnly) - private def rddInfo1 = new RDDInfo(1, "hostage", 200, memOnly) - private def rddInfo2 = new RDDInfo(2, "sanity", 300, memAndDisk) - private def rddInfo3 = new RDDInfo(3, "grace", 400, memAndDisk) + private def rddInfo0 = new RDDInfo(0, "freedom", 100, memOnly, Seq(10)) + private def rddInfo1 = new RDDInfo(1, "hostage", 200, memOnly, Seq(10)) + private def rddInfo2 = new RDDInfo(2, "sanity", 300, memAndDisk, Seq(10)) + private def rddInfo3 = new RDDInfo(3, "grace", 400, memAndDisk, Seq(10)) private val bm1 = BlockManagerId("big", "dog", 1) before { @@ -54,7 +54,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { assert(storageListener.rddInfoList.isEmpty) // 2 RDDs are known, but none are cached - val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(rddInfo0, rddInfo1), "details") + val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(rddInfo0, rddInfo1), Seq.empty, "details") bus.postToAll(SparkListenerStageSubmitted(stageInfo0)) assert(storageListener._rddInfoMap.size === 2) assert(storageListener.rddInfoList.isEmpty) @@ -64,15 +64,16 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { val rddInfo3Cached = rddInfo3 rddInfo2Cached.numCachedPartitions = 1 rddInfo3Cached.numCachedPartitions = 1 - val stageInfo1 = new StageInfo(1, 0, "0", 100, Seq(rddInfo2Cached, rddInfo3Cached), "details") + val stageInfo1 = new StageInfo( + 1, 0, "0", 100, Seq(rddInfo2Cached, rddInfo3Cached), Seq.empty, "details") bus.postToAll(SparkListenerStageSubmitted(stageInfo1)) assert(storageListener._rddInfoMap.size === 4) assert(storageListener.rddInfoList.size === 2) // Submitting RDDInfos with duplicate IDs does nothing - val rddInfo0Cached = new RDDInfo(0, "freedom", 100, StorageLevel.MEMORY_ONLY) + val rddInfo0Cached = new RDDInfo(0, "freedom", 100, StorageLevel.MEMORY_ONLY, Seq(10)) rddInfo0Cached.numCachedPartitions = 1 - val stageInfo0Cached = new StageInfo(0, 0, "0", 100, Seq(rddInfo0), "details") + val stageInfo0Cached = new StageInfo(0, 0, "0", 100, Seq(rddInfo0), Seq.empty, "details") bus.postToAll(SparkListenerStageSubmitted(stageInfo0Cached)) assert(storageListener._rddInfoMap.size === 4) assert(storageListener.rddInfoList.size === 2) @@ -88,7 +89,8 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { val rddInfo1Cached = rddInfo1 rddInfo0Cached.numCachedPartitions = 1 rddInfo1Cached.numCachedPartitions = 1 - val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(rddInfo0Cached, rddInfo1Cached), "details") + val stageInfo0 = new StageInfo( + 0, 0, "0", 100, Seq(rddInfo0Cached, rddInfo1Cached), Seq.empty, "details") bus.postToAll(SparkListenerStageSubmitted(stageInfo0)) assert(storageListener._rddInfoMap.size === 2) assert(storageListener.rddInfoList.size === 2) @@ -108,7 +110,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { val myRddInfo1 = rddInfo1 val myRddInfo2 = rddInfo2 val stageInfo0 = new StageInfo( - 0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), "details") + 0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), Seq.empty, "details") bus.postToAll(SparkListenerBlockManagerAdded(1L, bm1, 1000L)) bus.postToAll(SparkListenerStageSubmitted(stageInfo0)) assert(storageListener._rddInfoMap.size === 3) @@ -133,12 +135,12 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { bus.postToAll(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo, metrics1)) assert(storageListener._rddInfoMap(0).memSize === 800L) assert(storageListener._rddInfoMap(0).diskSize === 400L) - assert(storageListener._rddInfoMap(0).tachyonSize === 200L) + assert(storageListener._rddInfoMap(0).externalBlockStoreSize === 200L) assert(storageListener._rddInfoMap(0).numCachedPartitions === 3) assert(storageListener._rddInfoMap(0).isCached) assert(storageListener._rddInfoMap(1).memSize === 0L) assert(storageListener._rddInfoMap(1).diskSize === 240L) - assert(storageListener._rddInfoMap(1).tachyonSize === 0L) + assert(storageListener._rddInfoMap(1).externalBlockStoreSize === 0L) assert(storageListener._rddInfoMap(1).numCachedPartitions === 1) assert(storageListener._rddInfoMap(1).isCached) assert(!storageListener._rddInfoMap(2).isCached) @@ -155,7 +157,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { bus.postToAll(SparkListenerTaskEnd(2, 0, "obliteration", Success, taskInfo, metrics2)) assert(storageListener._rddInfoMap(0).memSize === 400L) assert(storageListener._rddInfoMap(0).diskSize === 400L) - assert(storageListener._rddInfoMap(0).tachyonSize === 200L) + assert(storageListener._rddInfoMap(0).externalBlockStoreSize === 200L) assert(storageListener._rddInfoMap(0).numCachedPartitions === 2) assert(storageListener._rddInfoMap(0).isCached) assert(!storageListener._rddInfoMap(1).isCached) @@ -166,10 +168,10 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { test("verify StorageTab contains all cached rdds") { - val rddInfo0 = new RDDInfo(0, "rdd0", 1, memOnly) - val rddInfo1 = new RDDInfo(1, "rdd1", 1 ,memOnly) - val stageInfo0 = new StageInfo(0, 0, "stage0", 1, Seq(rddInfo0), "details") - val stageInfo1 = new StageInfo(1, 0, "stage1", 1, Seq(rddInfo1), "details") + val rddInfo0 = new RDDInfo(0, "rdd0", 1, memOnly, Seq(4)) + val rddInfo1 = new RDDInfo(1, "rdd1", 1 ,memOnly, Seq(4)) + val stageInfo0 = new StageInfo(0, 0, "stage0", 1, Seq(rddInfo0), Seq.empty, "details") + val stageInfo1 = new StageInfo(1, 0, "stage1", 1, Seq(rddInfo1), Seq.empty, "details") val taskMetrics0 = new TaskMetrics val taskMetrics1 = new TaskMetrics val block0 = (RDDBlockId(0, 1), BlockStatus(memOnly, 100L, 0L, 0L)) diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala index c47162779bbba..446c3f24a74dd 100644 --- a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala @@ -17,10 +17,14 @@ package org.apache.spark.util +import java.io.NotSerializableException + import org.scalatest.FunSuite import org.apache.spark.LocalSparkContext._ -import org.apache.spark.{SparkContext, SparkException} +import org.apache.spark.{TaskContext, SparkContext, SparkException} +import org.apache.spark.partial.CountEvaluator +import org.apache.spark.rdd.RDD class ClosureCleanerSuite extends FunSuite { test("closures inside an object") { @@ -50,24 +54,80 @@ class ClosureCleanerSuite extends FunSuite { val obj = new TestClassWithNesting(1) assert(obj.run() === 96) // 4 * (1+2+3+4) + 4 * (1+2+3+4) + 16 * 1 } - + test("toplevel return statements in closures are identified at cleaning time") { - val ex = intercept[SparkException] { + intercept[ReturnStatementInClosureException] { TestObjectWithBogusReturns.run() } - - assert(ex.getMessage.contains("Return statements aren't allowed in Spark closures")) } test("return statements from named functions nested in closures don't raise exceptions") { val result = TestObjectWithNestedReturns.run() - assert(result == 1) + assert(result === 1) + } + + test("user provided closures are actually cleaned") { + + // We use return statements as an indication that a closure is actually being cleaned + // We expect closure cleaner to find the return statements in the user provided closures + def expectCorrectException(body: => Unit): Unit = { + try { + body + } catch { + case rse: ReturnStatementInClosureException => // Success! + case e @ (_: NotSerializableException | _: SparkException) => + fail(s"Expected ReturnStatementInClosureException, but got $e.\n" + + "This means the closure provided by user is not actually cleaned.") + } + } + + withSpark(new SparkContext("local", "test")) { sc => + val rdd = sc.parallelize(1 to 10) + val pairRdd = rdd.map { i => (i, i) } + expectCorrectException { TestUserClosuresActuallyCleaned.testMap(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testFlatMap(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testFilter(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testSortBy(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testGroupBy(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testKeyBy(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testMapPartitions(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testMapPartitionsWithIndex(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testZipPartitions2(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testZipPartitions3(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testZipPartitions4(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testForeach(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testForeachPartition(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testReduce(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testTreeReduce(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testFold(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testAggregate(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testTreeAggregate(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testCombineByKey(pairRdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testAggregateByKey(pairRdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testFoldByKey(pairRdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testReduceByKey(pairRdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testMapValues(pairRdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testFlatMapValues(pairRdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testForeachAsync(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testForeachPartitionAsync(rdd) } + expectCorrectException { TestUserClosuresActuallyCleaned.testRunJob1(sc) } + expectCorrectException { TestUserClosuresActuallyCleaned.testRunJob2(sc) } + expectCorrectException { TestUserClosuresActuallyCleaned.testRunApproximateJob(sc) } + expectCorrectException { TestUserClosuresActuallyCleaned.testSubmitJob(sc) } + } } } // A non-serializable class we create in closures to make sure that we aren't // keeping references to unneeded variables from our outer closures. -class NonSerializable {} +class NonSerializable(val id: Int = -1) { + override def equals(other: Any): Boolean = { + other match { + case o: NonSerializable => id == o.id + case _ => false + } + } +} object TestObject { def run(): Int = { @@ -180,3 +240,90 @@ class TestClassWithNesting(val y: Int) extends Serializable { } } } + +/** + * Test whether closures passed in through public APIs are actually cleaned. + * + * We put a return statement in each of these closures as a mechanism to detect whether the + * ClosureCleaner actually cleaned our closure. If it did, then it would throw an appropriate + * exception explicitly complaining about the return statement. Otherwise, we know the + * ClosureCleaner did not actually clean our closure, in which case we should fail the test. + */ +private object TestUserClosuresActuallyCleaned { + def testMap(rdd: RDD[Int]): Unit = { rdd.map { _ => return; 0 }.count() } + def testFlatMap(rdd: RDD[Int]): Unit = { rdd.flatMap { _ => return; Seq() }.count() } + def testFilter(rdd: RDD[Int]): Unit = { rdd.filter { _ => return; true }.count() } + def testSortBy(rdd: RDD[Int]): Unit = { rdd.sortBy { _ => return; 1 }.count() } + def testKeyBy(rdd: RDD[Int]): Unit = { rdd.keyBy { _ => return; 1 }.count() } + def testGroupBy(rdd: RDD[Int]): Unit = { rdd.groupBy { _ => return; 1 }.count() } + def testMapPartitions(rdd: RDD[Int]): Unit = { rdd.mapPartitions { it => return; it }.count() } + def testMapPartitionsWithIndex(rdd: RDD[Int]): Unit = { + rdd.mapPartitionsWithIndex { (_, it) => return; it }.count() + } + def testZipPartitions2(rdd: RDD[Int]): Unit = { + rdd.zipPartitions(rdd) { case (it1, it2) => return; it1 }.count() + } + def testZipPartitions3(rdd: RDD[Int]): Unit = { + rdd.zipPartitions(rdd, rdd) { case (it1, it2, it3) => return; it1 }.count() + } + def testZipPartitions4(rdd: RDD[Int]): Unit = { + rdd.zipPartitions(rdd, rdd, rdd) { case (it1, it2, it3, it4) => return; it1 }.count() + } + def testForeach(rdd: RDD[Int]): Unit = { rdd.foreach { _ => return } } + def testForeachPartition(rdd: RDD[Int]): Unit = { rdd.foreachPartition { _ => return } } + def testReduce(rdd: RDD[Int]): Unit = { rdd.reduce { case (_, _) => return; 1 } } + def testTreeReduce(rdd: RDD[Int]): Unit = { rdd.treeReduce { case (_, _) => return; 1 } } + def testFold(rdd: RDD[Int]): Unit = { rdd.fold(0) { case (_, _) => return; 1 } } + def testAggregate(rdd: RDD[Int]): Unit = { + rdd.aggregate(0)({ case (_, _) => return; 1 }, { case (_, _) => return; 1 }) + } + def testTreeAggregate(rdd: RDD[Int]): Unit = { + rdd.treeAggregate(0)({ case (_, _) => return; 1 }, { case (_, _) => return; 1 }) + } + + // Test pair RDD functions + def testCombineByKey(rdd: RDD[(Int, Int)]): Unit = { + rdd.combineByKey( + { _ => return; 1 }: Int => Int, + { case (_, _) => return; 1 }: (Int, Int) => Int, + { case (_, _) => return; 1 }: (Int, Int) => Int + ).count() + } + def testAggregateByKey(rdd: RDD[(Int, Int)]): Unit = { + rdd.aggregateByKey(0)({ case (_, _) => return; 1 }, { case (_, _) => return; 1 }).count() + } + def testFoldByKey(rdd: RDD[(Int, Int)]): Unit = { rdd.foldByKey(0) { case (_, _) => return; 1 } } + def testReduceByKey(rdd: RDD[(Int, Int)]): Unit = { rdd.reduceByKey { case (_, _) => return; 1 } } + def testMapValues(rdd: RDD[(Int, Int)]): Unit = { rdd.mapValues { _ => return; 1 } } + def testFlatMapValues(rdd: RDD[(Int, Int)]): Unit = { rdd.flatMapValues { _ => return; Seq() } } + + // Test async RDD actions + def testForeachAsync(rdd: RDD[Int]): Unit = { rdd.foreachAsync { _ => return } } + def testForeachPartitionAsync(rdd: RDD[Int]): Unit = { rdd.foreachPartitionAsync { _ => return } } + + // Test SparkContext runJob + def testRunJob1(sc: SparkContext): Unit = { + val rdd = sc.parallelize(1 to 10, 10) + sc.runJob(rdd, { (ctx: TaskContext, iter: Iterator[Int]) => return; 1 } ) + } + def testRunJob2(sc: SparkContext): Unit = { + val rdd = sc.parallelize(1 to 10, 10) + sc.runJob(rdd, { iter: Iterator[Int] => return; 1 } ) + } + def testRunApproximateJob(sc: SparkContext): Unit = { + val rdd = sc.parallelize(1 to 10, 10) + val evaluator = new CountEvaluator(1, 0.5) + sc.runApproximateJob( + rdd, { (ctx: TaskContext, iter: Iterator[Int]) => return; 1L }, evaluator, 1000) + } + def testSubmitJob(sc: SparkContext): Unit = { + val rdd = sc.parallelize(1 to 10, 10) + sc.submitJob( + rdd, + { _ => return; 1 }: Iterator[Int] => Int, + Seq.empty, + { case (_, _) => return }: (Int, Int) => Unit, + { return } + ) + } +} diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala new file mode 100644 index 0000000000000..59456790e89f0 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala @@ -0,0 +1,571 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +import java.io.NotSerializableException + +import scala.collection.mutable + +import org.scalatest.{BeforeAndAfterAll, FunSuite, PrivateMethodTester} + +import org.apache.spark.{SparkContext, SparkException} +import org.apache.spark.serializer.SerializerInstance + +/** + * Another test suite for the closure cleaner that is finer-grained. + * For tests involving end-to-end Spark jobs, see {{ClosureCleanerSuite}}. + */ +class ClosureCleanerSuite2 extends FunSuite with BeforeAndAfterAll with PrivateMethodTester { + + // Start a SparkContext so that the closure serializer is accessible + // We do not actually use this explicitly otherwise + private var sc: SparkContext = null + private var closureSerializer: SerializerInstance = null + + override def beforeAll(): Unit = { + sc = new SparkContext("local", "test") + closureSerializer = sc.env.closureSerializer.newInstance() + } + + override def afterAll(): Unit = { + sc.stop() + sc = null + closureSerializer = null + } + + // Some fields and methods to reference in inner closures later + private val someSerializableValue = 1 + private val someNonSerializableValue = new NonSerializable + private def someSerializableMethod() = 1 + private def someNonSerializableMethod() = new NonSerializable + + /** Assert that the given closure is serializable (or not). */ + private def assertSerializable(closure: AnyRef, serializable: Boolean): Unit = { + if (serializable) { + closureSerializer.serialize(closure) + } else { + intercept[NotSerializableException] { + closureSerializer.serialize(closure) + } + } + } + + /** + * Helper method for testing whether closure cleaning works as expected. + * This cleans the given closure twice, with and without transitive cleaning. + * + * @param closure closure to test cleaning with + * @param serializableBefore if true, verify that the closure is serializable + * before cleaning, otherwise assert that it is not + * @param serializableAfter if true, assert that the closure is serializable + * after cleaning otherwise assert that it is not + */ + private def verifyCleaning( + closure: AnyRef, + serializableBefore: Boolean, + serializableAfter: Boolean): Unit = { + verifyCleaning(closure, serializableBefore, serializableAfter, transitive = true) + verifyCleaning(closure, serializableBefore, serializableAfter, transitive = false) + } + + /** Helper method for testing whether closure cleaning works as expected. */ + private def verifyCleaning( + closure: AnyRef, + serializableBefore: Boolean, + serializableAfter: Boolean, + transitive: Boolean): Unit = { + assertSerializable(closure, serializableBefore) + // If the resulting closure is not serializable even after + // cleaning, we expect ClosureCleaner to throw a SparkException + if (serializableAfter) { + ClosureCleaner.clean(closure, checkSerializable = true, transitive) + } else { + intercept[SparkException] { + ClosureCleaner.clean(closure, checkSerializable = true, transitive) + } + } + assertSerializable(closure, serializableAfter) + } + + /** + * Return the fields accessed by the given closure by class. + * This also optionally finds the fields transitively referenced through methods invocations. + */ + private def findAccessedFields( + closure: AnyRef, + outerClasses: Seq[Class[_]], + findTransitively: Boolean): Map[Class[_], Set[String]] = { + val fields = new mutable.HashMap[Class[_], mutable.Set[String]] + outerClasses.foreach { c => fields(c) = new mutable.HashSet[String] } + ClosureCleaner.getClassReader(closure.getClass) + .accept(new FieldAccessFinder(fields, findTransitively), 0) + fields.mapValues(_.toSet).toMap + } + + // Accessors for private methods + private val _isClosure = PrivateMethod[Boolean]('isClosure) + private val _getInnerClosureClasses = PrivateMethod[List[Class[_]]]('getInnerClosureClasses) + private val _getOuterClasses = PrivateMethod[List[Class[_]]]('getOuterClasses) + private val _getOuterObjects = PrivateMethod[List[AnyRef]]('getOuterObjects) + + private def isClosure(obj: AnyRef): Boolean = { + ClosureCleaner invokePrivate _isClosure(obj) + } + + private def getInnerClosureClasses(closure: AnyRef): List[Class[_]] = { + ClosureCleaner invokePrivate _getInnerClosureClasses(closure) + } + + private def getOuterClasses(closure: AnyRef): List[Class[_]] = { + ClosureCleaner invokePrivate _getOuterClasses(closure) + } + + private def getOuterObjects(closure: AnyRef): List[AnyRef] = { + ClosureCleaner invokePrivate _getOuterObjects(closure) + } + + test("get inner closure classes") { + val closure1 = () => 1 + val closure2 = () => { () => 1 } + val closure3 = (i: Int) => { + (1 to i).map { x => x + 1 }.filter { x => x > 5 } + } + val closure4 = (j: Int) => { + (1 to j).flatMap { x => + (1 to x).flatMap { y => + (1 to y).map { z => z + 1 } + } + } + } + val inner1 = getInnerClosureClasses(closure1) + val inner2 = getInnerClosureClasses(closure2) + val inner3 = getInnerClosureClasses(closure3) + val inner4 = getInnerClosureClasses(closure4) + assert(inner1.isEmpty) + assert(inner2.size === 1) + assert(inner3.size === 2) + assert(inner4.size === 3) + assert(inner2.forall(isClosure)) + assert(inner3.forall(isClosure)) + assert(inner4.forall(isClosure)) + } + + test("get outer classes and objects") { + val localValue = someSerializableValue + val closure1 = () => 1 + val closure2 = () => localValue + val closure3 = () => someSerializableValue + val closure4 = () => someSerializableMethod() + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerClasses3 = getOuterClasses(closure3) + val outerClasses4 = getOuterClasses(closure4) + val outerObjects1 = getOuterObjects(closure1) + val outerObjects2 = getOuterObjects(closure2) + val outerObjects3 = getOuterObjects(closure3) + val outerObjects4 = getOuterObjects(closure4) + + // The classes and objects should have the same size + assert(outerClasses1.size === outerObjects1.size) + assert(outerClasses2.size === outerObjects2.size) + assert(outerClasses3.size === outerObjects3.size) + assert(outerClasses4.size === outerObjects4.size) + + // These do not have $outer pointers because they reference only local variables + assert(outerClasses1.isEmpty) + assert(outerClasses2.isEmpty) + + // These closures do have $outer pointers because they ultimately reference `this` + // The first $outer pointer refers to the closure defines this test (see FunSuite#test) + // The second $outer pointer refers to ClosureCleanerSuite2 + assert(outerClasses3.size === 2) + assert(outerClasses4.size === 2) + assert(isClosure(outerClasses3(0))) + assert(isClosure(outerClasses4(0))) + assert(outerClasses3(0) === outerClasses4(0)) // part of the same "FunSuite#test" scope + assert(outerClasses3(1) === this.getClass) + assert(outerClasses4(1) === this.getClass) + assert(outerObjects3(1) === this) + assert(outerObjects4(1) === this) + } + + test("get outer classes and objects with nesting") { + val localValue = someSerializableValue + + val test1 = () => { + val x = 1 + val closure1 = () => 1 + val closure2 = () => x + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerObjects1 = getOuterObjects(closure1) + val outerObjects2 = getOuterObjects(closure2) + assert(outerClasses1.size === outerObjects1.size) + assert(outerClasses2.size === outerObjects2.size) + // These inner closures only reference local variables, and so do not have $outer pointers + assert(outerClasses1.isEmpty) + assert(outerClasses2.isEmpty) + } + + val test2 = () => { + def y = 1 + val closure1 = () => 1 + val closure2 = () => y + val closure3 = () => localValue + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerClasses3 = getOuterClasses(closure3) + val outerObjects1 = getOuterObjects(closure1) + val outerObjects2 = getOuterObjects(closure2) + val outerObjects3 = getOuterObjects(closure3) + assert(outerClasses1.size === outerObjects1.size) + assert(outerClasses2.size === outerObjects2.size) + assert(outerClasses3.size === outerObjects3.size) + // Same as above, this closure only references local variables + assert(outerClasses1.isEmpty) + // This closure references the "test2" scope because it needs to find the method `y` + // Scope hierarchy: "test2" < "FunSuite#test" < ClosureCleanerSuite2 + assert(outerClasses2.size === 3) + // This closure references the "test2" scope because it needs to find the `localValue` + // defined outside of this scope + assert(outerClasses3.size === 3) + assert(isClosure(outerClasses2(0))) + assert(isClosure(outerClasses3(0))) + assert(isClosure(outerClasses2(1))) + assert(isClosure(outerClasses3(1))) + assert(outerClasses2(0) === outerClasses3(0)) // part of the same "test2" scope + assert(outerClasses2(1) === outerClasses3(1)) // part of the same "FunSuite#test" scope + assert(outerClasses2(2) === this.getClass) + assert(outerClasses3(2) === this.getClass) + assert(outerObjects2(2) === this) + assert(outerObjects3(2) === this) + } + + test1() + test2() + } + + test("find accessed fields") { + val localValue = someSerializableValue + val closure1 = () => 1 + val closure2 = () => localValue + val closure3 = () => someSerializableValue + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerClasses3 = getOuterClasses(closure3) + + val fields1 = findAccessedFields(closure1, outerClasses1, findTransitively = false) + val fields2 = findAccessedFields(closure2, outerClasses2, findTransitively = false) + val fields3 = findAccessedFields(closure3, outerClasses3, findTransitively = false) + assert(fields1.isEmpty) + assert(fields2.isEmpty) + assert(fields3.size === 2) + // This corresponds to the "FunSuite#test" closure. This is empty because the + // `someSerializableValue` belongs to its parent (i.e. ClosureCleanerSuite2). + assert(fields3(outerClasses3(0)).isEmpty) + // This corresponds to the ClosureCleanerSuite2. This is also empty, however, + // because accessing a `ClosureCleanerSuite2#someSerializableValue` actually involves a + // method call. Since we do not find fields transitively, we will not recursively trace + // through the fields referenced by this method. + assert(fields3(outerClasses3(1)).isEmpty) + + val fields1t = findAccessedFields(closure1, outerClasses1, findTransitively = true) + val fields2t = findAccessedFields(closure2, outerClasses2, findTransitively = true) + val fields3t = findAccessedFields(closure3, outerClasses3, findTransitively = true) + assert(fields1t.isEmpty) + assert(fields2t.isEmpty) + assert(fields3t.size === 2) + // Because we find fields transitively now, we are able to detect that we need the + // $outer pointer to get the field from the ClosureCleanerSuite2 + assert(fields3t(outerClasses3(0)).size === 1) + assert(fields3t(outerClasses3(0)).head === "$outer") + assert(fields3t(outerClasses3(1)).size === 1) + assert(fields3t(outerClasses3(1)).head.contains("someSerializableValue")) + } + + test("find accessed fields with nesting") { + val localValue = someSerializableValue + + val test1 = () => { + def a = localValue + 1 + val closure1 = () => 1 + val closure2 = () => a + val closure3 = () => localValue + val closure4 = () => someSerializableValue + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerClasses3 = getOuterClasses(closure3) + val outerClasses4 = getOuterClasses(closure4) + + // First, find only fields accessed directly, not transitively, by these closures + val fields1 = findAccessedFields(closure1, outerClasses1, findTransitively = false) + val fields2 = findAccessedFields(closure2, outerClasses2, findTransitively = false) + val fields3 = findAccessedFields(closure3, outerClasses3, findTransitively = false) + val fields4 = findAccessedFields(closure4, outerClasses4, findTransitively = false) + assert(fields1.isEmpty) + // Note that the size here represents the number of outer classes, not the number of fields + // "test1" < parameter of "FunSuite#test" < ClosureCleanerSuite2 + assert(fields2.size === 3) + // Since we do not find fields transitively here, we do not look into what `def a` references + assert(fields2(outerClasses2(0)).isEmpty) // This corresponds to the "test1" scope + assert(fields2(outerClasses2(1)).isEmpty) // This corresponds to the "FunSuite#test" scope + assert(fields2(outerClasses2(2)).isEmpty) // This corresponds to the ClosureCleanerSuite2 + assert(fields3.size === 3) + // Note that `localValue` is a field of the "test1" scope because `def a` references it, + // but NOT a field of the "FunSuite#test" scope because it is only a local variable there + assert(fields3(outerClasses3(0)).size === 1) + assert(fields3(outerClasses3(0)).head.contains("localValue")) + assert(fields3(outerClasses3(1)).isEmpty) + assert(fields3(outerClasses3(2)).isEmpty) + assert(fields4.size === 3) + // Because `val someSerializableValue` is an instance variable, even an explicit reference + // here actually involves a method call to access the underlying value of the variable. + // Because we are not finding fields transitively here, we do not consider the fields + // accessed by this "method" (i.e. the val's accessor). + assert(fields4(outerClasses4(0)).isEmpty) + assert(fields4(outerClasses4(1)).isEmpty) + assert(fields4(outerClasses4(2)).isEmpty) + + // Now do the same, but find fields that the closures transitively reference + val fields1t = findAccessedFields(closure1, outerClasses1, findTransitively = true) + val fields2t = findAccessedFields(closure2, outerClasses2, findTransitively = true) + val fields3t = findAccessedFields(closure3, outerClasses3, findTransitively = true) + val fields4t = findAccessedFields(closure4, outerClasses4, findTransitively = true) + assert(fields1t.isEmpty) + assert(fields2t.size === 3) + assert(fields2t(outerClasses2(0)).size === 1) // `def a` references `localValue` + assert(fields2t(outerClasses2(0)).head.contains("localValue")) + assert(fields2t(outerClasses2(1)).isEmpty) + assert(fields2t(outerClasses2(2)).isEmpty) + assert(fields3t.size === 3) + assert(fields3t(outerClasses3(0)).size === 1) // as before + assert(fields3t(outerClasses3(0)).head.contains("localValue")) + assert(fields3t(outerClasses3(1)).isEmpty) + assert(fields3t(outerClasses3(2)).isEmpty) + assert(fields4t.size === 3) + // Through a series of method calls, we are able to detect that we ultimately access + // ClosureCleanerSuite2's field `someSerializableValue`. Along the way, we also accessed + // a few $outer parent pointers to get to the outermost object. + assert(fields4t(outerClasses4(0)) === Set("$outer")) + assert(fields4t(outerClasses4(1)) === Set("$outer")) + assert(fields4t(outerClasses4(2)).size === 1) + assert(fields4t(outerClasses4(2)).head.contains("someSerializableValue")) + } + + test1() + } + + test("clean basic serializable closures") { + val localValue = someSerializableValue + val closure1 = () => 1 + val closure2 = () => Array[String]("a", "b", "c") + val closure3 = (s: String, arr: Array[Long]) => s + arr.mkString(", ") + val closure4 = () => localValue + val closure5 = () => new NonSerializable(5) // we're just serializing the class information + val closure1r = closure1() + val closure2r = closure2() + val closure3r = closure3("g", Array(1, 5, 8)) + val closure4r = closure4() + val closure5r = closure5() + + verifyCleaning(closure1, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure2, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure3, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure4, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure5, serializableBefore = true, serializableAfter = true) + + // Verify that closures can still be invoked and the result still the same + assert(closure1() === closure1r) + assert(closure2() === closure2r) + assert(closure3("g", Array(1, 5, 8)) === closure3r) + assert(closure4() === closure4r) + assert(closure5() === closure5r) + } + + test("clean basic non-serializable closures") { + val closure1 = () => this // ClosureCleanerSuite2 is not serializable + val closure5 = () => someSerializableValue + val closure3 = () => someSerializableMethod() + val closure4 = () => someNonSerializableValue + val closure2 = () => someNonSerializableMethod() + + // These are not cleanable because they ultimately reference the ClosureCleanerSuite2 + verifyCleaning(closure1, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure2, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure3, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure4, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure5, serializableBefore = false, serializableAfter = false) + } + + test("clean basic nested serializable closures") { + val localValue = someSerializableValue + val closure1 = (i: Int) => { + (1 to i).map { x => x + localValue } // 1 level of nesting + } + val closure2 = (j: Int) => { + (1 to j).flatMap { x => + (1 to x).map { y => y + localValue } // 2 levels + } + } + val closure3 = (k: Int, l: Int, m: Int) => { + (1 to k).flatMap(closure2) ++ // 4 levels + (1 to l).flatMap(closure1) ++ // 3 levels + (1 to m).map { x => x + 1 } // 2 levels + } + val closure1r = closure1(1) + val closure2r = closure2(2) + val closure3r = closure3(3, 4, 5) + + verifyCleaning(closure1, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure2, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure3, serializableBefore = true, serializableAfter = true) + + // Verify that closures can still be invoked and the result still the same + assert(closure1(1) === closure1r) + assert(closure2(2) === closure2r) + assert(closure3(3, 4, 5) === closure3r) + } + + test("clean basic nested non-serializable closures") { + def localSerializableMethod(): Int = someSerializableValue + val localNonSerializableValue = someNonSerializableValue + // These closures ultimately reference the ClosureCleanerSuite2 + // Note that even accessing `val` that is an instance variable involves a method call + val closure1 = (i: Int) => { (1 to i).map { x => x + someSerializableValue } } + val closure2 = (j: Int) => { (1 to j).map { x => x + someSerializableMethod() } } + val closure4 = (k: Int) => { (1 to k).map { x => x + localSerializableMethod() } } + // This closure references a local non-serializable value + val closure3 = (l: Int) => { (1 to l).map { x => localNonSerializableValue } } + // This is non-serializable no matter how many levels we nest it + val closure5 = (m: Int) => { + (1 to m).foreach { x => + (1 to x).foreach { y => + (1 to y).foreach { z => + someSerializableValue + } + } + } + } + + verifyCleaning(closure1, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure2, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure3, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure4, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure5, serializableBefore = false, serializableAfter = false) + } + + test("clean complicated nested serializable closures") { + val localValue = someSerializableValue + + // Here we assume that if the outer closure is serializable, + // then all inner closures must also be serializable + + // Reference local fields from all levels + val closure1 = (i: Int) => { + val a = 1 + (1 to i).flatMap { x => + val b = a + 1 + (1 to x).map { y => + y + a + b + localValue + } + } + } + + // Reference local fields and methods from all levels within the outermost closure + val closure2 = (i: Int) => { + val a1 = 1 + def a2 = 2 + (1 to i).flatMap { x => + val b1 = a1 + 1 + def b2 = a2 + 1 + (1 to x).map { y => + // If this references a method outside the outermost closure, then it will try to pull + // in the ClosureCleanerSuite2. This is why `localValue` here must be a local `val`. + y + a1 + a2 + b1 + b2 + localValue + } + } + } + + val closure1r = closure1(1) + val closure2r = closure2(2) + verifyCleaning(closure1, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure2, serializableBefore = true, serializableAfter = true) + assert(closure1(1) == closure1r) + assert(closure2(2) == closure2r) + } + + test("clean complicated nested non-serializable closures") { + val localValue = someSerializableValue + + // Note that we are not interested in cleaning the outer closures here (they are not cleanable) + // The only reason why they exist is to nest the inner closures + + val test1 = () => { + val a = localValue + val b = sc + val inner1 = (x: Int) => x + a + b.hashCode() + val inner2 = (x: Int) => x + a + + // This closure explicitly references a non-serializable field + // There is no way to clean it + verifyCleaning(inner1, serializableBefore = false, serializableAfter = false) + + // This closure is serializable to begin with since it does not need a pointer to + // the outer closure (it only references local variables) + verifyCleaning(inner2, serializableBefore = true, serializableAfter = true) + } + + // Same as above, but the `val a` becomes `def a` + // The difference here is that all inner closures now have pointers to the outer closure + val test2 = () => { + def a = localValue + val b = sc + val inner1 = (x: Int) => x + a + b.hashCode() + val inner2 = (x: Int) => x + a + + // As before, this closure is neither serializable nor cleanable + verifyCleaning(inner1, serializableBefore = false, serializableAfter = false) + + // This closure is no longer serializable because it now has a pointer to the outer closure, + // which is itself not serializable because it has a pointer to the ClosureCleanerSuite2. + // If we do not clean transitively, we will not null out this indirect reference. + verifyCleaning( + inner2, serializableBefore = false, serializableAfter = false, transitive = false) + + // If we clean transitively, we will find that method `a` does not actually reference the + // outer closure's parent (i.e. the ClosureCleanerSuite), so we can additionally null out + // the outer closure's parent pointer. This will make `inner2` serializable. + verifyCleaning( + inner2, serializableBefore = false, serializableAfter = true, transitive = true) + } + + // Same as above, but with more levels of nesting + val test3 = () => { () => test1() } + val test4 = () => { () => test2() } + val test5 = () => { () => { () => test3() } } + val test6 = () => { () => { () => test4() } } + + test1() + test2() + test3()() + test4()() + test5()()() + test6()()() + } + +} diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala index a2be724254d7c..0c5221d10d79d 100644 --- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala @@ -29,6 +29,7 @@ import org.scalatest.FunSuite import org.apache.spark._ import org.apache.spark.executor._ +import org.apache.spark.rdd.RDDOperationScope import org.apache.spark.scheduler._ import org.apache.spark.storage._ @@ -74,7 +75,8 @@ class JsonProtocolSuite extends FunSuite { val blockManagerRemoved = SparkListenerBlockManagerRemoved(2L, BlockManagerId("Scarce", "to be counted...", 100)) val unpersistRdd = SparkListenerUnpersistRDD(12345) - val applicationStart = SparkListenerApplicationStart("The winner of all", None, 42L, "Garfield") + val applicationStart = SparkListenerApplicationStart("The winner of all", Some("appId"), + 42L, "Garfield", Some("appAttempt")) val applicationEnd = SparkListenerApplicationEnd(42L) val logUrlMap = Map("stderr" -> "mystderr", "stdout" -> "mystdout").toMap val executorAdded = SparkListenerExecutorAdded(executorAddedTime, "exec1", @@ -161,7 +163,7 @@ class JsonProtocolSuite extends FunSuite { assertEquals(exceptionFailure, JsonProtocol.taskEndReasonFromJson(oldEvent)) } - test("StageInfo backward compatibility") { + test("StageInfo backward compatibility (details, accumulables)") { val info = makeStageInfo(1, 2, 3, 4L, 5L) val newJson = JsonProtocol.stageInfoToJson(info) @@ -274,9 +276,11 @@ class JsonProtocolSuite extends FunSuite { test("SparkListenerApplicationStart backwards compatibility") { // SparkListenerApplicationStart in Spark 1.0.0 do not have an "appId" property. - val applicationStart = SparkListenerApplicationStart("test", None, 1L, "user") + // SparkListenerApplicationStart pre-Spark 1.4 does not have "appAttemptId". + val applicationStart = SparkListenerApplicationStart("test", None, 1L, "user", None) val oldEvent = JsonProtocol.applicationStartToJson(applicationStart) .removeField({ _._1 == "App ID" }) + .removeField({ _._1 == "App Attempt ID" }) assert(applicationStart === JsonProtocol.applicationStartFromJson(oldEvent)) } @@ -294,7 +298,7 @@ class JsonProtocolSuite extends FunSuite { val stageIds = Seq[Int](1, 2, 3, 4) val stageInfos = stageIds.map(x => makeStageInfo(x, x * 200, x * 300, x * 400, x * 500)) val dummyStageInfos = - stageIds.map(id => new StageInfo(id, 0, "unknown", 0, Seq.empty, "unknown")) + stageIds.map(id => new StageInfo(id, 0, "unknown", 0, Seq.empty, Seq.empty, "unknown")) val jobStart = SparkListenerJobStart(10, jobSubmissionTime, stageInfos, properties) val oldEvent = JsonProtocol.jobStartToJson(jobStart).removeField({_._1 == "Stage Infos"}) val expectedJobStart = @@ -320,6 +324,25 @@ class JsonProtocolSuite extends FunSuite { assertEquals(expectedJobEnd, JsonProtocol.jobEndFromJson(oldEndEvent)) } + test("RDDInfo backward compatibility (scope, parent IDs)") { + // Prior to Spark 1.4.0, RDDInfo did not have the "Scope" and "Parent IDs" properties + val rddInfo = new RDDInfo( + 1, "one", 100, StorageLevel.NONE, Seq(1, 6, 8), Some(new RDDOperationScope("fable"))) + val oldRddInfoJson = JsonProtocol.rddInfoToJson(rddInfo) + .removeField({ _._1 == "Parent IDs"}) + .removeField({ _._1 == "Scope"}) + val expectedRddInfo = new RDDInfo(1, "one", 100, StorageLevel.NONE, Seq.empty, scope = None) + assertEquals(expectedRddInfo, JsonProtocol.rddInfoFromJson(oldRddInfoJson)) + } + + test("StageInfo backward compatibility (parent IDs)") { + // Prior to Spark 1.4.0, StageInfo did not have the "Parent IDs" property + val stageInfo = new StageInfo(1, 1, "me-stage", 1, Seq.empty, Seq(1, 2, 3), "details") + val oldStageInfo = JsonProtocol.stageInfoToJson(stageInfo).removeField({ _._1 == "Parent IDs"}) + val expectedStageInfo = new StageInfo(1, 1, "me-stage", 1, Seq.empty, Seq.empty, "details") + assertEquals(expectedStageInfo, JsonProtocol.stageInfoFromJson(oldStageInfo)) + } + /** -------------------------- * | Helper test running methods | * --------------------------- */ @@ -642,7 +665,7 @@ class JsonProtocolSuite extends FunSuite { } private def makeRddInfo(a: Int, b: Int, c: Int, d: Long, e: Long) = { - val r = new RDDInfo(a, "mayor", b, StorageLevel.MEMORY_AND_DISK) + val r = new RDDInfo(a, "mayor", b, StorageLevel.MEMORY_AND_DISK, Seq(1, 4, 7)) r.numCachedPartitions = c r.memSize = d r.diskSize = e @@ -651,7 +674,7 @@ class JsonProtocolSuite extends FunSuite { private def makeStageInfo(a: Int, b: Int, c: Int, d: Long, e: Long) = { val rddInfos = (0 until a % 5).map { i => makeRddInfo(a + i, b + i, c + i, d + i, e + i) } - val stageInfo = new StageInfo(a, 0, "greetings", b, rddInfos, "details") + val stageInfo = new StageInfo(a, 0, "greetings", b, rddInfos, Seq(100, 200, 300), "details") val (acc1, acc2) = (makeAccumulableInfo(1), makeAccumulableInfo(2)) stageInfo.accumulables(acc1.id) = acc1 stageInfo.accumulables(acc2.id) = acc2 @@ -744,6 +767,7 @@ class JsonProtocolSuite extends FunSuite { | "Stage Name": "greetings", | "Number of Tasks": 200, | "RDD Info": [], + | "ParentIDs" : [100, 200, 300], | "Details": "details", | "Accumulables": [ | { @@ -782,20 +806,22 @@ class JsonProtocolSuite extends FunSuite { | { | "RDD ID": 101, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 201, | "Number of Cached Partitions": 301, | "Memory Size": 401, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 501 | } | ], + | "ParentIDs" : [100, 200, 300], | "Details": "details", | "Accumulables": [ | { @@ -969,12 +995,12 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": false, | "Replication": 2 | }, | "Memory Size": 0, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 0 | } | } @@ -1052,12 +1078,12 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": false, | "Replication": 2 | }, | "Memory Size": 0, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 0 | } | } @@ -1135,12 +1161,12 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": false, | "Replication": 2 | }, | "Memory Size": 0, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 0 | } | } @@ -1165,20 +1191,22 @@ class JsonProtocolSuite extends FunSuite { | { | "RDD ID": 1, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 200, | "Number of Cached Partitions": 300, | "Memory Size": 400, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 500 | } | ], + | "Parent IDs" : [100, 200, 300], | "Details": "details", | "Accumulables": [ | { @@ -1204,36 +1232,39 @@ class JsonProtocolSuite extends FunSuite { | { | "RDD ID": 2, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 400, | "Number of Cached Partitions": 600, | "Memory Size": 800, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1000 | }, | { | "RDD ID": 3, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 401, | "Number of Cached Partitions": 601, | "Memory Size": 801, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1001 | } | ], + | "ParentIDs" : [100, 200, 300], | "Details": "details", | "Accumulables": [ | { @@ -1259,52 +1290,56 @@ class JsonProtocolSuite extends FunSuite { | { | "RDD ID": 3, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 600, | "Number of Cached Partitions": 900, | "Memory Size": 1200, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1500 | }, | { | "RDD ID": 4, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 601, | "Number of Cached Partitions": 901, | "Memory Size": 1201, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1501 | }, | { | "RDD ID": 5, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 602, | "Number of Cached Partitions": 902, | "Memory Size": 1202, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1502 | } | ], + | "ParentIDs" : [100, 200, 300], | "Details": "details", | "Accumulables": [ | { @@ -1330,68 +1365,73 @@ class JsonProtocolSuite extends FunSuite { | { | "RDD ID": 4, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 800, | "Number of Cached Partitions": 1200, | "Memory Size": 1600, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 2000 | }, | { | "RDD ID": 5, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 801, | "Number of Cached Partitions": 1201, | "Memory Size": 1601, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 2001 | }, | { | "RDD ID": 6, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 802, | "Number of Cached Partitions": 1202, | "Memory Size": 1602, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 2002 | }, | { | "RDD ID": 7, | "Name": "mayor", + | "Parent IDs": [1, 4, 7], | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 803, | "Number of Cached Partitions": 1203, | "Memory Size": 1603, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 2003 | } | ], + | "ParentIDs" : [100, 200, 300], | "Details": "details", | "Accumulables": [ | { @@ -1497,8 +1537,10 @@ class JsonProtocolSuite extends FunSuite { |{ | "Event": "SparkListenerApplicationStart", | "App Name": "The winner of all", + | "App ID": "appId", | "Timestamp": 42, - | "User": "Garfield" + | "User": "Garfield", + | "App Attempt ID": "appAttempt" |} """ diff --git a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala index 28915bd53354e..133a76f28e000 100644 --- a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala @@ -36,6 +36,15 @@ class DummyClass4(val d: DummyClass3) { val x: Int = 0 } +// dummy class to show class field blocks alignment. +class DummyClass5 extends DummyClass1 { + val x: Boolean = true +} + +class DummyClass6 extends DummyClass5 { + val y: Boolean = true +} + object DummyString { def apply(str: String) : DummyString = new DummyString(str.toArray) } @@ -50,6 +59,7 @@ class SizeEstimatorSuite override def beforeEach() { // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case + super.beforeEach() System.setProperty("os.arch", "amd64") System.setProperty("spark.test.useCompressedOops", "true") } @@ -62,6 +72,22 @@ class SizeEstimatorSuite assertResult(48)(SizeEstimator.estimate(new DummyClass4(new DummyClass3))) } + test("primitive wrapper objects") { + assertResult(16)(SizeEstimator.estimate(new java.lang.Boolean(true))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Byte("1"))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Character('1'))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Short("1"))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Integer(1))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Long(1))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Float(1.0))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Double(1.0d))) + } + + test("class field blocks rounding") { + assertResult(16)(SizeEstimator.estimate(new DummyClass5)) + assertResult(24)(SizeEstimator.estimate(new DummyClass6)) + } + // NOTE: The String class definition varies across JDK versions (1.6 vs. 1.7) and vendors // (Sun vs IBM). Use a DummyString class to make tests deterministic. test("strings") { @@ -102,18 +128,18 @@ class SizeEstimatorSuite val arr = new Array[Char](100000) assertResult(200016)(SizeEstimator.estimate(arr)) assertResult(480032)(SizeEstimator.estimate(Array.fill(10000)(new DummyString(arr)))) - + val buf = new ArrayBuffer[DummyString]() for (i <- 0 until 5000) { buf.append(new DummyString(new Array[Char](10))) } assertResult(340016)(SizeEstimator.estimate(buf.toArray)) - + for (i <- 0 until 5000) { buf.append(new DummyString(arr)) } assertResult(683912)(SizeEstimator.estimate(buf.toArray)) - + // If an array contains the *same* element many times, we should only count it once. val d1 = new DummyClass1 // 10 pointers plus 8-byte object @@ -155,5 +181,20 @@ class SizeEstimatorSuite assertResult(64)(SizeEstimator.estimate(DummyString("a"))) assertResult(64)(SizeEstimator.estimate(DummyString("ab"))) assertResult(72)(SizeEstimator.estimate(DummyString("abcdefgh"))) + + // primitive wrapper classes + assertResult(24)(SizeEstimator.estimate(new java.lang.Boolean(true))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Byte("1"))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Character('1'))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Short("1"))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Integer(1))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Long(1))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Float(1.0))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Double(1.0d))) + } + + test("class field blocks rounding on 64-bit VM without useCompressedOops") { + assertResult(24)(SizeEstimator.estimate(new DummyClass5)) + assertResult(32)(SizeEstimator.estimate(new DummyClass6)) } } diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index 62a3cbcdf69ea..651ead6ff1de2 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -35,9 +35,10 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.spark.network.util.ByteUnit +import org.apache.spark.Logging import org.apache.spark.SparkConf -class UtilsSuite extends FunSuite with ResetSystemProperties { +class UtilsSuite extends FunSuite with ResetSystemProperties with Logging { test("timeConversion") { // Test -1 @@ -68,7 +69,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { intercept[NumberFormatException] { Utils.timeStringAsMs("600l") } - + intercept[NumberFormatException] { Utils.timeStringAsMs("This breaks 600s") } @@ -99,7 +100,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(Utils.byteStringAsGb("1k") === 0) assert(Utils.byteStringAsGb("1t") === ByteUnit.TiB.toGiB(1)) assert(Utils.byteStringAsGb("1p") === ByteUnit.PiB.toGiB(1)) - + assert(Utils.byteStringAsMb("1") === 1) assert(Utils.byteStringAsMb("1m") === 1) assert(Utils.byteStringAsMb("1048575b") === 0) @@ -118,7 +119,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(Utils.byteStringAsKb("1g") === ByteUnit.GiB.toKiB(1)) assert(Utils.byteStringAsKb("1t") === ByteUnit.TiB.toKiB(1)) assert(Utils.byteStringAsKb("1p") === ByteUnit.PiB.toKiB(1)) - + assert(Utils.byteStringAsBytes("1") === 1) assert(Utils.byteStringAsBytes("1k") === ByteUnit.KiB.toBytes(1)) assert(Utils.byteStringAsBytes("1m") === ByteUnit.MiB.toBytes(1)) @@ -127,17 +128,17 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(Utils.byteStringAsBytes("1p") === ByteUnit.PiB.toBytes(1)) // Overflow handling, 1073741824p exceeds Long.MAX_VALUE if converted straight to Bytes - // This demonstrates that we can have e.g 1024^3 PB without overflowing. + // This demonstrates that we can have e.g 1024^3 PB without overflowing. assert(Utils.byteStringAsGb("1073741824p") === ByteUnit.PiB.toGiB(1073741824)) assert(Utils.byteStringAsMb("1073741824p") === ByteUnit.PiB.toMiB(1073741824)) - + // Run this to confirm it doesn't throw an exception - assert(Utils.byteStringAsBytes("9223372036854775807") === 9223372036854775807L) + assert(Utils.byteStringAsBytes("9223372036854775807") === 9223372036854775807L) assert(ByteUnit.PiB.toPiB(9223372036854775807L) === 9223372036854775807L) - + // Test overflow exception intercept[IllegalArgumentException] { - // This value exceeds Long.MAX when converted to bytes + // This value exceeds Long.MAX when converted to bytes Utils.byteStringAsBytes("9223372036854775808") } @@ -146,22 +147,22 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { // This value exceeds Long.MAX when converted to TB ByteUnit.PiB.toTiB(9223372036854775807L) } - + // Test fractional string intercept[NumberFormatException] { Utils.byteStringAsMb("0.064") } - + // Test fractional string intercept[NumberFormatException] { Utils.byteStringAsMb("0.064m") } - + // Test invalid strings intercept[NumberFormatException] { Utils.byteStringAsBytes("500ub") } - + // Test invalid strings intercept[NumberFormatException] { Utils.byteStringAsBytes("This breaks 600b") @@ -174,12 +175,12 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { intercept[NumberFormatException] { Utils.byteStringAsBytes("600gb This breaks") } - + intercept[NumberFormatException] { Utils.byteStringAsBytes("This 123mb breaks") } } - + test("bytesToString") { assert(Utils.bytesToString(10) === "10.0 B") assert(Utils.bytesToString(1500) === "1500.0 B") @@ -475,6 +476,15 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { } } + // Test for using the util function to change our log levels. + test("log4j log level change") { + Utils.setLogLevel(org.apache.log4j.Level.ALL) + assert(log.isInfoEnabled()) + Utils.setLogLevel(org.apache.log4j.Level.ERROR) + assert(!log.isInfoEnabled()) + assert(log.isErrorEnabled()) + } + test("deleteRecursively") { val tempDir1 = Utils.createTempDir() assert(tempDir1.exists()) diff --git a/core/src/test/scala/org/apache/spark/util/collection/ChainedBufferSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ChainedBufferSuite.scala new file mode 100644 index 0000000000000..c0c38cd4ac4ad --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/collection/ChainedBufferSuite.scala @@ -0,0 +1,143 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.nio.ByteBuffer + +import org.scalatest.FunSuite +import org.scalatest.Matchers._ + +class ChainedBufferSuite extends FunSuite { + test("write and read at start") { + // write from start of source array + val buffer = new ChainedBuffer(8) + buffer.capacity should be (0) + verifyWriteAndRead(buffer, 0, 0, 0, 4) + buffer.capacity should be (8) + + // write from middle of source array + verifyWriteAndRead(buffer, 0, 5, 0, 4) + buffer.capacity should be (8) + + // read to middle of target array + verifyWriteAndRead(buffer, 0, 0, 5, 4) + buffer.capacity should be (8) + + // write up to border + verifyWriteAndRead(buffer, 0, 0, 0, 8) + buffer.capacity should be (8) + + // expand into second buffer + verifyWriteAndRead(buffer, 0, 0, 0, 12) + buffer.capacity should be (16) + + // expand into multiple buffers + verifyWriteAndRead(buffer, 0, 0, 0, 28) + buffer.capacity should be (32) + } + + test("write and read at middle") { + val buffer = new ChainedBuffer(8) + + // fill to a middle point + verifyWriteAndRead(buffer, 0, 0, 0, 3) + + // write from start of source array + verifyWriteAndRead(buffer, 3, 0, 0, 4) + buffer.capacity should be (8) + + // write from middle of source array + verifyWriteAndRead(buffer, 3, 5, 0, 4) + buffer.capacity should be (8) + + // read to middle of target array + verifyWriteAndRead(buffer, 3, 0, 5, 4) + buffer.capacity should be (8) + + // write up to border + verifyWriteAndRead(buffer, 3, 0, 0, 5) + buffer.capacity should be (8) + + // expand into second buffer + verifyWriteAndRead(buffer, 3, 0, 0, 12) + buffer.capacity should be (16) + + // expand into multiple buffers + verifyWriteAndRead(buffer, 3, 0, 0, 28) + buffer.capacity should be (32) + } + + test("write and read at later buffer") { + val buffer = new ChainedBuffer(8) + + // fill to a middle point + verifyWriteAndRead(buffer, 0, 0, 0, 11) + + // write from start of source array + verifyWriteAndRead(buffer, 11, 0, 0, 4) + buffer.capacity should be (16) + + // write from middle of source array + verifyWriteAndRead(buffer, 11, 5, 0, 4) + buffer.capacity should be (16) + + // read to middle of target array + verifyWriteAndRead(buffer, 11, 0, 5, 4) + buffer.capacity should be (16) + + // write up to border + verifyWriteAndRead(buffer, 11, 0, 0, 5) + buffer.capacity should be (16) + + // expand into second buffer + verifyWriteAndRead(buffer, 11, 0, 0, 12) + buffer.capacity should be (24) + + // expand into multiple buffers + verifyWriteAndRead(buffer, 11, 0, 0, 28) + buffer.capacity should be (40) + } + + + // Used to make sure we're writing different bytes each time + var rangeStart = 0 + + /** + * @param buffer The buffer to write to and read from. + * @param offsetInBuffer The offset to write to in the buffer. + * @param offsetInSource The offset in the array that the bytes are written from. + * @param offsetInTarget The offset in the array to read the bytes into. + * @param length The number of bytes to read and write + */ + def verifyWriteAndRead( + buffer: ChainedBuffer, + offsetInBuffer: Int, + offsetInSource: Int, + offsetInTarget: Int, + length: Int): Unit = { + val source = new Array[Byte](offsetInSource + length) + (rangeStart until rangeStart + length).map(_.toByte).copyToArray(source, offsetInSource) + buffer.write(offsetInBuffer, source, offsetInSource, length) + val target = new Array[Byte](offsetInTarget + length) + buffer.read(offsetInBuffer, target, offsetInTarget, length) + ByteBuffer.wrap(source, offsetInSource, length) should be + (ByteBuffer.wrap(target, offsetInTarget, length)) + + rangeStart += 100 + } +} diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala index 9ff067f86af44..7a98723bc6472 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala @@ -19,19 +19,24 @@ package org.apache.spark.util.collection import scala.collection.mutable.ArrayBuffer -import org.scalatest.{PrivateMethodTester, FunSuite} - -import org.apache.spark._ +import org.scalatest.{FunSuite, PrivateMethodTester} import scala.util.Random +import org.apache.spark._ +import org.apache.spark.serializer.{JavaSerializer, KryoSerializer} + class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMethodTester { - private def createSparkConf(loadDefaults: Boolean): SparkConf = { + private def createSparkConf(loadDefaults: Boolean, kryo: Boolean): SparkConf = { val conf = new SparkConf(loadDefaults) - // Make the Java serializer write a reset instruction (TC_RESET) after each object to test - // for a bug we had with bytes written past the last object in a batch (SPARK-2792) - conf.set("spark.serializer.objectStreamReset", "1") - conf.set("spark.serializer", "org.apache.spark.serializer.JavaSerializer") + if (kryo) { + conf.set("spark.serializer", classOf[KryoSerializer].getName) + } else { + // Make the Java serializer write a reset instruction (TC_RESET) after each object to test + // for a bug we had with bytes written past the last object in a batch (SPARK-2792) + conf.set("spark.serializer.objectStreamReset", "1") + conf.set("spark.serializer", classOf[JavaSerializer].getName) + } // Ensure that we actually have multiple batches per spill file conf.set("spark.shuffle.spill.batchSize", "10") conf @@ -47,8 +52,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(!sorter.invokePrivate(bypassMergeSort()), "sorter bypassed merge-sort") } - test("empty data stream") { - val conf = new SparkConf(false) + test("empty data stream with kryo ser") { + emptyDataStream(createSparkConf(false, true)) + } + + test("empty data stream with java ser") { + emptyDataStream(createSparkConf(false, false)) + } + + def emptyDataStream(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -81,8 +93,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe sorter4.stop() } - test("few elements per partition") { - val conf = createSparkConf(false) + test("few elements per partition with kryo ser") { + fewElementsPerPartition(createSparkConf(false, true)) + } + + test("few elements per partition with java ser") { + fewElementsPerPartition(createSparkConf(false, false)) + } + + def fewElementsPerPartition(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -123,8 +142,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe sorter4.stop() } - test("empty partitions with spilling") { - val conf = createSparkConf(false) + test("empty partitions with spilling with kryo ser") { + emptyPartitionsWithSpilling(createSparkConf(false, true)) + } + + test("empty partitions with spilling with java ser") { + emptyPartitionsWithSpilling(createSparkConf(false, false)) + } + + def emptyPartitionsWithSpilling(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.spill.initialMemoryThreshold", "512") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") @@ -149,8 +175,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe sorter.stop() } - test("empty partitions with spilling, bypass merge-sort") { - val conf = createSparkConf(false) + test("empty partitions with spilling, bypass merge-sort with kryo ser") { + emptyPartitionerWithSpillingBypassMergeSort(createSparkConf(false, true)) + } + + test("empty partitions with spilling, bypass merge-sort with java ser") { + emptyPartitionerWithSpillingBypassMergeSort(createSparkConf(false, false)) + } + + def emptyPartitionerWithSpillingBypassMergeSort(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.spill.initialMemoryThreshold", "512") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") @@ -174,8 +207,17 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe sorter.stop() } - test("spilling in local cluster") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + test("spilling in local cluster with kryo ser") { + // Load defaults, otherwise SPARK_HOME is not found + testSpillingInLocalCluster(createSparkConf(true, true)) + } + + test("spilling in local cluster with java ser") { + // Load defaults, otherwise SPARK_HOME is not found + testSpillingInLocalCluster(createSparkConf(true, false)) + } + + def testSpillingInLocalCluster(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -245,8 +287,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(resultE === (0 until 100000).map(i => (i/4, i)).toSeq) } - test("spilling in local cluster with many reduce tasks") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + test("spilling in local cluster with many reduce tasks with kryo ser") { + spillingInLocalClusterWithManyReduceTasks(createSparkConf(true, true)) + } + + test("spilling in local cluster with many reduce tasks with java ser") { + spillingInLocalClusterWithManyReduceTasks(createSparkConf(true, false)) + } + + def spillingInLocalClusterWithManyReduceTasks(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local-cluster[2,1,512]", "test", conf) @@ -317,7 +366,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in sorter") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + val conf = createSparkConf(true, false) // Load defaults, otherwise SPARK_HOME is not found conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -328,7 +377,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe val sorter = new ExternalSorter[Int, Int, Int]( None, Some(new HashPartitioner(3)), Some(ord), None) assertDidNotBypassMergeSort(sorter) - sorter.insertAll((0 until 100000).iterator.map(i => (i, i))) + sorter.insertAll((0 until 120000).iterator.map(i => (i, i))) assert(diskBlockManager.getAllFiles().length > 0) sorter.stop() assert(diskBlockManager.getAllBlocks().length === 0) @@ -336,15 +385,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe val sorter2 = new ExternalSorter[Int, Int, Int]( None, Some(new HashPartitioner(3)), Some(ord), None) assertDidNotBypassMergeSort(sorter2) - sorter2.insertAll((0 until 100000).iterator.map(i => (i, i))) + sorter2.insertAll((0 until 120000).iterator.map(i => (i, i))) assert(diskBlockManager.getAllFiles().length > 0) - assert(sorter2.iterator.toSet === (0 until 100000).map(i => (i, i)).toSet) + assert(sorter2.iterator.toSet === (0 until 120000).map(i => (i, i)).toSet) sorter2.stop() assert(diskBlockManager.getAllBlocks().length === 0) } test("cleanup of intermediate files in sorter, bypass merge-sort") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + val conf = createSparkConf(true, false) // Load defaults, otherwise SPARK_HOME is not found conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -367,7 +416,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in sorter if there are errors") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + val conf = createSparkConf(true, false) // Load defaults, otherwise SPARK_HOME is not found conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -379,8 +428,8 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe None, Some(new HashPartitioner(3)), Some(ord), None) assertDidNotBypassMergeSort(sorter) intercept[SparkException] { - sorter.insertAll((0 until 100000).iterator.map(i => { - if (i == 99990) { + sorter.insertAll((0 until 120000).iterator.map(i => { + if (i == 119990) { throw new SparkException("Intentional failure") } (i, i) @@ -392,7 +441,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in sorter if there are errors, bypass merge-sort") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + val conf = createSparkConf(true, false) // Load defaults, otherwise SPARK_HOME is not found conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -414,7 +463,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in shuffle") { - val conf = createSparkConf(false) + val conf = createSparkConf(false, false) conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -429,7 +478,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in shuffle with errors") { - val conf = createSparkConf(false) + val conf = createSparkConf(false, false) conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -450,8 +499,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(diskBlockManager.getAllFiles().length === 2) } - test("no partial aggregation or sorting") { - val conf = createSparkConf(false) + test("no partial aggregation or sorting with kryo ser") { + noPartialAggregationOrSorting(createSparkConf(false, true)) + } + + test("no partial aggregation or sorting with java ser") { + noPartialAggregationOrSorting(createSparkConf(false, false)) + } + + def noPartialAggregationOrSorting(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -465,8 +521,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("partial aggregation without spill") { - val conf = createSparkConf(false) + test("partial aggregation without spill with kryo ser") { + partialAggregationWithoutSpill(createSparkConf(false, true)) + } + + test("partial aggregation without spill with java ser") { + partialAggregationWithoutSpill(createSparkConf(false, false)) + } + + def partialAggregationWithoutSpill(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -481,8 +544,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("partial aggregation with spill, no ordering") { - val conf = createSparkConf(false) + test("partial aggregation with spill, no ordering with kryo ser") { + partialAggregationWIthSpillNoOrdering(createSparkConf(false, true)) + } + + test("partial aggregation with spill, no ordering with java ser") { + partialAggregationWIthSpillNoOrdering(createSparkConf(false, false)) + } + + def partialAggregationWIthSpillNoOrdering(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -497,8 +567,16 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("partial aggregation with spill, with ordering") { - val conf = createSparkConf(false) + test("partial aggregation with spill, with ordering with kryo ser") { + partialAggregationWithSpillWithOrdering(createSparkConf(false, true)) + } + + + test("partial aggregation with spill, with ordering with java ser") { + partialAggregationWithSpillWithOrdering(createSparkConf(false, false)) + } + + def partialAggregationWithSpillWithOrdering(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -506,7 +584,10 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe val agg = new Aggregator[Int, Int, Int](i => i, (i, j) => i + j, (i, j) => i + j) val ord = implicitly[Ordering[Int]] val sorter = new ExternalSorter(Some(agg), Some(new HashPartitioner(3)), Some(ord), None) - sorter.insertAll((0 until 100000).iterator.map(i => (i / 2, i))) + + // avoid combine before spill + sorter.insertAll((0 until 50000).iterator.map(i => (i , 2 * i))) + sorter.insertAll((0 until 50000).iterator.map(i => (i, 2 * i + 1))) val results = sorter.partitionedIterator.map{case (p, vs) => (p, vs.toSet)}.toSet val expected = (0 until 3).map(p => { (p, (0 until 50000).map(i => (i, i * 4 + 1)).filter(_._1 % 3 == p).toSet) @@ -514,8 +595,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("sorting without aggregation, no spill") { - val conf = createSparkConf(false) + test("sorting without aggregation, no spill with kryo ser") { + sortingWithoutAggregationNoSpill(createSparkConf(false, true)) + } + + test("sorting without aggregation, no spill with java ser") { + sortingWithoutAggregationNoSpill(createSparkConf(false, false)) + } + + def sortingWithoutAggregationNoSpill(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -531,8 +619,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("sorting without aggregation, with spill") { - val conf = createSparkConf(false) + test("sorting without aggregation, with spill with kryo ser") { + sortingWithoutAggregationWithSpill(createSparkConf(false, true)) + } + + test("sorting without aggregation, with spill with java ser") { + sortingWithoutAggregationWithSpill(createSparkConf(false, false)) + } + + def sortingWithoutAggregationWithSpill(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -549,7 +644,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("spilling with hash collisions") { - val conf = createSparkConf(true) + val conf = createSparkConf(true, false) conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -606,7 +701,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("spilling with many hash collisions") { - val conf = createSparkConf(true) + val conf = createSparkConf(true, false) conf.set("spark.shuffle.memoryFraction", "0.0001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -629,7 +724,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("spilling with hash collisions using the Int.MaxValue key") { - val conf = createSparkConf(true) + val conf = createSparkConf(true, false) conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -653,7 +748,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("spilling with null keys and values") { - val conf = createSparkConf(true) + val conf = createSparkConf(true, false) conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -682,7 +777,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("conditions for bypassing merge-sort") { - val conf = createSparkConf(false) + val conf = createSparkConf(false, false) conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -715,8 +810,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assertDidNotBypassMergeSort(sorter4) } - test("sort without breaking sorting contracts") { - val conf = createSparkConf(true) + test("sort without breaking sorting contracts with kryo ser") { + sortWithoutBreakingSortingContracts(createSparkConf(true, true)) + } + + test("sort without breaking sorting contracts with java ser") { + sortWithoutBreakingSortingContracts(createSparkConf(true, false)) + } + + def sortWithoutBreakingSortingContracts(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.01") conf.set("spark.shuffle.manager", "sort") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) diff --git a/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala new file mode 100644 index 0000000000000..b5a2d9ef720c1 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala @@ -0,0 +1,149 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream, InputStream} + +import com.google.common.io.ByteStreams + +import org.scalatest.FunSuite +import org.scalatest.Matchers._ + +import org.apache.spark.SparkConf +import org.apache.spark.serializer.KryoSerializer +import org.apache.spark.storage.{FileSegment, BlockObjectWriter} + +class PartitionedSerializedPairBufferSuite extends FunSuite { + test("OrderedInputStream single record") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct = SomeStruct("something", 5) + buffer.insert(4, 10, struct) + + val bytes = ByteStreams.toByteArray(buffer.orderedInputStream) + + val baos = new ByteArrayOutputStream() + val stream = serializerInstance.serializeStream(baos) + stream.writeObject(10) + stream.writeObject(struct) + stream.close() + + baos.toByteArray should be (bytes) + } + + test("insert single record") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct = SomeStruct("something", 5) + buffer.insert(4, 10, struct) + val elements = buffer.partitionedDestructiveSortedIterator(None).toArray + elements.size should be (1) + elements.head should be (((4, 10), struct)) + } + + test("insert multiple records") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct1 = SomeStruct("something1", 8) + buffer.insert(6, 1, struct1) + val struct2 = SomeStruct("something2", 9) + buffer.insert(4, 2, struct2) + val struct3 = SomeStruct("something3", 10) + buffer.insert(5, 3, struct3) + + val elements = buffer.partitionedDestructiveSortedIterator(None).toArray + elements.size should be (3) + elements(0) should be (((4, 2), struct2)) + elements(1) should be (((5, 3), struct3)) + elements(2) should be (((6, 1), struct1)) + } + + test("write single record") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct = SomeStruct("something", 5) + buffer.insert(4, 10, struct) + val it = buffer.destructiveSortedWritablePartitionedIterator(None) + val writer = new SimpleBlockObjectWriter + assert(it.hasNext) + it.nextPartition should be (4) + it.writeNext(writer) + assert(!it.hasNext) + + val stream = serializerInstance.deserializeStream(writer.getInputStream) + stream.readObject[AnyRef]() should be (10) + stream.readObject[AnyRef]() should be (struct) + } + + test("write multiple records") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct1 = SomeStruct("something1", 8) + buffer.insert(6, 1, struct1) + val struct2 = SomeStruct("something2", 9) + buffer.insert(4, 2, struct2) + val struct3 = SomeStruct("something3", 10) + buffer.insert(5, 3, struct3) + + val it = buffer.destructiveSortedWritablePartitionedIterator(None) + val writer = new SimpleBlockObjectWriter + assert(it.hasNext) + it.nextPartition should be (4) + it.writeNext(writer) + assert(it.hasNext) + it.nextPartition should be (5) + it.writeNext(writer) + assert(it.hasNext) + it.nextPartition should be (6) + it.writeNext(writer) + assert(!it.hasNext) + + val stream = serializerInstance.deserializeStream(writer.getInputStream) + val iter = stream.asIterator + iter.next() should be (2) + iter.next() should be (struct2) + iter.next() should be (3) + iter.next() should be (struct3) + iter.next() should be (1) + iter.next() should be (struct1) + assert(!iter.hasNext) + } +} + +case class SomeStruct(val str: String, val num: Int) + +class SimpleBlockObjectWriter extends BlockObjectWriter(null) { + val baos = new ByteArrayOutputStream() + + override def write(bytes: Array[Byte], offs: Int, len: Int): Unit = { + baos.write(bytes, offs, len) + } + + def getInputStream(): InputStream = new ByteArrayInputStream(baos.toByteArray) + + override def open(): BlockObjectWriter = this + override def close(): Unit = { } + override def isOpen: Boolean = true + override def commitAndClose(): Unit = { } + override def revertPartialWritesAndClose(): Unit = { } + override def fileSegment(): FileSegment = null + override def write(key: Any, value: Any): Unit = { } + override def recordWritten(): Unit = { } + override def write(b: Int): Unit = { } +} diff --git a/dev/run-tests b/dev/run-tests index 861d1671182c2..05c63bce4d40d 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -236,7 +236,7 @@ echo "=========================================================================" CURRENT_BLOCK=$BLOCK_PYSPARK_UNIT_TESTS # add path for python 3 in jenkins -export PATH="${PATH}:/home/anaonda/envs/py3k/bin" +export PATH="${PATH}:/home/anaconda/envs/py3k/bin" ./python/run-tests echo "" diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins index 030f2cdddb350..f452ab66efcd8 100755 --- a/dev/run-tests-jenkins +++ b/dev/run-tests-jenkins @@ -62,7 +62,7 @@ TESTS_TIMEOUT="150m" # format: http://linux.die.net/man/1/timeout PR_TESTS=( "pr_merge_ability" "pr_public_classes" - "pr_new_dependencies" +# DISABLED (pwendell) "pr_new_dependencies" ) function post_message () { diff --git a/examples/src/main/r/wordcount.R b/docker/spark-mesos/Dockerfile similarity index 58% rename from examples/src/main/r/wordcount.R rename to docker/spark-mesos/Dockerfile index b734cb0ecf55b..b90aef3655dee 100644 --- a/examples/src/main/r/wordcount.R +++ b/docker/spark-mesos/Dockerfile @@ -1,3 +1,5 @@ +# This is an example Dockerfile for creating a Spark image which can be +# references by the Spark property 'spark.mesos.executor.docker.image' # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with @@ -15,28 +17,14 @@ # limitations under the License. # -library(SparkR) +FROM mesosphere/mesos:0.20.1 -args <- commandArgs(trailing = TRUE) +# Update the base ubuntu image with dependencies needed for Spark +RUN apt-get update && \ + apt-get install -y python libnss3 openjdk-7-jre-headless curl -if (length(args) != 1) { - print("Usage: wordcount ") - q("no") -} - -# Initialize Spark context -sc <- sparkR.init(appName = "RwordCount") -lines <- textFile(sc, args[[1]]) - -words <- flatMap(lines, - function(line) { - strsplit(line, " ")[[1]] - }) -wordCount <- lapply(words, function(word) { list(word, 1L) }) - -counts <- reduceByKey(wordCount, "+", 2L) -output <- collect(counts) - -for (wordcount in output) { - cat(wordcount[[1]], ": ", wordcount[[2]], "\n") -} +RUN mkdir /opt/spark && \ + curl http://www.apache.org/dyn/closer.cgi/spark/spark-1.4.0/spark-1.4.0-bin-hadoop2.4.tgz \ + | tar -xzC /opt +ENV SPARK_HOME /opt/spark +ENV MESOS_NATIVE_JAVA_LIBRARY /usr/local/lib/libmesos.so diff --git a/docs/building-spark.md b/docs/building-spark.md index ea79c5bc276d3..287fcd3c4034f 100644 --- a/docs/building-spark.md +++ b/docs/building-spark.md @@ -66,7 +66,6 @@ Because HDFS is not protocol-compatible across versions, if you want to read fro Hadoop versionProfile required - 0.23.xhadoop-0.23 1.x to 2.1.x(none) 2.2.xhadoop-2.2 2.3.xhadoop-2.3 @@ -82,9 +81,6 @@ mvn -Dhadoop.version=1.2.1 -DskipTests clean package # Cloudera CDH 4.2.0 with MapReduce v1 mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package - -# Apache Hadoop 0.23.x -mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package {% endhighlight %} You can enable the "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". Spark only supports YARN versions 2.2.0 and later. diff --git a/docs/configuration.md b/docs/configuration.md index 72105feba4919..0de824546c751 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -1,4 +1,4 @@ ---- +-- layout: global displayTitle: Spark Configuration title: Configuration @@ -843,19 +843,27 @@ Apart from these, the following properties are also available, and may be useful - spark.tachyonStore.baseDir + spark.externalBlockStore.blockManager + org.apache.spark.storage.TachyonBlockManager + + Implementation of external block manager (file system) that store RDDs. The file system's URL is set by + spark.externalBlockStore.url. + + + + spark.externalBlockStore.baseDir System.getProperty("java.io.tmpdir") - Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by - spark.tachyonStore.url. It can also be a comma-separated list of multiple + Directories of the external block store that store RDDs. The file system's URL is set by + spark.externalBlockStore.url It can also be a comma-separated list of multiple directories on Tachyon file system. - spark.tachyonStore.url - tachyon://localhost:19998 + spark.externalBlockStore.url + tachyon://localhost:19998 for Tachyon - The URL of the underlying Tachyon file system in the TachyonStore. + The URL of the underlying external blocker file system in the external block store. @@ -1456,6 +1464,23 @@ Apart from these, the following properties are also available, and may be useful for more details. + + spark.streaming.kafka.maxRetries + 1 + + Maximum number of consecutive retries the driver will make in order to find + the latest offsets on the leader of each partition (a default value of 1 + means that the driver will make a maximum of 2 attempts). Only applies to + the new Kafka direct stream API. + + + + spark.streaming.ui.retainedBatches + 1000 + + How many batches the Spark Streaming UI and status APIs remember before garbage collecting. + + #### Cluster Managers diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md index 87dcc58feb494..96bd69ca3b33b 100644 --- a/docs/hadoop-third-party-distributions.md +++ b/docs/hadoop-third-party-distributions.md @@ -29,9 +29,6 @@ the _exact_ Hadoop version you are running to avoid any compatibility errors. ReleaseVersion code CDH 4.X.X (YARN mode)2.0.0-cdh4.X.X CDH 4.X.X2.0.0-mr1-cdh4.X.X - CDH 3u60.20.2-cdh3u6 - CDH 3u50.20.2-cdh3u5 - CDH 3u40.20.2-cdh3u4 diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index 76140282a2dd0..7b397e30b2d90 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -216,7 +216,7 @@ model = ALS.train(ratings, rank, numIterations) testdata = ratings.map(lambda p: (p[0], p[1])) predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2])) ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions) -MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y) / ratesAndPreds.count() +MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean() print("Mean Squared Error = " + str(MSE)) # Save and load model diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 8f53d8201a089..5f1d6daeb27f0 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -184,6 +184,16 @@ acquire. By default, it will acquire *all* cores in the cluster (that get offere only makes sense if you run just one application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example). +# Mesos Docker Support + +Spark can make use of a Mesos Docker containerizer by setting the property `spark.mesos.executor.docker.image` +in your [SparkConf](configuration.html#spark-properties). + +The Docker image used must have an appropriate version of Spark already part of the image, or you can +have Mesos download Spark via the usual methods. + +Requires Mesos version 0.20.1 or later. + # Running Alongside Hadoop You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a @@ -237,6 +247,38 @@ See the [configuration page](configuration.html) for information on Spark config The value can be a floating point number. + + spark.mesos.executor.docker.image + (none) + + Set the name of the docker image that the Spark executors will run in. The selected + image must have Spark installed, as well as a compatible version of the Mesos library. + The installed path of Spark in the image can be specified with spark.mesos.executor.home; + the installed path of the Mesos library can be specified with spark.executorEnv.MESOS_NATIVE_LIBRARY. + + + + spark.mesos.executor.docker.volumes + (none) + + Set the list of volumes which will be mounted into the Docker image, which was set using + spark.mesos.executor.docker.image. The format of this property is a comma-separated list of + mappings following the form passed to docker run -v. That is they take the form: + + 
[host_path:]container_path[:ro|:rw]
+ + + + spark.mesos.executor.docker.portmaps + (none) + + Set the list of incoming ports exposed by the Docker image, which was set using + spark.mesos.executor.docker.image. The format of this property is a comma-separated list of + mappings which take the form: + + 
host_port:container_port[:tcp|:udp]
+ + spark.mesos.executor.home driver side SPARK_HOME diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 0968fc5ad632b..4fb4a90307ec8 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -133,6 +133,13 @@ Most of the configs are the same for Spark on YARN as for other deployment modes Same as spark.yarn.driver.memoryOverhead, but for the Application Master in client mode. + + spark.yarn.am.port + (random) + + Port for the YARN Application Master to listen on. In YARN client mode, this is used to communicate between the Spark driver running on a gateway and the Application Master running on YARN. In YARN cluster mode, this is used for the dynamic executor feature, where it handles the kill from the scheduler backend. + + spark.yarn.queue default @@ -189,6 +196,13 @@ Most of the configs are the same for Spark on YARN as for other deployment modes In cluster mode, use spark.driver.extraJavaOptions instead. + + spark.yarn.am.extraLibraryPath + (none) + + Set a special library path to use when launching the application master in client mode. + + spark.yarn.maxAppAttempts yarn.resourcemanager.am.max-attempts in YARN diff --git a/docs/security.md b/docs/security.md index c034ba12ff1fc..d4ffa60e59a33 100644 --- a/docs/security.md +++ b/docs/security.md @@ -32,6 +32,8 @@ SSL must be configured on each node and configured for each component involved i ### YARN mode The key-store can be prepared on the client side and then distributed and used by the executors as the part of the application. It is possible because the user is able to deploy files before the application is started in YARN by using `spark.yarn.dist.files` or `spark.yarn.dist.archives` configuration settings. The responsibility for encryption of transferring these files is on YARN side and has nothing to do with Spark. +For long-running apps like Spark Streaming apps to be able to write to HDFS, it is possible to pass a principal and keytab to `spark-submit` via the `--principal` and `--keytab` parameters respectively. The keytab passed in will be copied over to the machine running the Application Master via the Hadoop Distributed Cache (securely - if YARN is configured with SSL and HDFS encryption is enabled). The Kerberos login will be periodically renewed using this principal and keytab and the delegation tokens required for HDFS will be generated periodically so the application can continue writing to HDFS. + ### Standalone mode The user needs to provide key-stores and configuration options for master and workers. They have to be set by attaching appropriate Java system properties in `SPARK_MASTER_OPTS` and in `SPARK_WORKER_OPTS` environment variables, or just in `SPARK_DAEMON_JAVA_OPTS`. In this mode, the user may allow the executors to use the SSL settings inherited from the worker which spawned that executor. It can be accomplished by setting `spark.ssl.useNodeLocalConf` to `true`. If that parameter is set, the settings provided by user on the client side, are not used by the executors. diff --git a/docs/tuning.md b/docs/tuning.md index 1cb223e74f382..572c7270e4999 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -94,11 +94,13 @@ We will then cover tuning Spark's cache size and the Java garbage collector. ## Determining Memory Consumption -The best way to size the amount of memory consumption your dataset will require is to create an RDD, put it into cache, and look at the SparkContext logs on your driver program. The logs will tell you how much memory each partition is consuming, which you can aggregate to get the total size of the RDD. You will see messages like this: +The best way to size the amount of memory consumption a dataset will require is to create an RDD, put it +into cache, and look at the "Storage" page in the web UI. The page will tell you how much memory the RDD +is occupying. - INFO BlockManagerMasterActor: Added rdd_0_1 in memory on mbk.local:50311 (size: 717.5 KB, free: 332.3 MB) - -This means that partition 1 of RDD 0 consumed 717.5 KB. +To estimate the memory consumption of a particular object, use `SizeEstimator`'s `estimate` method +This is useful for experimenting with different data layouts to trim memory usage, as well as +determining the amount of space a broadcast variable will occupy on each executor heap. ## Tuning Data Structures diff --git a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala index 387c0e421334b..f407367a54f6c 100644 --- a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala +++ b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala @@ -17,9 +17,9 @@ package org.apache.spark.examples.streaming -import java.util.Properties +import java.util.HashMap -import kafka.producer._ +import org.apache.kafka.clients.producer.{ProducerConfig, KafkaProducer, ProducerRecord} import org.apache.spark.streaming._ import org.apache.spark.streaming.kafka._ @@ -77,23 +77,25 @@ object KafkaWordCountProducer { val Array(brokers, topic, messagesPerSec, wordsPerMessage) = args // Zookeeper connection properties - val props = new Properties() - props.put("metadata.broker.list", brokers) - props.put("serializer.class", "kafka.serializer.StringEncoder") + val props = new HashMap[String, Object]() + props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers) + props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, + "org.apache.kafka.common.serialization.StringSerializer") + props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, + "org.apache.kafka.common.serialization.StringSerializer") - val config = new ProducerConfig(props) - val producer = new Producer[String, String](config) + val producer = new KafkaProducer[String, String](props) // Send some messages while(true) { - val messages = (1 to messagesPerSec.toInt).map { messageNum => + (1 to messagesPerSec.toInt).foreach { messageNum => val str = (1 to wordsPerMessage.toInt).map(x => scala.util.Random.nextInt(10).toString) .mkString(" ") - new KeyedMessage[String, String](topic, str) - }.toArray + val message = new ProducerRecord[String, String](topic, null, str) + producer.send(message) + } - producer.send(messages: _*) Thread.sleep(100) } } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java index eaf00d09f550d..eac4f898a475d 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java @@ -28,8 +28,6 @@ import org.apache.spark.ml.classification.ClassificationModel; import org.apache.spark.ml.param.IntParam; import org.apache.spark.ml.param.ParamMap; -import org.apache.spark.ml.param.Params; -import org.apache.spark.ml.param.Params$; import org.apache.spark.mllib.linalg.BLAS; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; @@ -100,11 +98,12 @@ public static void main(String[] args) throws Exception { /** * Example of defining a type of {@link Classifier}. * - * NOTE: This is private since it is an example. In practice, you may not want it to be private. + * Note: Some IDEs (e.g., IntelliJ) will complain that this will not compile due to + * {@link org.apache.spark.ml.param.Params#set} using incompatible return types. + * However, this should still compile and run successfully. */ class MyJavaLogisticRegression - extends Classifier - implements Params { + extends Classifier { /** * Param for max number of iterations @@ -129,42 +128,37 @@ MyJavaLogisticRegression setMaxIter(int value) { // This method is used by fit(). // In Java, we have to make it public since Java does not understand Scala's protected modifier. - public MyJavaLogisticRegressionModel train(DataFrame dataset, ParamMap paramMap) { + public MyJavaLogisticRegressionModel train(DataFrame dataset) { // Extract columns from data using helper method. - JavaRDD oldDataset = extractLabeledPoints(dataset, paramMap).toJavaRDD(); + JavaRDD oldDataset = extractLabeledPoints(dataset).toJavaRDD(); // Do learning to estimate the weight vector. int numFeatures = oldDataset.take(1).get(0).features().size(); Vector weights = Vectors.zeros(numFeatures); // Learning would happen here. // Create a model, and return it. - return new MyJavaLogisticRegressionModel(this, paramMap, weights); + return new MyJavaLogisticRegressionModel(this, weights); } } /** * Example of defining a type of {@link ClassificationModel}. * - * NOTE: This is private since it is an example. In practice, you may not want it to be private. + * Note: Some IDEs (e.g., IntelliJ) will complain that this will not compile due to + * {@link org.apache.spark.ml.param.Params#set} using incompatible return types. + * However, this should still compile and run successfully. */ class MyJavaLogisticRegressionModel - extends ClassificationModel implements Params { + extends ClassificationModel { private MyJavaLogisticRegression parent_; public MyJavaLogisticRegression parent() { return parent_; } - private ParamMap fittingParamMap_; - public ParamMap fittingParamMap() { return fittingParamMap_; } - private Vector weights_; public Vector weights() { return weights_; } - public MyJavaLogisticRegressionModel( - MyJavaLogisticRegression parent_, - ParamMap fittingParamMap_, - Vector weights_) { + public MyJavaLogisticRegressionModel(MyJavaLogisticRegression parent_, Vector weights_) { this.parent_ = parent_; - this.fittingParamMap_ = fittingParamMap_; this.weights_ = weights_; } @@ -208,10 +202,8 @@ public Vector predictRaw(Vector features) { * In Java, we have to make this method public since Java does not understand Scala's protected * modifier. */ - public MyJavaLogisticRegressionModel copy() { - MyJavaLogisticRegressionModel m = - new MyJavaLogisticRegressionModel(parent_, fittingParamMap_, weights_); - Params$.MODULE$.inheritValues(this.extractParamMap(), this, m); - return m; + @Override + public MyJavaLogisticRegressionModel copy(ParamMap extra) { + return copyValues(new MyJavaLogisticRegressionModel(parent_, weights_), extra); } } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java index 4e02acce696e6..29158d5c85651 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java @@ -71,7 +71,7 @@ public static void main(String[] args) { // we can view the parameters it used during fit(). // This prints the parameter (name: value) pairs, where names are unique IDs for this // LogisticRegression instance. - System.out.println("Model 1 was fit using parameters: " + model1.fittingParamMap()); + System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap()); // We may alternatively specify parameters using a ParamMap. ParamMap paramMap = new ParamMap(); @@ -87,7 +87,7 @@ public static void main(String[] args) { // Now learn a new model using the paramMapCombined parameters. // paramMapCombined overrides all parameters set earlier via lr.set* methods. LogisticRegressionModel model2 = lr.fit(training, paramMapCombined); - System.out.println("Model 2 was fit using parameters: " + model2.fittingParamMap()); + System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap()); // Prepare test documents. List localTest = Lists.newArrayList( diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py index 19391506463f0..1456c87312841 100755 --- a/examples/src/main/python/kmeans.py +++ b/examples/src/main/python/kmeans.py @@ -68,14 +68,14 @@ def closestPoint(p, centers): closest = data.map( lambda p: (closestPoint(p, kPoints), (p, 1))) pointStats = closest.reduceByKey( - lambda (x1, y1), (x2, y2): (x1 + x2, y1 + y2)) + lambda (p1, c1), (p2, c2): (p1 + p2, c1 + c2)) newPoints = pointStats.map( - lambda xy: (xy[0], xy[1][0] / xy[1][1])).collect() + lambda st: (st[0], st[1][0] / st[1][1])).collect() - tempDist = sum(np.sum((kPoints[x] - y) ** 2) for (x, y) in newPoints) + tempDist = sum(np.sum((kPoints[iK] - p) ** 2) for (iK, p) in newPoints) - for (x, y) in newPoints: - kPoints[x] = y + for (iK, p) in newPoints: + kPoints[iK] = p print("Final centers: " + str(kPoints)) diff --git a/examples/src/main/python/mllib/kmeans.py b/examples/src/main/python/mllib/kmeans.py index f901a87fa63ac..002fc75799648 100755 --- a/examples/src/main/python/mllib/kmeans.py +++ b/examples/src/main/python/mllib/kmeans.py @@ -43,4 +43,5 @@ def parseVector(line): k = int(sys.argv[2]) model = KMeans.train(data, k) print("Final centers: " + str(model.clusterCenters)) + print("Total Cost: " + str(model.computeCost(data))) sc.stop() diff --git a/examples/src/main/r/dataframe.R b/examples/src/main/r/dataframe.R new file mode 100644 index 0000000000000..53b817144f6ac --- /dev/null +++ b/examples/src/main/r/dataframe.R @@ -0,0 +1,54 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(SparkR) + +# Initialize SparkContext and SQLContext +sc <- sparkR.init(appName="SparkR-DataFrame-example") +sqlContext <- sparkRSQL.init(sc) + +# Create a simple local data.frame +localDF <- data.frame(name=c("John", "Smith", "Sarah"), age=c(19, 23, 18)) + +# Convert local data frame to a SparkR DataFrame +df <- createDataFrame(sqlContext, localDF) + +# Print its schema +printSchema(df) +# root +# |-- name: string (nullable = true) +# |-- age: double (nullable = true) + +# Create a DataFrame from a JSON file +path <- file.path(Sys.getenv("SPARK_HOME"), "examples/src/main/resources/people.json") +peopleDF <- jsonFile(sqlContext, path) +printSchema(peopleDF) + +# Register this DataFrame as a table. +registerTempTable(peopleDF, "people") + +# SQL statements can be run by using the sql methods provided by sqlContext +teenagers <- sql(sqlContext, "SELECT name FROM people WHERE age >= 13 AND age <= 19") + +# Call collect to get a local data.frame +teenagersLocalDF <- collect(teenagers) + +# Print the teenagers in our dataset +print(teenagersLocalDF) + +# Stop the SparkContext now +sparkR.stop() diff --git a/examples/src/main/r/kmeans.R b/examples/src/main/r/kmeans.R deleted file mode 100644 index 6e6b5cb93789c..0000000000000 --- a/examples/src/main/r/kmeans.R +++ /dev/null @@ -1,93 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -library(SparkR) - -# Logistic regression in Spark. -# Note: unlike the example in Scala, a point here is represented as a vector of -# doubles. - -parseVectors <- function(lines) { - lines <- strsplit(as.character(lines) , " ", fixed = TRUE) - list(matrix(as.numeric(unlist(lines)), ncol = length(lines[[1]]))) -} - -dist.fun <- function(P, C) { - apply( - C, - 1, - function(x) { - colSums((t(P) - x)^2) - } - ) -} - -closestPoint <- function(P, C) { - max.col(-dist.fun(P, C)) -} -# Main program - -args <- commandArgs(trailing = TRUE) - -if (length(args) != 3) { - print("Usage: kmeans ") - q("no") -} - -sc <- sparkR.init(appName = "RKMeans") -K <- as.integer(args[[2]]) -convergeDist <- as.double(args[[3]]) - -lines <- textFile(sc, args[[1]]) -points <- cache(lapplyPartition(lines, parseVectors)) -# kPoints <- take(points, K) -kPoints <- do.call(rbind, takeSample(points, FALSE, K, 16189L)) -tempDist <- 1.0 - -while (tempDist > convergeDist) { - closest <- lapplyPartition( - lapply(points, - function(p) { - cp <- closestPoint(p, kPoints); - mapply(list, unique(cp), split.data.frame(cbind(1, p), cp), SIMPLIFY=FALSE) - }), - function(x) {do.call(c, x) - }) - - pointStats <- reduceByKey(closest, - function(p1, p2) { - t(colSums(rbind(p1, p2))) - }, - 2L) - - newPoints <- do.call( - rbind, - collect(lapply(pointStats, - function(tup) { - point.sum <- tup[[2]][, -1] - point.count <- tup[[2]][, 1] - point.sum/point.count - }))) - - D <- dist.fun(kPoints, newPoints) - tempDist <- sum(D[cbind(1:3, max.col(-D))]) - kPoints <- newPoints - cat("Finished iteration (delta = ", tempDist, ")\n") -} - -cat("Final centers:\n") -writeLines(unlist(lapply(kPoints, paste, collapse = " "))) diff --git a/examples/src/main/r/linear_solver_mnist.R b/examples/src/main/r/linear_solver_mnist.R deleted file mode 100644 index c864a4232d010..0000000000000 --- a/examples/src/main/r/linear_solver_mnist.R +++ /dev/null @@ -1,107 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Instructions: https://github.com/amplab-extras/SparkR-pkg/wiki/SparkR-Example:-Digit-Recognition-on-EC2 - -library(SparkR) -library(Matrix) - -args <- commandArgs(trailing = TRUE) - -# number of random features; default to 1100 -D <- ifelse(length(args) > 0, as.integer(args[[1]]), 1100) -# number of partitions for training dataset -trainParts <- 12 -# dimension of digits -d <- 784 -# number of test examples -NTrain <- 60000 -# number of training examples -NTest <- 10000 -# scale of features -gamma <- 4e-4 - -sc <- sparkR.init(appName = "SparkR-LinearSolver") - -# You can also use HDFS path to speed things up: -# hdfs:///train-mnist-dense-with-labels.data -file <- textFile(sc, "/data/train-mnist-dense-with-labels.data", trainParts) - -W <- gamma * matrix(nrow=D, ncol=d, data=rnorm(D*d)) -b <- 2 * pi * matrix(nrow=D, ncol=1, data=runif(D)) -broadcastW <- broadcast(sc, W) -broadcastB <- broadcast(sc, b) - -includePackage(sc, Matrix) -numericLines <- lapplyPartitionsWithIndex(file, - function(split, part) { - matList <- sapply(part, function(line) { - as.numeric(strsplit(line, ",", fixed=TRUE)[[1]]) - }, simplify=FALSE) - mat <- Matrix(ncol=d+1, data=unlist(matList, F, F), - sparse=T, byrow=T) - mat - }) - -featureLabels <- cache(lapplyPartition( - numericLines, - function(part) { - label <- part[,1] - mat <- part[,-1] - ones <- rep(1, nrow(mat)) - features <- cos( - mat %*% t(value(broadcastW)) + (matrix(ncol=1, data=ones) %*% t(value(broadcastB)))) - onesMat <- Matrix(ones) - featuresPlus <- cBind(features, onesMat) - labels <- matrix(nrow=nrow(mat), ncol=10, data=-1) - for (i in 1:nrow(mat)) { - labels[i, label[i]] <- 1 - } - list(label=labels, features=featuresPlus) - })) - -FTF <- Reduce("+", collect(lapplyPartition(featureLabels, - function(part) { - t(part$features) %*% part$features - }), flatten=F)) - -FTY <- Reduce("+", collect(lapplyPartition(featureLabels, - function(part) { - t(part$features) %*% part$label - }), flatten=F)) - -# solve for the coefficient matrix -C <- solve(FTF, FTY) - -test <- Matrix(as.matrix(read.csv("/data/test-mnist-dense-with-labels.data", - header=F), sparse=T)) -testData <- test[,-1] -testLabels <- matrix(ncol=1, test[,1]) - -err <- 0 - -# contstruct the feature maps for all examples from this digit -featuresTest <- cos(testData %*% t(value(broadcastW)) + - (matrix(ncol=1, data=rep(1, NTest)) %*% t(value(broadcastB)))) -featuresTest <- cBind(featuresTest, Matrix(rep(1, NTest))) - -# extract the one vs. all assignment -results <- featuresTest %*% C -labelsGot <- apply(results, 1, which.max) -err <- sum(testLabels != labelsGot) / nrow(testLabels) - -cat("\nFinished running. The error rate is: ", err, ".\n") diff --git a/examples/src/main/r/logistic_regression.R b/examples/src/main/r/logistic_regression.R deleted file mode 100644 index 2a86aa98160d3..0000000000000 --- a/examples/src/main/r/logistic_regression.R +++ /dev/null @@ -1,62 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -library(SparkR) - -args <- commandArgs(trailing = TRUE) - -if (length(args) != 3) { - print("Usage: logistic_regression ") - q("no") -} - -# Initialize Spark context -sc <- sparkR.init(appName = "LogisticRegressionR") -iterations <- as.integer(args[[2]]) -D <- as.integer(args[[3]]) - -readPartition <- function(part){ - part = strsplit(part, " ", fixed = T) - list(matrix(as.numeric(unlist(part)), ncol = length(part[[1]]))) -} - -# Read data points and convert each partition to a matrix -points <- cache(lapplyPartition(textFile(sc, args[[1]]), readPartition)) - -# Initialize w to a random value -w <- runif(n=D, min = -1, max = 1) -cat("Initial w: ", w, "\n") - -# Compute logistic regression gradient for a matrix of data points -gradient <- function(partition) { - partition = partition[[1]] - Y <- partition[, 1] # point labels (first column of input file) - X <- partition[, -1] # point coordinates - - # For each point (x, y), compute gradient function - dot <- X %*% w - logit <- 1 / (1 + exp(-Y * dot)) - grad <- t(X) %*% ((logit - 1) * Y) - list(grad) -} - -for (i in 1:iterations) { - cat("On iteration ", i, "\n") - w <- w - reduce(lapplyPartition(points, gradient), "+") -} - -cat("Final w: ", w, "\n") diff --git a/examples/src/main/r/pi.R b/examples/src/main/r/pi.R deleted file mode 100644 index aa7a833e147a0..0000000000000 --- a/examples/src/main/r/pi.R +++ /dev/null @@ -1,46 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -library(SparkR) - -args <- commandArgs(trailing = TRUE) - -sc <- sparkR.init(appName = "PiR") - -slices <- ifelse(length(args) > 1, as.integer(args[[2]]), 2) - -n <- 100000 * slices - -piFunc <- function(elem) { - rands <- runif(n = 2, min = -1, max = 1) - val <- ifelse((rands[1]^2 + rands[2]^2) < 1, 1.0, 0.0) - val -} - - -piFuncVec <- function(elems) { - message(length(elems)) - rands1 <- runif(n = length(elems), min = -1, max = 1) - rands2 <- runif(n = length(elems), min = -1, max = 1) - val <- ifelse((rands1^2 + rands2^2) < 1, 1.0, 0.0) - sum(val) -} - -rdd <- parallelize(sc, 1:n, slices) -count <- reduce(lapplyPartition(rdd, piFuncVec), sum) -cat("Pi is roughly", 4.0 * count / n, "\n") -cat("Num elements in RDD ", count(rdd), "\n") diff --git a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala index f4684b42b5d41..849887d23c9cf 100644 --- a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala +++ b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala @@ -28,7 +28,19 @@ object HBaseTest { def main(args: Array[String]) { val sparkConf = new SparkConf().setAppName("HBaseTest") val sc = new SparkContext(sparkConf) + + // please ensure HBASE_CONF_DIR is on classpath of spark driver + // e.g: set it through spark.driver.extraClassPath property + // in spark-defaults.conf or through --driver-class-path + // command line option of spark-submit + val conf = HBaseConfiguration.create() + + if (args.length < 1) { + System.err.println("Usage: HBaseTest ") + System.exit(1) + } + // Other options for configuring scan behavior are available. More information available at // http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html conf.set(TableInputFormat.INPUT_TABLE, args(0)) diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala index f73eac1e2b906..04fc0a033014a 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala @@ -99,7 +99,7 @@ object LocalKMeans { var pointStats = mappings.map { pair => pair._2.reduceLeft [(Int, (Vector[Double], Int))] { - case ((id1, (x1, y1)), (id2, (x2, y2))) => (id1, (x1 + x2, y1 + y2)) + case ((id1, (p1, c1)), (id2, (p2, c2))) => (id1, (p1 + p2, c1 + c2)) } } diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala index 48e8d11cdf95b..b514d9123f5e7 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala @@ -79,7 +79,7 @@ object SparkKMeans { while(tempDist > convergeDist) { val closest = data.map (p => (closestPoint(p, kPoints), (p, 1))) - val pointStats = closest.reduceByKey{case ((x1, y1), (x2, y2)) => (x1 + x2, y1 + y2)} + val pointStats = closest.reduceByKey{case ((p1, c1), (p2, c2)) => (p1 + p2, c1 + c2)} val newPoints = pointStats.map {pair => (pair._1, pair._2._1 * (1.0 / pair._2._2))}.collectAsMap() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala index 9002e99d82ad3..8340d91101ab3 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala @@ -276,16 +276,14 @@ object DecisionTreeExample { // Get the trained Decision Tree from the fitted PipelineModel algo match { case "classification" => - val treeModel = pipelineModel.getModel[DecisionTreeClassificationModel]( - dt.asInstanceOf[DecisionTreeClassifier]) + val treeModel = pipelineModel.stages.last.asInstanceOf[DecisionTreeClassificationModel] if (treeModel.numNodes < 20) { println(treeModel.toDebugString) // Print full model. } else { println(treeModel) // Print model summary. } case "regression" => - val treeModel = pipelineModel.getModel[DecisionTreeRegressionModel]( - dt.asInstanceOf[DecisionTreeRegressor]) + val treeModel = pipelineModel.stages.last.asInstanceOf[DecisionTreeRegressionModel] if (treeModel.numNodes < 20) { println(treeModel.toDebugString) // Print full model. } else { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala index 2245fa429fda3..2a2d0677272a0 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala @@ -18,13 +18,12 @@ package org.apache.spark.examples.ml import org.apache.spark.{SparkConf, SparkContext} -import org.apache.spark.ml.classification.{Classifier, ClassifierParams, ClassificationModel} -import org.apache.spark.ml.param.{Params, IntParam, ParamMap} +import org.apache.spark.ml.classification.{ClassificationModel, Classifier, ClassifierParams} +import org.apache.spark.ml.param.{IntParam, ParamMap} import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.sql.{DataFrame, Row, SQLContext} - /** * A simple example demonstrating how to write your own learning algorithm using Estimator, * Transformer, and other abstractions. @@ -99,7 +98,7 @@ private trait MyLogisticRegressionParams extends ClassifierParams { * class since the maxIter parameter is only used during training (not in the Model). */ val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") - def getMaxIter: Int = getOrDefault(maxIter) + def getMaxIter: Int = $(maxIter) } /** @@ -117,18 +116,16 @@ private class MyLogisticRegression def setMaxIter(value: Int): this.type = set(maxIter, value) // This method is used by fit() - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): MyLogisticRegressionModel = { + override protected def train(dataset: DataFrame): MyLogisticRegressionModel = { // Extract columns from data using helper method. - val oldDataset = extractLabeledPoints(dataset, paramMap) + val oldDataset = extractLabeledPoints(dataset) // Do learning to estimate the weight vector. val numFeatures = oldDataset.take(1)(0).features.size val weights = Vectors.zeros(numFeatures) // Learning would happen here. // Create a model, and return it. - new MyLogisticRegressionModel(this, paramMap, weights) + new MyLogisticRegressionModel(this, weights) } } @@ -139,7 +136,6 @@ private class MyLogisticRegression */ private class MyLogisticRegressionModel( override val parent: MyLogisticRegression, - override val fittingParamMap: ParamMap, val weights: Vector) extends ClassificationModel[Vector, MyLogisticRegressionModel] with MyLogisticRegressionParams { @@ -176,9 +172,7 @@ private class MyLogisticRegressionModel( * * This is used for the default implementation of [[transform()]]. */ - override protected def copy(): MyLogisticRegressionModel = { - val m = new MyLogisticRegressionModel(parent, fittingParamMap, weights) - Params.inheritValues(extractParamMap(), this, m) - m + override def copy(extra: ParamMap): MyLogisticRegressionModel = { + copyValues(new MyLogisticRegressionModel(parent, weights), extra) } } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala index 5fccb142d4c3d..c5899b6683c79 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala @@ -201,14 +201,14 @@ object GBTExample { // Get the trained GBT from the fitted PipelineModel algo match { case "classification" => - val rfModel = pipelineModel.getModel[GBTClassificationModel](dt.asInstanceOf[GBTClassifier]) + val rfModel = pipelineModel.stages.last.asInstanceOf[GBTClassificationModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { println(rfModel) // Print model summary. } case "regression" => - val rfModel = pipelineModel.getModel[GBTRegressionModel](dt.asInstanceOf[GBTRegressor]) + val rfModel = pipelineModel.stages.last.asInstanceOf[GBTRegressionModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala index 9b909324ec82a..7f88d2681bcaa 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala @@ -209,16 +209,14 @@ object RandomForestExample { // Get the trained Random Forest from the fitted PipelineModel algo match { case "classification" => - val rfModel = pipelineModel.getModel[RandomForestClassificationModel]( - dt.asInstanceOf[RandomForestClassifier]) + val rfModel = pipelineModel.stages.last.asInstanceOf[RandomForestClassificationModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { println(rfModel) // Print model summary. } case "regression" => - val rfModel = pipelineModel.getModel[RandomForestRegressionModel]( - dt.asInstanceOf[RandomForestRegressor]) + val rfModel = pipelineModel.stages.last.asInstanceOf[RandomForestRegressionModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala index bf805149d0af6..e8a991f50e338 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala @@ -63,7 +63,7 @@ object SimpleParamsExample { // we can view the parameters it used during fit(). // This prints the parameter (name: value) pairs, where names are unique IDs for this // LogisticRegression instance. - println("Model 1 was fit using parameters: " + model1.fittingParamMap) + println("Model 1 was fit using parameters: " + model1.parent.extractParamMap()) // We may alternatively specify parameters using a ParamMap, // which supports several methods for specifying parameters. @@ -78,7 +78,7 @@ object SimpleParamsExample { // Now learn a new model using the paramMapCombined parameters. // paramMapCombined overrides all parameters set earlier via lr.set* methods. val model2 = lr.fit(training.toDF(), paramMapCombined) - println("Model 2 was fit using parameters: " + model2.fittingParamMap) + println("Model 2 was fit using parameters: " + model2.parent.extractParamMap()) // Prepare test data. val test = sc.parallelize(Seq( diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala index 2edea9b5b69ba..43c1b865b64a1 100644 --- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala +++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala @@ -57,11 +57,11 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging before(beforeFunction()) - test("flume polling test") { + ignore("flume polling test") { testMultipleTimes(testFlumePolling) } - test("flume polling test multiple hosts") { + ignore("flume polling test multiple hosts") { testMultipleTimes(testFlumePollingMultipleHost) } diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml index f695cff410a18..243ce6eaca658 100644 --- a/external/kafka/pom.xml +++ b/external/kafka/pom.xml @@ -44,7 +44,7 @@ org.apache.kafka kafka_${scala.binary.version} - 0.8.1.1 + 0.8.2.1 com.sun.jmx diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala index 1b1fc8051d052..6715aede7928a 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala @@ -17,7 +17,6 @@ package org.apache.spark.streaming.kafka - import scala.annotation.tailrec import scala.collection.mutable import scala.reflect.{classTag, ClassTag} @@ -27,10 +26,10 @@ import kafka.message.MessageAndMetadata import kafka.serializer.Decoder import org.apache.spark.{Logging, SparkException} -import org.apache.spark.rdd.RDD -import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset import org.apache.spark.streaming.{StreamingContext, Time} import org.apache.spark.streaming.dstream._ +import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset +import org.apache.spark.streaming.scheduler.InputInfo /** * A stream of {@link org.apache.spark.streaming.kafka.KafkaRDD} where @@ -117,6 +116,11 @@ class DirectKafkaInputDStream[ val rdd = KafkaRDD[K, V, U, T, R]( context.sparkContext, kafkaParams, currentOffsets, untilOffsets, messageHandler) + // Report the record number of this batch interval to InputInfoTracker. + val numRecords = rdd.offsetRanges.map(r => r.untilOffset - r.fromOffset).sum + val inputInfo = InputInfo(id, numRecords) + ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo) + currentOffsets = untilOffsets.map(kv => kv._1 -> kv._2.offset) Some(rdd) } diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala index bd767031c1849..6cf254a7b69cb 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala @@ -20,9 +20,10 @@ package org.apache.spark.streaming.kafka import scala.util.control.NonFatal import scala.util.Random import scala.collection.mutable.ArrayBuffer +import scala.collection.JavaConverters._ import java.util.Properties import kafka.api._ -import kafka.common.{ErrorMapping, OffsetMetadataAndError, TopicAndPartition} +import kafka.common.{ErrorMapping, OffsetAndMetadata, OffsetMetadataAndError, TopicAndPartition} import kafka.consumer.{ConsumerConfig, SimpleConsumer} import org.apache.spark.SparkException @@ -220,12 +221,22 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { // https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol#AGuideToTheKafkaProtocol-OffsetCommit/FetchAPI // scalastyle:on + // this 0 here indicates api version, in this case the original ZK backed api. + private def defaultConsumerApiVersion: Short = 0 + /** Requires Kafka >= 0.8.1.1 */ def getConsumerOffsets( groupId: String, topicAndPartitions: Set[TopicAndPartition] + ): Either[Err, Map[TopicAndPartition, Long]] = + getConsumerOffsets(groupId, topicAndPartitions, defaultConsumerApiVersion) + + def getConsumerOffsets( + groupId: String, + topicAndPartitions: Set[TopicAndPartition], + consumerApiVersion: Short ): Either[Err, Map[TopicAndPartition, Long]] = { - getConsumerOffsetMetadata(groupId, topicAndPartitions).right.map { r => + getConsumerOffsetMetadata(groupId, topicAndPartitions, consumerApiVersion).right.map { r => r.map { kv => kv._1 -> kv._2.offset } @@ -236,9 +247,16 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { def getConsumerOffsetMetadata( groupId: String, topicAndPartitions: Set[TopicAndPartition] + ): Either[Err, Map[TopicAndPartition, OffsetMetadataAndError]] = + getConsumerOffsetMetadata(groupId, topicAndPartitions, defaultConsumerApiVersion) + + def getConsumerOffsetMetadata( + groupId: String, + topicAndPartitions: Set[TopicAndPartition], + consumerApiVersion: Short ): Either[Err, Map[TopicAndPartition, OffsetMetadataAndError]] = { var result = Map[TopicAndPartition, OffsetMetadataAndError]() - val req = OffsetFetchRequest(groupId, topicAndPartitions.toSeq) + val req = OffsetFetchRequest(groupId, topicAndPartitions.toSeq, consumerApiVersion) val errs = new Err withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer => val resp = consumer.fetchOffsets(req) @@ -266,24 +284,39 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { def setConsumerOffsets( groupId: String, offsets: Map[TopicAndPartition, Long] + ): Either[Err, Map[TopicAndPartition, Short]] = + setConsumerOffsets(groupId, offsets, defaultConsumerApiVersion) + + def setConsumerOffsets( + groupId: String, + offsets: Map[TopicAndPartition, Long], + consumerApiVersion: Short ): Either[Err, Map[TopicAndPartition, Short]] = { - setConsumerOffsetMetadata(groupId, offsets.map { kv => - kv._1 -> OffsetMetadataAndError(kv._2) - }) + val meta = offsets.map { kv => + kv._1 -> OffsetAndMetadata(kv._2) + } + setConsumerOffsetMetadata(groupId, meta, consumerApiVersion) } /** Requires Kafka >= 0.8.1.1 */ def setConsumerOffsetMetadata( groupId: String, - metadata: Map[TopicAndPartition, OffsetMetadataAndError] + metadata: Map[TopicAndPartition, OffsetAndMetadata] + ): Either[Err, Map[TopicAndPartition, Short]] = + setConsumerOffsetMetadata(groupId, metadata, defaultConsumerApiVersion) + + def setConsumerOffsetMetadata( + groupId: String, + metadata: Map[TopicAndPartition, OffsetAndMetadata], + consumerApiVersion: Short ): Either[Err, Map[TopicAndPartition, Short]] = { var result = Map[TopicAndPartition, Short]() - val req = OffsetCommitRequest(groupId, metadata) + val req = OffsetCommitRequest(groupId, metadata, consumerApiVersion) val errs = new Err val topicAndPartitions = metadata.keySet withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer => val resp = consumer.commitOffsets(req) - val respMap = resp.requestInfo + val respMap = resp.commitStatus val needed = topicAndPartitions.diff(result.keySet) needed.foreach { tp: TopicAndPartition => respMap.get(tp).foreach { err: Short => diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala index 13e9475065979..6dc4e9517d5a4 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala @@ -29,10 +29,12 @@ import scala.language.postfixOps import scala.util.control.NonFatal import kafka.admin.AdminUtils +import kafka.api.Request +import kafka.common.TopicAndPartition import kafka.producer.{KeyedMessage, Producer, ProducerConfig} import kafka.serializer.StringEncoder import kafka.server.{KafkaConfig, KafkaServer} -import kafka.utils.ZKStringSerializer +import kafka.utils.{ZKStringSerializer, ZkUtils} import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer} import org.I0Itec.zkclient.ZkClient @@ -227,12 +229,35 @@ private class KafkaTestUtils extends Logging { tryAgain(1) } - private def waitUntilMetadataIsPropagated(topic: String, partition: Int): Unit = { + /** Wait until the leader offset for the given topic/partition equals the specified offset */ + def waitUntilLeaderOffset( + topic: String, + partition: Int, + offset: Long): Unit = { eventually(Time(10000), Time(100)) { + val kc = new KafkaCluster(Map("metadata.broker.list" -> brokerAddress)) + val tp = TopicAndPartition(topic, partition) + val llo = kc.getLatestLeaderOffsets(Set(tp)).right.get.apply(tp).offset assert( - server.apis.metadataCache.containsTopicAndPartition(topic, partition), - s"Partition [$topic, $partition] metadata not propagated after timeout" - ) + llo == offset, + s"$topic $partition $offset not reached after timeout") + } + } + + private def waitUntilMetadataIsPropagated(topic: String, partition: Int): Unit = { + def isPropagated = server.apis.metadataCache.getPartitionInfo(topic, partition) match { + case Some(partitionState) => + val leaderAndInSyncReplicas = partitionState.leaderIsrAndControllerEpoch.leaderAndIsr + + ZkUtils.getLeaderForPartition(zkClient, topic, partition).isDefined && + Request.isValidBrokerId(leaderAndInSyncReplicas.leader) && + leaderAndInSyncReplicas.isr.size >= 1 + + case _ => + false + } + eventually(Time(10000), Time(100)) { + assert(isPropagated, s"Partition [$topic, $partition] metadata not propagated after timeout") } } diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala index 0721ddaf7055a..d7cf500577c2a 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala @@ -31,6 +31,7 @@ import kafka.message.MessageAndMetadata import kafka.serializer.{DefaultDecoder, Decoder, StringDecoder} import org.apache.spark.api.java.function.{Function => JFunction} +import org.apache.spark.streaming.util.WriteAheadLogUtils import org.apache.spark.{SparkContext, SparkException} import org.apache.spark.annotation.Experimental import org.apache.spark.rdd.RDD @@ -80,7 +81,7 @@ object KafkaUtils { topics: Map[String, Int], storageLevel: StorageLevel ): ReceiverInputDStream[(K, V)] = { - val walEnabled = ssc.conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false) + val walEnabled = WriteAheadLogUtils.enableReceiverLog(ssc.conf) new KafkaInputDStream[K, V, U, T](ssc, kafkaParams, topics, walEnabled, storageLevel) } diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java index a9dc6e50613ca..5cf379635354f 100644 --- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java @@ -72,6 +72,9 @@ public void testKafkaRDD() throws InterruptedException { HashMap kafkaParams = new HashMap(); kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress()); + kafkaTestUtils.waitUntilLeaderOffset(topic1, 0, topic1data.length); + kafkaTestUtils.waitUntilLeaderOffset(topic2, 0, topic2data.length); + OffsetRange[] offsetRanges = { OffsetRange.create(topic1, 0, 0, 1), OffsetRange.create(topic2, 0, 0, 1) diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala index 415730f5559c5..b6d314dfc7783 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala @@ -18,6 +18,7 @@ package org.apache.spark.streaming.kafka import java.io.File +import java.util.concurrent.atomic.AtomicLong import scala.collection.mutable import scala.collection.mutable.ArrayBuffer @@ -34,6 +35,7 @@ import org.apache.spark.{Logging, SparkConf, SparkContext} import org.apache.spark.rdd.RDD import org.apache.spark.streaming.{Milliseconds, StreamingContext, Time} import org.apache.spark.streaming.dstream.DStream +import org.apache.spark.streaming.scheduler._ import org.apache.spark.util.Utils class DirectKafkaStreamSuite @@ -290,7 +292,6 @@ class DirectKafkaStreamSuite }, "Recovered ranges are not the same as the ones generated" ) - // Restart context, give more data and verify the total at the end // If the total is write that means each records has been received only once ssc.start() @@ -301,6 +302,44 @@ class DirectKafkaStreamSuite ssc.stop() } + test("Direct Kafka stream report input information") { + val topic = "report-test" + val data = Map("a" -> 7, "b" -> 9) + kafkaTestUtils.createTopic(topic) + kafkaTestUtils.sendMessages(topic, data) + + val totalSent = data.values.sum + val kafkaParams = Map( + "metadata.broker.list" -> kafkaTestUtils.brokerAddress, + "auto.offset.reset" -> "smallest" + ) + + import DirectKafkaStreamSuite._ + ssc = new StreamingContext(sparkConf, Milliseconds(200)) + val collector = new InputInfoCollector + ssc.addStreamingListener(collector) + + val stream = withClue("Error creating direct stream") { + KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder]( + ssc, kafkaParams, Set(topic)) + } + + val allReceived = new ArrayBuffer[(String, String)] + + stream.foreachRDD { rdd => allReceived ++= rdd.collect() } + ssc.start() + eventually(timeout(20000.milliseconds), interval(200.milliseconds)) { + assert(allReceived.size === totalSent, + "didn't get expected number of messages, messages:\n" + allReceived.mkString("\n")) + + // Calculate all the record number collected in the StreamingListener. + assert(collector.numRecordsSubmitted.get() === totalSent) + assert(collector.numRecordsStarted.get() === totalSent) + assert(collector.numRecordsCompleted.get() === totalSent) + } + ssc.stop() + } + /** Get the generated offset ranges from the DirectKafkaStream */ private def getOffsetRanges[K, V]( kafkaStream: DStream[(K, V)]): Seq[(Time, Array[OffsetRange])] = { @@ -313,4 +352,22 @@ class DirectKafkaStreamSuite object DirectKafkaStreamSuite { val collectedData = new mutable.ArrayBuffer[String]() var total = -1L + + class InputInfoCollector extends StreamingListener { + val numRecordsSubmitted = new AtomicLong(0L) + val numRecordsStarted = new AtomicLong(0L) + val numRecordsCompleted = new AtomicLong(0L) + + override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted): Unit = { + numRecordsSubmitted.addAndGet(batchSubmitted.batchInfo.numRecords) + } + + override def onBatchStarted(batchStarted: StreamingListenerBatchStarted): Unit = { + numRecordsStarted.addAndGet(batchStarted.batchInfo.numRecords) + } + + override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted): Unit = { + numRecordsCompleted.addAndGet(batchCompleted.batchInfo.numRecords) + } + } } diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala index 7d26ce50875b3..39c3fb448ff57 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala @@ -53,14 +53,15 @@ class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { } test("basic usage") { - val topic = "topicbasic" + val topic = s"topicbasic-${Random.nextInt}" kafkaTestUtils.createTopic(topic) val messages = Set("the", "quick", "brown", "fox") kafkaTestUtils.sendMessages(topic, messages.toArray) - val kafkaParams = Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress, - "group.id" -> s"test-consumer-${Random.nextInt(10000)}") + "group.id" -> s"test-consumer-${Random.nextInt}") + + kafkaTestUtils.waitUntilLeaderOffset(topic, 0, messages.size) val offsetRanges = Array(OffsetRange(topic, 0, 0, messages.size)) @@ -73,27 +74,38 @@ class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { test("iterator boundary conditions") { // the idea is to find e.g. off-by-one errors between what kafka has available and the rdd - val topic = "topic1" + val topic = s"topicboundary-${Random.nextInt}" val sent = Map("a" -> 5, "b" -> 3, "c" -> 10) kafkaTestUtils.createTopic(topic) val kafkaParams = Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress, - "group.id" -> s"test-consumer-${Random.nextInt(10000)}") + "group.id" -> s"test-consumer-${Random.nextInt}") val kc = new KafkaCluster(kafkaParams) // this is the "lots of messages" case kafkaTestUtils.sendMessages(topic, sent) + val sentCount = sent.values.sum + kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sentCount) + // rdd defined from leaders after sending messages, should get the number sent val rdd = getRdd(kc, Set(topic)) assert(rdd.isDefined) - assert(rdd.get.count === sent.values.sum, "didn't get all sent messages") - val ranges = rdd.get.asInstanceOf[HasOffsetRanges] - .offsetRanges.map(o => TopicAndPartition(o.topic, o.partition) -> o.untilOffset).toMap + val ranges = rdd.get.asInstanceOf[HasOffsetRanges].offsetRanges + val rangeCount = ranges.map(o => o.untilOffset - o.fromOffset).sum - kc.setConsumerOffsets(kafkaParams("group.id"), ranges) + assert(rangeCount === sentCount, "offset range didn't include all sent messages") + assert(rdd.get.count === sentCount, "didn't get all sent messages") + + val rangesMap = ranges.map(o => TopicAndPartition(o.topic, o.partition) -> o.untilOffset).toMap + + // make sure consumer offsets are committed before the next getRdd call + kc.setConsumerOffsets(kafkaParams("group.id"), rangesMap).fold( + err => throw new Exception(err.mkString("\n")), + _ => () + ) // this is the "0 messages" case val rdd2 = getRdd(kc, Set(topic)) @@ -101,6 +113,8 @@ class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { val sentOnlyOne = Map("d" -> 1) kafkaTestUtils.sendMessages(topic, sentOnlyOne) + kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sentCount + 1) + assert(rdd2.isDefined) assert(rdd2.get.count === 0, "got messages when there shouldn't be any") diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala index 86f611d55aa8a..7edd627b20918 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala @@ -372,6 +372,31 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali PageRank.runUntilConvergence(graph, tol, resetProb) } + + /** + * Run personalized PageRank for a given vertex, such that all random walks + * are started relative to the source node. + * + * @see [[org.apache.spark.graphx.lib.PageRank$#runUntilConvergenceWithOptions]] + */ + def personalizedPageRank(src: VertexId, tol: Double, + resetProb: Double = 0.15) : Graph[Double, Double] = { + PageRank.runUntilConvergenceWithOptions(graph, tol, resetProb, Some(src)) + } + + /** + * Run Personalized PageRank for a fixed number of iterations with + * with all iterations originating at the source node + * returning a graph with vertex attributes + * containing the PageRank and edge attributes the normalized edge weight. + * + * @see [[org.apache.spark.graphx.lib.PageRank$#runWithOptions]] + */ + def staticPersonalizedPageRank(src: VertexId, numIter: Int, + resetProb: Double = 0.15) : Graph[Double, Double] = { + PageRank.runWithOptions(graph, numIter, resetProb, Some(src)) + } + /** * Run PageRank for a fixed number of iterations returning a graph with vertex attributes * containing the PageRank and edge attributes the normalized edge weight. diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala index 042e366a29f58..bc974b2f04e70 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala @@ -18,6 +18,7 @@ package org.apache.spark.graphx.lib import scala.reflect.ClassTag +import scala.language.postfixOps import org.apache.spark.Logging import org.apache.spark.graphx._ @@ -60,6 +61,7 @@ import org.apache.spark.graphx._ */ object PageRank extends Logging { + /** * Run PageRank for a fixed number of iterations returning a graph * with vertex attributes containing the PageRank and edge @@ -74,10 +76,33 @@ object PageRank extends Logging { * * @return the graph containing with each vertex containing the PageRank and each edge * containing the normalized weight. + */ + def run[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED], numIter: Int, + resetProb: Double = 0.15): Graph[Double, Double] = + { + runWithOptions(graph, numIter, resetProb) + } + + /** + * Run PageRank for a fixed number of iterations returning a graph + * with vertex attributes containing the PageRank and edge + * attributes the normalized edge weight. + * + * @tparam VD the original vertex attribute (not used) + * @tparam ED the original edge attribute (not used) + * + * @param graph the graph on which to compute PageRank + * @param numIter the number of iterations of PageRank to run + * @param resetProb the random reset probability (alpha) + * @param srcId the source vertex for a Personalized Page Rank (optional) + * + * @return the graph containing with each vertex containing the PageRank and each edge + * containing the normalized weight. * */ - def run[VD: ClassTag, ED: ClassTag]( - graph: Graph[VD, ED], numIter: Int, resetProb: Double = 0.15): Graph[Double, Double] = + def runWithOptions[VD: ClassTag, ED: ClassTag]( + graph: Graph[VD, ED], numIter: Int, resetProb: Double = 0.15, + srcId: Option[VertexId] = None): Graph[Double, Double] = { // Initialize the PageRank graph with each edge attribute having // weight 1/outDegree and each vertex with attribute 1.0. @@ -89,6 +114,10 @@ object PageRank extends Logging { // Set the vertex attributes to the initial pagerank values .mapVertices( (id, attr) => resetProb ) + val personalized = srcId isDefined + val src: VertexId = srcId.getOrElse(-1L) + def delta(u: VertexId, v: VertexId):Double = { if (u == v) 1.0 else 0.0 } + var iteration = 0 var prevRankGraph: Graph[Double, Double] = null while (iteration < numIter) { @@ -103,8 +132,14 @@ object PageRank extends Logging { // that didn't receive a message. Requires a shuffle for broadcasting updated ranks to the // edge partitions. prevRankGraph = rankGraph + val rPrb = if (personalized) { + (src: VertexId ,id: VertexId) => resetProb * delta(src,id) + } else { + (src: VertexId, id: VertexId) => resetProb + } + rankGraph = rankGraph.joinVertices(rankUpdates) { - (id, oldRank, msgSum) => resetProb + (1.0 - resetProb) * msgSum + (id, oldRank, msgSum) => rPrb(src,id) + (1.0 - resetProb) * msgSum }.cache() rankGraph.edges.foreachPartition(x => {}) // also materializes rankGraph.vertices @@ -133,7 +168,29 @@ object PageRank extends Logging { * containing the normalized weight. */ def runUntilConvergence[VD: ClassTag, ED: ClassTag]( - graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15): Graph[Double, Double] = + graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15): Graph[Double, Double] = + { + runUntilConvergenceWithOptions(graph, tol, resetProb) + } + + /** + * Run a dynamic version of PageRank returning a graph with vertex attributes containing the + * PageRank and edge attributes containing the normalized edge weight. + * + * @tparam VD the original vertex attribute (not used) + * @tparam ED the original edge attribute (not used) + * + * @param graph the graph on which to compute PageRank + * @param tol the tolerance allowed at convergence (smaller => more accurate). + * @param resetProb the random reset probability (alpha) + * @param srcId the source vertex for a Personalized Page Rank (optional) + * + * @return the graph containing with each vertex containing the PageRank and each edge + * containing the normalized weight. + */ + def runUntilConvergenceWithOptions[VD: ClassTag, ED: ClassTag]( + graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15, + srcId: Option[VertexId] = None): Graph[Double, Double] = { // Initialize the pagerankGraph with each edge attribute // having weight 1/outDegree and each vertex with attribute 1.0. @@ -148,6 +205,10 @@ object PageRank extends Logging { .mapVertices( (id, attr) => (0.0, 0.0) ) .cache() + val personalized = srcId.isDefined + val src: VertexId = srcId.getOrElse(-1L) + + // Define the three functions needed to implement PageRank in the GraphX // version of Pregel def vertexProgram(id: VertexId, attr: (Double, Double), msgSum: Double): (Double, Double) = { @@ -156,7 +217,18 @@ object PageRank extends Logging { (newPR, newPR - oldPR) } - def sendMessage(edge: EdgeTriplet[(Double, Double), Double]): Iterator[(VertexId, Double)] = { + def personalizedVertexProgram(id: VertexId, attr: (Double, Double), + msgSum: Double): (Double, Double) = { + val (oldPR, lastDelta) = attr + var teleport = oldPR + val delta = if (src==id) 1.0 else 0.0 + teleport = oldPR*delta + + val newPR = teleport + (1.0 - resetProb) * msgSum + (newPR, newPR - oldPR) + } + + def sendMessage(edge: EdgeTriplet[(Double, Double), Double]) = { if (edge.srcAttr._2 > tol) { Iterator((edge.dstId, edge.srcAttr._2 * edge.attr)) } else { @@ -170,8 +242,17 @@ object PageRank extends Logging { val initialMessage = resetProb / (1.0 - resetProb) // Execute a dynamic version of Pregel. + val vp = if (personalized) { + (id: VertexId, attr: (Double, Double),msgSum: Double) => + personalizedVertexProgram(id, attr, msgSum) + } else { + (id: VertexId, attr: (Double, Double), msgSum: Double) => + vertexProgram(id, attr, msgSum) + } + Pregel(pagerankGraph, initialMessage, activeDirection = EdgeDirection.Out)( - vertexProgram, sendMessage, messageCombiner) + vp, sendMessage, messageCombiner) .mapVertices((vid, attr) => attr._1) } // end of deltaPageRank + } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala index 95804b07b1db0..3f3c9dfd7b3dd 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala @@ -92,6 +92,36 @@ class PageRankSuite extends FunSuite with LocalSparkContext { } } // end of test Star PageRank + test("Star PersonalPageRank") { + withSpark { sc => + val nVertices = 100 + val starGraph = GraphGenerators.starGraph(sc, nVertices).cache() + val resetProb = 0.15 + val errorTol = 1.0e-5 + + val staticRanks1 = starGraph.staticPersonalizedPageRank(0,numIter = 1, resetProb).vertices + val staticRanks2 = starGraph.staticPersonalizedPageRank(0,numIter = 2, resetProb) + .vertices.cache() + + // Static PageRank should only take 2 iterations to converge + val notMatching = staticRanks1.innerZipJoin(staticRanks2) { (vid, pr1, pr2) => + if (pr1 != pr2) 1 else 0 + }.map { case (vid, test) => test }.sum + assert(notMatching === 0) + + val staticErrors = staticRanks2.map { case (vid, pr) => + val correct = (vid > 0 && pr == resetProb) || + (vid == 0 && math.abs(pr - (resetProb + (1.0 - resetProb) * (resetProb * + (nVertices - 1)) )) < 1.0E-5) + if (!correct) 1 else 0 + } + assert(staticErrors.sum === 0) + + val dynamicRanks = starGraph.personalizedPageRank(0,0, resetProb).vertices.cache() + assert(compareRanks(staticRanks2, dynamicRanks) < errorTol) + } + } // end of test Star PageRank + test("Grid PageRank") { withSpark { sc => val rows = 10 @@ -128,4 +158,21 @@ class PageRankSuite extends FunSuite with LocalSparkContext { assert(compareRanks(staticRanks, dynamicRanks) < errorTol) } } + + test("Chain PersonalizedPageRank") { + withSpark { sc => + val chain1 = (0 until 9).map(x => (x, x + 1) ) + val rawEdges = sc.parallelize(chain1, 1).map { case (s,d) => (s.toLong, d.toLong) } + val chain = Graph.fromEdgeTuples(rawEdges, 1.0).cache() + val resetProb = 0.15 + val tol = 0.0001 + val numIter = 10 + val errorTol = 1.0e-1 + + val staticRanks = chain.staticPersonalizedPageRank(4, numIter, resetProb).vertices + val dynamicRanks = chain.personalizedPageRank(4, tol, resetProb).vertices + + assert(compareRanks(staticRanks, dynamicRanks) < errorTol) + } + } } diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java index a73c9c87e3126..7d387d406edae 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java @@ -190,6 +190,10 @@ private List buildSparkSubmitCommand(Map env) throws IOE firstNonEmptyValue(SparkLauncher.DRIVER_EXTRA_CLASSPATH, conf, props) : null; List cmd = buildJavaCommand(extraClassPath); + // Take Thrift Server as daemon + if (isThriftServer(mainClass)) { + addOptionString(cmd, System.getenv("SPARK_DAEMON_JAVA_OPTS")); + } addOptionString(cmd, System.getenv("SPARK_SUBMIT_OPTS")); addOptionString(cmd, System.getenv("SPARK_JAVA_OPTS")); @@ -201,7 +205,11 @@ private List buildSparkSubmitCommand(Map env) throws IOE // - SPARK_DRIVER_MEMORY env variable // - SPARK_MEM env variable // - default value (512m) - String memory = firstNonEmpty(firstNonEmptyValue(SparkLauncher.DRIVER_MEMORY, conf, props), + // Take Thrift Server as daemon + String tsMemory = + isThriftServer(mainClass) ? System.getenv("SPARK_DAEMON_MEMORY") : null; + String memory = firstNonEmpty(tsMemory, + firstNonEmptyValue(SparkLauncher.DRIVER_MEMORY, conf, props), System.getenv("SPARK_DRIVER_MEMORY"), System.getenv("SPARK_MEM"), DEFAULT_MEM); cmd.add("-Xms" + memory); cmd.add("-Xmx" + memory); @@ -292,6 +300,15 @@ private boolean isClientMode(Properties userProps) { (!userMaster.equals("yarn-cluster") && deployMode == null); } + /** + * Return whether the given main class represents a thrift server. + */ + private boolean isThriftServer(String mainClass) { + return (mainClass != null && + mainClass.equals("org.apache.spark.sql.hive.thriftserver.HiveThriftServer2")); + } + + private class OptionParser extends SparkSubmitOptionParser { @Override diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java index 8526d2e7cfa3f..229000087688f 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java @@ -69,8 +69,10 @@ class SparkSubmitOptionParser { // YARN-only options. protected final String ARCHIVES = "--archives"; protected final String EXECUTOR_CORES = "--executor-cores"; - protected final String QUEUE = "--queue"; + protected final String KEYTAB = "--keytab"; protected final String NUM_EXECUTORS = "--num-executors"; + protected final String PRINCIPAL = "--principal"; + protected final String QUEUE = "--queue"; /** * This is the canonical list of spark-submit options. Each entry in the array contains the @@ -96,11 +98,13 @@ class SparkSubmitOptionParser { { EXECUTOR_MEMORY }, { FILES }, { JARS }, + { KEYTAB }, { KILL_SUBMISSION }, { MASTER }, { NAME }, { NUM_EXECUTORS }, { PACKAGES }, + { PRINCIPAL }, { PROPERTIES_FILE }, { PROXY_USER }, { PY_FILES }, diff --git a/make-distribution.sh b/make-distribution.sh index cb65932b4abc0..1bfa9acb1fe6e 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -26,6 +26,7 @@ set -o pipefail set -e +set -x # Figure out where the Spark framework is installed SPARK_HOME="$(cd "`dirname "$0"`"; pwd)" @@ -57,7 +58,7 @@ while (( "$#" )); do --hadoop) echo "Error: '--hadoop' is no longer supported:" echo "Error: use Maven profiles and options -Dhadoop.version and -Dyarn.version instead." - echo "Error: Related profiles include hadoop-0.23, hdaoop-2.2, hadoop-2.3 and hadoop-2.4." + echo "Error: Related profiles include hadoop-2.2, hadoop-2.3 and hadoop-2.4." exit_with_usage ;; --with-yarn) @@ -126,7 +127,7 @@ if [ ! $(command -v "$MVN") ] ; then exit -1; fi -VERSION=$("$MVN" help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1) +VERSION=$("$MVN" help:evaluate -Dexpression=project.version $@ 2>/dev/null | grep -v "INFO" | tail -n 1) SCALA_VERSION=$("$MVN" help:evaluate -Dexpression=scala.binary.version $@ 2>/dev/null\ | grep -v "INFO"\ | tail -n 1) diff --git a/mllib/pom.xml b/mllib/pom.xml index 5dfab36c76907..a3c57ae26000b 100644 --- a/mllib/pom.xml +++ b/mllib/pom.xml @@ -109,6 +109,21 @@ test-jar test + + org.jpmml + pmml-model + 1.1.15 + + + com.sun.xml.fastinfoset + FastInfoset + + + com.sun.istack + istack-commons-runtime + + + diff --git a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala index d6b3503ebdd9a..7f3f3262a644f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala @@ -34,13 +34,16 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { * Fits a single model to the input data with optional parameters. * * @param dataset input dataset - * @param paramPairs Optional list of param pairs. - * These values override any specified in this Estimator's embedded ParamMap. + * @param firstParamPair the first param pair, overrides embedded params + * @param otherParamPairs other param pairs. These values override any specified in this + * Estimator's embedded ParamMap. * @return fitted model */ @varargs - def fit(dataset: DataFrame, paramPairs: ParamPair[_]*): M = { - val map = ParamMap(paramPairs: _*) + def fit(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): M = { + val map = new ParamMap() + .put(firstParamPair) + .put(otherParamPairs: _*) fit(dataset, map) } @@ -52,12 +55,19 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { * These values override any specified in this Estimator's embedded ParamMap. * @return fitted model */ - def fit(dataset: DataFrame, paramMap: ParamMap): M + def fit(dataset: DataFrame, paramMap: ParamMap): M = { + copy(paramMap).fit(dataset) + } + + /** + * Fits a model to the input data. + */ + def fit(dataset: DataFrame): M /** * Fits multiple models to the input data with multiple sets of parameters. * The default implementation uses a for loop on each parameter map. - * Subclasses could overwrite this to optimize multi-model training. + * Subclasses could override this to optimize multi-model training. * * @param dataset input dataset * @param paramMaps An array of parameter maps. @@ -67,4 +77,8 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { def fit(dataset: DataFrame, paramMaps: Array[ParamMap]): Seq[M] = { paramMaps.map(fit(dataset, _)) } + + override def copy(extra: ParamMap): Estimator[M] = { + super.copy(extra).asInstanceOf[Estimator[M]] + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala index 8b4b5fd8af986..5f2f8c94e9ff7 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala @@ -18,8 +18,7 @@ package org.apache.spark.ml import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.util.Identifiable +import org.apache.spark.ml.param.{ParamMap, Params} import org.apache.spark.sql.DataFrame /** @@ -27,7 +26,7 @@ import org.apache.spark.sql.DataFrame * Abstract class for evaluators that compute metrics from predictions. */ @AlphaComponent -abstract class Evaluator extends Identifiable { +abstract class Evaluator extends Params { /** * Evaluates the output. @@ -36,5 +35,18 @@ abstract class Evaluator extends Identifiable { * @param paramMap parameter map that specifies the input columns and output metrics * @return metric */ - def evaluate(dataset: DataFrame, paramMap: ParamMap): Double + def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = { + this.copy(paramMap).evaluate(dataset) + } + + /** + * Evaluates the output. + * @param dataset a dataset that contains labels/observations and predictions. + * @return metric + */ + def evaluate(dataset: DataFrame): Double + + override def copy(extra: ParamMap): Evaluator = { + super.copy(extra).asInstanceOf[Evaluator] + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Model.scala b/mllib/src/main/scala/org/apache/spark/ml/Model.scala index a491bc7ee8295..9974efe7b1d25 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Model.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Model.scala @@ -34,9 +34,8 @@ abstract class Model[M <: Model[M]] extends Transformer { */ val parent: Estimator[M] - /** - * Fitting parameters, such that parent.fit(..., fittingParamMap) could reproduce the model. - * Note: For ensembles' component Models, this value can be null. - */ - val fittingParamMap: ParamMap + override def copy(extra: ParamMap): M = { + // The default implementation of Params.copy doesn't work for models. + throw new NotImplementedError(s"${this.getClass} doesn't implement copy(extra: ParamMap)") + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index 8eddf79cdfe28..33d430f5671ee 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -21,7 +21,7 @@ import scala.collection.mutable.ListBuffer import org.apache.spark.Logging import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.param.{Param, ParamMap} +import org.apache.spark.ml.param.{Param, ParamMap, Params} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types.StructType @@ -30,40 +30,41 @@ import org.apache.spark.sql.types.StructType * A stage in a pipeline, either an [[Estimator]] or a [[Transformer]]. */ @AlphaComponent -abstract class PipelineStage extends Serializable with Logging { +abstract class PipelineStage extends Params with Logging { /** * :: DeveloperApi :: * - * Derives the output schema from the input schema and parameters. - * The schema describes the columns and types of the data. - * - * @param schema Input schema to this stage - * @param paramMap Parameters passed to this stage - * @return Output schema from this stage + * Derives the output schema from the input schema. */ @DeveloperApi - def transformSchema(schema: StructType, paramMap: ParamMap): StructType + def transformSchema(schema: StructType): StructType /** + * :: DeveloperApi :: + * * Derives the output schema from the input schema and parameters, optionally with logging. * * This should be optimistic. If it is unclear whether the schema will be valid, then it should * be assumed valid until proven otherwise. */ + @DeveloperApi protected def transformSchema( schema: StructType, - paramMap: ParamMap, logging: Boolean): StructType = { if (logging) { logDebug(s"Input schema: ${schema.json}") } - val outputSchema = transformSchema(schema, paramMap) + val outputSchema = transformSchema(schema) if (logging) { logDebug(s"Expected output schema: ${outputSchema.json}") } outputSchema } + + override def copy(extra: ParamMap): PipelineStage = { + super.copy(extra).asInstanceOf[PipelineStage] + } } /** @@ -81,10 +82,25 @@ abstract class PipelineStage extends Serializable with Logging { @AlphaComponent class Pipeline extends Estimator[PipelineModel] { - /** param for pipeline stages */ + /** + * param for pipeline stages + * @group param + */ val stages: Param[Array[PipelineStage]] = new Param(this, "stages", "stages of the pipeline") + + /** @group setParam */ def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this } - def getStages: Array[PipelineStage] = getOrDefault(stages) + + /** @group getParam */ + def getStages: Array[PipelineStage] = $(stages).clone() + + override def validateParams(paramMap: ParamMap): Unit = { + val map = extractParamMap(paramMap) + getStages.foreach { + case pStage: Params => pStage.validateParams(map) + case _ => + } + } /** * Fits the pipeline to the input dataset with additional parameters. If a stage is an @@ -96,13 +112,11 @@ class Pipeline extends Estimator[PipelineModel] { * pipeline stages. If there are no stages, the output model acts as an identity transformer. * * @param dataset input dataset - * @param paramMap parameter map * @return fitted pipeline */ - override def fit(dataset: DataFrame, paramMap: ParamMap): PipelineModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val theStages = map(stages) + override def fit(dataset: DataFrame): PipelineModel = { + transformSchema(dataset.schema, logging = true) + val theStages = $(stages) // Search for the last estimator. var indexOfLastEstimator = -1 theStages.view.zipWithIndex.foreach { case (stage, index) => @@ -118,7 +132,7 @@ class Pipeline extends Estimator[PipelineModel] { if (index <= indexOfLastEstimator) { val transformer = stage match { case estimator: Estimator[_] => - estimator.fit(curDataset, paramMap) + estimator.fit(curDataset) case t: Transformer => t case _ => @@ -126,7 +140,7 @@ class Pipeline extends Estimator[PipelineModel] { s"Do not support stage $stage of type ${stage.getClass}") } if (index < indexOfLastEstimator) { - curDataset = transformer.transform(curDataset, paramMap) + curDataset = transformer.transform(curDataset) } transformers += transformer } else { @@ -134,15 +148,20 @@ class Pipeline extends Estimator[PipelineModel] { } } - new PipelineModel(this, map, transformers.toArray) + new PipelineModel(this, transformers.toArray) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val theStages = map(stages) - require(theStages.toSet.size == theStages.size, + override def copy(extra: ParamMap): Pipeline = { + val map = extractParamMap(extra) + val newStages = map(stages).map(_.copy(extra)) + new Pipeline().setStages(newStages) + } + + override def transformSchema(schema: StructType): StructType = { + val theStages = $(stages) + require(theStages.toSet.size == theStages.length, "Cannot have duplicate components in a pipeline.") - theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur, paramMap)) + theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur)) } } @@ -153,38 +172,24 @@ class Pipeline extends Estimator[PipelineModel] { @AlphaComponent class PipelineModel private[ml] ( override val parent: Pipeline, - override val fittingParamMap: ParamMap, - private[ml] val stages: Array[Transformer]) + val stages: Array[Transformer]) extends Model[PipelineModel] with Logging { - /** - * Gets the model produced by the input estimator. Throws an NoSuchElementException is the input - * estimator does not exist in the pipeline. - */ - def getModel[M <: Model[M]](stage: Estimator[M]): M = { - val matched = stages.filter { - case m: Model[_] => m.parent.eq(stage) - case _ => false - } - if (matched.isEmpty) { - throw new NoSuchElementException(s"Cannot find stage $stage from the pipeline.") - } else if (matched.size > 1) { - throw new IllegalStateException(s"Cannot have duplicate estimators in the sample pipeline.") - } else { - matched.head.asInstanceOf[M] - } + override def validateParams(): Unit = { + super.validateParams() + stages.foreach(_.validateParams()) + } + + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur)) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = fittingParamMap ++ extractParamMap(paramMap) - transformSchema(dataset.schema, map, logging = true) - stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur, map)) + override def transformSchema(schema: StructType): StructType = { + stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = fittingParamMap ++ extractParamMap(paramMap) - stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur, map)) + override def copy(extra: ParamMap): PipelineModel = { + new PipelineModel(parent, stages) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala similarity index 63% rename from mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala rename to mllib/src/main/scala/org/apache/spark/ml/Predictor.scala index 195333a5cc47f..0e53877de92db 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala @@ -15,36 +15,28 @@ * limitations under the License. */ -package org.apache.spark.ml.impl.estimator +package org.apache.spark.ml -import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.util.SchemaUtils -import org.apache.spark.ml.{Estimator, Model} +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ -import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, DoubleType, StructType} - +import org.apache.spark.sql.{DataFrame, Row} /** - * :: DeveloperApi :: - * - * Trait for parameters for prediction (regression and classification). - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. + * (private[ml]) Trait for parameters for prediction (regression and classification). */ -@DeveloperApi -private[spark] trait PredictorParams extends Params +private[ml] trait PredictorParams extends Params with HasLabelCol with HasFeaturesCol with HasPredictionCol { /** * Validates and transforms the input schema with the provided param map. * @param schema input schema - * @param paramMap additional parameters * @param fitting whether this is in fitting * @param featuresDataType SQL DataType for FeaturesType. * E.g., [[org.apache.spark.mllib.linalg.VectorUDT]] for vector features. @@ -52,22 +44,20 @@ private[spark] trait PredictorParams extends Params */ protected def validateAndTransformSchema( schema: StructType, - paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val map = extractParamMap(paramMap) // TODO: Support casting Array[Double] and Array[Float] to Vector when FeaturesType = Vector - SchemaUtils.checkColumnType(schema, map(featuresCol), featuresDataType) + SchemaUtils.checkColumnType(schema, $(featuresCol), featuresDataType) if (fitting) { // TODO: Allow other numeric types - SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType) } - SchemaUtils.appendColumn(schema, map(predictionCol), DoubleType) + SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) } } /** - * :: AlphaComponent :: + * :: DeveloperApi :: * * Abstraction for prediction problems (regression and classification). * @@ -77,11 +67,9 @@ private[spark] trait PredictorParams extends Params * parameter to specify the concrete type. * @tparam M Specialization of [[PredictionModel]]. If you subclass this type, use this type * parameter to specify the concrete type for the corresponding model. - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. */ -@AlphaComponent -private[spark] abstract class Predictor[ +@DeveloperApi +abstract class Predictor[ FeaturesType, Learner <: Predictor[FeaturesType, Learner, M], M <: PredictionModel[FeaturesType, M]] @@ -96,34 +84,28 @@ private[spark] abstract class Predictor[ /** @group setParam */ def setPredictionCol(value: String): Learner = set(predictionCol, value).asInstanceOf[Learner] - override def fit(dataset: DataFrame, paramMap: ParamMap): M = { + override def fit(dataset: DataFrame): M = { // This handles a few items such as schema validation. // Developers only need to implement train(). - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val model = train(dataset, map) - Params.inheritValues(map, this, model) // copy params to model - model + transformSchema(dataset.schema, logging = true) + copyValues(train(dataset)) + } + + override def copy(extra: ParamMap): Learner = { + super.copy(extra).asInstanceOf[Learner] } /** - * :: DeveloperApi :: - * * Train a model using the given dataset and parameters. * Developers can implement this instead of [[fit()]] to avoid dealing with schema validation * and copying parameters into the model. * * @param dataset Training dataset - * @param paramMap Parameter map. Unlike [[fit()]]'s paramMap, this paramMap has already - * been combined with the embedded ParamMap. * @return Fitted model */ - @DeveloperApi - protected def train(dataset: DataFrame, paramMap: ParamMap): M + protected def train(dataset: DataFrame): M /** - * :: DeveloperApi :: - * * Returns the SQL DataType corresponding to the FeaturesType type parameter. * * This is used by [[validateAndTransformSchema()]]. @@ -131,20 +113,18 @@ private[spark] abstract class Predictor[ * * The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector. */ - @DeveloperApi protected def featuresDataType: DataType = new VectorUDT - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap, fitting = true, featuresDataType) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema, fitting = true, featuresDataType) } /** * Extract [[labelCol]] and [[featuresCol]] from the given dataset, * and put it in an RDD with strong types. */ - protected def extractLabeledPoints(dataset: DataFrame, paramMap: ParamMap): RDD[LabeledPoint] = { - val map = extractParamMap(paramMap) - dataset.select(map(labelCol), map(featuresCol)) + protected def extractLabeledPoints(dataset: DataFrame): RDD[LabeledPoint] = { + dataset.select($(labelCol), $(featuresCol)) .map { case Row(label: Double, features: Vector) => LabeledPoint(label, features) } @@ -152,7 +132,7 @@ private[spark] abstract class Predictor[ } /** - * :: AlphaComponent :: + * :: DeveloperApi :: * * Abstraction for a model for prediction tasks (regression and classification). * @@ -160,11 +140,9 @@ private[spark] abstract class Predictor[ * E.g., [[org.apache.spark.mllib.linalg.VectorUDT]] for vector features. * @tparam M Specialization of [[PredictionModel]]. If you subclass this type, use this type * parameter to specify the concrete type for the corresponding model. - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. */ -@AlphaComponent -private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType, M]] +@DeveloperApi +abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType, M]] extends Model[M] with PredictorParams { /** @group setParam */ @@ -174,8 +152,6 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel def setPredictionCol(value: String): M = set(predictionCol, value).asInstanceOf[M] /** - * :: DeveloperApi :: - * * Returns the SQL DataType corresponding to the FeaturesType type parameter. * * This is used by [[validateAndTransformSchema()]]. @@ -183,11 +159,10 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel * * The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector. */ - @DeveloperApi protected def featuresDataType: DataType = new VectorUDT - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap, fitting = false, featuresDataType) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema, fitting = false, featuresDataType) } /** @@ -195,30 +170,12 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel * the predictions as a new column [[predictionCol]]. * * @param dataset input dataset - * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset with [[predictionCol]] of type [[Double]] */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - // This default implementation should be overridden as needed. - - // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - - // Prepare model - val tmpModel = if (paramMap.size != 0) { - val tmpModel = this.copy() - Params.inheritValues(paramMap, parent, tmpModel) - tmpModel - } else { - this - } - - if (map(predictionCol) != "") { - val pred: FeaturesType => Double = (features) => { - tmpModel.predict(features) - } - dataset.withColumn(map(predictionCol), callUDF(pred, DoubleType, col(map(featuresCol)))) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + if ($(predictionCol).nonEmpty) { + dataset.withColumn($(predictionCol), callUDF(predict _, DoubleType, col($(featuresCol)))) } else { this.logWarning(s"$uid: Predictor.transform() was called as NOOP" + " since no output columns were set.") @@ -227,17 +184,8 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel } /** - * :: DeveloperApi :: - * * Predict label for the given features. * This internal method is used to implement [[transform()]] and output [[predictionCol]]. */ - @DeveloperApi protected def predict(features: FeaturesType): Double - - /** - * Create a copy of the model. - * The copy is shallow, except for the embedded paramMap, which gets a deep copy. - */ - protected def copy(): M } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala index 0acda71ec6045..d96b54e511e9c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala @@ -37,13 +37,18 @@ abstract class Transformer extends PipelineStage with Params { /** * Transforms the dataset with optional parameters * @param dataset input dataset - * @param paramPairs optional list of param pairs, overwrite embedded params + * @param firstParamPair the first param pair, overwrite embedded params + * @param otherParamPairs other param pairs, overwrite embedded params * @return transformed dataset */ @varargs - def transform(dataset: DataFrame, paramPairs: ParamPair[_]*): DataFrame = { + def transform( + dataset: DataFrame, + firstParamPair: ParamPair[_], + otherParamPairs: ParamPair[_]*): DataFrame = { val map = new ParamMap() - paramPairs.foreach(map.put(_)) + .put(firstParamPair) + .put(otherParamPairs: _*) transform(dataset, map) } @@ -53,7 +58,18 @@ abstract class Transformer extends PipelineStage with Params { * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset */ - def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame + def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + this.copy(paramMap).transform(dataset) + } + + /** + * Transforms the input dataset. + */ + def transform(dataset: DataFrame): DataFrame + + override def copy(extra: ParamMap): Transformer = { + super.copy(extra).asInstanceOf[Transformer] + } } /** @@ -74,7 +90,7 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O * account of the embedded param map. So the param values should be determined solely by the input * param map. */ - protected def createTransformFunc(paramMap: ParamMap): IN => OUT + protected def createTransformFunc: IN => OUT /** * Returns the data type of the output column. @@ -86,22 +102,20 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O */ protected def validateInputType(inputType: DataType): Unit = {} - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputType = schema(map(inputCol)).dataType + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType validateInputType(inputType) - if (schema.fieldNames.contains(map(outputCol))) { - throw new IllegalArgumentException(s"Output column ${map(outputCol)} already exists.") + if (schema.fieldNames.contains($(outputCol))) { + throw new IllegalArgumentException(s"Output column ${$(outputCol)} already exists.") } val outputFields = schema.fields :+ - StructField(map(outputCol), outputDataType, nullable = false) + StructField($(outputCol), outputDataType, nullable = false) StructType(outputFields) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - dataset.withColumn(map(outputCol), - callUDF(this.createTransformFunc(map), outputDataType, dataset(map(inputCol)))) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + dataset.withColumn($(outputCol), + callUDF(this.createTransformFunc, outputDataType, dataset($(inputCol)))) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala index 29339c98f51cf..263d580fe2dd3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala @@ -17,9 +17,8 @@ package org.apache.spark.ml.classification -import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams} -import org.apache.spark.ml.param.{ParamMap, Params} +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.ml.{PredictionModel, PredictorParams, Predictor} import org.apache.spark.ml.param.shared.HasRawPredictionCol import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} @@ -29,44 +28,36 @@ import org.apache.spark.sql.types.{DataType, DoubleType, StructType} /** - * :: DeveloperApi :: - * Params for classification. - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. + * (private[spark]) Params for classification. */ -@DeveloperApi -private[spark] trait ClassifierParams extends PredictorParams - with HasRawPredictionCol { +private[spark] trait ClassifierParams + extends PredictorParams with HasRawPredictionCol { override protected def validateAndTransformSchema( schema: StructType, - paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = extractParamMap(paramMap) - SchemaUtils.appendColumn(parentSchema, map(rawPredictionCol), new VectorUDT) + val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType) + SchemaUtils.appendColumn(parentSchema, $(rawPredictionCol), new VectorUDT) } } /** - * :: AlphaComponent :: + * :: DeveloperApi :: + * * Single-label binary or multiclass classification. * Classes are indexed {0, 1, ..., numClasses - 1}. * * @tparam FeaturesType Type of input features. E.g., [[Vector]] * @tparam E Concrete Estimator type * @tparam M Concrete Model type - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. */ -@AlphaComponent -private[spark] abstract class Classifier[ +@DeveloperApi +abstract class Classifier[ FeaturesType, E <: Classifier[FeaturesType, E, M], M <: ClassificationModel[FeaturesType, M]] - extends Predictor[FeaturesType, E, M] - with ClassifierParams { + extends Predictor[FeaturesType, E, M] with ClassifierParams { /** @group setParam */ def setRawPredictionCol(value: String): E = set(rawPredictionCol, value).asInstanceOf[E] @@ -75,17 +66,15 @@ private[spark] abstract class Classifier[ } /** - * :: AlphaComponent :: + * :: DeveloperApi :: + * * Model produced by a [[Classifier]]. * Classes are indexed {0, 1, ..., numClasses - 1}. * * @tparam FeaturesType Type of input features. E.g., [[Vector]] * @tparam M Concrete Model type - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. */ -@AlphaComponent -private[spark] +@DeveloperApi abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[FeaturesType, M]] extends PredictionModel[FeaturesType, M] with ClassifierParams { @@ -102,27 +91,30 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur * - raw predictions (confidences) as [[rawPredictionCol]] of type [[Vector]]. * * @param dataset input dataset - * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - // This default implementation should be overridden as needed. - - // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - - // Prepare model - val tmpModel = if (paramMap.size != 0) { - val tmpModel = this.copy() - Params.inheritValues(paramMap, parent, tmpModel) - tmpModel - } else { - this + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + + // Output selected columns only. + // This is a bit complicated since it tries to avoid repeated computation. + var outputData = dataset + var numColsOutput = 0 + if (getRawPredictionCol != "") { + outputData = outputData.withColumn(getRawPredictionCol, + callUDF(predictRaw _, new VectorUDT, col(getFeaturesCol))) + numColsOutput += 1 + } + if (getPredictionCol != "") { + val predUDF = if (getRawPredictionCol != "") { + callUDF(raw2prediction _, DoubleType, col(getRawPredictionCol)) + } else { + callUDF(predict _, DoubleType, col(getFeaturesCol)) + } + outputData = outputData.withColumn(getPredictionCol, predUDF) + numColsOutput += 1 } - val (numColsOutput, outputData) = - ClassificationModel.transformColumnsImpl[FeaturesType](dataset, tmpModel, map) if (numColsOutput == 0) { logWarning(s"$uid: ClassificationModel.transform() was called as NOOP" + " since no output columns were set.") @@ -131,22 +123,17 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur } /** - * :: DeveloperApi :: - * * Predict label for the given features. * This internal method is used to implement [[transform()]] and output [[predictionCol]]. * * This default implementation for classification predicts the index of the maximum value * from [[predictRaw()]]. */ - @DeveloperApi override protected def predict(features: FeaturesType): Double = { - predictRaw(features).toArray.zipWithIndex.maxBy(_._1)._2 + raw2prediction(predictRaw(features)) } /** - * :: DeveloperApi :: - * * Raw prediction for each possible label. * The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives * a measure of confidence in each possible label (where larger = more confident). @@ -156,52 +143,12 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur * This raw prediction may be any real number, where a larger value indicates greater * confidence for that label. */ - @DeveloperApi protected def predictRaw(features: FeaturesType): Vector -} - -private[ml] object ClassificationModel { - /** - * Added prediction column(s). This is separated from [[ClassificationModel.transform()]] - * since it is used by [[org.apache.spark.ml.classification.ProbabilisticClassificationModel]]. - * @param dataset Input dataset - * @param map Parameter map. This will NOT be merged with the embedded paramMap; the merge - * should already be done. - * @return (number of columns added, transformed dataset) + * Given a vector of raw predictions, select the predicted label. + * This may be overridden to support thresholds which favor particular labels. + * @return predicted label */ - def transformColumnsImpl[FeaturesType]( - dataset: DataFrame, - model: ClassificationModel[FeaturesType, _], - map: ParamMap): (Int, DataFrame) = { - - // Output selected columns only. - // This is a bit complicated since it tries to avoid repeated computation. - var tmpData = dataset - var numColsOutput = 0 - if (map(model.rawPredictionCol) != "") { - // output raw prediction - val features2raw: FeaturesType => Vector = model.predictRaw - tmpData = tmpData.withColumn(map(model.rawPredictionCol), - callUDF(features2raw, new VectorUDT, col(map(model.featuresCol)))) - numColsOutput += 1 - if (map(model.predictionCol) != "") { - val raw2pred: Vector => Double = (rawPred) => { - rawPred.toArray.zipWithIndex.maxBy(_._1)._2 - } - tmpData = tmpData.withColumn(map(model.predictionCol), - callUDF(raw2pred, DoubleType, col(map(model.rawPredictionCol)))) - numColsOutput += 1 - } - } else if (map(model.predictionCol) != "") { - // output prediction - val features2pred: FeaturesType => Double = model.predict - tmpData = tmpData.withColumn(map(model.predictionCol), - callUDF(features2pred, DoubleType, col(map(model.featuresCol)))) - numColsOutput += 1 - } - (numColsOutput, tmpData) - } - + protected def raw2prediction(rawPrediction: Vector): Double = rawPrediction.toDense.argmax } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala index ee2a8dc6db171..dcebea1d4b015 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala @@ -18,10 +18,9 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.impl.estimator.{Predictor, PredictionModel} -import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap} -import org.apache.spark.ml.tree.{DecisionTreeModel, Node} +import org.apache.spark.ml.{PredictionModel, Predictor} +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.tree.{TreeClassifierParams, DecisionTreeParams, DecisionTreeModel, Node} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint @@ -31,7 +30,6 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeMo import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -64,22 +62,20 @@ final class DecisionTreeClassifier override def setImpurity(value: String): this.type = super.setImpurity(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): DecisionTreeClassificationModel = { + override protected def train(dataset: DataFrame): DecisionTreeClassificationModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("DecisionTreeClassifier was given input" + - s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + s" with invalid label column ${$(labelCol)}, without the number of classes" + " specified. See StringIndexer.") // TODO: Automatically index labels: SPARK-7126 } - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = getOldStrategy(categoricalFeatures, numClasses) val oldModel = OldDecisionTree.train(oldDataset, strategy) - DecisionTreeClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + DecisionTreeClassificationModel.fromOld(oldModel, this, categoricalFeatures) } /** (private[ml]) Create a Strategy instance to use with the old API. */ @@ -106,7 +102,6 @@ object DecisionTreeClassifier { @AlphaComponent final class DecisionTreeClassificationModel private[ml] ( override val parent: DecisionTreeClassifier, - override val fittingParamMap: ParamMap, override val rootNode: Node) extends PredictionModel[Vector, DecisionTreeClassificationModel] with DecisionTreeModel with Serializable { @@ -118,10 +113,8 @@ final class DecisionTreeClassificationModel private[ml] ( rootNode.predict(features) } - override protected def copy(): DecisionTreeClassificationModel = { - val m = new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): DecisionTreeClassificationModel = { + copyValues(new DecisionTreeClassificationModel(parent, rootNode), extra) } override def toString: String = { @@ -140,12 +133,11 @@ private[ml] object DecisionTreeClassificationModel { def fromOld( oldModel: OldDecisionTreeModel, parent: DecisionTreeClassifier, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): DecisionTreeClassificationModel = { require(oldModel.algo == OldAlgo.Classification, s"Cannot convert non-classification DecisionTreeModel (old API) to" + s" DecisionTreeClassificationModel (new API). Algo is: ${oldModel.algo}") val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) - new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) + new DecisionTreeClassificationModel(parent, rootNode) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala index d2e052fbbbf22..ae51b05a0c42d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala @@ -21,22 +21,20 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} -import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Param, Params, ParamMap} +import org.apache.spark.ml.{PredictionModel, Predictor} +import org.apache.spark.ml.param.{Param, ParamMap} import org.apache.spark.ml.regression.DecisionTreeRegressionModel -import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} +import org.apache.spark.ml.tree.{GBTParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} -import org.apache.spark.mllib.tree.loss.{Loss => OldLoss, LogLoss => OldLogLoss} +import org.apache.spark.mllib.tree.loss.{LogLoss => OldLogLoss, Loss => OldLoss} import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -103,21 +101,16 @@ final class GBTClassifier */ val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" + " tries to minimize (case-insensitive). Supported options:" + - s" ${GBTClassifier.supportedLossTypes.mkString(", ")}") + s" ${GBTClassifier.supportedLossTypes.mkString(", ")}", + (value: String) => GBTClassifier.supportedLossTypes.contains(value.toLowerCase)) setDefault(lossType -> "logistic") /** @group setParam */ - def setLossType(value: String): this.type = { - val lossStr = value.toLowerCase - require(GBTClassifier.supportedLossTypes.contains(lossStr), "GBTClassifier was given bad loss" + - s" type: $value. Supported options: ${GBTClassifier.supportedLossTypes.mkString(", ")}") - set(lossType, lossStr) - this - } + def setLossType(value: String): this.type = set(lossType, value) /** @group getParam */ - def getLossType: String = getOrDefault(lossType) + def getLossType: String = $(lossType).toLowerCase /** (private[ml]) Convert new loss to old loss. */ override private[ml] def getOldLossType: OldLoss = { @@ -129,25 +122,23 @@ final class GBTClassifier } } - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): GBTClassificationModel = { + override protected def train(dataset: DataFrame): GBTClassificationModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("GBTClassifier was given input" + - s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + s" with invalid label column ${$(labelCol)}, without the number of classes" + " specified. See StringIndexer.") // TODO: Automatically index labels: SPARK-7126 } require(numClasses == 2, s"GBTClassifier only supports binary classification but was given numClasses = $numClasses") - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Classification) val oldGBT = new OldGBT(boostingStrategy) val oldModel = oldGBT.run(oldDataset) - GBTClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + GBTClassificationModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -170,7 +161,6 @@ object GBTClassifier { @AlphaComponent final class GBTClassificationModel( override val parent: GBTClassifier, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeRegressionModel], private val _treeWeights: Array[Double]) extends PredictionModel[Vector, GBTClassificationModel] @@ -193,10 +183,8 @@ final class GBTClassificationModel( if (prediction > 0.0) 1.0 else 0.0 } - override protected def copy(): GBTClassificationModel = { - val m = new GBTClassificationModel(parent, fittingParamMap, _trees, _treeWeights) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): GBTClassificationModel = { + copyValues(new GBTClassificationModel(parent, _trees, _treeWeights), extra) } override def toString: String = { @@ -215,14 +203,13 @@ private[ml] object GBTClassificationModel { def fromOld( oldModel: OldGBTModel, parent: GBTClassifier, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): GBTClassificationModel = { require(oldModel.algo == OldAlgo.Classification, "Cannot convert GradientBoostedTreesModel" + s" with algo=${oldModel.algo} (old API) to GBTClassificationModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new GBTClassificationModel(parent, fittingParamMap, newTrees, oldModel.treeWeights) + new GBTClassificationModel(parent, newTrees, oldModel.treeWeights) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index cc8b0721cf2b6..550369d18cfec 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -21,12 +21,10 @@ import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS -import org.apache.spark.mllib.linalg.{VectorUDT, BLAS, Vector, Vectors} +import org.apache.spark.mllib.linalg._ import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.functions._ import org.apache.spark.storage.StorageLevel - /** * Params for logistic regression. */ @@ -59,9 +57,9 @@ class LogisticRegression /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) - override protected def train(dataset: DataFrame, paramMap: ParamMap): LogisticRegressionModel = { + override protected def train(dataset: DataFrame): LogisticRegressionModel = { // Extract columns from data. If dataset is persisted, do not persist oldDataset. - val oldDataset = extractLabeledPoints(dataset, paramMap) + val oldDataset = extractLabeledPoints(dataset) val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE if (handlePersistence) { oldDataset.persist(StorageLevel.MEMORY_AND_DISK) @@ -69,17 +67,17 @@ class LogisticRegression // Train model val lr = new LogisticRegressionWithLBFGS() - .setIntercept(paramMap(fitIntercept)) + .setIntercept($(fitIntercept)) lr.optimizer - .setRegParam(paramMap(regParam)) - .setNumIterations(paramMap(maxIter)) + .setRegParam($(regParam)) + .setNumIterations($(maxIter)) val oldModel = lr.run(oldDataset) - val lrm = new LogisticRegressionModel(this, paramMap, oldModel.weights, oldModel.intercept) + val lrm = new LogisticRegressionModel(this, oldModel.weights, oldModel.intercept) if (handlePersistence) { oldDataset.unpersist() } - lrm + copyValues(lrm) } } @@ -92,7 +90,6 @@ class LogisticRegression @AlphaComponent class LogisticRegressionModel private[ml] ( override val parent: LogisticRegression, - override val fittingParamMap: ParamMap, val weights: Vector, val intercept: Double) extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] @@ -101,78 +98,17 @@ class LogisticRegressionModel private[ml] ( /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) + /** Margin (rawPrediction) for class label 1. For binary classification only. */ private val margin: Vector => Double = (features) => { BLAS.dot(features, weights) + intercept } + /** Score (probability) for class label 1. For binary classification only. */ private val score: Vector => Double = (features) => { val m = margin(features) 1.0 / (1.0 + math.exp(-m)) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - // This is overridden (a) to be more efficient (avoiding re-computing values when creating - // multiple output columns) and (b) to handle threshold, which the abstractions do not use. - // TODO: We should abstract away the steps defined by UDFs below so that the abstractions - // can call whichever UDFs are needed to create the output columns. - - // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - - val map = extractParamMap(paramMap) - - // Output selected columns only. - // This is a bit complicated since it tries to avoid repeated computation. - // rawPrediction (-margin, margin) - // probability (1.0-score, score) - // prediction (max margin) - var tmpData = dataset - var numColsOutput = 0 - if (map(rawPredictionCol) != "") { - val features2raw: Vector => Vector = (features) => predictRaw(features) - tmpData = tmpData.withColumn(map(rawPredictionCol), - callUDF(features2raw, new VectorUDT, col(map(featuresCol)))) - numColsOutput += 1 - } - if (map(probabilityCol) != "") { - if (map(rawPredictionCol) != "") { - val raw2prob = udf { (rawPreds: Vector) => - val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1))) - Vectors.dense(1.0 - prob1, prob1): Vector - } - tmpData = tmpData.withColumn(map(probabilityCol), raw2prob(col(map(rawPredictionCol)))) - } else { - val features2prob = udf { (features: Vector) => predictProbabilities(features) : Vector } - tmpData = tmpData.withColumn(map(probabilityCol), features2prob(col(map(featuresCol)))) - } - numColsOutput += 1 - } - if (map(predictionCol) != "") { - val t = map(threshold) - if (map(probabilityCol) != "") { - val predict = udf { probs: Vector => - if (probs(1) > t) 1.0 else 0.0 - } - tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(probabilityCol)))) - } else if (map(rawPredictionCol) != "") { - val predict = udf { rawPreds: Vector => - val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1))) - if (prob1 > t) 1.0 else 0.0 - } - tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(rawPredictionCol)))) - } else { - val predict = udf { features: Vector => this.predict(features) } - tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(featuresCol)))) - } - numColsOutput += 1 - } - if (numColsOutput == 0) { - this.logWarning(s"$uid: LogisticRegressionModel.transform() was called as NOOP" + - " since no output columns were set.") - } - tmpData - } - override val numClasses: Int = 2 /** @@ -183,19 +119,43 @@ class LogisticRegressionModel private[ml] ( if (score(features) > getThreshold) 1 else 0 } - override protected def predictProbabilities(features: Vector): Vector = { - val s = score(features) - Vectors.dense(1.0 - s, s) + override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = { + rawPrediction match { + case dv: DenseVector => + var i = 0 + while (i < dv.size) { + dv.values(i) = 1.0 / (1.0 + math.exp(-dv.values(i))) + i += 1 + } + dv + case sv: SparseVector => + throw new RuntimeException("Unexpected error in LogisticRegressionModel:" + + " raw2probabilitiesInPlace encountered SparseVector") + } } override protected def predictRaw(features: Vector): Vector = { val m = margin(features) - Vectors.dense(0.0, m) + Vectors.dense(-m, m) + } + + override def copy(extra: ParamMap): LogisticRegressionModel = { + copyValues(new LogisticRegressionModel(parent, weights, intercept), extra) + } + + override protected def raw2prediction(rawPrediction: Vector): Double = { + val t = getThreshold + val rawThreshold = if (t == 0.0) { + Double.NegativeInfinity + } else if (t == 1.0) { + Double.PositiveInfinity + } else { + Math.log(t / (1.0 - t)) + } + if (rawPrediction(1) > rawThreshold) 1 else 0 } - override protected def copy(): LogisticRegressionModel = { - val m = new LogisticRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(this.extractParamMap(), this, m) - m + override protected def probability2prediction(probability: Vector): Double = { + if (probability(1) > getThreshold) 1 else 0 } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala index 10404548ccfde..330ae2938f4e0 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala @@ -17,45 +17,40 @@ package org.apache.spark.ml.classification -import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.param.{ParamMap, Params} +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.param.shared._ import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ -import org.apache.spark.sql.types.{DataType, StructType} +import org.apache.spark.sql.types.{DoubleType, DataType, StructType} /** - * Params for probabilistic classification. + * (private[classification]) Params for probabilistic classification. */ private[classification] trait ProbabilisticClassifierParams extends ClassifierParams with HasProbabilityCol { override protected def validateAndTransformSchema( schema: StructType, - paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = extractParamMap(paramMap) - SchemaUtils.appendColumn(parentSchema, map(probabilityCol), new VectorUDT) + val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType) + SchemaUtils.appendColumn(parentSchema, $(probabilityCol), new VectorUDT) } } /** - * :: AlphaComponent :: + * :: DeveloperApi :: * * Single-label binary or multiclass classifier which can output class conditional probabilities. * * @tparam FeaturesType Type of input features. E.g., [[Vector]] * @tparam E Concrete Estimator type * @tparam M Concrete Model type - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. */ -@AlphaComponent +@DeveloperApi private[spark] abstract class ProbabilisticClassifier[ FeaturesType, E <: ProbabilisticClassifier[FeaturesType, E, M], @@ -68,17 +63,15 @@ private[spark] abstract class ProbabilisticClassifier[ /** - * :: AlphaComponent :: + * :: DeveloperApi :: * * Model produced by a [[ProbabilisticClassifier]]. * Classes are indexed {0, 1, ..., numClasses - 1}. * * @tparam FeaturesType Type of input features. E.g., [[Vector]] * @tparam M Concrete Model type - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. */ -@AlphaComponent +@DeveloperApi private[spark] abstract class ProbabilisticClassificationModel[ FeaturesType, M <: ProbabilisticClassificationModel[FeaturesType, M]] @@ -95,56 +88,82 @@ private[spark] abstract class ProbabilisticClassificationModel[ * - probability of each class as [[probabilityCol]] of type [[Vector]]. * * @param dataset input dataset - * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - // This default implementation should be overridden as needed. - - // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - - // Prepare model - val tmpModel = if (paramMap.size != 0) { - val tmpModel = this.copy() - Params.inheritValues(paramMap, parent, tmpModel) - tmpModel - } else { - this - } - - val (numColsOutput, outputData) = - ClassificationModel.transformColumnsImpl[FeaturesType](dataset, tmpModel, map) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) // Output selected columns only. - if (map(probabilityCol) != "") { - // output probabilities - val features2probs: FeaturesType => Vector = (features) => { - tmpModel.predictProbabilities(features) + // This is a bit complicated since it tries to avoid repeated computation. + var outputData = dataset + var numColsOutput = 0 + if ($(rawPredictionCol).nonEmpty) { + outputData = outputData.withColumn(getRawPredictionCol, + callUDF(predictRaw _, new VectorUDT, col(getFeaturesCol))) + numColsOutput += 1 + } + if ($(probabilityCol).nonEmpty) { + val probUDF = if ($(rawPredictionCol).nonEmpty) { + callUDF(raw2probability _, new VectorUDT, col($(rawPredictionCol))) + } else { + callUDF(predictProbability _, new VectorUDT, col($(featuresCol))) } - outputData.withColumn(map(probabilityCol), - callUDF(features2probs, new VectorUDT, col(map(featuresCol)))) - } else { - if (numColsOutput == 0) { - this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" + - " since no output columns were set.") + outputData = outputData.withColumn($(probabilityCol), probUDF) + numColsOutput += 1 + } + if ($(predictionCol).nonEmpty) { + val predUDF = if ($(rawPredictionCol).nonEmpty) { + callUDF(raw2prediction _, DoubleType, col($(rawPredictionCol))) + } else if ($(probabilityCol).nonEmpty) { + callUDF(probability2prediction _, DoubleType, col($(probabilityCol))) + } else { + callUDF(predict _, DoubleType, col($(featuresCol))) } - outputData + outputData = outputData.withColumn($(predictionCol), predUDF) + numColsOutput += 1 } + + if (numColsOutput == 0) { + this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" + + " since no output columns were set.") + } + outputData } /** - * :: DeveloperApi :: + * Estimate the probability of each class given the raw prediction, + * doing the computation in-place. + * These predictions are also called class conditional probabilities. + * + * This internal method is used to implement [[transform()]] and output [[probabilityCol]]. * + * @return Estimated class conditional probabilities (modified input vector) + */ + protected def raw2probabilityInPlace(rawPrediction: Vector): Vector + + /** Non-in-place version of [[raw2probabilityInPlace()]] */ + protected def raw2probability(rawPrediction: Vector): Vector = { + val probs = rawPrediction.copy + raw2probabilityInPlace(probs) + } + + /** * Predict the probability of each class given the features. * These predictions are also called class conditional probabilities. * - * WARNING: Not all models output well-calibrated probability estimates! These probabilities - * should be treated as confidences, not precise probabilities. - * * This internal method is used to implement [[transform()]] and output [[probabilityCol]]. + * + * @return Estimated class conditional probabilities + */ + protected def predictProbability(features: FeaturesType): Vector = { + val rawPreds = predictRaw(features) + raw2probabilityInPlace(rawPreds) + } + + /** + * Given a vector of class conditional probabilities, select the predicted label. + * This may be overridden to support thresholds which favor particular labels. + * @return predicted label */ - @DeveloperApi - protected def predictProbabilities(features: FeaturesType): Vector + protected def probability2prediction(probability: Vector): Double = probability.toDense.argmax } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala index cfd6508fce890..9954893f14359 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala @@ -20,20 +20,18 @@ package org.apache.spark.ml.classification import scala.collection.mutable import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} -import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap} -import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} +import org.apache.spark.ml.{PredictionModel, Predictor} +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.tree.{RandomForestParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -81,24 +79,22 @@ final class RandomForestClassifier override def setFeatureSubsetStrategy(value: String): this.type = super.setFeatureSubsetStrategy(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): RandomForestClassificationModel = { + override protected def train(dataset: DataFrame): RandomForestClassificationModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("RandomForestClassifier was given input" + - s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + s" with invalid label column ${$(labelCol)}, without the number of classes" + " specified. See StringIndexer.") // TODO: Automatically index labels: SPARK-7126 } - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity) val oldModel = OldRandomForest.trainClassifier( oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) - RandomForestClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + RandomForestClassificationModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -123,7 +119,6 @@ object RandomForestClassifier { @AlphaComponent final class RandomForestClassificationModel private[ml] ( override val parent: RandomForestClassifier, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeClassificationModel]) extends PredictionModel[Vector, RandomForestClassificationModel] with TreeEnsembleModel with Serializable { @@ -150,10 +145,8 @@ final class RandomForestClassificationModel private[ml] ( votes.maxBy(_._2)._1 } - override protected def copy(): RandomForestClassificationModel = { - val m = new RandomForestClassificationModel(parent, fittingParamMap, _trees) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): RandomForestClassificationModel = { + copyValues(new RandomForestClassificationModel(parent, _trees), extra) } override def toString: String = { @@ -172,14 +165,13 @@ private[ml] object RandomForestClassificationModel { def fromOld( oldModel: OldRandomForestModel, parent: RandomForestClassifier, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): RandomForestClassificationModel = { require(oldModel.algo == OldAlgo.Classification, "Cannot convert RandomForestModel" + s" with algo=${oldModel.algo} (old API) to RandomForestClassificationModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeClassificationModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures) } - new RandomForestClassificationModel(parent, fittingParamMap, newTrees) + new RandomForestClassificationModel(parent, newTrees) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala index c865eb9fe092d..e5a73c6087a11 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala @@ -33,8 +33,7 @@ import org.apache.spark.sql.types.DoubleType * Evaluator for binary classification, which expects two input columns: score and label. */ @AlphaComponent -class BinaryClassificationEvaluator extends Evaluator with Params - with HasRawPredictionCol with HasLabelCol { +class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol with HasLabelCol { /** * param for metric name in evaluation @@ -44,7 +43,7 @@ class BinaryClassificationEvaluator extends Evaluator with Params "metric name in evaluation (areaUnderROC|areaUnderPR)") /** @group getParam */ - def getMetricName: String = getOrDefault(metricName) + def getMetricName: String = $(metricName) /** @group setParam */ def setMetricName(value: String): this.type = set(metricName, value) @@ -57,20 +56,18 @@ class BinaryClassificationEvaluator extends Evaluator with Params setDefault(metricName -> "areaUnderROC") - override def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = { - val map = extractParamMap(paramMap) - + override def evaluate(dataset: DataFrame): Double = { val schema = dataset.schema - SchemaUtils.checkColumnType(schema, map(rawPredictionCol), new VectorUDT) - SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, $(rawPredictionCol), new VectorUDT) + SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType) // TODO: When dataset metadata has been implemented, check rawPredictionCol vector length = 2. - val scoreAndLabels = dataset.select(map(rawPredictionCol), map(labelCol)) + val scoreAndLabels = dataset.select($(rawPredictionCol), $(labelCol)) .map { case Row(rawPrediction: Vector, label: Double) => (rawPrediction(1), label) } val metrics = new BinaryClassificationMetrics(scoreAndLabels) - val metric = map(metricName) match { + val metric = $(metricName) match { case "areaUnderROC" => metrics.areaUnderROC() case "areaUnderPR" => diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala new file mode 100644 index 0000000000000..6eb1db6971111 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala @@ -0,0 +1,83 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.Transformer +import org.apache.spark.ml.attribute.BinaryAttribute +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.sql._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.{DoubleType, StructType} + +/** + * :: AlphaComponent :: + * Binarize a column of continuous features given a threshold. + */ +@AlphaComponent +final class Binarizer extends Transformer with HasInputCol with HasOutputCol { + + /** + * Param for threshold used to binarize continuous features. + * The features greater than the threshold, will be binarized to 1.0. + * The features equal to or less than the threshold, will be binarized to 0.0. + * @group param + */ + val threshold: DoubleParam = + new DoubleParam(this, "threshold", "threshold used to binarize continuous features") + + /** @group getParam */ + def getThreshold: Double = $(threshold) + + /** @group setParam */ + def setThreshold(value: Double): this.type = set(threshold, value) + + setDefault(threshold -> 0.0) + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val td = $(threshold) + val binarizer = udf { in: Double => if (in > td) 1.0 else 0.0 } + val outputColName = $(outputCol) + val metadata = BinaryAttribute.defaultAttr.withName(outputColName).toMetadata() + dataset.select(col("*"), + binarizer(col($(inputCol))).as(outputColName, metadata)) + } + + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType) + + val inputFields = schema.fields + val outputColName = $(outputCol) + + require(inputFields.forall(_.name != outputColName), + s"Output column $outputColName already exists.") + + val attr = BinaryAttribute.defaultAttr.withName(outputColName) + val outputFields = inputFields :+ attr.toStructField() + StructType(outputFields) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala index b20f2fc49a8f6..c305a819a8966 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala @@ -19,9 +19,9 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{IntParam, ParamMap} +import org.apache.spark.ml.param.{IntParam, ParamValidators} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.types.DataType /** @@ -32,21 +32,23 @@ import org.apache.spark.sql.types.DataType class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { /** - * number of features + * Number of features. Should be > 0. + * (default = 2^18^) * @group param */ - val numFeatures = new IntParam(this, "numFeatures", "number of features") + val numFeatures = new IntParam(this, "numFeatures", "number of features (> 0)", + ParamValidators.gt(0)) + + setDefault(numFeatures -> (1 << 18)) /** @group getParam */ - def getNumFeatures: Int = getOrDefault(numFeatures) + def getNumFeatures: Int = $(numFeatures) /** @group setParam */ def setNumFeatures(value: Int): this.type = set(numFeatures, value) - setDefault(numFeatures -> (1 << 18)) - - override protected def createTransformFunc(paramMap: ParamMap): Iterable[_] => Vector = { - val hashingTF = new feature.HashingTF(paramMap(numFeatures)) + override protected def createTransformFunc: Iterable[_] => Vector = { + val hashingTF = new feature.HashingTF($(numFeatures)) hashingTF.transform } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala index e6a62d998bb97..d901a20aed002 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala @@ -43,7 +43,7 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol setDefault(minDocFreq -> 0) /** @group getParam */ - def getMinDocFreq: Int = getOrDefault(minDocFreq) + def getMinDocFreq: Int = $(minDocFreq) /** @group setParam */ def setMinDocFreq(value: Int): this.type = set(minDocFreq, value) @@ -51,10 +51,9 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol /** * Validate and transform the input schema. */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - SchemaUtils.checkColumnType(schema, map(inputCol), new VectorUDT) - SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT) + protected def validateAndTransformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT) + SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT) } } @@ -71,18 +70,15 @@ final class IDF extends Estimator[IDFModel] with IDFBase { /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): IDFModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val idf = new feature.IDF(map(minDocFreq)).fit(input) - val model = new IDFModel(this, map, idf) - Params.inheritValues(map, this, model) - model + override def fit(dataset: DataFrame): IDFModel = { + transformSchema(dataset.schema, logging = true) + val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v } + val idf = new feature.IDF($(minDocFreq)).fit(input) + copyValues(new IDFModel(this, idf)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -93,7 +89,6 @@ final class IDF extends Estimator[IDFModel] with IDFBase { @AlphaComponent class IDFModel private[ml] ( override val parent: IDF, - override val fittingParamMap: ParamMap, idfModel: feature.IDFModel) extends Model[IDFModel] with IDFBase { @@ -103,14 +98,13 @@ class IDFModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) val idf = udf { vec: Vector => idfModel.transform(vec) } - dataset.withColumn(map(outputCol), idf(col(map(inputCol)))) + dataset.withColumn($(outputCol), idf(col($(inputCol)))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala index decaeb0da6246..755b46a64c7f1 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala @@ -19,9 +19,9 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{DoubleParam, ParamMap} +import org.apache.spark.ml.param.{DoubleParam, ParamValidators} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.types.DataType /** @@ -32,21 +32,22 @@ import org.apache.spark.sql.types.DataType class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { /** - * Normalization in L^p^ space, p = 2 by default. + * Normalization in L^p^ space. Must be >= 1. + * (default: p = 2) * @group param */ - val p = new DoubleParam(this, "p", "the p norm value") + val p = new DoubleParam(this, "p", "the p norm value", ParamValidators.gtEq(1)) + + setDefault(p -> 2.0) /** @group getParam */ - def getP: Double = getOrDefault(p) + def getP: Double = $(p) /** @group setParam */ def setP(value: Double): this.type = set(p, value) - setDefault(p -> 2.0) - - override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { - val normalizer = new feature.Normalizer(paramMap(p)) + override protected def createTransformFunc: Vector => Vector = { + val normalizer = new feature.Normalizer($(p)) normalizer.transform } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala new file mode 100644 index 0000000000000..46514ae5f0e84 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala @@ -0,0 +1,107 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.SparkException +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.UnaryTransformer +import org.apache.spark.ml.attribute.{Attribute, BinaryAttribute, NominalAttribute} +import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT} +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.sql.types.{DataType, DoubleType, StructType} + +/** + * A one-hot encoder that maps a column of label indices to a column of binary vectors, with + * at most a single one-value. By default, the binary vector has an element for each category, so + * with 5 categories, an input value of 2.0 would map to an output vector of + * (0.0, 0.0, 1.0, 0.0, 0.0). If includeFirst is set to false, the first category is omitted, so the + * output vector for the previous example would be (0.0, 1.0, 0.0, 0.0) and an input value + * of 0.0 would map to a vector of all zeros. Including the first category makes the vector columns + * linearly dependent because they sum up to one. + */ +@AlphaComponent +class OneHotEncoder extends UnaryTransformer[Double, Vector, OneHotEncoder] + with HasInputCol with HasOutputCol { + + /** + * Whether to include a component in the encoded vectors for the first category, defaults to true. + * @group param + */ + final val includeFirst: BooleanParam = + new BooleanParam(this, "includeFirst", "include first category") + setDefault(includeFirst -> true) + + private var categories: Array[String] = _ + + /** @group setParam */ + def setIncludeFirst(value: Boolean): this.type = set(includeFirst, value) + + /** @group setParam */ + override def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + override def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType) + val inputFields = schema.fields + val outputColName = $(outputCol) + require(inputFields.forall(_.name != $(outputCol)), + s"Output column ${$(outputCol)} already exists.") + + val inputColAttr = Attribute.fromStructField(schema($(inputCol))) + categories = inputColAttr match { + case nominal: NominalAttribute => + nominal.values.getOrElse((0 until nominal.numValues.get).map(_.toString).toArray) + case binary: BinaryAttribute => binary.values.getOrElse(Array("0", "1")) + case _ => + throw new SparkException(s"OneHotEncoder input column ${$(inputCol)} is not nominal") + } + + val attrValues = (if ($(includeFirst)) categories else categories.drop(1)).toArray + val attr = NominalAttribute.defaultAttr.withName(outputColName).withValues(attrValues) + val outputFields = inputFields :+ attr.toStructField() + StructType(outputFields) + } + + protected override def createTransformFunc(): (Double) => Vector = { + val first = $(includeFirst) + val vecLen = if (first) categories.length else categories.length - 1 + val oneValue = Array(1.0) + val emptyValues = Array[Double]() + val emptyIndices = Array[Int]() + label: Double => { + val values = if (first || label != 0.0) oneValue else emptyValues + val indices = if (first) { + Array(label.toInt) + } else if (label != 0.0) { + Array(label.toInt - 1) + } else { + emptyIndices + } + Vectors.sparse(vecLen, indices, values) + } + } + + /** + * Returns the data type of the output column. + */ + protected def outputDataType: DataType = new VectorUDT +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala index d855f04799ae7..63e190c8aae53 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala @@ -21,7 +21,7 @@ import scala.collection.mutable import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{IntParam, ParamMap} +import org.apache.spark.ml.param.{IntParam, ParamValidators} import org.apache.spark.mllib.linalg._ import org.apache.spark.sql.types.DataType @@ -37,21 +37,23 @@ import org.apache.spark.sql.types.DataType class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExpansion] { /** - * The polynomial degree to expand, which should be larger than 1. + * The polynomial degree to expand, which should be >= 1. A value of 1 means no expansion. + * Default: 2 * @group param */ - val degree = new IntParam(this, "degree", "the polynomial degree to expand") + val degree = new IntParam(this, "degree", "the polynomial degree to expand (>= 1)", + ParamValidators.gt(1)) + setDefault(degree -> 2) /** @group getParam */ - def getDegree: Int = getOrDefault(degree) + def getDegree: Int = $(degree) /** @group setParam */ def setDegree(value: Int): this.type = set(degree, value) - override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { v => - val d = paramMap(degree) - PolynomialExpansion.expand(v, d) + override protected def createTransformFunc: Vector => Vector = { v => + PolynomialExpansion.expand(v, $(degree)) } override protected def outputDataType: DataType = new VectorUDT() diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala index 447851ec034d6..7cad59ff3fa37 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala @@ -31,17 +31,19 @@ import org.apache.spark.sql.types.{StructField, StructType} * Params for [[StandardScaler]] and [[StandardScalerModel]]. */ private[feature] trait StandardScalerParams extends Params with HasInputCol with HasOutputCol { - + /** - * False by default. Centers the data with mean before scaling. + * Centers the data with mean before scaling. * It will build a dense output, so this does not work on sparse input * and will raise an exception. + * Default: false * @group param */ val withMean: BooleanParam = new BooleanParam(this, "withMean", "Center data with mean") /** - * True by default. Scales the data to unit standard deviation. + * Scales the data to unit standard deviation. + * Default: true * @group param */ val withStd: BooleanParam = new BooleanParam(this, "withStd", "Scale to unit standard deviation") @@ -56,7 +58,7 @@ private[feature] trait StandardScalerParams extends Params with HasInputCol with class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams { setDefault(withMean -> false, withStd -> true) - + /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) @@ -69,25 +71,21 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP /** @group setParam */ def setWithStd(value: Boolean): this.type = set(withStd, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): StandardScalerModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val scaler = new feature.StandardScaler(withMean = map(withMean), withStd = map(withStd)) + override def fit(dataset: DataFrame): StandardScalerModel = { + transformSchema(dataset.schema, logging = true) + val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v } + val scaler = new feature.StandardScaler(withMean = $(withMean), withStd = $(withStd)) val scalerModel = scaler.fit(input) - val model = new StandardScalerModel(this, map, scalerModel) - Params.inheritValues(map, this, model) - model + copyValues(new StandardScalerModel(this, scalerModel)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputType = schema(map(inputCol)).dataType + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], - s"Input column ${map(inputCol)} must be a vector column") - require(!schema.fieldNames.contains(map(outputCol)), - s"Output column ${map(outputCol)} already exists.") - val outputFields = schema.fields :+ StructField(map(outputCol), new VectorUDT, false) + s"Input column ${$(inputCol)} must be a vector column") + require(!schema.fieldNames.contains($(outputCol)), + s"Output column ${$(outputCol)} already exists.") + val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false) StructType(outputFields) } } @@ -99,7 +97,6 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP @AlphaComponent class StandardScalerModel private[ml] ( override val parent: StandardScaler, - override val fittingParamMap: ParamMap, scaler: feature.StandardScalerModel) extends Model[StandardScalerModel] with StandardScalerParams { @@ -109,21 +106,19 @@ class StandardScalerModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val scale = udf((v: Vector) => { scaler.transform(v) } : Vector) - dataset.withColumn(map(outputCol), scale(col(map(inputCol)))) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val scale = udf { scaler.transform _ } + dataset.withColumn($(outputCol), scale(col($(inputCol)))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputType = schema(map(inputCol)).dataType + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], - s"Input column ${map(inputCol)} must be a vector column") - require(!schema.fieldNames.contains(map(outputCol)), - s"Output column ${map(outputCol)} already exists.") - val outputFields = schema.fields :+ StructField(map(outputCol), new VectorUDT, false) + s"Input column ${$(inputCol)} must be a vector column") + require(!schema.fieldNames.contains($(outputCol)), + s"Output column ${$(outputCol)} already exists.") + val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false) StructType(outputFields) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala index 9db3b29e10d69..3d78537ad84cb 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala @@ -34,18 +34,17 @@ import org.apache.spark.util.collection.OpenHashMap private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol { /** Validates and transforms the input schema. */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputColName = map(inputCol) + protected def validateAndTransformSchema(schema: StructType): StructType = { + val inputColName = $(inputCol) val inputDataType = schema(inputColName).dataType require(inputDataType == StringType || inputDataType.isInstanceOf[NumericType], s"The input column $inputColName must be either string type or numeric type, " + s"but got $inputDataType.") val inputFields = schema.fields - val outputColName = map(outputCol) + val outputColName = $(outputCol) require(inputFields.forall(_.name != outputColName), s"Output column $outputColName already exists.") - val attr = NominalAttribute.defaultAttr.withName(map(outputCol)) + val attr = NominalAttribute.defaultAttr.withName($(outputCol)) val outputFields = inputFields :+ attr.toStructField() StructType(outputFields) } @@ -69,19 +68,16 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase // TODO: handle unseen labels - override def fit(dataset: DataFrame, paramMap: ParamMap): StringIndexerModel = { - val map = extractParamMap(paramMap) - val counts = dataset.select(col(map(inputCol)).cast(StringType)) + override def fit(dataset: DataFrame): StringIndexerModel = { + val counts = dataset.select(col($(inputCol)).cast(StringType)) .map(_.getString(0)) .countByValue() val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray - val model = new StringIndexerModel(this, map, labels) - Params.inheritValues(map, this, model) - model + copyValues(new StringIndexerModel(this, labels)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -92,7 +88,6 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase @AlphaComponent class StringIndexerModel private[ml] ( override val parent: StringIndexer, - override val fittingParamMap: ParamMap, labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase { private val labelToIndex: OpenHashMap[String, Double] = { @@ -112,8 +107,7 @@ class StringIndexerModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { val indexer = udf { label: String => if (labelToIndex.contains(label)) { labelToIndex(label) @@ -122,14 +116,14 @@ class StringIndexerModel private[ml] ( throw new SparkException(s"Unseen label: $label.") } } - val outputColName = map(outputCol) + val outputColName = $(outputCol) val metadata = NominalAttribute.defaultAttr .withName(outputColName).withValues(labels).toMetadata() dataset.select(col("*"), - indexer(dataset(map(inputCol)).cast(StringType)).as(outputColName, metadata)) + indexer(dataset($(inputCol)).cast(StringType)).as(outputColName, metadata)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala index 376a004858b4c..2863b7621526e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala @@ -19,8 +19,8 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{ParamMap, IntParam, BooleanParam, Param} -import org.apache.spark.sql.types.{DataType, StringType, ArrayType} +import org.apache.spark.ml.param._ +import org.apache.spark.sql.types.{ArrayType, DataType, StringType} /** * :: AlphaComponent :: @@ -29,7 +29,7 @@ import org.apache.spark.sql.types.{DataType, StringType, ArrayType} @AlphaComponent class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { - override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { + override protected def createTransformFunc: String => Seq[String] = { _.toLowerCase.split("\\s") } @@ -43,29 +43,30 @@ class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { /** * :: AlphaComponent :: * A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default) - * or using it to split the text (set matching to false). Optional parameters also allow to fold - * the text to lowercase prior to it being tokenized and to filer tokens using a minimal length. + * or using it to split the text (set matching to false). Optional parameters also allow filtering + * tokens using a minimal length. * It returns an array of strings that can be empty. - * The default parameters are regex = "\\p{L}+|[^\\p{L}\\s]+", matching = true, - * lowercase = false, minTokenLength = 1 */ @AlphaComponent class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenizer] { /** - * param for minimum token length, default is one to avoid returning empty strings + * Minimum token length, >= 0. + * Default: 1, to avoid returning empty strings * @group param */ - val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length") + val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length (>= 0)", + ParamValidators.gtEq(0)) /** @group setParam */ def setMinTokenLength(value: Int): this.type = set(minTokenLength, value) /** @group getParam */ - def getMinTokenLength: Int = getOrDefault(minTokenLength) + def getMinTokenLength: Int = $(minTokenLength) /** - * param sets regex as splitting on gaps (true) or matching tokens (false) + * Indicates whether regex splits on gaps (true) or matching tokens (false). + * Default: false * @group param */ val gaps: BooleanParam = new BooleanParam(this, "gaps", "Set regex to match gaps or tokens") @@ -74,10 +75,11 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def setGaps(value: Boolean): this.type = set(gaps, value) /** @group getParam */ - def getGaps: Boolean = getOrDefault(gaps) + def getGaps: Boolean = $(gaps) /** - * param sets regex pattern used by tokenizer + * Regex pattern used by tokenizer. + * Default: `"\\p{L}+|[^\\p{L}\\s]+"` * @group param */ val pattern: Param[String] = new Param(this, "pattern", "regex pattern used for tokenizing") @@ -86,14 +88,14 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def setPattern(value: String): this.type = set(pattern, value) /** @group getParam */ - def getPattern: String = getOrDefault(pattern) + def getPattern: String = $(pattern) setDefault(minTokenLength -> 1, gaps -> false, pattern -> "\\p{L}+|[^\\p{L}\\s]+") - override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { str => - val re = paramMap(pattern).r - val tokens = if (paramMap(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq - val minLength = paramMap(minTokenLength) + override protected def createTransformFunc: String => Seq[String] = { str => + val re = $(pattern).r + val tokens = if ($(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq + val minLength = $(minTokenLength) tokens.filter(_.length >= minLength) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala index 7b2a451ca5ee5..8f2e62a8e2081 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -22,12 +22,9 @@ import scala.collection.mutable.ArrayBuilder import org.apache.spark.SparkException import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.Transformer -import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors} -import org.apache.spark.sql.{Column, DataFrame, Row} -import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute -import org.apache.spark.sql.catalyst.expressions.{Alias, Cast, CreateStruct} +import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ @@ -44,28 +41,25 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { val assembleFunc = udf { r: Row => VectorAssembler.assemble(r.toSeq: _*) } val schema = dataset.schema - val inputColNames = map(inputCols) + val inputColNames = $(inputCols) val args = inputColNames.map { c => schema(c).dataType match { - case DoubleType => UnresolvedAttribute(c) - case t if t.isInstanceOf[VectorUDT] => UnresolvedAttribute(c) - case _: NumericType | BooleanType => - Alias(Cast(UnresolvedAttribute(c), DoubleType), s"${c}_double_$uid")() + case DoubleType => dataset(c) + case _: VectorUDT => dataset(c) + case _: NumericType | BooleanType => dataset(c).cast(DoubleType).as(s"${c}_double_$uid") } } - dataset.select(col("*"), assembleFunc(new Column(CreateStruct(args))).as(map(outputCol))) + dataset.select(col("*"), assembleFunc(struct(args : _*)).as($(outputCol))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputColNames = map(inputCols) - val outputColName = map(outputCol) + override def transformSchema(schema: StructType): StructType = { + val inputColNames = $(inputCols) + val outputColName = $(outputCol) val inputDataTypes = inputColNames.map(name => schema(name).dataType) inputDataTypes.foreach { case _: NumericType | BooleanType => diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala index 452faa06e2021..07ea579d69893 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala @@ -18,36 +18,36 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.ml.{Estimator, Model} -import org.apache.spark.ml.attribute.{BinaryAttribute, NumericAttribute, NominalAttribute, - Attribute, AttributeGroup} -import org.apache.spark.ml.param.{IntParam, ParamMap, Params} +import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, BinaryAttribute, NominalAttribute, NumericAttribute} +import org.apache.spark.ml.param.{IntParam, ParamValidators, Params} import org.apache.spark.ml.param.shared._ -import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT} -import org.apache.spark.sql.{Row, DataFrame} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, VectorUDT} +import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions.callUDF import org.apache.spark.sql.types.{StructField, StructType} import org.apache.spark.util.collection.OpenHashSet - /** Private trait for params for VectorIndexer and VectorIndexerModel */ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOutputCol { /** * Threshold for the number of values a categorical feature can take. * If a feature is found to have > maxCategories values, then it is declared continuous. + * Must be >= 2. * * (default = 20) */ val maxCategories = new IntParam(this, "maxCategories", - "Threshold for the number of values a categorical feature can take." + - " If a feature is found to have > maxCategories values, then it is declared continuous.") - - /** @group getParam */ - def getMaxCategories: Int = getOrDefault(maxCategories) + "Threshold for the number of values a categorical feature can take (>= 2)." + + " If a feature is found to have > maxCategories values, then it is declared continuous.", + ParamValidators.gtEq(2)) setDefault(maxCategories -> 20) + + /** @group getParam */ + def getMaxCategories: Int = $(maxCategories) } /** @@ -90,11 +90,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerParams { /** @group setParam */ - def setMaxCategories(value: Int): this.type = { - require(value > 1, - s"DatasetIndexer given maxCategories = value, but requires maxCategories > 1.") - set(maxCategories, value) - } + def setMaxCategories(value: Int): this.type = set(maxCategories, value) /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) @@ -102,33 +98,29 @@ class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerPara /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): VectorIndexerModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val firstRow = dataset.select(map(inputCol)).take(1) + override def fit(dataset: DataFrame): VectorIndexerModel = { + transformSchema(dataset.schema, logging = true) + val firstRow = dataset.select($(inputCol)).take(1) require(firstRow.length == 1, s"VectorIndexer cannot be fit on an empty dataset.") val numFeatures = firstRow(0).getAs[Vector](0).size - val vectorDataset = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val maxCats = map(maxCategories) + val vectorDataset = dataset.select($(inputCol)).map { case Row(v: Vector) => v } + val maxCats = $(maxCategories) val categoryStats: VectorIndexer.CategoryStats = vectorDataset.mapPartitions { iter => val localCatStats = new VectorIndexer.CategoryStats(numFeatures, maxCats) iter.foreach(localCatStats.addVector) Iterator(localCatStats) }.reduce((stats1, stats2) => stats1.merge(stats2)) - val model = new VectorIndexerModel(this, map, numFeatures, categoryStats.getCategoryMaps) - Params.inheritValues(map, this, model) - model + copyValues(new VectorIndexerModel(this, numFeatures, categoryStats.getCategoryMaps)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + override def transformSchema(schema: StructType): StructType = { // We do not transfer feature metadata since we do not know what types of features we will // produce in transform(). - val map = extractParamMap(paramMap) val dataType = new VectorUDT - require(map.contains(inputCol), s"VectorIndexer requires input column parameter: $inputCol") - require(map.contains(outputCol), s"VectorIndexer requires output column parameter: $outputCol") - SchemaUtils.checkColumnType(schema, map(inputCol), dataType) - SchemaUtils.appendColumn(schema, map(outputCol), dataType) + require(isDefined(inputCol), s"VectorIndexer requires input column parameter: $inputCol") + require(isDefined(outputCol), s"VectorIndexer requires output column parameter: $outputCol") + SchemaUtils.checkColumnType(schema, $(inputCol), dataType) + SchemaUtils.appendColumn(schema, $(outputCol), dataType) } } @@ -233,6 +225,7 @@ private object VectorIndexer { * - Continuous features (columns) are left unchanged. * This also appends metadata to the output column, marking features as Numeric (continuous), * Nominal (categorical), or Binary (either continuous or categorical). + * Non-ML metadata is not carried over from the input to the output column. * * This maintains vector sparsity. * @@ -244,7 +237,6 @@ private object VectorIndexer { @AlphaComponent class VectorIndexerModel private[ml] ( override val parent: VectorIndexer, - override val fittingParamMap: ParamMap, val numFeatures: Int, val categoryMaps: Map[Int, Map[Double, Int]]) extends Model[VectorIndexerModel] with VectorIndexerParams { @@ -283,34 +275,40 @@ class VectorIndexerModel private[ml] ( // TODO: Check more carefully about whether this whole class will be included in a closure. + /** Per-vector transform function */ private val transformFunc: Vector => Vector = { - val sortedCategoricalFeatureIndices = categoryMaps.keys.toArray.sorted + val sortedCatFeatureIndices = categoryMaps.keys.toArray.sorted val localVectorMap = categoryMaps - val f: Vector => Vector = { - case dv: DenseVector => - val tmpv = dv.copy - localVectorMap.foreach { case (featureIndex: Int, categoryMap: Map[Double, Int]) => - tmpv.values(featureIndex) = categoryMap(tmpv(featureIndex)) - } - tmpv - case sv: SparseVector => - // We use the fact that categorical value 0 is always mapped to index 0. - val tmpv = sv.copy - var catFeatureIdx = 0 // index into sortedCategoricalFeatureIndices - var k = 0 // index into non-zero elements of sparse vector - while (catFeatureIdx < sortedCategoricalFeatureIndices.length && k < tmpv.indices.length) { - val featureIndex = sortedCategoricalFeatureIndices(catFeatureIdx) - if (featureIndex < tmpv.indices(k)) { - catFeatureIdx += 1 - } else if (featureIndex > tmpv.indices(k)) { - k += 1 - } else { - tmpv.values(k) = localVectorMap(featureIndex)(tmpv.values(k)) - catFeatureIdx += 1 - k += 1 + val localNumFeatures = numFeatures + val f: Vector => Vector = { (v: Vector) => + assert(v.size == localNumFeatures, "VectorIndexerModel expected vector of length" + + s" $numFeatures but found length ${v.size}") + v match { + case dv: DenseVector => + val tmpv = dv.copy + localVectorMap.foreach { case (featureIndex: Int, categoryMap: Map[Double, Int]) => + tmpv.values(featureIndex) = categoryMap(tmpv(featureIndex)) } - } - tmpv + tmpv + case sv: SparseVector => + // We use the fact that categorical value 0 is always mapped to index 0. + val tmpv = sv.copy + var catFeatureIdx = 0 // index into sortedCatFeatureIndices + var k = 0 // index into non-zero elements of sparse vector + while (catFeatureIdx < sortedCatFeatureIndices.length && k < tmpv.indices.length) { + val featureIndex = sortedCatFeatureIndices(catFeatureIdx) + if (featureIndex < tmpv.indices(k)) { + catFeatureIdx += 1 + } else if (featureIndex > tmpv.indices(k)) { + k += 1 + } else { + tmpv.values(k) = localVectorMap(featureIndex)(tmpv.values(k)) + catFeatureIdx += 1 + k += 1 + } + } + tmpv + } } f } @@ -321,41 +319,33 @@ class VectorIndexerModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val newField = prepOutputField(dataset.schema, map) - val newCol = callUDF(transformFunc, new VectorUDT, dataset(map(inputCol))) - // For now, just check the first row of inputCol for vector length. - val firstRow = dataset.select(map(inputCol)).take(1) - if (firstRow.length != 0) { - val actualNumFeatures = firstRow(0).getAs[Vector](0).size - require(numFeatures == actualNumFeatures, "VectorIndexerModel expected vector of length" + - s" $numFeatures but found length $actualNumFeatures") - } - dataset.withColumn(map(outputCol), newCol.as(map(outputCol), newField.metadata)) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val newField = prepOutputField(dataset.schema) + val newCol = callUDF(transformFunc, new VectorUDT, dataset($(inputCol))) + dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) + override def transformSchema(schema: StructType): StructType = { val dataType = new VectorUDT - require(map.contains(inputCol), + require(isDefined(inputCol), s"VectorIndexerModel requires input column parameter: $inputCol") - require(map.contains(outputCol), + require(isDefined(outputCol), s"VectorIndexerModel requires output column parameter: $outputCol") - SchemaUtils.checkColumnType(schema, map(inputCol), dataType) + SchemaUtils.checkColumnType(schema, $(inputCol), dataType) - val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + // If the input metadata specifies numFeatures, compare with expected numFeatures. + val origAttrGroup = AttributeGroup.fromStructField(schema($(inputCol))) val origNumFeatures: Option[Int] = if (origAttrGroup.attributes.nonEmpty) { Some(origAttrGroup.attributes.get.length) } else { origAttrGroup.numAttributes } require(origNumFeatures.forall(_ == numFeatures), "VectorIndexerModel expected" + - s" $numFeatures features, but input column ${map(inputCol)} had metadata specifying" + + s" $numFeatures features, but input column ${$(inputCol)} had metadata specifying" + s" ${origAttrGroup.numAttributes.get} features.") - val newField = prepOutputField(schema, map) + val newField = prepOutputField(schema) val outputFields = schema.fields :+ newField StructType(outputFields) } @@ -363,11 +353,10 @@ class VectorIndexerModel private[ml] ( /** * Prepare the output column field, including per-feature metadata. * @param schema Input schema - * @param map Parameter map (with this class' embedded parameter map folded in) - * @return Output column field + * @return Output column field. This field does not contain non-ML metadata. */ - private def prepOutputField(schema: StructType, map: ParamMap): StructField = { - val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + private def prepOutputField(schema: StructType): StructField = { + val origAttrGroup = AttributeGroup.fromStructField(schema($(inputCol))) val featureAttributes: Array[Attribute] = if (origAttrGroup.attributes.nonEmpty) { // Convert original attributes to modified attributes val origAttrs: Array[Attribute] = origAttrGroup.attributes.get @@ -390,7 +379,7 @@ class VectorIndexerModel private[ml] ( } else { partialFeatureAttributes } - val newAttributeGroup = new AttributeGroup(map(outputCol), featureAttributes) - newAttributeGroup.toStructField(schema(map(inputCol)).metadata) + val newAttributeGroup = new AttributeGroup($(outputCol), featureAttributes) + newAttributeGroup.toStructField() } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala new file mode 100644 index 0000000000000..34ff92970129f --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala @@ -0,0 +1,179 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.{Estimator, Model} +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.mllib.feature +import org.apache.spark.mllib.linalg.{VectorUDT, Vectors} +import org.apache.spark.mllib.linalg.BLAS._ +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ + +/** + * Params for [[Word2Vec]] and [[Word2VecModel]]. + */ +private[feature] trait Word2VecBase extends Params + with HasInputCol with HasOutputCol with HasMaxIter with HasStepSize with HasSeed { + + /** + * The dimension of the code that you want to transform from words. + */ + final val vectorSize = new IntParam( + this, "vectorSize", "the dimension of codes after transforming from words") + setDefault(vectorSize -> 100) + + /** @group getParam */ + def getVectorSize: Int = $(vectorSize) + + /** + * Number of partitions for sentences of words. + */ + final val numPartitions = new IntParam( + this, "numPartitions", "number of partitions for sentences of words") + setDefault(numPartitions -> 1) + + /** @group getParam */ + def getNumPartitions: Int = $(numPartitions) + + /** + * The minimum number of times a token must appear to be included in the word2vec model's + * vocabulary. + */ + final val minCount = new IntParam(this, "minCount", "the minimum number of times a token must " + + "appear to be included in the word2vec model's vocabulary") + setDefault(minCount -> 5) + + /** @group getParam */ + def getMinCount: Int = $(minCount) + + setDefault(stepSize -> 0.025) + setDefault(maxIter -> 1) + setDefault(seed -> 42L) + + /** + * Validate and transform the input schema. + */ + protected def validateAndTransformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), new ArrayType(StringType, true)) + SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT) + } +} + +/** + * :: AlphaComponent :: + * Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further + * natural language processing or machine learning process. + */ +@AlphaComponent +final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase { + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + /** @group setParam */ + def setVectorSize(value: Int): this.type = set(vectorSize, value) + + /** @group setParam */ + def setStepSize(value: Double): this.type = set(stepSize, value) + + /** @group setParam */ + def setNumPartitions(value: Int): this.type = set(numPartitions, value) + + /** @group setParam */ + def setMaxIter(value: Int): this.type = set(maxIter, value) + + /** @group setParam */ + def setSeed(value: Long): this.type = set(seed, value) + + /** @group setParam */ + def setMinCount(value: Int): this.type = set(minCount, value) + + override def fit(dataset: DataFrame): Word2VecModel = { + transformSchema(dataset.schema, logging = true) + val input = dataset.select($(inputCol)).map(_.getAs[Seq[String]](0)) + val wordVectors = new feature.Word2Vec() + .setLearningRate($(stepSize)) + .setMinCount($(minCount)) + .setNumIterations($(maxIter)) + .setNumPartitions($(numPartitions)) + .setSeed($(seed)) + .setVectorSize($(vectorSize)) + .fit(input) + copyValues(new Word2VecModel(this, wordVectors)) + } + + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) + } +} + +/** + * :: AlphaComponent :: + * Model fitted by [[Word2Vec]]. + */ +@AlphaComponent +class Word2VecModel private[ml] ( + override val parent: Word2Vec, + wordVectors: feature.Word2VecModel) + extends Model[Word2VecModel] with Word2VecBase { + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + /** + * Transform a sentence column to a vector column to represent the whole sentence. The transform + * is performed by averaging all word vectors it contains. + */ + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val bWordVectors = dataset.sqlContext.sparkContext.broadcast(wordVectors) + val word2Vec = udf { sentence: Seq[String] => + if (sentence.size == 0) { + Vectors.sparse($(vectorSize), Array.empty[Int], Array.empty[Double]) + } else { + val cum = Vectors.zeros($(vectorSize)) + val model = bWordVectors.value.getVectors + for (word <- sentence) { + if (model.contains(word)) { + axpy(1.0, bWordVectors.value.transform(word), cum) + } else { + // pass words which not belong to model + } + } + scal(1.0 / sentence.size, cum) + cum + } + } + dataset.withColumn($(outputCol), word2Vec(col($(inputCol)))) + } + + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 014e124e440a3..51ce19d29cd29 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -23,7 +23,7 @@ import java.util.NoSuchElementException import scala.annotation.varargs import scala.collection.mutable -import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} +import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.util.Identifiable /** @@ -34,10 +34,35 @@ import org.apache.spark.ml.util.Identifiable * @param parent parent object * @param name param name * @param doc documentation + * @param isValid optional validation method which indicates if a value is valid. + * See [[ParamValidators]] for factory methods for common validation functions. * @tparam T param value type */ @AlphaComponent -class Param[T] (val parent: Params, val name: String, val doc: String) extends Serializable { +class Param[T] (val parent: Params, val name: String, val doc: String, val isValid: T => Boolean) + extends Serializable { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue[T]) + + /** + * Assert that the given value is valid for this parameter. + * + * Note: Parameter checks involving interactions between multiple parameters should be + * implemented in [[Params.validateParams()]]. Checks for input/output columns should be + * implemented in [[org.apache.spark.ml.PipelineStage.transformSchema()]]. + * + * DEVELOPERS: This method is only called by [[ParamPair]], which means that all parameters + * should be specified via [[ParamPair]]. + * + * @throws IllegalArgumentException if the value is invalid + */ + private[param] def validate(value: T): Unit = { + if (!isValid(value)) { + throw new IllegalArgumentException(s"$parent parameter $name given invalid value $value." + + s" Parameter description: $toString") + } + } /** * Creates a param pair with the given value (for Java). @@ -65,38 +90,129 @@ class Param[T] (val parent: Params, val name: String, val doc: String) extends S } } +/** + * Factory methods for common validation functions for [[Param.isValid]]. + * The numerical methods only support Int, Long, Float, and Double. + */ +object ParamValidators { + + /** (private[param]) Default validation always return true */ + private[param] def alwaysTrue[T]: T => Boolean = (_: T) => true + + /** + * Private method for checking numerical types and converting to Double. + * This is mainly for the sake of compilation; type checks are really handled + * by [[Params]] setters and the [[ParamPair]] constructor. + */ + private def getDouble[T](value: T): Double = value match { + case x: Int => x.toDouble + case x: Long => x.toDouble + case x: Float => x.toDouble + case x: Double => x.toDouble + case _ => + // The type should be checked before this is ever called. + throw new IllegalArgumentException("Numerical Param validation failed because" + + s" of unexpected input type: ${value.getClass}") + } + + /** Check if value > lowerBound */ + def gt[T](lowerBound: Double): T => Boolean = { (value: T) => + getDouble(value) > lowerBound + } + + /** Check if value >= lowerBound */ + def gtEq[T](lowerBound: Double): T => Boolean = { (value: T) => + getDouble(value) >= lowerBound + } + + /** Check if value < upperBound */ + def lt[T](upperBound: Double): T => Boolean = { (value: T) => + getDouble(value) < upperBound + } + + /** Check if value <= upperBound */ + def ltEq[T](upperBound: Double): T => Boolean = { (value: T) => + getDouble(value) <= upperBound + } + + /** + * Check for value in range lowerBound to upperBound. + * @param lowerInclusive If true, check for value >= lowerBound. + * If false, check for value > lowerBound. + * @param upperInclusive If true, check for value <= upperBound. + * If false, check for value < upperBound. + */ + def inRange[T]( + lowerBound: Double, + upperBound: Double, + lowerInclusive: Boolean, + upperInclusive: Boolean): T => Boolean = { (value: T) => + val x: Double = getDouble(value) + val lowerValid = if (lowerInclusive) x >= lowerBound else x > lowerBound + val upperValid = if (upperInclusive) x <= upperBound else x < upperBound + lowerValid && upperValid + } + + /** Version of [[inRange()]] which uses inclusive be default: [lowerBound, upperBound] */ + def inRange[T](lowerBound: Double, upperBound: Double): T => Boolean = { + inRange[T](lowerBound, upperBound, lowerInclusive = true, upperInclusive = true) + } + + /** Check for value in an allowed set of values. */ + def inArray[T](allowed: Array[T]): T => Boolean = { (value: T) => + allowed.contains(value) + } + + /** Check for value in an allowed set of values. */ + def inArray[T](allowed: java.util.List[T]): T => Boolean = { (value: T) => + allowed.contains(value) + } +} + // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ... /** Specialized version of [[Param[Double]]] for Java. */ -class DoubleParam(parent: Params, name: String, doc: String) - extends Param[Double](parent, name, doc) { +class DoubleParam(parent: Params, name: String, doc: String, isValid: Double => Boolean) + extends Param[Double](parent, name, doc, isValid) { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue) override def w(value: Double): ParamPair[Double] = super.w(value) } /** Specialized version of [[Param[Int]]] for Java. */ -class IntParam(parent: Params, name: String, doc: String) - extends Param[Int](parent, name, doc) { +class IntParam(parent: Params, name: String, doc: String, isValid: Int => Boolean) + extends Param[Int](parent, name, doc, isValid) { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue) override def w(value: Int): ParamPair[Int] = super.w(value) } /** Specialized version of [[Param[Float]]] for Java. */ -class FloatParam(parent: Params, name: String, doc: String) - extends Param[Float](parent, name, doc) { +class FloatParam(parent: Params, name: String, doc: String, isValid: Float => Boolean) + extends Param[Float](parent, name, doc, isValid) { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue) override def w(value: Float): ParamPair[Float] = super.w(value) } /** Specialized version of [[Param[Long]]] for Java. */ -class LongParam(parent: Params, name: String, doc: String) - extends Param[Long](parent, name, doc) { +class LongParam(parent: Params, name: String, doc: String, isValid: Long => Boolean) + extends Param[Long](parent, name, doc, isValid) { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue) override def w(value: Long): ParamPair[Long] = super.w(value) } /** Specialized version of [[Param[Boolean]]] for Java. */ -class BooleanParam(parent: Params, name: String, doc: String) +class BooleanParam(parent: Params, name: String, doc: String) // No need for isValid extends Param[Boolean](parent, name, doc) { override def w(value: Boolean): ParamPair[Boolean] = super.w(value) @@ -105,7 +221,11 @@ class BooleanParam(parent: Params, name: String, doc: String) /** * A param amd its value. */ -case class ParamPair[T](param: Param[T], value: T) +case class ParamPair[T](param: Param[T], value: T) { + // This is *the* place Param.validate is called. Whenever a parameter is specified, we should + // always construct a ParamPair so that validate is called. + param.validate(value) +} /** * :: AlphaComponent :: @@ -132,14 +252,30 @@ trait Params extends Identifiable with Serializable { /** * Validates parameter values stored internally plus the input parameter map. * Raises an exception if any parameter is invalid. - */ - def validate(paramMap: ParamMap): Unit = {} + * + * This only needs to check for interactions between parameters. + * Parameter value checks which do not depend on other parameters are handled by + * [[Param.validate()]]. This method does not handle input/output column parameters; + * those are checked during schema validation. + */ + def validateParams(paramMap: ParamMap): Unit = { + copy(paramMap).validateParams() + } /** * Validates parameter values stored internally. * Raise an exception if any parameter value is invalid. - */ - def validate(): Unit = validate(ParamMap.empty) + * + * This only needs to check for interactions between parameters. + * Parameter value checks which do not depend on other parameters are handled by + * [[Param.validate()]]. This method does not handle input/output column parameters; + * those are checked during schema validation. + */ + def validateParams(): Unit = { + params.filter(isDefined _).foreach { param => + param.asInstanceOf[Param[Any]].validate($(param)) + } + } /** * Returns the documentation of all params. @@ -158,6 +294,11 @@ trait Params extends Identifiable with Serializable { defaultParamMap.contains(param) || paramMap.contains(param) } + /** Tests whether this instance contains a param with a given name. */ + def hasParam(paramName: String): Boolean = { + params.exists(_.name == paramName) + } + /** Gets a param by its name. */ def getParam(paramName: String): Param[Any] = { params.find(_.name == paramName).getOrElse { @@ -207,6 +348,9 @@ trait Params extends Identifiable with Serializable { get(param).orElse(getDefault(param)).get } + /** An alias for [[getOrDefault()]]. */ + protected final def $[T](param: Param[T]): T = getOrDefault(param) + /** * Sets a default value for a param. * @param param param to set the default value. Make sure that this param is initialized before @@ -221,6 +365,10 @@ trait Params extends Identifiable with Serializable { /** * Sets default values for a list of params. + * + * Note: Java developers should use the single-parameter [[setDefault()]]. + * Annotating this with varargs causes compilation failures. + * * @param paramPairs a list of param pairs that specify params and their default values to set * respectively. Make sure that the params are initialized before this method * gets called. @@ -248,19 +396,31 @@ trait Params extends Identifiable with Serializable { defaultParamMap.contains(param) } + /** + * Creates a copy of this instance with a randomly generated uid and some extra params. + * The default implementation calls the default constructor to create a new instance, then + * copies the embedded and extra parameters over and returns the new instance. + * Subclasses should override this method if the default approach is not sufficient. + */ + def copy(extra: ParamMap): Params = { + val that = this.getClass.newInstance() + copyValues(that, extra) + that + } + /** * Extracts the embedded default param values and user-supplied values, and then merges them with * extra values from input into a flat param map, where the latter value is used if there exist * conflicts, i.e., with ordering: default param values < user-supplied values < extraParamMap. */ - protected final def extractParamMap(extraParamMap: ParamMap): ParamMap = { + final def extractParamMap(extraParamMap: ParamMap): ParamMap = { defaultParamMap ++ paramMap ++ extraParamMap } /** * [[extractParamMap]] with no extra values. */ - protected final def extractParamMap(): ParamMap = { + final def extractParamMap(): ParamMap = { extractParamMap(ParamMap.empty) } @@ -274,37 +434,32 @@ trait Params extends Identifiable with Serializable { private def shouldOwn(param: Param[_]): Unit = { require(param.parent.eq(this), s"Param $param does not belong to $this.") } -} -/** - * :: DeveloperApi :: - * - * Helper functionality for developers. - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. - */ -@DeveloperApi -private[spark] object Params { - - /** - * Copies parameter values from the parent estimator to the child model it produced. - * @param paramMap the param map that holds parameters of the parent - * @param parent the parent estimator - * @param child the child model - */ - def inheritValues[E <: Params, M <: E]( - paramMap: ParamMap, - parent: E, - child: M): Unit = { - val childParams = child.params.map(_.name).toSet - parent.params.foreach { param => - if (paramMap.contains(param) && childParams.contains(param.name)) { - child.set(child.getParam(param.name), paramMap(param)) + /** + * Copies param values from this instance to another instance for params shared by them. + * @param to the target instance + * @param extra extra params to be copied + * @return the target instance with param values copied + */ + protected def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T = { + val map = extractParamMap(extra) + params.foreach { param => + if (map.contains(param) && to.hasParam(param.name)) { + to.set(param.name, map(param)) } } + to } } +/** + * Java-friendly wrapper for [[Params]]. + * Java developers who need to extend [[Params]] should use this class instead. + * If you need to extend a abstract class which already extends [[Params]], then that abstract + * class should be Java-friendly as well. + */ +abstract class JavaParams extends Params + /** * :: AlphaComponent :: * A param to value map. @@ -313,6 +468,12 @@ private[spark] object Params { final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) extends Serializable { + /* DEVELOPERS: About validating parameter values + * This and ParamPair are the only two collections of parameters. + * This class should always create ParamPairs when + * specifying new parameter values. ParamPair will then call Param.validate(). + */ + /** * Creates an empty param map. */ @@ -321,10 +482,7 @@ final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) /** * Puts a (param, value) pair (overwrites if the input param exists). */ - def put[T](param: Param[T], value: T): this.type = { - map(param.asInstanceOf[Param[Any]]) = value - this - } + def put[T](param: Param[T], value: T): this.type = put(ParamPair(param, value)) /** * Puts a list of param pairs (overwrites if the input params exists). @@ -332,7 +490,7 @@ final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) @varargs def put(paramPairs: ParamPair[_]*): this.type = { paramPairs.foreach { p => - put(p.param.asInstanceOf[Param[Any]], p.value) + map(p.param.asInstanceOf[Param[Any]]) = p.value } this } diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index 3f7e8f5a0b22c..0e1ff97a8bf60 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -31,24 +31,34 @@ private[shared] object SharedParamsCodeGen { def main(args: Array[String]): Unit = { val params = Seq( - ParamDesc[Double]("regParam", "regularization parameter"), - ParamDesc[Int]("maxIter", "max number of iterations"), + ParamDesc[Double]("regParam", "regularization parameter (>= 0)", + isValid = "ParamValidators.gtEq(0)"), + ParamDesc[Int]("maxIter", "max number of iterations (>= 0)", + isValid = "ParamValidators.gtEq(0)"), ParamDesc[String]("featuresCol", "features column name", Some("\"features\"")), ParamDesc[String]("labelCol", "label column name", Some("\"label\"")), ParamDesc[String]("predictionCol", "prediction column name", Some("\"prediction\"")), ParamDesc[String]("rawPredictionCol", "raw prediction (a.k.a. confidence) column name", Some("\"rawPrediction\"")), - ParamDesc[String]("probabilityCol", - "column name for predicted class conditional probabilities", Some("\"probability\"")), - ParamDesc[Double]("threshold", "threshold in binary classification prediction"), + ParamDesc[String]("probabilityCol", "Column name for predicted class conditional" + + " probabilities. Note: Not all models output well-calibrated probability estimates!" + + " These probabilities should be treated as confidences, not precise probabilities.", + Some("\"probability\"")), + ParamDesc[Double]("threshold", + "threshold in binary classification prediction, in range [0, 1]", + isValid = "ParamValidators.inRange(0, 1)"), ParamDesc[String]("inputCol", "input column name"), ParamDesc[Array[String]]("inputCols", "input column names"), ParamDesc[String]("outputCol", "output column name"), - ParamDesc[Int]("checkpointInterval", "checkpoint interval"), + ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)", + isValid = "ParamValidators.gtEq(1)"), ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")), ParamDesc[Long]("seed", "random seed", Some("Utils.random.nextLong()")), - ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter"), - ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms")) + ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]." + + " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", + isValid = "ParamValidators.inRange(0, 1)"), + ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms"), + ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization.")) val code = genSharedParams(params) val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala" @@ -61,7 +71,8 @@ private[shared] object SharedParamsCodeGen { private case class ParamDesc[T: ClassTag]( name: String, doc: String, - defaultValueStr: Option[String] = None) { + defaultValueStr: Option[String] = None, + isValid: String = "") { require(name.matches("[a-z][a-zA-Z0-9]*"), s"Param name $name is invalid.") require(doc.nonEmpty) // TODO: more rigorous on doc @@ -112,23 +123,26 @@ private[shared] object SharedParamsCodeGen { | setDefault($name, $v) |""".stripMargin }.getOrElse("") + val isValid = if (param.isValid != "") { + ", " + param.isValid + } else { + "" + } s""" |/** - | * :: DeveloperApi :: - | * Trait for shared param $name$defaultValueDoc. + | * (private[ml]) Trait for shared param $name$defaultValueDoc. | */ - |@DeveloperApi - |trait Has$Name extends Params { + |private[ml] trait Has$Name extends Params { | | /** | * Param for $doc. | * @group param | */ - | final val $name: $Param = new $Param(this, "$name", "$doc") + | final val $name: $Param = new $Param(this, "$name", "$doc"$isValid) |$setDefault | /** @group getParam */ - | final def get$Name: $T = getOrDefault($name) + | final def get$Name: $T = $$($name) |} |""".stripMargin } @@ -155,7 +169,6 @@ private[shared] object SharedParamsCodeGen { | |package org.apache.spark.ml.param.shared | - |import org.apache.spark.annotation.DeveloperApi |import org.apache.spark.ml.param._ |import org.apache.spark.util.Utils | diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index 7d2c76d6c62c8..87f86807c3c91 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -17,7 +17,6 @@ package org.apache.spark.ml.param.shared -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.param._ import org.apache.spark.util.Utils @@ -26,45 +25,39 @@ import org.apache.spark.util.Utils // scalastyle:off /** - * :: DeveloperApi :: - * Trait for shared param regParam. + * (private[ml]) Trait for shared param regParam. */ -@DeveloperApi -trait HasRegParam extends Params { +private[ml] trait HasRegParam extends Params { /** - * Param for regularization parameter. + * Param for regularization parameter (>= 0). * @group param */ - final val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter") + final val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter (>= 0)", ParamValidators.gtEq(0)) /** @group getParam */ - final def getRegParam: Double = getOrDefault(regParam) + final def getRegParam: Double = $(regParam) } /** - * :: DeveloperApi :: - * Trait for shared param maxIter. + * (private[ml]) Trait for shared param maxIter. */ -@DeveloperApi -trait HasMaxIter extends Params { +private[ml] trait HasMaxIter extends Params { /** - * Param for max number of iterations. + * Param for max number of iterations (>= 0). * @group param */ - final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") + final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations (>= 0)", ParamValidators.gtEq(0)) /** @group getParam */ - final def getMaxIter: Int = getOrDefault(maxIter) + final def getMaxIter: Int = $(maxIter) } /** - * :: DeveloperApi :: - * Trait for shared param featuresCol (default: "features"). + * (private[ml]) Trait for shared param featuresCol (default: "features"). */ -@DeveloperApi -trait HasFeaturesCol extends Params { +private[ml] trait HasFeaturesCol extends Params { /** * Param for features column name. @@ -75,15 +68,13 @@ trait HasFeaturesCol extends Params { setDefault(featuresCol, "features") /** @group getParam */ - final def getFeaturesCol: String = getOrDefault(featuresCol) + final def getFeaturesCol: String = $(featuresCol) } /** - * :: DeveloperApi :: - * Trait for shared param labelCol (default: "label"). + * (private[ml]) Trait for shared param labelCol (default: "label"). */ -@DeveloperApi -trait HasLabelCol extends Params { +private[ml] trait HasLabelCol extends Params { /** * Param for label column name. @@ -94,15 +85,13 @@ trait HasLabelCol extends Params { setDefault(labelCol, "label") /** @group getParam */ - final def getLabelCol: String = getOrDefault(labelCol) + final def getLabelCol: String = $(labelCol) } /** - * :: DeveloperApi :: - * Trait for shared param predictionCol (default: "prediction"). + * (private[ml]) Trait for shared param predictionCol (default: "prediction"). */ -@DeveloperApi -trait HasPredictionCol extends Params { +private[ml] trait HasPredictionCol extends Params { /** * Param for prediction column name. @@ -113,15 +102,13 @@ trait HasPredictionCol extends Params { setDefault(predictionCol, "prediction") /** @group getParam */ - final def getPredictionCol: String = getOrDefault(predictionCol) + final def getPredictionCol: String = $(predictionCol) } /** - * :: DeveloperApi :: - * Trait for shared param rawPredictionCol (default: "rawPrediction"). + * (private[ml]) Trait for shared param rawPredictionCol (default: "rawPrediction"). */ -@DeveloperApi -trait HasRawPredictionCol extends Params { +private[ml] trait HasRawPredictionCol extends Params { /** * Param for raw prediction (a.k.a. confidence) column name. @@ -132,51 +119,45 @@ trait HasRawPredictionCol extends Params { setDefault(rawPredictionCol, "rawPrediction") /** @group getParam */ - final def getRawPredictionCol: String = getOrDefault(rawPredictionCol) + final def getRawPredictionCol: String = $(rawPredictionCol) } /** - * :: DeveloperApi :: - * Trait for shared param probabilityCol (default: "probability"). + * (private[ml]) Trait for shared param probabilityCol (default: "probability"). */ -@DeveloperApi -trait HasProbabilityCol extends Params { +private[ml] trait HasProbabilityCol extends Params { /** - * Param for column name for predicted class conditional probabilities. + * Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.. * @group param */ - final val probabilityCol: Param[String] = new Param[String](this, "probabilityCol", "column name for predicted class conditional probabilities") + final val probabilityCol: Param[String] = new Param[String](this, "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.") setDefault(probabilityCol, "probability") /** @group getParam */ - final def getProbabilityCol: String = getOrDefault(probabilityCol) + final def getProbabilityCol: String = $(probabilityCol) } /** - * :: DeveloperApi :: - * Trait for shared param threshold. + * (private[ml]) Trait for shared param threshold. */ -@DeveloperApi -trait HasThreshold extends Params { +private[ml] trait HasThreshold extends Params { /** - * Param for threshold in binary classification prediction. + * Param for threshold in binary classification prediction, in range [0, 1]. * @group param */ - final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction") + final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction, in range [0, 1]", ParamValidators.inRange(0, 1)) /** @group getParam */ - final def getThreshold: Double = getOrDefault(threshold) + final def getThreshold: Double = $(threshold) } /** - * :: DeveloperApi :: - * Trait for shared param inputCol. + * (private[ml]) Trait for shared param inputCol. */ -@DeveloperApi -trait HasInputCol extends Params { +private[ml] trait HasInputCol extends Params { /** * Param for input column name. @@ -185,15 +166,13 @@ trait HasInputCol extends Params { final val inputCol: Param[String] = new Param[String](this, "inputCol", "input column name") /** @group getParam */ - final def getInputCol: String = getOrDefault(inputCol) + final def getInputCol: String = $(inputCol) } /** - * :: DeveloperApi :: - * Trait for shared param inputCols. + * (private[ml]) Trait for shared param inputCols. */ -@DeveloperApi -trait HasInputCols extends Params { +private[ml] trait HasInputCols extends Params { /** * Param for input column names. @@ -202,15 +181,13 @@ trait HasInputCols extends Params { final val inputCols: Param[Array[String]] = new Param[Array[String]](this, "inputCols", "input column names") /** @group getParam */ - final def getInputCols: Array[String] = getOrDefault(inputCols) + final def getInputCols: Array[String] = $(inputCols) } /** - * :: DeveloperApi :: - * Trait for shared param outputCol. + * (private[ml]) Trait for shared param outputCol. */ -@DeveloperApi -trait HasOutputCol extends Params { +private[ml] trait HasOutputCol extends Params { /** * Param for output column name. @@ -219,32 +196,28 @@ trait HasOutputCol extends Params { final val outputCol: Param[String] = new Param[String](this, "outputCol", "output column name") /** @group getParam */ - final def getOutputCol: String = getOrDefault(outputCol) + final def getOutputCol: String = $(outputCol) } /** - * :: DeveloperApi :: - * Trait for shared param checkpointInterval. + * (private[ml]) Trait for shared param checkpointInterval. */ -@DeveloperApi -trait HasCheckpointInterval extends Params { +private[ml] trait HasCheckpointInterval extends Params { /** - * Param for checkpoint interval. + * Param for checkpoint interval (>= 1). * @group param */ - final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval") + final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval (>= 1)", ParamValidators.gtEq(1)) /** @group getParam */ - final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) + final def getCheckpointInterval: Int = $(checkpointInterval) } /** - * :: DeveloperApi :: - * Trait for shared param fitIntercept (default: true). + * (private[ml]) Trait for shared param fitIntercept (default: true). */ -@DeveloperApi -trait HasFitIntercept extends Params { +private[ml] trait HasFitIntercept extends Params { /** * Param for whether to fit an intercept term. @@ -255,15 +228,13 @@ trait HasFitIntercept extends Params { setDefault(fitIntercept, true) /** @group getParam */ - final def getFitIntercept: Boolean = getOrDefault(fitIntercept) + final def getFitIntercept: Boolean = $(fitIntercept) } /** - * :: DeveloperApi :: - * Trait for shared param seed (default: Utils.random.nextLong()). + * (private[ml]) Trait for shared param seed (default: Utils.random.nextLong()). */ -@DeveloperApi -trait HasSeed extends Params { +private[ml] trait HasSeed extends Params { /** * Param for random seed. @@ -274,32 +245,28 @@ trait HasSeed extends Params { setDefault(seed, Utils.random.nextLong()) /** @group getParam */ - final def getSeed: Long = getOrDefault(seed) + final def getSeed: Long = $(seed) } /** - * :: DeveloperApi :: - * Trait for shared param elasticNetParam. + * (private[ml]) Trait for shared param elasticNetParam. */ -@DeveloperApi -trait HasElasticNetParam extends Params { +private[ml] trait HasElasticNetParam extends Params { /** - * Param for the ElasticNet mixing parameter. + * Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.. * @group param */ - final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter") + final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", ParamValidators.inRange(0, 1)) /** @group getParam */ - final def getElasticNetParam: Double = getOrDefault(elasticNetParam) + final def getElasticNetParam: Double = $(elasticNetParam) } /** - * :: DeveloperApi :: - * Trait for shared param tol. + * (private[ml]) Trait for shared param tol. */ -@DeveloperApi -trait HasTol extends Params { +private[ml] trait HasTol extends Params { /** * Param for the convergence tolerance for iterative algorithms. @@ -308,6 +275,21 @@ trait HasTol extends Params { final val tol: DoubleParam = new DoubleParam(this, "tol", "the convergence tolerance for iterative algorithms") /** @group getParam */ - final def getTol: Double = getOrDefault(tol) + final def getTol: Double = $(tol) +} + +/** + * (private[ml]) Trait for shared param stepSize. + */ +private[ml] trait HasStepSize extends Params { + + /** + * Param for Step size to be used for each iteration of optimization.. + * @group param + */ + final val stepSize: DoubleParam = new DoubleParam(this, "stepSize", "Step size to be used for each iteration of optimization.") + + /** @group getParam */ + final def getStepSize: Double = $(stepSize) } // scalastyle:on diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index bd793beba35b6..6cf4b40075281 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -52,108 +52,117 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR with HasPredictionCol with HasCheckpointInterval { /** - * Param for rank of the matrix factorization. + * Param for rank of the matrix factorization (>= 1). + * Default: 10 * @group param */ - val rank = new IntParam(this, "rank", "rank of the factorization") + val rank = new IntParam(this, "rank", "rank of the factorization", ParamValidators.gtEq(1)) /** @group getParam */ - def getRank: Int = getOrDefault(rank) + def getRank: Int = $(rank) /** - * Param for number of user blocks. + * Param for number of user blocks (>= 1). + * Default: 10 * @group param */ - val numUserBlocks = new IntParam(this, "numUserBlocks", "number of user blocks") + val numUserBlocks = new IntParam(this, "numUserBlocks", "number of user blocks", + ParamValidators.gtEq(1)) /** @group getParam */ - def getNumUserBlocks: Int = getOrDefault(numUserBlocks) + def getNumUserBlocks: Int = $(numUserBlocks) /** - * Param for number of item blocks. + * Param for number of item blocks (>= 1). + * Default: 10 * @group param */ - val numItemBlocks = - new IntParam(this, "numItemBlocks", "number of item blocks") + val numItemBlocks = new IntParam(this, "numItemBlocks", "number of item blocks", + ParamValidators.gtEq(1)) /** @group getParam */ - def getNumItemBlocks: Int = getOrDefault(numItemBlocks) + def getNumItemBlocks: Int = $(numItemBlocks) /** * Param to decide whether to use implicit preference. + * Default: false * @group param */ val implicitPrefs = new BooleanParam(this, "implicitPrefs", "whether to use implicit preference") /** @group getParam */ - def getImplicitPrefs: Boolean = getOrDefault(implicitPrefs) + def getImplicitPrefs: Boolean = $(implicitPrefs) /** - * Param for the alpha parameter in the implicit preference formulation. + * Param for the alpha parameter in the implicit preference formulation (>= 0). + * Default: 1.0 * @group param */ - val alpha = new DoubleParam(this, "alpha", "alpha for implicit preference") + val alpha = new DoubleParam(this, "alpha", "alpha for implicit preference", + ParamValidators.gtEq(0)) /** @group getParam */ - def getAlpha: Double = getOrDefault(alpha) + def getAlpha: Double = $(alpha) /** * Param for the column name for user ids. + * Default: "user" * @group param */ val userCol = new Param[String](this, "userCol", "column name for user ids") /** @group getParam */ - def getUserCol: String = getOrDefault(userCol) + def getUserCol: String = $(userCol) /** * Param for the column name for item ids. + * Default: "item" * @group param */ val itemCol = new Param[String](this, "itemCol", "column name for item ids") /** @group getParam */ - def getItemCol: String = getOrDefault(itemCol) + def getItemCol: String = $(itemCol) /** * Param for the column name for ratings. + * Default: "rating" * @group param */ val ratingCol = new Param[String](this, "ratingCol", "column name for ratings") /** @group getParam */ - def getRatingCol: String = getOrDefault(ratingCol) + def getRatingCol: String = $(ratingCol) /** * Param for whether to apply nonnegativity constraints. + * Default: false * @group param */ val nonnegative = new BooleanParam( this, "nonnegative", "whether to use nonnegative constraint for least squares") /** @group getParam */ - def getNonnegative: Boolean = getOrDefault(nonnegative) + def getNonnegative: Boolean = $(nonnegative) setDefault(rank -> 10, maxIter -> 10, regParam -> 0.1, numUserBlocks -> 10, numItemBlocks -> 10, implicitPrefs -> false, alpha -> 1.0, userCol -> "user", itemCol -> "item", - ratingCol -> "rating", nonnegative -> false) + ratingCol -> "rating", nonnegative -> false, checkpointInterval -> 10) /** * Validates and transforms the input schema. * @param schema input schema - * @param paramMap extra params * @return output schema */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - assert(schema(map(userCol)).dataType == IntegerType) - assert(schema(map(itemCol)).dataType== IntegerType) - val ratingType = schema(map(ratingCol)).dataType - assert(ratingType == FloatType || ratingType == DoubleType) - val predictionColName = map(predictionCol) - assert(!schema.fieldNames.contains(predictionColName), + protected def validateAndTransformSchema(schema: StructType): StructType = { + require(schema($(userCol)).dataType == IntegerType) + require(schema($(itemCol)).dataType== IntegerType) + val ratingType = schema($(ratingCol)).dataType + require(ratingType == FloatType || ratingType == DoubleType) + val predictionColName = $(predictionCol) + require(!schema.fieldNames.contains(predictionColName), s"Prediction column $predictionColName already exists.") - val newFields = schema.fields :+ StructField(map(predictionCol), FloatType, nullable = false) + val newFields = schema.fields :+ StructField($(predictionCol), FloatType, nullable = false) StructType(newFields) } } @@ -163,7 +172,6 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR */ class ALSModel private[ml] ( override val parent: ALS, - override val fittingParamMap: ParamMap, k: Int, userFactors: RDD[(Int, Array[Float])], itemFactors: RDD[(Int, Array[Float])]) @@ -172,9 +180,8 @@ class ALSModel private[ml] ( /** @group setParam */ def setPredictionCol(value: String): this.type = set(predictionCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { import dataset.sqlContext.implicits._ - val map = extractParamMap(paramMap) val users = userFactors.toDF("id", "features") val items = itemFactors.toDF("id", "features") @@ -188,13 +195,13 @@ class ALSModel private[ml] ( } } dataset - .join(users, dataset(map(userCol)) === users("id"), "left") - .join(items, dataset(map(itemCol)) === items("id"), "left") - .select(dataset("*"), predict(users("features"), items("features")).as(map(predictionCol))) + .join(users, dataset($(userCol)) === users("id"), "left") + .join(items, dataset($(itemCol)) === items("id"), "left") + .select(dataset("*"), predict(users("features"), items("features")).as($(predictionCol))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -281,29 +288,22 @@ class ALS extends Estimator[ALSModel] with ALSParams { this } - setMaxIter(20) - setRegParam(1.0) - setCheckpointInterval(10) - - override def fit(dataset: DataFrame, paramMap: ParamMap): ALSModel = { - val map = extractParamMap(paramMap) + override def fit(dataset: DataFrame): ALSModel = { val ratings = dataset - .select(col(map(userCol)), col(map(itemCol)), col(map(ratingCol)).cast(FloatType)) + .select(col($(userCol)), col($(itemCol)), col($(ratingCol)).cast(FloatType)) .map { row => Rating(row.getInt(0), row.getInt(1), row.getFloat(2)) } - val (userFactors, itemFactors) = ALS.train(ratings, rank = map(rank), - numUserBlocks = map(numUserBlocks), numItemBlocks = map(numItemBlocks), - maxIter = map(maxIter), regParam = map(regParam), implicitPrefs = map(implicitPrefs), - alpha = map(alpha), nonnegative = map(nonnegative), - checkpointInterval = map(checkpointInterval)) - val model = new ALSModel(this, map, map(rank), userFactors, itemFactors) - Params.inheritValues(map, this, model) - model + val (userFactors, itemFactors) = ALS.train(ratings, rank = $(rank), + numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks), + maxIter = $(maxIter), regParam = $(regParam), implicitPrefs = $(implicitPrefs), + alpha = $(alpha), nonnegative = $(nonnegative), + checkpointInterval = $(checkpointInterval)) + copyValues(new ALSModel(this, $(rank), userFactors, itemFactors)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala index 756725a64b0f3..f8f0b161a4812 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala @@ -18,10 +18,9 @@ package org.apache.spark.ml.regression import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} -import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap} -import org.apache.spark.ml.tree.{DecisionTreeModel, Node} +import org.apache.spark.ml.{PredictionModel, Predictor} +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.tree.{TreeRegressorParams, DecisionTreeParams, DecisionTreeModel, Node} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint @@ -31,7 +30,6 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeMo import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -63,15 +61,13 @@ final class DecisionTreeRegressor override def setImpurity(value: String): this.type = super.setImpurity(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): DecisionTreeRegressionModel = { + override protected def train(dataset: DataFrame): DecisionTreeRegressionModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = getOldStrategy(categoricalFeatures) val oldModel = OldDecisionTree.train(oldDataset, strategy) - DecisionTreeRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(oldModel, this, categoricalFeatures) } /** (private[ml]) Create a Strategy instance to use with the old API. */ @@ -96,7 +92,6 @@ object DecisionTreeRegressor { @AlphaComponent final class DecisionTreeRegressionModel private[ml] ( override val parent: DecisionTreeRegressor, - override val fittingParamMap: ParamMap, override val rootNode: Node) extends PredictionModel[Vector, DecisionTreeRegressionModel] with DecisionTreeModel with Serializable { @@ -108,10 +103,8 @@ final class DecisionTreeRegressionModel private[ml] ( rootNode.predict(features) } - override protected def copy(): DecisionTreeRegressionModel = { - val m = new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): DecisionTreeRegressionModel = { + copyValues(new DecisionTreeRegressionModel(parent, rootNode), extra) } override def toString: String = { @@ -130,12 +123,11 @@ private[ml] object DecisionTreeRegressionModel { def fromOld( oldModel: OldDecisionTreeModel, parent: DecisionTreeRegressor, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): DecisionTreeRegressionModel = { require(oldModel.algo == OldAlgo.Regression, s"Cannot convert non-regression DecisionTreeModel (old API) to" + s" DecisionTreeRegressionModel (new API). Algo is: ${oldModel.algo}") val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) - new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) + new DecisionTreeRegressionModel(parent, rootNode) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala index c784cf39ed31a..461905c12701a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala @@ -21,22 +21,19 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} -import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap, Param} -import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} +import org.apache.spark.ml.{PredictionModel, Predictor} +import org.apache.spark.ml.param.{Param, ParamMap} +import org.apache.spark.ml.tree.{GBTParams, TreeRegressorParams, DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} -import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss, - SquaredError => OldSquaredError} +import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss, SquaredError => OldSquaredError} import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -102,21 +99,16 @@ final class GBTRegressor */ val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" + " tries to minimize (case-insensitive). Supported options:" + - s" ${GBTRegressor.supportedLossTypes.mkString(", ")}") + s" ${GBTRegressor.supportedLossTypes.mkString(", ")}", + (value: String) => GBTRegressor.supportedLossTypes.contains(value.toLowerCase)) setDefault(lossType -> "squared") /** @group setParam */ - def setLossType(value: String): this.type = { - val lossStr = value.toLowerCase - require(GBTRegressor.supportedLossTypes.contains(lossStr), "GBTRegressor was given bad loss" + - s" type: $value. Supported options: ${GBTRegressor.supportedLossTypes.mkString(", ")}") - set(lossType, lossStr) - this - } + def setLossType(value: String): this.type = set(lossType, value) /** @group getParam */ - def getLossType: String = getOrDefault(lossType) + def getLossType: String = $(lossType).toLowerCase /** (private[ml]) Convert new loss to old loss. */ override private[ml] def getOldLossType: OldLoss = { @@ -129,16 +121,14 @@ final class GBTRegressor } } - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): GBTRegressionModel = { + override protected def train(dataset: DataFrame): GBTRegressionModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression) val oldGBT = new OldGBT(boostingStrategy) val oldModel = oldGBT.run(oldDataset) - GBTRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + GBTRegressionModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -160,7 +150,6 @@ object GBTRegressor { @AlphaComponent final class GBTRegressionModel( override val parent: GBTRegressor, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeRegressionModel], private val _treeWeights: Array[Double]) extends PredictionModel[Vector, GBTRegressionModel] @@ -183,10 +172,8 @@ final class GBTRegressionModel( if (prediction > 0.0) 1.0 else 0.0 } - override protected def copy(): GBTRegressionModel = { - val m = new GBTRegressionModel(parent, fittingParamMap, _trees, _treeWeights) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): GBTRegressionModel = { + copyValues(new GBTRegressionModel(parent, _trees, _treeWeights), extra) } override def toString: String = { @@ -205,14 +192,13 @@ private[ml] object GBTRegressionModel { def fromOld( oldModel: OldGBTModel, parent: GBTRegressor, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): GBTRegressionModel = { require(oldModel.algo == OldAlgo.Regression, "Cannot convert GradientBoostedTreesModel" + s" with algo=${oldModel.algo} (old API) to GBTRegressionModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new GBTRegressionModel(parent, fittingParamMap, newTrees, oldModel.treeWeights) + new GBTRegressionModel(parent, newTrees, oldModel.treeWeights) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index f92c6816eb54c..e63c9a3eead52 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -19,17 +19,19 @@ package org.apache.spark.ml.regression import scala.collection.mutable -import breeze.linalg.{norm => brzNorm, DenseVector => BDV} -import breeze.optimize.{LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN} -import breeze.optimize.{CachedDiffFunction, DiffFunction} +import breeze.linalg.{DenseVector => BDV, norm => brzNorm} +import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, + OWLQN => BreezeOWLQN} +import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.PredictorParams +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol} -import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.linalg.BLAS._ import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame import org.apache.spark.storage.StorageLevel @@ -38,17 +40,27 @@ import org.apache.spark.util.StatCounter /** * Params for linear regression. */ -private[regression] trait LinearRegressionParams extends RegressorParams +private[regression] trait LinearRegressionParams extends PredictorParams with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol /** * :: AlphaComponent :: * * Linear regression. + * + * The learning objective is to minimize the squared error, with regularization. + * The specific squared error loss function used is: + * L = 1/2n ||A weights - y||^2^ + * + * This support multiple types of regularization: + * - none (a.k.a. ordinary least squares) + * - L2 (ridge regression) + * - L1 (Lasso) + * - L2 + L1 (elastic net) */ @AlphaComponent class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] - with LinearRegressionParams { + with LinearRegressionParams with Logging { /** * Set the regularization parameter. @@ -85,15 +97,13 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress def setTol(value: Double): this.type = set(tol, value) setDefault(tol -> 1E-6) - override protected def train(dataset: DataFrame, paramMap: ParamMap): LinearRegressionModel = { + override protected def train(dataset: DataFrame): LinearRegressionModel = { // Extract columns from data. If dataset is persisted, do not persist instances. - val instances = extractLabeledPoints(dataset, paramMap).map { + val instances = extractLabeledPoints(dataset).map { case LabeledPoint(label: Double, features: Vector) => (label, features) } val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE - if (handlePersistence) { - instances.persist(StorageLevel.MEMORY_AND_DISK) - } + if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK) val (summarizer, statCounter) = instances.treeAggregate( (new MultivariateOnlineSummarizer, new StatCounter))( { @@ -110,22 +120,31 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress val yMean = statCounter.mean val yStd = math.sqrt(statCounter.variance) + // If the yStd is zero, then the intercept is yMean with zero weights; + // as a result, training is not needed. + if (yStd == 0.0) { + logWarning(s"The standard deviation of the label is zero, so the weights will be zeros " + + s"and the intercept will be the mean of the label; as a result, training is not needed.") + if (handlePersistence) instances.unpersist() + return new LinearRegressionModel(this, Vectors.sparse(numFeatures, Seq()), yMean) + } + val featuresMean = summarizer.mean.toArray val featuresStd = summarizer.variance.toArray.map(math.sqrt) // Since we implicitly do the feature scaling when we compute the cost function // to improve the convergence, the effective regParam will be changed. - val effectiveRegParam = paramMap(regParam) / yStd - val effectiveL1RegParam = paramMap(elasticNetParam) * effectiveRegParam - val effectiveL2RegParam = (1.0 - paramMap(elasticNetParam)) * effectiveRegParam + val effectiveRegParam = $(regParam) / yStd + val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam + val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam val costFun = new LeastSquaresCostFun(instances, yStd, yMean, featuresStd, featuresMean, effectiveL2RegParam) - val optimizer = if (paramMap(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { - new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol)) + val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { + new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol)) } else { - new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, paramMap(tol)) + new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegParam, $(tol)) } val initialWeights = Vectors.zeros(numFeatures) @@ -141,7 +160,6 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress } lossHistory += state.value - // TODO: Based on the sparsity of weights, we may convert the weights to the sparse vector. // The weights are trained in the scaled space; we're converting them back to // the original space. val weights = { @@ -158,11 +176,10 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress // converged. See the following discussion for detail. // http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet val intercept = yMean - dot(weights, Vectors.dense(featuresMean)) + if (handlePersistence) instances.unpersist() - if (handlePersistence) { - instances.unpersist() - } - new LinearRegressionModel(this, paramMap, weights, intercept) + // TODO: Converts to sparse format based on the storage, but may base on the scoring speed. + new LinearRegressionModel(this, weights.compressed, intercept) } } @@ -174,7 +191,6 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress @AlphaComponent class LinearRegressionModel private[ml] ( override val parent: LinearRegression, - override val fittingParamMap: ParamMap, val weights: Vector, val intercept: Double) extends RegressionModel[Vector, LinearRegressionModel] @@ -184,10 +200,8 @@ class LinearRegressionModel private[ml] ( dot(features, weights) + intercept } - override protected def copy(): LinearRegressionModel = { - val m = new LinearRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(extractParamMap(), this, m) - m + override def copy(extra: ParamMap): LinearRegressionModel = { + copyValues(new LinearRegressionModel(parent, weights, intercept), extra) } } @@ -198,15 +212,84 @@ class LinearRegressionModel private[ml] ( * Two LeastSquaresAggregator can be merged together to have a summary of loss and gradient of * the corresponding joint dataset. * - - * * Compute gradient and loss for a Least-squared loss function, as used in linear regression. - * This is correct for the averaged least squares loss function (mean squared error) - * L = 1/2n ||A weights-y||^2 - * See also the documentation for the precise formulation. + * For improving the convergence rate during the optimization process, and also preventing against + * features with very large variances exerting an overly large influence during model training, + * package like R's GLMNET performs the scaling to unit variance and removing the mean to reduce + * the condition number, and then trains the model in scaled space but returns the weights in + * the original scale. See page 9 in http://cran.r-project.org/web/packages/glmnet/glmnet.pdf + * + * However, we don't want to apply the `StandardScaler` on the training dataset, and then cache + * the standardized dataset since it will create a lot of overhead. As a result, we perform the + * scaling implicitly when we compute the objective function. The following is the mathematical + * derivation. + * + * Note that we don't deal with intercept by adding bias here, because the intercept + * can be computed using closed form after the coefficients are converged. + * See this discussion for detail. + * http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet * - * @param weights weights/coefficients corresponding to features + * The objective function in the scaled space is given by + * {{{ + * L = 1/2n ||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2, + * }}} + * where \bar{x_i} is the mean of x_i, \hat{x_i} is the standard deviation of x_i, + * \bar{y} is the mean of label, and \hat{y} is the standard deviation of label. * - * @param updater Updater to be used to update weights after every iteration. + * This can be rewritten as + * {{{ + * L = 1/2n ||\sum_i (w_i/\hat{x_i})x_i - \sum_i (w_i/\hat{x_i})\bar{x_i} - y / \hat{y} + * + \bar{y} / \hat{y}||^2 + * = 1/2n ||\sum_i w_i^\prime x_i - y / \hat{y} + offset||^2 = 1/2n diff^2 + * }}} + * where w_i^\prime^ is the effective weights defined by w_i/\hat{x_i}, offset is + * {{{ + * - \sum_i (w_i/\hat{x_i})\bar{x_i} + \bar{y} / \hat{y}. + * }}}, and diff is + * {{{ + * \sum_i w_i^\prime x_i - y / \hat{y} + offset + * }}} + * + * Note that the effective weights and offset don't depend on training dataset, + * so they can be precomputed. + * + * Now, the first derivative of the objective function in scaled space is + * {{{ + * \frac{\partial L}{\partial\w_i} = diff/N (x_i - \bar{x_i}) / \hat{x_i} + * }}} + * However, ($x_i - \bar{x_i}$) will densify the computation, so it's not + * an ideal formula when the training dataset is sparse format. + * + * This can be addressed by adding the dense \bar{x_i} / \har{x_i} terms + * in the end by keeping the sum of diff. The first derivative of total + * objective function from all the samples is + * {{{ + * \frac{\partial L}{\partial\w_i} = + * 1/N \sum_j diff_j (x_{ij} - \bar{x_i}) / \hat{x_i} + * = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) - diffSum \bar{x_i}) / \hat{x_i}) + * = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) + correction_i) + * }}}, + * where correction_i = - diffSum \bar{x_i}) / \hat{x_i} + * + * A simple math can show that diffSum is actually zero, so we don't even + * need to add the correction terms in the end. From the definition of diff, + * {{{ + * diffSum = \sum_j (\sum_i w_i(x_{ij} - \bar{x_i}) / \hat{x_i} - (y_j - \bar{y}) / \hat{y}) + * = N * (\sum_i w_i(\bar{x_i} - \bar{x_i}) / \hat{x_i} - (\bar{y_j} - \bar{y}) / \hat{y}) + * = 0 + * }}} + * + * As a result, the first derivative of the total objective function only depends on + * the training dataset, which can be easily computed in distributed fashion, and is + * sparse format friendly. + * {{{ + * \frac{\partial L}{\partial\w_i} = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) + * }}}, + * + * @param weights The weights/coefficients corresponding to the features. + * @param labelStd The standard deviation value of the label. + * @param labelMean The mean value of the label. + * @param featuresStd The standard deviation values of the features. + * @param featuresMean The mean values of the features. */ private class LeastSquaresAggregator( weights: Vector, @@ -215,9 +298,8 @@ private class LeastSquaresAggregator( featuresStd: Array[Double], featuresMean: Array[Double]) extends Serializable { - private var totalCnt: Long = 0 + private var totalCnt: Long = 0L private var lossSum = 0.0 - private var diffSum = 0.0 private val (effectiveWeightsArray: Array[Double], offset: Double, dim: Int) = { val weightsArray = weights.toArray.clone() @@ -234,9 +316,10 @@ private class LeastSquaresAggregator( } (weightsArray, -sum + labelMean / labelStd, weightsArray.length) } + private val effectiveWeightsVector = Vectors.dense(effectiveWeightsArray) - private val gradientSumArray: Array[Double] = Array.ofDim[Double](dim) + private val gradientSumArray = Array.ofDim[Double](dim) /** * Add a new training data to this LeastSquaresAggregator, and update the loss and gradient @@ -261,7 +344,6 @@ private class LeastSquaresAggregator( } } lossSum += diff * diff / 2.0 - diffSum += diff } totalCnt += 1 @@ -283,7 +365,6 @@ private class LeastSquaresAggregator( if (other.totalCnt != 0) { totalCnt += other.totalCnt lossSum += other.lossSum - diffSum += other.diffSum var i = 0 val localThisGradientSumArray = this.gradientSumArray @@ -302,18 +383,6 @@ private class LeastSquaresAggregator( def gradient: Vector = { val result = Vectors.dense(gradientSumArray.clone()) - - val correction = { - val temp = effectiveWeightsArray.clone() - var i = 0 - while (i < temp.length) { - temp(i) *= featuresMean(i) - i += 1 - } - Vectors.dense(temp) - } - - axpy(-diffSum, correction, result) scal(1.0 / totalCnt, result) result } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala index 2171ef3d32c26..dbc628927433d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala @@ -18,20 +18,18 @@ package org.apache.spark.ml.regression import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} -import org.apache.spark.ml.impl.tree.{RandomForestParams, TreeRegressorParams} -import org.apache.spark.ml.param.{Params, ParamMap} -import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} +import org.apache.spark.ml.{PredictionModel, Predictor} +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.tree.{RandomForestParams, TreeRegressorParams, DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -77,17 +75,15 @@ final class RandomForestRegressor override def setFeatureSubsetStrategy(value: String): this.type = super.setFeatureSubsetStrategy(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): RandomForestRegressionModel = { + override protected def train(dataset: DataFrame): RandomForestRegressionModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity) val oldModel = OldRandomForest.trainRegressor( oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) - RandomForestRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + RandomForestRegressionModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -110,7 +106,6 @@ object RandomForestRegressor { @AlphaComponent final class RandomForestRegressionModel private[ml] ( override val parent: RandomForestRegressor, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeRegressionModel]) extends PredictionModel[Vector, RandomForestRegressionModel] with TreeEnsembleModel with Serializable { @@ -132,10 +127,8 @@ final class RandomForestRegressionModel private[ml] ( _trees.map(_.rootNode.predict(features)).sum / numTrees } - override protected def copy(): RandomForestRegressionModel = { - val m = new RandomForestRegressionModel(parent, fittingParamMap, _trees) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): RandomForestRegressionModel = { + copyValues(new RandomForestRegressionModel(parent, _trees), extra) } override def toString: String = { @@ -154,14 +147,13 @@ private[ml] object RandomForestRegressionModel { def fromOld( oldModel: OldRandomForestModel, parent: RandomForestRegressor, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): RandomForestRegressionModel = { require(oldModel.algo == OldAlgo.Regression, "Cannot convert RandomForestModel" + s" with algo=${oldModel.algo} (old API) to RandomForestRegressionModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new RandomForestRegressionModel(parent, fittingParamMap, newTrees) + new RandomForestRegressionModel(parent, newTrees) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala index d679085eeafe1..c72ef29680329 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala @@ -17,62 +17,40 @@ package org.apache.spark.ml.regression -import org.apache.spark.annotation.{DeveloperApi, AlphaComponent} -import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams} +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.ml.{PredictionModel, PredictorParams, Predictor} -/** - * :: DeveloperApi :: - * Params for regression. - * Currently empty, but may add functionality later. - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. - */ -@DeveloperApi -private[spark] trait RegressorParams extends PredictorParams /** - * :: AlphaComponent :: + * :: DeveloperApi :: * * Single-label regression * * @tparam FeaturesType Type of input features. E.g., [[org.apache.spark.mllib.linalg.Vector]] * @tparam Learner Concrete Estimator type * @tparam M Concrete Model type - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. */ -@AlphaComponent +@DeveloperApi private[spark] abstract class Regressor[ FeaturesType, Learner <: Regressor[FeaturesType, Learner, M], M <: RegressionModel[FeaturesType, M]] - extends Predictor[FeaturesType, Learner, M] - with RegressorParams { + extends Predictor[FeaturesType, Learner, M] with PredictorParams { // TODO: defaultEvaluator (follow-up PR) } /** - * :: AlphaComponent :: + * :: DeveloperApi :: * * Model produced by a [[Regressor]]. * * @tparam FeaturesType Type of input features. E.g., [[org.apache.spark.mllib.linalg.Vector]] * @tparam M Concrete Model type. - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. */ -@AlphaComponent -private[spark] abstract class RegressionModel[FeaturesType, M <: RegressionModel[FeaturesType, M]] - extends PredictionModel[FeaturesType, M] with RegressorParams { - - /** - * :: DeveloperApi :: - * - * Predict real-valued label for the given features. - * This internal method is used to implement [[transform()]] and output [[predictionCol]]. - */ - @DeveloperApi - protected def predict(features: FeaturesType): Double +@DeveloperApi +abstract class RegressionModel[FeaturesType, M <: RegressionModel[FeaturesType, M]] + extends PredictionModel[FeaturesType, M] with PredictorParams { + // TODO: defaultEvaluator (follow-up PR) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala similarity index 75% rename from mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala rename to mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala index ab6281b9b2e34..816fcedf2efb3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala @@ -15,19 +15,16 @@ * limitations under the License. */ -package org.apache.spark.ml.impl.tree +package org.apache.spark.ml.tree import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.ml.impl.estimator.PredictorParams +import org.apache.spark.ml.PredictorParams import org.apache.spark.ml.param._ -import org.apache.spark.ml.param.shared.{HasSeed, HasMaxIter} -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, - BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy} -import org.apache.spark.mllib.tree.impurity.{Gini => OldGini, Entropy => OldEntropy, - Impurity => OldImpurity, Variance => OldVariance} +import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance} import org.apache.spark.mllib.tree.loss.{Loss => OldLoss} - /** * :: DeveloperApi :: * Parameters for Decision Tree-based algorithms. @@ -38,14 +35,15 @@ import org.apache.spark.mllib.tree.loss.{Loss => OldLoss} private[ml] trait DecisionTreeParams extends PredictorParams { /** - * Maximum depth of the tree. + * Maximum depth of the tree (>= 0). * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. * (default = 5) * @group param */ final val maxDepth: IntParam = - new IntParam(this, "maxDepth", "Maximum depth of the tree." + - " E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.") + new IntParam(this, "maxDepth", "Maximum depth of the tree. (>= 0)" + + " E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.", + ParamValidators.gtEq(0)) /** * Maximum number of bins used for discretizing continuous features and for choosing how to split @@ -56,7 +54,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams { */ final val maxBins: IntParam = new IntParam(this, "maxBins", "Max number of bins for" + " discretizing continuous features. Must be >=2 and >= number of categories for any" + - " categorical feature.") + " categorical feature.", ParamValidators.gtEq(2)) /** * Minimum number of instances each child must have after split. @@ -69,7 +67,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams { final val minInstancesPerNode: IntParam = new IntParam(this, "minInstancesPerNode", "Minimum" + " number of instances each child must have after split. If a split causes the left or right" + " child to have fewer than minInstancesPerNode, the split will be discarded as invalid." + - " Should be >= 1.") + " Should be >= 1.", ParamValidators.gtEq(1)) /** * Minimum information gain for a split to be considered at a tree node. @@ -85,7 +83,8 @@ private[ml] trait DecisionTreeParams extends PredictorParams { * @group expertParam */ final val maxMemoryInMB: IntParam = new IntParam(this, "maxMemoryInMB", - "Maximum memory in MB allocated to histogram aggregation.") + "Maximum memory in MB allocated to histogram aggregation.", + ParamValidators.gtEq(0)) /** * If false, the algorithm will pass trees to executors to match instances with nodes. @@ -111,67 +110,53 @@ private[ml] trait DecisionTreeParams extends PredictorParams { final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "Specifies" + " how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get" + " checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the" + - " checkpoint directory is set in the SparkContext. Must be >= 1.") + " checkpoint directory is set in the SparkContext. Must be >= 1.", + ParamValidators.gtEq(1)) setDefault(maxDepth -> 5, maxBins -> 32, minInstancesPerNode -> 1, minInfoGain -> 0.0, maxMemoryInMB -> 256, cacheNodeIds -> false, checkpointInterval -> 10) /** @group setParam */ - def setMaxDepth(value: Int): this.type = { - require(value >= 0, s"maxDepth parameter must be >= 0. Given bad value: $value") - set(maxDepth, value) - } + def setMaxDepth(value: Int): this.type = set(maxDepth, value) /** @group getParam */ - final def getMaxDepth: Int = getOrDefault(maxDepth) + final def getMaxDepth: Int = $(maxDepth) /** @group setParam */ - def setMaxBins(value: Int): this.type = { - require(value >= 2, s"maxBins parameter must be >= 2. Given bad value: $value") - set(maxBins, value) - } + def setMaxBins(value: Int): this.type = set(maxBins, value) /** @group getParam */ - final def getMaxBins: Int = getOrDefault(maxBins) + final def getMaxBins: Int = $(maxBins) /** @group setParam */ - def setMinInstancesPerNode(value: Int): this.type = { - require(value >= 1, s"minInstancesPerNode parameter must be >= 1. Given bad value: $value") - set(minInstancesPerNode, value) - } + def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value) /** @group getParam */ - final def getMinInstancesPerNode: Int = getOrDefault(minInstancesPerNode) + final def getMinInstancesPerNode: Int = $(minInstancesPerNode) /** @group setParam */ def setMinInfoGain(value: Double): this.type = set(minInfoGain, value) /** @group getParam */ - final def getMinInfoGain: Double = getOrDefault(minInfoGain) + final def getMinInfoGain: Double = $(minInfoGain) /** @group expertSetParam */ - def setMaxMemoryInMB(value: Int): this.type = { - require(value > 0, s"maxMemoryInMB parameter must be > 0. Given bad value: $value") - set(maxMemoryInMB, value) - } + def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value) /** @group expertGetParam */ - final def getMaxMemoryInMB: Int = getOrDefault(maxMemoryInMB) + final def getMaxMemoryInMB: Int = $(maxMemoryInMB) /** @group expertSetParam */ def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value) /** @group expertGetParam */ - final def getCacheNodeIds: Boolean = getOrDefault(cacheNodeIds) + final def getCacheNodeIds: Boolean = $(cacheNodeIds) /** @group expertSetParam */ - def setCheckpointInterval(value: Int): this.type = { - require(value >= 1, s"checkpointInterval parameter must be >= 1. Given bad value: $value") - set(checkpointInterval, value) - } + def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value) /** @group expertGetParam */ - final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) + final def getCheckpointInterval: Int = $(checkpointInterval) /** (private[ml]) Create a Strategy instance to use with the old API. */ private[ml] def getOldStrategy( @@ -209,21 +194,16 @@ private[ml] trait TreeClassifierParams extends Params { */ final val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + " information gain calculation (case-insensitive). Supported options:" + - s" ${TreeClassifierParams.supportedImpurities.mkString(", ")}") + s" ${TreeClassifierParams.supportedImpurities.mkString(", ")}", + (value: String) => TreeClassifierParams.supportedImpurities.contains(value.toLowerCase)) setDefault(impurity -> "gini") /** @group setParam */ - def setImpurity(value: String): this.type = { - val impurityStr = value.toLowerCase - require(TreeClassifierParams.supportedImpurities.contains(impurityStr), - s"Tree-based classifier was given unrecognized impurity: $value." + - s" Supported options: ${TreeClassifierParams.supportedImpurities.mkString(", ")}") - set(impurity, impurityStr) - } + def setImpurity(value: String): this.type = set(impurity, value) /** @group getParam */ - final def getImpurity: String = getOrDefault(impurity) + final def getImpurity: String = $(impurity).toLowerCase /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -256,21 +236,16 @@ private[ml] trait TreeRegressorParams extends Params { */ final val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + " information gain calculation (case-insensitive). Supported options:" + - s" ${TreeRegressorParams.supportedImpurities.mkString(", ")}") + s" ${TreeRegressorParams.supportedImpurities.mkString(", ")}", + (value: String) => TreeRegressorParams.supportedImpurities.contains(value.toLowerCase)) setDefault(impurity -> "variance") /** @group setParam */ - def setImpurity(value: String): this.type = { - val impurityStr = value.toLowerCase - require(TreeRegressorParams.supportedImpurities.contains(impurityStr), - s"Tree-based regressor was given unrecognized impurity: $value." + - s" Supported options: ${TreeRegressorParams.supportedImpurities.mkString(", ")}") - set(impurity, impurityStr) - } + def setImpurity(value: String): this.type = set(impurity, value) /** @group getParam */ - final def getImpurity: String = getOrDefault(impurity) + final def getImpurity: String = $(impurity).toLowerCase /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -299,24 +274,21 @@ private[ml] object TreeRegressorParams { private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed { /** - * Fraction of the training data used for learning each decision tree. + * Fraction of the training data used for learning each decision tree, in range (0, 1]. * (default = 1.0) * @group param */ final val subsamplingRate: DoubleParam = new DoubleParam(this, "subsamplingRate", - "Fraction of the training data used for learning each decision tree.") + "Fraction of the training data used for learning each decision tree, in range (0, 1].", + ParamValidators.inRange(0, 1, lowerInclusive = false, upperInclusive = true)) setDefault(subsamplingRate -> 1.0) /** @group setParam */ - def setSubsamplingRate(value: Double): this.type = { - require(value > 0.0 && value <= 1.0, - s"Subsampling rate must be in range (0,1]. Bad rate: $value") - set(subsamplingRate, value) - } + def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value) /** @group getParam */ - final def getSubsamplingRate: Double = getOrDefault(subsamplingRate) + final def getSubsamplingRate: Double = $(subsamplingRate) /** @group setParam */ def setSeed(value: Long): this.type = set(seed, value) @@ -350,7 +322,8 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams { * (default = 20) * @group param */ - final val numTrees: IntParam = new IntParam(this, "numTrees", "Number of trees to train (>= 1)") + final val numTrees: IntParam = new IntParam(this, "numTrees", "Number of trees to train (>= 1)", + ParamValidators.gtEq(1)) /** * The number of features to consider for splits at each tree node. @@ -378,30 +351,23 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams { */ final val featureSubsetStrategy: Param[String] = new Param[String](this, "featureSubsetStrategy", "The number of features to consider for splits at each tree node." + - s" Supported options: ${RandomForestParams.supportedFeatureSubsetStrategies.mkString(", ")}") + s" Supported options: ${RandomForestParams.supportedFeatureSubsetStrategies.mkString(", ")}", + (value: String) => + RandomForestParams.supportedFeatureSubsetStrategies.contains(value.toLowerCase)) setDefault(numTrees -> 20, featureSubsetStrategy -> "auto") /** @group setParam */ - def setNumTrees(value: Int): this.type = { - require(value >= 1, s"Random Forest numTrees parameter cannot be $value; it must be >= 1.") - set(numTrees, value) - } + def setNumTrees(value: Int): this.type = set(numTrees, value) /** @group getParam */ - final def getNumTrees: Int = getOrDefault(numTrees) + final def getNumTrees: Int = $(numTrees) /** @group setParam */ - def setFeatureSubsetStrategy(value: String): this.type = { - val strategyStr = value.toLowerCase - require(RandomForestParams.supportedFeatureSubsetStrategies.contains(strategyStr), - s"RandomForestParams was given unrecognized featureSubsetStrategy: $value. Supported" + - s" options: ${RandomForestParams.supportedFeatureSubsetStrategies.mkString(", ")}") - set(featureSubsetStrategy, strategyStr) - } + def setFeatureSubsetStrategy(value: String): this.type = set(featureSubsetStrategy, value) /** @group getParam */ - final def getFeatureSubsetStrategy: String = getOrDefault(featureSubsetStrategy) + final def getFeatureSubsetStrategy: String = $(featureSubsetStrategy).toLowerCase } private[ml] object RandomForestParams { @@ -426,7 +392,8 @@ private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter { * @group param */ final val stepSize: DoubleParam = new DoubleParam(this, "stepSize", "Step size (a.k.a." + - " learning rate) in interval (0, 1] for shrinking the contribution of each estimator") + " learning rate) in interval (0, 1] for shrinking the contribution of each estimator", + ParamValidators.inRange(0, 1, lowerInclusive = false, upperInclusive = true)) /* TODO: Add this doc when we add this param. SPARK-7132 * Threshold for stopping early when runWithValidation is used. @@ -442,20 +409,13 @@ private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter { setDefault(maxIter -> 20, stepSize -> 0.1) /** @group setParam */ - def setMaxIter(value: Int): this.type = { - require(value >= 1, s"Gradient Boosting maxIter parameter cannot be $value; it must be >= 1.") - set(maxIter, value) - } + def setMaxIter(value: Int): this.type = set(maxIter, value) /** @group setParam */ - def setStepSize(value: Double): this.type = { - require(value > 0.0 && value <= 1.0, - s"GBT given invalid step size ($value). Value should be in (0,1].") - set(stepSize, value) - } + def setStepSize(value: Double): this.type = set(stepSize, value) /** @group getParam */ - final def getStepSize: Double = getOrDefault(stepSize) + final def getStepSize: Double = $(stepSize) /** (private[ml]) Create a BoostingStrategy instance to use with the old API. */ private[ml] def getOldBoostingStrategy( diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index 4bb4ed813c006..9208127eb1d79 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -22,7 +22,7 @@ import com.github.fommil.netlib.F2jBLAS import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml._ -import org.apache.spark.ml.param.{IntParam, Param, ParamMap, Params} +import org.apache.spark.ml.param._ import org.apache.spark.mllib.util.MLUtils import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types.StructType @@ -39,7 +39,7 @@ private[ml] trait CrossValidatorParams extends Params { val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection") /** @group getParam */ - def getEstimator: Estimator[_] = getOrDefault(estimator) + def getEstimator: Estimator[_] = $(estimator) /** * param for estimator param maps @@ -49,25 +49,29 @@ private[ml] trait CrossValidatorParams extends Params { new Param(this, "estimatorParamMaps", "param maps for the estimator") /** @group getParam */ - def getEstimatorParamMaps: Array[ParamMap] = getOrDefault(estimatorParamMaps) + def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps) /** - * param for the evaluator for selection + * param for the evaluator used to select hyper-parameters that maximize the cross-validated + * metric * @group param */ - val evaluator: Param[Evaluator] = new Param(this, "evaluator", "evaluator for selection") + val evaluator: Param[Evaluator] = new Param(this, "evaluator", + "evaluator used to select hyper-parameters that maximize the cross-validated metric") /** @group getParam */ - def getEvaluator: Evaluator = getOrDefault(evaluator) + def getEvaluator: Evaluator = $(evaluator) /** - * param for number of folds for cross validation + * Param for number of folds for cross validation. Must be >= 2. + * Default: 3 * @group param */ - val numFolds: IntParam = new IntParam(this, "numFolds", "number of folds for cross validation") + val numFolds: IntParam = new IntParam(this, "numFolds", + "number of folds for cross validation (>= 2)", ParamValidators.gtEq(2)) /** @group getParam */ - def getNumFolds: Int = getOrDefault(numFolds) + def getNumFolds: Int = $(numFolds) setDefault(numFolds -> 3) } @@ -93,17 +97,22 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP /** @group setParam */ def setNumFolds(value: Int): this.type = set(numFolds, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): CrossValidatorModel = { - val map = extractParamMap(paramMap) + override def validateParams(paramMap: ParamMap): Unit = { + getEstimatorParamMaps.foreach { eMap => + getEstimator.validateParams(eMap ++ paramMap) + } + } + + override def fit(dataset: DataFrame): CrossValidatorModel = { val schema = dataset.schema - transformSchema(dataset.schema, paramMap, logging = true) + transformSchema(dataset.schema, logging = true) val sqlCtx = dataset.sqlContext - val est = map(estimator) - val eval = map(evaluator) - val epm = map(estimatorParamMaps) - val numModels = epm.size - val metrics = new Array[Double](epm.size) - val splits = MLUtils.kFold(dataset.rdd, map(numFolds), 0) + val est = $(estimator) + val eval = $(evaluator) + val epm = $(estimatorParamMaps) + val numModels = epm.length + val metrics = new Array[Double](epm.length) + val splits = MLUtils.kFold(dataset.rdd, $(numFolds), 0) splits.zipWithIndex.foreach { case ((training, validation), splitIndex) => val trainingDataset = sqlCtx.createDataFrame(training, schema).cache() val validationDataset = sqlCtx.createDataFrame(validation, schema).cache() @@ -113,27 +122,25 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP trainingDataset.unpersist() var i = 0 while (i < numModels) { - val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) + // TODO: duplicate evaluator to take extra params from input + val metric = eval.evaluate(models(i).transform(validationDataset, epm(i))) logDebug(s"Got metric $metric for model trained with ${epm(i)}.") metrics(i) += metric i += 1 } validationDataset.unpersist() } - f2jBLAS.dscal(numModels, 1.0 / map(numFolds), metrics, 1) + f2jBLAS.dscal(numModels, 1.0 / $(numFolds), metrics, 1) logInfo(s"Average cross-validation metrics: ${metrics.toSeq}") val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1) logInfo(s"Best set of parameters:\n${epm(bestIndex)}") logInfo(s"Best cross-validation metric: $bestMetric.") val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]] - val cvModel = new CrossValidatorModel(this, map, bestModel) - Params.inheritValues(map, this, cvModel) - cvModel + copyValues(new CrossValidatorModel(this, bestModel)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - map(estimator).transformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + $(estimator).transformSchema(schema) } } @@ -144,15 +151,18 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP @AlphaComponent class CrossValidatorModel private[ml] ( override val parent: CrossValidator, - override val fittingParamMap: ParamMap, val bestModel: Model[_]) extends Model[CrossValidatorModel] with CrossValidatorParams { - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - bestModel.transform(dataset, paramMap) + override def validateParams(paramMap: ParamMap): Unit = { + bestModel.validateParams(paramMap) + } + + override def transform(dataset: DataFrame): DataFrame = { + bestModel.transform(dataset) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - bestModel.transformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + bestModel.transformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 6237b64c8f984..426306d78c1c3 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -282,6 +282,24 @@ private[python] class PythonMLLibAPI extends Serializable { map(_.asInstanceOf[Object]).asJava } + /** + * Java stub for Python mllib IsotonicRegression.run() + */ + def trainIsotonicRegressionModel( + data: JavaRDD[Vector], + isotonic: Boolean): JList[Object] = { + val isotonicRegressionAlg = new IsotonicRegression().setIsotonic(isotonic) + val input = data.rdd.map { x => + (x(0), x(1), x(2)) + }.persist(StorageLevel.MEMORY_AND_DISK) + try { + val model = isotonicRegressionAlg.run(input) + List[AnyRef](model.boundaryVector, model.predictionVector).asJava + } finally { + data.rdd.unpersist(blocking = false) + } + } + /** * Java stub for Python mllib KMeans.run() */ @@ -291,12 +309,16 @@ private[python] class PythonMLLibAPI extends Serializable { maxIterations: Int, runs: Int, initializationMode: String, - seed: java.lang.Long): KMeansModel = { + seed: java.lang.Long, + initializationSteps: Int, + epsilon: Double): KMeansModel = { val kMeansAlg = new KMeans() .setK(k) .setMaxIterations(maxIterations) .setRuns(runs) .setInitializationMode(initializationMode) + .setInitializationSteps(initializationSteps) + .setEpsilon(epsilon) if (seed != null) kMeansAlg.setSeed(seed) @@ -307,6 +329,15 @@ private[python] class PythonMLLibAPI extends Serializable { } } + /** + * Java stub for Python mllib KMeansModel.computeCost() + */ + def computeCostKmeansModel( + data: JavaRDD[Vector], + centers: java.util.ArrayList[Vector]): Double = { + new KMeansModel(centers).computeCost(data) + } + /** * Java stub for Python mllib GaussianMixture.run() * Returns a list containing weights, mean and covariance of each mixture component. @@ -1015,6 +1046,61 @@ private[spark] object SerDe extends Serializable { } } + // Pickler for SparseMatrix + private[python] class SparseMatrixPickler extends BasePickler[SparseMatrix] { + + def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = { + val s = obj.asInstanceOf[SparseMatrix] + val order = ByteOrder.nativeOrder() + + val colPtrsBytes = new Array[Byte](4 * s.colPtrs.length) + val indicesBytes = new Array[Byte](4 * s.rowIndices.length) + val valuesBytes = new Array[Byte](8 * s.values.length) + val isTransposed = if (s.isTransposed) 1 else 0 + ByteBuffer.wrap(colPtrsBytes).order(order).asIntBuffer().put(s.colPtrs) + ByteBuffer.wrap(indicesBytes).order(order).asIntBuffer().put(s.rowIndices) + ByteBuffer.wrap(valuesBytes).order(order).asDoubleBuffer().put(s.values) + + out.write(Opcodes.MARK) + out.write(Opcodes.BININT) + out.write(PickleUtils.integer_to_bytes(s.numRows)) + out.write(Opcodes.BININT) + out.write(PickleUtils.integer_to_bytes(s.numCols)) + out.write(Opcodes.BINSTRING) + out.write(PickleUtils.integer_to_bytes(colPtrsBytes.length)) + out.write(colPtrsBytes) + out.write(Opcodes.BINSTRING) + out.write(PickleUtils.integer_to_bytes(indicesBytes.length)) + out.write(indicesBytes) + out.write(Opcodes.BINSTRING) + out.write(PickleUtils.integer_to_bytes(valuesBytes.length)) + out.write(valuesBytes) + out.write(Opcodes.BININT) + out.write(PickleUtils.integer_to_bytes(isTransposed)) + out.write(Opcodes.TUPLE) + } + + def construct(args: Array[Object]): Object = { + if (args.length != 6) { + throw new PickleException("should be 6") + } + val order = ByteOrder.nativeOrder() + val colPtrsBytes = getBytes(args(2)) + val indicesBytes = getBytes(args(3)) + val valuesBytes = getBytes(args(4)) + val colPtrs = new Array[Int](colPtrsBytes.length / 4) + val rowIndices = new Array[Int](indicesBytes.length / 4) + val values = new Array[Double](valuesBytes.length / 8) + ByteBuffer.wrap(colPtrsBytes).order(order).asIntBuffer().get(colPtrs) + ByteBuffer.wrap(indicesBytes).order(order).asIntBuffer().get(rowIndices) + ByteBuffer.wrap(valuesBytes).order(order).asDoubleBuffer().get(values) + val isTransposed = args(5).asInstanceOf[Int] == 1 + new SparseMatrix( + args(0).asInstanceOf[Int], args(1).asInstanceOf[Int], colPtrs, rowIndices, values, + isTransposed) + } + } + // Pickler for SparseVector private[python] class SparseVectorPickler extends BasePickler[SparseVector] { @@ -1099,6 +1185,7 @@ private[spark] object SerDe extends Serializable { if (!initialized) { new DenseVectorPickler().register() new DenseMatrixPickler().register() + new SparseMatrixPickler().register() new SparseVectorPickler().register() new LabeledPointPickler().register() new RatingPickler().register() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala index 057b628c6a586..bd2e9079ce1ae 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala @@ -23,6 +23,7 @@ import org.apache.spark.mllib.classification.impl.GLMClassificationModel import org.apache.spark.mllib.linalg.BLAS.dot import org.apache.spark.mllib.linalg.{DenseVector, Vector} import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.{DataValidators, Saveable, Loader} import org.apache.spark.rdd.RDD @@ -46,7 +47,7 @@ class LogisticRegressionModel ( val numFeatures: Int, val numClasses: Int) extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable - with Saveable { + with Saveable with PMMLExportable { if (numClasses == 2) { require(weights.size == numFeatures, diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala index 52fb62dcff1b4..33104cf06c6ea 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala @@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental import org.apache.spark.mllib.classification.impl.GLMClassificationModel import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.{DataValidators, Loader, Saveable} import org.apache.spark.rdd.RDD @@ -36,7 +37,7 @@ class SVMModel ( override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable - with Saveable { + with Saveable with PMMLExportable { private var threshold: Option[Double] = Some(0.0) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala index e4e411a3c8b42..ba228b11fcec3 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala @@ -25,6 +25,7 @@ import org.json4s.jackson.JsonMethods._ import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext @@ -34,7 +35,8 @@ import org.apache.spark.sql.Row /** * A clustering model for K-means. Each point belongs to the cluster with the closest center. */ -class KMeansModel (val clusterCenters: Array[Vector]) extends Saveable with Serializable { +class KMeansModel ( + val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable { /** A Java-friendly constructor that takes an Iterable of Vectors. */ def this(centers: java.lang.Iterable[Vector]) = this(centers.asScala.toArray) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala index 37bf88b73b911..c8daa2388e868 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala @@ -78,35 +78,29 @@ class LDA private ( * * This is the parameter to a symmetric Dirichlet distribution. */ - def getDocConcentration: Double = { - if (this.docConcentration == -1) { - (50.0 / k) + 1.0 - } else { - this.docConcentration - } - } + def getDocConcentration: Double = this.docConcentration /** * Concentration parameter (commonly named "alpha") for the prior placed on documents' * distributions over topics ("theta"). * - * This is the parameter to a symmetric Dirichlet distribution. + * This is the parameter to a symmetric Dirichlet distribution, where larger values + * mean more smoothing (more regularization). * - * This value should be > 1.0, where larger values mean more smoothing (more regularization). * If set to -1, then docConcentration is set automatically. * (default = -1 = automatic) * - * Automatic setting of parameter: - * - For EM: default = (50 / k) + 1. - * - The 50/k is common in LDA libraries. - * - The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM. - * - * Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), - * but values in (0,1) are not yet supported. + * Optimizer-specific parameter settings: + * - EM + * - Value should be > 1.0 + * - default = (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows + * Asuncion et al. (2009), who recommend a +1 adjustment for EM. + * - Online + * - Value should be >= 0 + * - default = (1.0 / k), following the implementation from + * [[https://github.com/Blei-Lab/onlineldavb]]. */ def setDocConcentration(docConcentration: Double): this.type = { - require(docConcentration > 1.0 || docConcentration == -1.0, - s"LDA docConcentration must be > 1.0 (or -1 for auto), but was set to $docConcentration") this.docConcentration = docConcentration this } @@ -126,13 +120,7 @@ class LDA private ( * Note: The topics' distributions over terms are called "beta" in the original LDA paper * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009. */ - def getTopicConcentration: Double = { - if (this.topicConcentration == -1) { - 1.1 - } else { - this.topicConcentration - } - } + def getTopicConcentration: Double = this.topicConcentration /** * Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' @@ -143,21 +131,20 @@ class LDA private ( * Note: The topics' distributions over terms are called "beta" in the original LDA paper * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009. * - * This value should be > 0.0. * If set to -1, then topicConcentration is set automatically. * (default = -1 = automatic) * - * Automatic setting of parameter: - * - For EM: default = 0.1 + 1. - * - The 0.1 gives a small amount of smoothing. - * - The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM. - * - * Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), - * but values in (0,1) are not yet supported. + * Optimizer-specific parameter settings: + * - EM + * - Value should be > 1.0 + * - default = 0.1 + 1, where 0.1 gives a small amount of smoothing and +1 follows + * Asuncion et al. (2009), who recommend a +1 adjustment for EM. + * - Online + * - Value should be >= 0 + * - default = (1.0 / k), following the implementation from + * [[https://github.com/Blei-Lab/onlineldavb]]. */ def setTopicConcentration(topicConcentration: Double): this.type = { - require(topicConcentration > 1.0 || topicConcentration == -1.0, - s"LDA topicConcentration must be > 1.0 (or -1 for auto), but was set to $topicConcentration") this.topicConcentration = topicConcentration this } @@ -223,14 +210,15 @@ class LDA private ( /** * Set the LDAOptimizer used to perform the actual calculation by algorithm name. - * Currently "em" is supported. + * Currently "em", "online" is supported. */ def setOptimizer(optimizerName: String): this.type = { this.ldaOptimizer = optimizerName.toLowerCase match { case "em" => new EMLDAOptimizer + case "online" => new OnlineLDAOptimizer case other => - throw new IllegalArgumentException(s"Only em is supported but got $other.") + throw new IllegalArgumentException(s"Only em, online are supported but got $other.") } this } @@ -245,8 +233,7 @@ class LDA private ( * @return Inferred LDA model */ def run(documents: RDD[(Long, Vector)]): LDAModel = { - val state = ldaOptimizer.initialState(documents, k, getDocConcentration, getTopicConcentration, - seed, checkpointInterval) + val state = ldaOptimizer.initialize(documents, this) var iter = 0 val iterationTimes = Array.fill[Double](maxIterations)(0) while (iter < maxIterations) { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala index ffd72a294c6c6..093aa0f315ab2 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala @@ -19,13 +19,15 @@ package org.apache.spark.mllib.clustering import java.util.Random -import breeze.linalg.{DenseVector => BDV, normalize} +import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, sum, normalize, kron} +import breeze.numerics.{digamma, exp, abs} +import breeze.stats.distributions.{Gamma, RandBasis} import org.apache.spark.annotation.Experimental import org.apache.spark.graphx._ import org.apache.spark.graphx.impl.GraphImpl import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer -import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.linalg.{Matrices, SparseVector, DenseVector, Vector} import org.apache.spark.rdd.RDD /** @@ -35,7 +37,7 @@ import org.apache.spark.rdd.RDD * hold optimizer-specific parameters for users to set. */ @Experimental -trait LDAOptimizer{ +trait LDAOptimizer { /* DEVELOPERS NOTE: @@ -49,13 +51,7 @@ trait LDAOptimizer{ * Initializer for the optimizer. LDA passes the common parameters to the optimizer and * the internal structure can be initialized properly. */ - private[clustering] def initialState( - docs: RDD[(Long, Vector)], - k: Int, - docConcentration: Double, - topicConcentration: Double, - randomSeed: Long, - checkpointInterval: Int): LDAOptimizer + private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer private[clustering] def next(): LDAOptimizer @@ -80,12 +76,12 @@ trait LDAOptimizer{ * */ @Experimental -class EMLDAOptimizer extends LDAOptimizer{ +class EMLDAOptimizer extends LDAOptimizer { import LDA._ /** - * Following fields will only be initialized through initialState method + * The following fields will only be initialized through the initialize() method */ private[clustering] var graph: Graph[TopicCounts, TokenCount] = null private[clustering] var k: Int = 0 @@ -98,13 +94,23 @@ class EMLDAOptimizer extends LDAOptimizer{ /** * Compute bipartite term/doc graph. */ - private[clustering] override def initialState( - docs: RDD[(Long, Vector)], - k: Int, - docConcentration: Double, - topicConcentration: Double, - randomSeed: Long, - checkpointInterval: Int): LDAOptimizer = { + override private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer = { + + val docConcentration = lda.getDocConcentration + val topicConcentration = lda.getTopicConcentration + val k = lda.getK + + // Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), + // but values in (0,1) are not yet supported. + require(docConcentration > 1.0 || docConcentration == -1.0, s"LDA docConcentration must be" + + s" > 1.0 (or -1 for auto) for EM Optimizer, but was set to $docConcentration") + require(topicConcentration > 1.0 || topicConcentration == -1.0, s"LDA topicConcentration " + + s"must be > 1.0 (or -1 for auto) for EM Optimizer, but was set to $topicConcentration") + + this.docConcentration = if (docConcentration == -1) (50.0 / k) + 1.0 else docConcentration + this.topicConcentration = if (topicConcentration == -1) 1.1 else topicConcentration + val randomSeed = lda.getSeed + // For each document, create an edge (Document -> Term) for each unique term in the document. val edges: RDD[Edge[TokenCount]] = docs.flatMap { case (docID: Long, termCounts: Vector) => // Add edges for terms with non-zero counts. @@ -113,11 +119,9 @@ class EMLDAOptimizer extends LDAOptimizer{ } } - val vocabSize = docs.take(1).head._2.size - // Create vertices. // Initially, we use random soft assignments of tokens to topics (random gamma). - def createVertices(): RDD[(VertexId, TopicCounts)] = { + val docTermVertices: RDD[(VertexId, TopicCounts)] = { val verticesTMP: RDD[(VertexId, TopicCounts)] = edges.mapPartitionsWithIndex { case (partIndex, partEdges) => val random = new Random(partIndex + randomSeed) @@ -130,22 +134,18 @@ class EMLDAOptimizer extends LDAOptimizer{ verticesTMP.reduceByKey(_ + _) } - val docTermVertices = createVertices() - // Partition such that edges are grouped by document this.graph = Graph(docTermVertices, edges).partitionBy(PartitionStrategy.EdgePartition1D) this.k = k - this.vocabSize = vocabSize - this.docConcentration = docConcentration - this.topicConcentration = topicConcentration - this.checkpointInterval = checkpointInterval + this.vocabSize = docs.take(1).head._2.size + this.checkpointInterval = lda.getCheckpointInterval this.graphCheckpointer = new PeriodicGraphCheckpointer[TopicCounts, TokenCount](graph, checkpointInterval) this.globalTopicTotals = computeGlobalTopicTotals() this } - private[clustering] override def next(): EMLDAOptimizer = { + override private[clustering] def next(): EMLDAOptimizer = { require(graph != null, "graph is null, EMLDAOptimizer not initialized.") val eta = topicConcentration @@ -202,9 +202,269 @@ class EMLDAOptimizer extends LDAOptimizer{ graph.vertices.filter(isTermVertex).values.fold(BDV.zeros[Double](numTopics))(_ += _) } - private[clustering] override def getLDAModel(iterationTimes: Array[Double]): LDAModel = { + override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = { require(graph != null, "graph is null, EMLDAOptimizer not initialized.") this.graphCheckpointer.deleteAllCheckpoints() new DistributedLDAModel(this, iterationTimes) } } + + +/** + * :: Experimental :: + * + * An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA + * algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic + * distribution adaptively. + * + * Original Online LDA paper: + * Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010. + */ +@Experimental +class OnlineLDAOptimizer extends LDAOptimizer { + + // LDA common parameters + private var k: Int = 0 + private var corpusSize: Long = 0 + private var vocabSize: Int = 0 + + /** alias for docConcentration */ + private var alpha: Double = 0 + + /** (private[clustering] for debugging) Get docConcentration */ + private[clustering] def getAlpha: Double = alpha + + /** alias for topicConcentration */ + private var eta: Double = 0 + + /** (private[clustering] for debugging) Get topicConcentration */ + private[clustering] def getEta: Double = eta + + private var randomGenerator: java.util.Random = null + + // Online LDA specific parameters + // Learning rate is: (tau_0 + t)^{-kappa} + private var tau_0: Double = 1024 + private var kappa: Double = 0.51 + private var miniBatchFraction: Double = 0.05 + + // internal data structure + private var docs: RDD[(Long, Vector)] = null + + /** Dirichlet parameter for the posterior over topics */ + private var lambda: BDM[Double] = null + + /** (private[clustering] for debugging) Get parameter for topics */ + private[clustering] def getLambda: BDM[Double] = lambda + + /** Current iteration (count of invocations of [[next()]]) */ + private var iteration: Int = 0 + private var gammaShape: Double = 100 + + /** + * A (positive) learning parameter that downweights early iterations. Larger values make early + * iterations count less. + */ + def getTau_0: Double = this.tau_0 + + /** + * A (positive) learning parameter that downweights early iterations. Larger values make early + * iterations count less. + * Default: 1024, following the original Online LDA paper. + */ + def setTau_0(tau_0: Double): this.type = { + require(tau_0 > 0, s"LDA tau_0 must be positive, but was set to $tau_0") + this.tau_0 = tau_0 + this + } + + /** + * Learning rate: exponential decay rate + */ + def getKappa: Double = this.kappa + + /** + * Learning rate: exponential decay rate---should be between + * (0.5, 1.0] to guarantee asymptotic convergence. + * Default: 0.51, based on the original Online LDA paper. + */ + def setKappa(kappa: Double): this.type = { + require(kappa >= 0, s"Online LDA kappa must be nonnegative, but was set to $kappa") + this.kappa = kappa + this + } + + /** + * Mini-batch fraction, which sets the fraction of document sampled and used in each iteration + */ + def getMiniBatchFraction: Double = this.miniBatchFraction + + /** + * Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in + * each iteration. + * + * Note that this should be adjusted in synch with [[LDA.setMaxIterations()]] + * so the entire corpus is used. Specifically, set both so that + * maxIterations * miniBatchFraction >= 1. + * + * Default: 0.05, i.e., 5% of total documents. + */ + def setMiniBatchFraction(miniBatchFraction: Double): this.type = { + require(miniBatchFraction > 0.0 && miniBatchFraction <= 1.0, + s"Online LDA miniBatchFraction must be in range (0,1], but was set to $miniBatchFraction") + this.miniBatchFraction = miniBatchFraction + this + } + + /** + * (private[clustering]) + * Set the Dirichlet parameter for the posterior over topics. + * This is only used for testing now. In the future, it can help support training stop/resume. + */ + private[clustering] def setLambda(lambda: BDM[Double]): this.type = { + this.lambda = lambda + this + } + + /** + * (private[clustering]) + * Used for random initialization of the variational parameters. + * Larger value produces values closer to 1.0. + * This is only used for testing currently. + */ + private[clustering] def setGammaShape(shape: Double): this.type = { + this.gammaShape = shape + this + } + + override private[clustering] def initialize( + docs: RDD[(Long, Vector)], + lda: LDA): OnlineLDAOptimizer = { + this.k = lda.getK + this.corpusSize = docs.count() + this.vocabSize = docs.first()._2.size + this.alpha = if (lda.getDocConcentration == -1) 1.0 / k else lda.getDocConcentration + this.eta = if (lda.getTopicConcentration == -1) 1.0 / k else lda.getTopicConcentration + this.randomGenerator = new Random(lda.getSeed) + + this.docs = docs + + // Initialize the variational distribution q(beta|lambda) + this.lambda = getGammaMatrix(k, vocabSize) + this.iteration = 0 + this + } + + override private[clustering] def next(): OnlineLDAOptimizer = { + val batch = docs.sample(withReplacement = true, miniBatchFraction, randomGenerator.nextLong()) + if (batch.isEmpty()) return this + submitMiniBatch(batch) + } + + /** + * Submit a subset (like 1%, decide by the miniBatchFraction) of the corpus to the Online LDA + * model, and it will update the topic distribution adaptively for the terms appearing in the + * subset. + */ + private[clustering] def submitMiniBatch(batch: RDD[(Long, Vector)]): OnlineLDAOptimizer = { + iteration += 1 + val k = this.k + val vocabSize = this.vocabSize + val Elogbeta = dirichletExpectation(lambda) + val expElogbeta = exp(Elogbeta) + val alpha = this.alpha + val gammaShape = this.gammaShape + + val stats: RDD[BDM[Double]] = batch.mapPartitions { docs => + val stat = BDM.zeros[Double](k, vocabSize) + docs.foreach { doc => + val termCounts = doc._2 + val (ids: List[Int], cts: Array[Double]) = termCounts match { + case v: DenseVector => ((0 until v.size).toList, v.values) + case v: SparseVector => (v.indices.toList, v.values) + case v => throw new IllegalArgumentException("Online LDA does not support vector type " + + v.getClass) + } + + // Initialize the variational distribution q(theta|gamma) for the mini-batch + var gammad = new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k).t // 1 * K + var Elogthetad = digamma(gammad) - digamma(sum(gammad)) // 1 * K + var expElogthetad = exp(Elogthetad) // 1 * K + val expElogbetad = expElogbeta(::, ids).toDenseMatrix // K * ids + + var phinorm = expElogthetad * expElogbetad + 1e-100 // 1 * ids + var meanchange = 1D + val ctsVector = new BDV[Double](cts).t // 1 * ids + + // Iterate between gamma and phi until convergence + while (meanchange > 1e-3) { + val lastgamma = gammad + // 1*K 1 * ids ids * k + gammad = (expElogthetad :* ((ctsVector / phinorm) * expElogbetad.t)) + alpha + Elogthetad = digamma(gammad) - digamma(sum(gammad)) + expElogthetad = exp(Elogthetad) + phinorm = expElogthetad * expElogbetad + 1e-100 + meanchange = sum(abs(gammad - lastgamma)) / k + } + + val m1 = expElogthetad.t + val m2 = (ctsVector / phinorm).t.toDenseVector + var i = 0 + while (i < ids.size) { + stat(::, ids(i)) := stat(::, ids(i)) + m1 * m2(i) + i += 1 + } + } + Iterator(stat) + } + + val statsSum: BDM[Double] = stats.reduce(_ += _) + val batchResult = statsSum :* expElogbeta + + // Note that this is an optimization to avoid batch.count + update(batchResult, iteration, (miniBatchFraction * corpusSize).ceil.toInt) + this + } + + override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = { + new LocalLDAModel(Matrices.fromBreeze(lambda).transpose) + } + + /** + * Update lambda based on the batch submitted. batchSize can be different for each iteration. + */ + private[clustering] def update(stat: BDM[Double], iter: Int, batchSize: Int): Unit = { + val tau_0 = this.getTau_0 + val kappa = this.getKappa + + // weight of the mini-batch. + val weight = math.pow(tau_0 + iter, -kappa) + + // Update lambda based on documents. + lambda = lambda * (1 - weight) + + (stat * (corpusSize.toDouble / batchSize.toDouble) + eta) * weight + } + + /** + * Get a random matrix to initialize lambda + */ + private def getGammaMatrix(row: Int, col: Int): BDM[Double] = { + val randBasis = new RandBasis(new org.apache.commons.math3.random.MersenneTwister( + randomGenerator.nextLong())) + val gammaRandomGenerator = new Gamma(gammaShape, 1.0 / gammaShape)(randBasis) + val temp = gammaRandomGenerator.sample(row * col).toArray + new BDM[Double](col, row, temp).t + } + + /** + * For theta ~ Dir(alpha), computes E[log(theta)] given alpha. Currently the implementation + * uses digamma which is accurate but expensive. + */ + private def dirichletExpectation(alpha: BDM[Double]): BDM[Double] = { + val rowSum = sum(alpha(breeze.linalg.*, ::)) + val digAlpha = digamma(alpha) + val digRowSum = digamma(rowSum) + val result = digAlpha(::, breeze.linalg.*) - digRowSum + result + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index 188d1e542b5b5..f6bcdf83cd337 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -587,6 +587,28 @@ class DenseVector(val values: Array[Double]) extends Vector { } new SparseVector(size, ii, vv) } + + /** + * Find the index of a maximal element. Returns the first maximal element in case of a tie. + * Returns -1 if vector has length 0. + */ + private[spark] def argmax: Int = { + if (size == 0) { + -1 + } else { + var maxIdx = 0 + var maxValue = values(0) + var i = 1 + while (i < size) { + if (values(i) > maxValue) { + maxIdx = i + maxValue = values(i) + } + i += 1 + } + maxIdx + } + } } object DenseVector { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala new file mode 100644 index 0000000000000..354e90f3eeaa6 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala @@ -0,0 +1,74 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml + +import java.io.{File, OutputStream, StringWriter} +import javax.xml.transform.stream.StreamResult + +import org.jpmml.model.JAXBUtil + +import org.apache.spark.SparkContext +import org.apache.spark.mllib.pmml.export.PMMLModelExportFactory + +/** + * Export model to the PMML format + * Predictive Model Markup Language (PMML) is an XML-based file format + * developed by the Data Mining Group (www.dmg.org). + */ +trait PMMLExportable { + + /** + * Export the model to the stream result in PMML format + */ + private def toPMML(streamResult: StreamResult): Unit = { + val pmmlModelExport = PMMLModelExportFactory.createPMMLModelExport(this) + JAXBUtil.marshalPMML(pmmlModelExport.getPmml, streamResult) + } + + /** + * Export the model to a local file in PMML format + */ + def toPMML(localPath: String): Unit = { + toPMML(new StreamResult(new File(localPath))) + } + + /** + * Export the model to a directory on a distributed file system in PMML format + */ + def toPMML(sc: SparkContext, path: String): Unit = { + val pmml = toPMML() + sc.parallelize(Array(pmml), 1).saveAsTextFile(path) + } + + /** + * Export the model to the OutputStream in PMML format + */ + def toPMML(outputStream: OutputStream): Unit = { + toPMML(new StreamResult(outputStream)) + } + + /** + * Export the model to a String in PMML format + */ + def toPMML(): String = { + val writer = new StringWriter + toPMML(new StreamResult(writer)) + writer.toString + } + +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala new file mode 100644 index 0000000000000..34b447584e521 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala @@ -0,0 +1,90 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import scala.{Array => SArray} + +import org.dmg.pmml._ + +import org.apache.spark.mllib.regression.GeneralizedLinearModel + +/** + * PMML Model Export for GeneralizedLinearModel class with binary ClassificationModel + */ +private[mllib] class BinaryClassificationPMMLModelExport( + model : GeneralizedLinearModel, + description : String, + normalizationMethod : RegressionNormalizationMethodType, + threshold: Double) + extends PMMLModelExport { + + populateBinaryClassificationPMML() + + /** + * Export the input LogisticRegressionModel or SVMModel to PMML format. + */ + private def populateBinaryClassificationPMML(): Unit = { + pmml.getHeader.setDescription(description) + + if (model.weights.size > 0) { + val fields = new SArray[FieldName](model.weights.size) + val dataDictionary = new DataDictionary + val miningSchema = new MiningSchema + val regressionTableYES = new RegressionTable(model.intercept).withTargetCategory("1") + var interceptNO = threshold + if (RegressionNormalizationMethodType.LOGIT == normalizationMethod) { + if (threshold <= 0) { + interceptNO = Double.MinValue + } else if (threshold >= 1) { + interceptNO = Double.MaxValue + } else { + interceptNO = -math.log(1 / threshold - 1) + } + } + val regressionTableNO = new RegressionTable(interceptNO).withTargetCategory("0") + val regressionModel = new RegressionModel() + .withFunctionName(MiningFunctionType.CLASSIFICATION) + .withMiningSchema(miningSchema) + .withModelName(description) + .withNormalizationMethod(normalizationMethod) + .withRegressionTables(regressionTableYES, regressionTableNO) + + for (i <- 0 until model.weights.size) { + fields(i) = FieldName.create("field_" + i) + dataDictionary.withDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE)) + miningSchema + .withMiningFields(new MiningField(fields(i)) + .withUsageType(FieldUsageType.ACTIVE)) + regressionTableYES.withNumericPredictors(new NumericPredictor(fields(i), model.weights(i))) + } + + // add target field + val targetField = FieldName.create("target") + dataDictionary + .withDataFields(new DataField(targetField, OpType.CATEGORICAL, DataType.STRING)) + miningSchema + .withMiningFields(new MiningField(targetField) + .withUsageType(FieldUsageType.TARGET)) + + dataDictionary.withNumberOfFields(dataDictionary.getDataFields.size) + + pmml.setDataDictionary(dataDictionary) + pmml.withModels(regressionModel) + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala new file mode 100644 index 0000000000000..1874786af0002 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala @@ -0,0 +1,75 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import scala.{Array => SArray} + +import org.dmg.pmml._ + +import org.apache.spark.mllib.regression.GeneralizedLinearModel + +/** + * PMML Model Export for GeneralizedLinearModel abstract class + */ +private[mllib] class GeneralizedLinearPMMLModelExport( + model: GeneralizedLinearModel, + description: String) + extends PMMLModelExport { + + populateGeneralizedLinearPMML(model) + + /** + * Export the input GeneralizedLinearModel model to PMML format. + */ + private def populateGeneralizedLinearPMML(model: GeneralizedLinearModel): Unit = { + pmml.getHeader.setDescription(description) + + if (model.weights.size > 0) { + val fields = new SArray[FieldName](model.weights.size) + val dataDictionary = new DataDictionary + val miningSchema = new MiningSchema + val regressionTable = new RegressionTable(model.intercept) + val regressionModel = new RegressionModel() + .withFunctionName(MiningFunctionType.REGRESSION) + .withMiningSchema(miningSchema) + .withModelName(description) + .withRegressionTables(regressionTable) + + for (i <- 0 until model.weights.size) { + fields(i) = FieldName.create("field_" + i) + dataDictionary.withDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE)) + miningSchema + .withMiningFields(new MiningField(fields(i)) + .withUsageType(FieldUsageType.ACTIVE)) + regressionTable.withNumericPredictors(new NumericPredictor(fields(i), model.weights(i))) + } + + // for completeness add target field + val targetField = FieldName.create("target") + dataDictionary.withDataFields(new DataField(targetField, OpType.CONTINUOUS, DataType.DOUBLE)) + miningSchema + .withMiningFields(new MiningField(targetField) + .withUsageType(FieldUsageType.TARGET)) + + dataDictionary.withNumberOfFields(dataDictionary.getDataFields.size) + + pmml.setDataDictionary(dataDictionary) + pmml.withModels(regressionModel) + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala new file mode 100644 index 0000000000000..069e7afc9fca0 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala @@ -0,0 +1,83 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import scala.{Array => SArray} + +import org.dmg.pmml._ + +import org.apache.spark.mllib.clustering.KMeansModel + +/** + * PMML Model Export for KMeansModel class + */ +private[mllib] class KMeansPMMLModelExport(model : KMeansModel) extends PMMLModelExport{ + + populateKMeansPMML(model) + + /** + * Export the input KMeansModel model to PMML format. + */ + private def populateKMeansPMML(model : KMeansModel): Unit = { + pmml.getHeader.setDescription("k-means clustering") + + if (model.clusterCenters.length > 0) { + val clusterCenter = model.clusterCenters(0) + val fields = new SArray[FieldName](clusterCenter.size) + val dataDictionary = new DataDictionary + val miningSchema = new MiningSchema + val comparisonMeasure = new ComparisonMeasure() + .withKind(ComparisonMeasure.Kind.DISTANCE) + .withMeasure(new SquaredEuclidean()) + val clusteringModel = new ClusteringModel() + .withModelName("k-means") + .withMiningSchema(miningSchema) + .withComparisonMeasure(comparisonMeasure) + .withFunctionName(MiningFunctionType.CLUSTERING) + .withModelClass(ClusteringModel.ModelClass.CENTER_BASED) + .withNumberOfClusters(model.clusterCenters.length) + + for (i <- 0 until clusterCenter.size) { + fields(i) = FieldName.create("field_" + i) + dataDictionary.withDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE)) + miningSchema + .withMiningFields(new MiningField(fields(i)) + .withUsageType(FieldUsageType.ACTIVE)) + clusteringModel.withClusteringFields( + new ClusteringField(fields(i)).withCompareFunction(CompareFunctionType.ABS_DIFF)) + } + + dataDictionary.withNumberOfFields(dataDictionary.getDataFields.size) + + for (i <- 0 until model.clusterCenters.length) { + val cluster = new Cluster() + .withName("cluster_" + i) + .withArray(new org.dmg.pmml.Array() + .withType(Array.Type.REAL) + .withN(clusterCenter.size) + .withValue(model.clusterCenters(i).toArray.mkString(" "))) + // we don't have the size of the single cluster but only the centroids (withValue) + // .withSize(value) + clusteringModel.withClusters(cluster) + } + + pmml.setDataDictionary(dataDictionary) + pmml.withModels(clusteringModel) + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala new file mode 100644 index 0000000000000..ebdeae50bb32f --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala @@ -0,0 +1,47 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import java.text.SimpleDateFormat +import java.util.Date + +import scala.beans.BeanProperty + +import org.dmg.pmml.{Application, Header, PMML, Timestamp} + +private[mllib] trait PMMLModelExport { + + /** + * Holder of the exported model in PMML format + */ + @BeanProperty + val pmml: PMML = new PMML + + setHeader(pmml) + + private def setHeader(pmml: PMML): Unit = { + val version = getClass.getPackage.getImplementationVersion + val app = new Application().withName("Apache Spark MLlib").withVersion(version) + val timestamp = new Timestamp() + .withContent(new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss").format(new Date())) + val header = new Header() + .withApplication(app) + .withTimestamp(timestamp) + pmml.setHeader(header) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala new file mode 100644 index 0000000000000..c16e83d6a067d --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala @@ -0,0 +1,64 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.dmg.pmml.RegressionNormalizationMethodType + +import org.apache.spark.mllib.classification.LogisticRegressionModel +import org.apache.spark.mllib.classification.SVMModel +import org.apache.spark.mllib.clustering.KMeansModel +import org.apache.spark.mllib.regression.LassoModel +import org.apache.spark.mllib.regression.LinearRegressionModel +import org.apache.spark.mllib.regression.RidgeRegressionModel + +private[mllib] object PMMLModelExportFactory { + + /** + * Factory object to help creating the necessary PMMLModelExport implementation + * taking as input the machine learning model (for example KMeansModel). + */ + def createPMMLModelExport(model: Any): PMMLModelExport = { + model match { + case kmeans: KMeansModel => + new KMeansPMMLModelExport(kmeans) + case linear: LinearRegressionModel => + new GeneralizedLinearPMMLModelExport(linear, "linear regression") + case ridge: RidgeRegressionModel => + new GeneralizedLinearPMMLModelExport(ridge, "ridge regression") + case lasso: LassoModel => + new GeneralizedLinearPMMLModelExport(lasso, "lasso regression") + case svm: SVMModel => + new BinaryClassificationPMMLModelExport( + svm, "linear SVM", RegressionNormalizationMethodType.NONE, + svm.getThreshold.getOrElse(0.0)) + case logistic: LogisticRegressionModel => + if (logistic.numClasses == 2) { + new BinaryClassificationPMMLModelExport( + logistic, "logistic regression", RegressionNormalizationMethodType.LOGIT, + logistic.getThreshold.getOrElse(0.5)) + } else { + throw new IllegalArgumentException( + "PMML Export not supported for Multinomial Logistic Regression") + } + case _ => + throw new IllegalArgumentException( + "PMML Export not supported for model: " + model.getClass.getName) + } + } + +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala index 9213fd3f595c3..5af55aaf84802 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala @@ -42,13 +42,11 @@ class MLPairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) extends Se self.aggregateByKey(new BoundedPriorityQueue[V](num)(ord))( seqOp = (queue, item) => { queue += item - queue }, combOp = (queue1, queue2) => { queue1 ++= queue2 - queue1 } - ).mapValues(_.toArray.sorted(ord.reverse)) + ).mapValues(_.toArray.reverse) // This is an min-heap, so we reverse the order. } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala index 36cbf060d9998..88c2148403313 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -20,14 +20,18 @@ package org.apache.spark.mllib.recommendation import java.io.IOException import java.lang.{Integer => JavaInteger} +import scala.collection.mutable + +import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.hadoop.fs.Path import org.json4s._ import org.json4s.JsonDSL._ import org.json4s.jackson.JsonMethods._ -import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.spark.{Logging, SparkContext} import org.apache.spark.api.java.{JavaPairRDD, JavaRDD} +import org.apache.spark.mllib.linalg._ +import org.apache.spark.mllib.rdd.MLPairRDDFunctions._ import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD import org.apache.spark.sql.{Row, SQLContext} @@ -57,7 +61,7 @@ class MatrixFactorizationModel( /** Validates factors and warns users if there are performance concerns. */ private def validateFeatures(name: String, features: RDD[(Int, Array[Double])]): Unit = { - require(features.first()._2.size == rank, + require(features.first()._2.length == rank, s"$name feature dimension does not match the rank $rank.") if (features.partitioner.isEmpty) { logWarning(s"$name factor does not have a partitioner. " @@ -72,19 +76,19 @@ class MatrixFactorizationModel( def predict(user: Int, product: Int): Double = { val userVector = userFeatures.lookup(user).head val productVector = productFeatures.lookup(product).head - blas.ddot(userVector.length, userVector, 1, productVector, 1) + blas.ddot(rank, userVector, 1, productVector, 1) } /** - * Predict the rating of many users for many products. - * The output RDD has an element per each element in the input RDD (including all duplicates) - * unless a user or product is missing in the training set. - * - * @param usersProducts RDD of (user, product) pairs. - * @return RDD of Ratings. - */ + * Predict the rating of many users for many products. + * The output RDD has an element per each element in the input RDD (including all duplicates) + * unless a user or product is missing in the training set. + * + * @param usersProducts RDD of (user, product) pairs. + * @return RDD of Ratings. + */ def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = { - val users = userFeatures.join(usersProducts).map{ + val users = userFeatures.join(usersProducts).map { case (user, (uFeatures, product)) => (product, (user, uFeatures)) } users.join(productFeatures).map { @@ -112,7 +116,7 @@ class MatrixFactorizationModel( * recommended the product is. */ def recommendProducts(user: Int, num: Int): Array[Rating] = - recommend(userFeatures.lookup(user).head, productFeatures, num) + MatrixFactorizationModel.recommend(userFeatures.lookup(user).head, productFeatures, num) .map(t => Rating(user, t._1, t._2)) /** @@ -128,7 +132,7 @@ class MatrixFactorizationModel( * recommended the user is. */ def recommendUsers(product: Int, num: Int): Array[Rating] = - recommend(productFeatures.lookup(product).head, userFeatures, num) + MatrixFactorizationModel.recommend(productFeatures.lookup(product).head, userFeatures, num) .map(t => Rating(t._1, product, t._2)) protected override val formatVersion: String = "1.0" @@ -137,20 +141,113 @@ class MatrixFactorizationModel( MatrixFactorizationModel.SaveLoadV1_0.save(this, path) } + /** + * Recommends topK products for all users. + * + * @param num how many products to return for every user. + * @return [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of + * rating objects which contains the same userId, recommended productID and a "score" in the + * rating field. Semantics of score is same as recommendProducts API + */ + def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] = { + MatrixFactorizationModel.recommendForAll(rank, userFeatures, productFeatures, num).map { + case (user, top) => + val ratings = top.map { case (product, rating) => Rating(user, product, rating) } + (user, ratings) + } + } + + + /** + * Recommends topK users for all products. + * + * @param num how many users to return for every product. + * @return [(Int, Array[Rating])] objects, where every tuple contains a productID and an array + * of rating objects which contains the recommended userId, same productID and a "score" in the + * rating field. Semantics of score is same as recommendUsers API + */ + def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])] = { + MatrixFactorizationModel.recommendForAll(rank, productFeatures, userFeatures, num).map { + case (product, top) => + val ratings = top.map { case (user, rating) => Rating(user, product, rating) } + (product, ratings) + } + } +} + +object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { + + import org.apache.spark.mllib.util.Loader._ + + /** + * Makes recommendations for a single user (or product). + */ private def recommend( recommendToFeatures: Array[Double], recommendableFeatures: RDD[(Int, Array[Double])], num: Int): Array[(Int, Double)] = { - val scored = recommendableFeatures.map { case (id,features) => + val scored = recommendableFeatures.map { case (id, features) => (id, blas.ddot(features.length, recommendToFeatures, 1, features, 1)) } scored.top(num)(Ordering.by(_._2)) } -} -object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { + /** + * Makes recommendations for all users (or products). + * @param rank rank + * @param srcFeatures src features to receive recommendations + * @param dstFeatures dst features used to make recommendations + * @param num number of recommendations for each record + * @return an RDD of (srcId: Int, recommendations), where recommendations are stored as an array + * of (dstId, rating) pairs. + */ + private def recommendForAll( + rank: Int, + srcFeatures: RDD[(Int, Array[Double])], + dstFeatures: RDD[(Int, Array[Double])], + num: Int): RDD[(Int, Array[(Int, Double)])] = { + val srcBlocks = blockify(rank, srcFeatures) + val dstBlocks = blockify(rank, dstFeatures) + val ratings = srcBlocks.cartesian(dstBlocks).flatMap { + case ((srcIds, srcFactors), (dstIds, dstFactors)) => + val m = srcIds.length + val n = dstIds.length + val ratings = srcFactors.transpose.multiply(dstFactors) + val output = new Array[(Int, (Int, Double))](m * n) + var k = 0 + ratings.foreachActive { (i, j, r) => + output(k) = (srcIds(i), (dstIds(j), r)) + k += 1 + } + output.toSeq + } + ratings.topByKey(num)(Ordering.by(_._2)) + } - import org.apache.spark.mllib.util.Loader._ + /** + * Blockifies features to use Level-3 BLAS. + */ + private def blockify( + rank: Int, + features: RDD[(Int, Array[Double])]): RDD[(Array[Int], DenseMatrix)] = { + val blockSize = 4096 // TODO: tune the block size + val blockStorage = rank * blockSize + features.mapPartitions { iter => + iter.grouped(blockSize).map { grouped => + val ids = mutable.ArrayBuilder.make[Int] + ids.sizeHint(blockSize) + val factors = mutable.ArrayBuilder.make[Double] + factors.sizeHint(blockStorage) + var i = 0 + grouped.foreach { case (id, factor) => + ids += id + factors ++= factor + i += 1 + } + (ids.result(), new DenseMatrix(rank, i, factors.result())) + } + } + } override def load(sc: SparkContext, path: String): MatrixFactorizationModel = { val (loadedClassName, formatVersion, _) = loadMetadata(sc, path) @@ -214,4 +311,5 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { new Path(dataPath(path), "product").toUri.toString } } + } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala index 9fd60ff7a0c79..26be30ff9d6fd 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -225,7 +225,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] throw new SparkException("Input validation failed.") } - /* + /** * Scaling columns to unit variance as a heuristic to reduce the condition number: * * During the optimization process, the convergence (rate) depends on the condition number of diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala index 1d7617046b6c7..be2a00c2dfea4 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala @@ -21,18 +21,20 @@ import java.io.Serializable import java.lang.{Double => JDouble} import java.util.Arrays.binarySearch +import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer import org.json4s._ import org.json4s.JsonDSL._ import org.json4s.jackson.JsonMethods._ +import org.apache.spark.SparkContext import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD} +import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD -import org.apache.spark.SparkContext -import org.apache.spark.sql.{DataFrame, SQLContext} +import org.apache.spark.sql.SQLContext /** * :: Experimental :: @@ -57,6 +59,13 @@ class IsotonicRegressionModel ( assertOrdered(boundaries) assertOrdered(predictions)(predictionOrd) + /** A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter. */ + def this(boundaries: java.lang.Iterable[Double], + predictions: java.lang.Iterable[Double], + isotonic: java.lang.Boolean) = { + this(boundaries.asScala.toArray, predictions.asScala.toArray, isotonic) + } + /** Asserts the input array is monotone with the given ordering. */ private def assertOrdered(xs: Array[Double])(implicit ord: Ordering[Double]): Unit = { var i = 1 @@ -132,6 +141,12 @@ class IsotonicRegressionModel ( } } + /** A convenient method for boundaries called by the Python API. */ + private[mllib] def boundaryVector: Vector = Vectors.dense(boundaries) + + /** A convenient method for boundaries called by the Python API. */ + private[mllib] def predictionVector: Vector = Vectors.dense(predictions) + override def save(sc: SparkContext, path: String): Unit = { IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala index e8b03816573cf..4f482384f0f38 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala @@ -20,6 +20,7 @@ package org.apache.spark.mllib.regression import org.apache.spark.SparkContext import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression.impl.GLMRegressionModel import org.apache.spark.mllib.util.{Saveable, Loader} import org.apache.spark.rdd.RDD @@ -34,7 +35,7 @@ class LassoModel ( override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) - with RegressionModel with Serializable with Saveable { + with RegressionModel with Serializable with Saveable with PMMLExportable { override protected def predictPoint( dataMatrix: Vector, diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala index 6fa7ad52a5b33..9453c4f66c216 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala @@ -20,6 +20,7 @@ package org.apache.spark.mllib.regression import org.apache.spark.SparkContext import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression.impl.GLMRegressionModel import org.apache.spark.mllib.util.{Saveable, Loader} import org.apache.spark.rdd.RDD @@ -34,7 +35,7 @@ class LinearRegressionModel ( override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable - with Saveable { + with Saveable with PMMLExportable { override protected def predictPoint( dataMatrix: Vector, diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala index 309f9af466457..e0c03d8180c7a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala @@ -20,6 +20,7 @@ package org.apache.spark.mllib.regression import org.apache.spark.SparkContext import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression.impl.GLMRegressionModel import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD @@ -35,7 +36,7 @@ class RidgeRegressionModel ( override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) - with RegressionModel with Serializable with Saveable { + with RegressionModel with Serializable with Saveable with PMMLExportable { override protected def predictPoint( dataMatrix: Vector, diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala index 23b291eee070b..8a821d1b23bab 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala @@ -101,7 +101,7 @@ private[stat] object PearsonCorrelation extends Correlation with Logging { Matrices.fromBreeze(cov) } - private def closeToZero(value: Double, threshhold: Double = 1e-12): Boolean = { - math.abs(value) <= threshhold + private def closeToZero(value: Double, threshold: Double = 1e-12): Boolean = { + math.abs(value) <= threshold } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala index deac390130128..1f779584dcffd 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala @@ -180,7 +180,9 @@ object GradientBoostedTrees extends Logging { val persistedInput = if (input.getStorageLevel == StorageLevel.NONE) { input.persist(StorageLevel.MEMORY_AND_DISK) true - } else false + } else { + false + } timer.stop("init") diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala index f1a6ed230186e..f73896e37c05e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala @@ -107,7 +107,10 @@ private[tree] object DecisionTreeMetadata extends Logging { numTrees: Int, featureSubsetStrategy: String): DecisionTreeMetadata = { - val numFeatures = input.take(1)(0).features.size + val numFeatures = input.map(_.features.size).take(1).headOption.getOrElse { + throw new IllegalArgumentException(s"DecisionTree requires size of input RDD > 0, " + + s"but was given by empty one.") + } val numExamples = input.count() val numClasses = strategy.algo match { case Classification => strategy.numClasses diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala index d7bb943e84f53..b1a4517344970 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala @@ -75,8 +75,8 @@ object LinearDataGenerator { seed: Int, eps: Double = 0.1): Seq[LabeledPoint] = { generateLinearInput(intercept, weights, - Array.fill[Double](weights.size)(0.0), - Array.fill[Double](weights.size)(1.0 / 3.0), + Array.fill[Double](weights.length)(0.0), + Array.fill[Double](weights.length)(1.0 / 3.0), nPoints, seed, eps)} /** @@ -103,17 +103,15 @@ object LinearDataGenerator { val rnd = new Random(seed) val x = Array.fill[Array[Double]](nPoints)( - Array.fill[Double](weights.length)(rnd.nextDouble)) + Array.fill[Double](weights.length)(rnd.nextDouble())) - x.map(vector => { - // This doesn't work if `vector` is a sparse vector. - val vectorArray = vector.toArray + x.foreach { v => var i = 0 - while (i < vectorArray.size) { - vectorArray(i) = (vectorArray(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i) + while (i < v.length) { + v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i) i += 1 } - }) + } val y = x.map { xi => blas.ddot(weights.length, xi, 1, weights, 1) + intercept + eps * rnd.nextGaussian() diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java index 3f8e59de0f05c..7e7189a2b1d53 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java @@ -84,9 +84,10 @@ public void logisticRegressionWithSetters() { .setThreshold(0.6) .setProbabilityCol("myProbability"); LogisticRegressionModel model = lr.fit(dataset); - assert(model.fittingParamMap().apply(lr.maxIter()).equals(10)); - assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0)); - assert(model.fittingParamMap().apply(lr.threshold()).equals(0.6)); + LogisticRegression parent = model.parent(); + assert(parent.getMaxIter() == 10); + assert(parent.getRegParam() == 1.0); + assert(parent.getThreshold() == 0.6); assert(model.getThreshold() == 0.6); // Modify model params, and check that the params worked. @@ -109,9 +110,10 @@ public void logisticRegressionWithSetters() { // Call fit() with new params, and check as many params as we can. LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.threshold().w(0.4), lr.probabilityCol().w("theProb")); - assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5)); - assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1)); - assert(model2.fittingParamMap().apply(lr.threshold()).equals(0.4)); + LogisticRegression parent2 = model2.parent(); + assert(parent2.getMaxIter() == 5); + assert(parent2.getRegParam() == 0.1); + assert(parent2.getThreshold() == 0.4); assert(model2.getThreshold() == 0.4); assert(model2.getProbabilityCol().equals("theProb")); } diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java new file mode 100644 index 0000000000000..e7df10dfa63ac --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java @@ -0,0 +1,66 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.param; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaSparkContext; + +/** + * Test Param and related classes in Java + */ +public class JavaParamsSuite { + + private transient JavaSparkContext jsc; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaParamsSuite"); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void testParams() { + JavaTestParams testParams = new JavaTestParams(); + Assert.assertEquals(testParams.getMyIntParam(), 1); + testParams.setMyIntParam(2).setMyDoubleParam(0.4).setMyStringParam("a"); + Assert.assertEquals(testParams.getMyDoubleParam(), 0.4, 0.0); + Assert.assertEquals(testParams.getMyStringParam(), "a"); + } + + @Test + public void testParamValidate() { + ParamValidators.gt(1.0); + ParamValidators.gtEq(1.0); + ParamValidators.lt(1.0); + ParamValidators.ltEq(1.0); + ParamValidators.inRange(0, 1, true, false); + ParamValidators.inRange(0, 1); + ParamValidators.inArray(Lists.newArrayList(0, 1, 3)); + ParamValidators.inArray(Lists.newArrayList("a", "b")); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java new file mode 100644 index 0000000000000..8abe575610d19 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.param; + +import java.util.List; + +import com.google.common.collect.Lists; + +/** + * A subclass of Params for testing. + */ +public class JavaTestParams extends JavaParams { + + public IntParam myIntParam; + + public int getMyIntParam() { return (Integer)getOrDefault(myIntParam); } + + public JavaTestParams setMyIntParam(int value) { + set(myIntParam, value); return this; + } + + public DoubleParam myDoubleParam; + + public double getMyDoubleParam() { return (Double)getOrDefault(myDoubleParam); } + + public JavaTestParams setMyDoubleParam(double value) { + set(myDoubleParam, value); return this; + } + + public Param myStringParam; + + public String getMyStringParam() { return (String)getOrDefault(myStringParam); } + + public JavaTestParams setMyStringParam(String value) { + set(myStringParam, value); return this; + } + + public JavaTestParams() { + myIntParam = new IntParam(this, "myIntParam", "this is an int param", ParamValidators.gt(0)); + myDoubleParam = new DoubleParam(this, "myDoubleParam", "this is a double param", + ParamValidators.inRange(0.0, 1.0)); + List validStrings = Lists.newArrayList("a", "b"); + myStringParam = new Param(this, "myStringParam", "this is a string param", + ParamValidators.inArray(validStrings)); + setDefault(myIntParam, 1); + setDefault(myDoubleParam, 0.5); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java index 0cc36c8d56d70..a82b86d560b6e 100644 --- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java @@ -23,14 +23,15 @@ import org.junit.After; import org.junit.Before; import org.junit.Test; +import static org.junit.Assert.assertEquals; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; -import static org.apache.spark.mllib.classification.LogisticRegressionSuite - .generateLogisticInputAsList; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.SQLContext; +import static org.apache.spark.mllib.classification.LogisticRegressionSuite + .generateLogisticInputAsList; public class JavaLinearRegressionSuite implements Serializable { @@ -65,8 +66,8 @@ public void linearRegressionDefaultParams() { DataFrame predictions = jsql.sql("SELECT label, prediction FROM prediction"); predictions.collect(); // Check defaults - assert(model.getFeaturesCol().equals("features")); - assert(model.getPredictionCol().equals("prediction")); + assertEquals("features", model.getFeaturesCol()); + assertEquals("prediction", model.getPredictionCol()); } @Test @@ -76,14 +77,16 @@ public void linearRegressionWithSetters() { .setMaxIter(10) .setRegParam(1.0); LinearRegressionModel model = lr.fit(dataset); - assert(model.fittingParamMap().apply(lr.maxIter()).equals(10)); - assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0)); + LinearRegression parent = model.parent(); + assertEquals(10, parent.getMaxIter()); + assertEquals(1.0, parent.getRegParam(), 0.0); // Call fit() with new params, and check as many params as we can. LinearRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.predictionCol().w("thePred")); - assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5)); - assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1)); - assert(model2.getPredictionCol().equals("thePred")); + LinearRegression parent2 = model2.parent(); + assertEquals(5, parent2.getMaxIter()); + assertEquals(0.1, parent2.getRegParam(), 0.0); + assertEquals("thePred", model2.getPredictionCol()); } } diff --git a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java index 0bb6b489f2757..08eeca53f0721 100644 --- a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java @@ -68,8 +68,8 @@ public void crossValidationWithLogisticRegression() { .setEvaluator(eval) .setNumFolds(3); CrossValidatorModel cvModel = cv.fit(dataset); - ParamMap bestParamMap = cvModel.bestModel().fittingParamMap(); - Assert.assertEquals(0.001, bestParamMap.apply(lr.regParam())); - Assert.assertEquals(10, bestParamMap.apply(lr.maxIter())); + LogisticRegression parent = (LogisticRegression) cvModel.bestModel().parent(); + Assert.assertEquals(0.001, parent.getRegParam(), 0.0); + Assert.assertEquals(10, parent.getMaxIter()); } } diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java index fbe171b4b1ab1..f394d903966de 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java @@ -20,7 +20,6 @@ import java.io.Serializable; import java.util.ArrayList; -import org.apache.spark.api.java.JavaRDD; import scala.Tuple2; import org.junit.After; @@ -30,6 +29,7 @@ import org.junit.Test; import org.apache.spark.api.java.JavaPairRDD; +import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.mllib.linalg.Matrix; import org.apache.spark.mllib.linalg.Vector; @@ -109,11 +109,45 @@ public void distributedLDAModel() { assert(model.logPrior() < 0.0); } + @Test + public void OnlineOptimizerCompatibility() { + int k = 3; + double topicSmoothing = 1.2; + double termSmoothing = 1.2; + + // Train a model + OnlineLDAOptimizer op = new OnlineLDAOptimizer() + .setTau_0(1024) + .setKappa(0.51) + .setGammaShape(1e40) + .setMiniBatchFraction(0.5); + + LDA lda = new LDA(); + lda.setK(k) + .setDocConcentration(topicSmoothing) + .setTopicConcentration(termSmoothing) + .setMaxIterations(5) + .setSeed(12345) + .setOptimizer(op); + + LDAModel model = lda.run(corpus); + + // Check: basic parameters + assertEquals(model.k(), k); + assertEquals(model.vocabSize(), tinyVocabSize); + + // Check: topic summaries + Tuple2[] roundedTopicSummary = model.describeTopics(); + assertEquals(roundedTopicSummary.length, k); + Tuple2[] roundedLocalTopicSummary = model.describeTopics(); + assertEquals(roundedLocalTopicSummary.length, k); + } + private static int tinyK = LDASuite$.MODULE$.tinyK(); private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize(); private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics(); private static Tuple2[] tinyTopicDescription = LDASuite$.MODULE$.tinyTopicDescription(); - JavaPairRDD corpus; + private JavaPairRDD corpus; } diff --git a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala index 2f175fb117941..2b04a3034782e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala @@ -42,30 +42,32 @@ class PipelineSuite extends FunSuite { val dataset3 = mock[DataFrame] val dataset4 = mock[DataFrame] - when(estimator0.fit(meq(dataset0), any[ParamMap]())).thenReturn(model0) - when(model0.transform(meq(dataset0), any[ParamMap]())).thenReturn(dataset1) + when(estimator0.copy(any[ParamMap])).thenReturn(estimator0) + when(model0.copy(any[ParamMap])).thenReturn(model0) + when(transformer1.copy(any[ParamMap])).thenReturn(transformer1) + when(estimator2.copy(any[ParamMap])).thenReturn(estimator2) + when(model2.copy(any[ParamMap])).thenReturn(model2) + when(transformer3.copy(any[ParamMap])).thenReturn(transformer3) + + when(estimator0.fit(meq(dataset0))).thenReturn(model0) + when(model0.transform(meq(dataset0))).thenReturn(dataset1) when(model0.parent).thenReturn(estimator0) - when(transformer1.transform(meq(dataset1), any[ParamMap])).thenReturn(dataset2) - when(estimator2.fit(meq(dataset2), any[ParamMap]())).thenReturn(model2) - when(model2.transform(meq(dataset2), any[ParamMap]())).thenReturn(dataset3) + when(transformer1.transform(meq(dataset1))).thenReturn(dataset2) + when(estimator2.fit(meq(dataset2))).thenReturn(model2) + when(model2.transform(meq(dataset2))).thenReturn(dataset3) when(model2.parent).thenReturn(estimator2) - when(transformer3.transform(meq(dataset3), any[ParamMap]())).thenReturn(dataset4) + when(transformer3.transform(meq(dataset3))).thenReturn(dataset4) val pipeline = new Pipeline() .setStages(Array(estimator0, transformer1, estimator2, transformer3)) val pipelineModel = pipeline.fit(dataset0) - assert(pipelineModel.stages.size === 4) + assert(pipelineModel.stages.length === 4) assert(pipelineModel.stages(0).eq(model0)) assert(pipelineModel.stages(1).eq(transformer1)) assert(pipelineModel.stages(2).eq(model2)) assert(pipelineModel.stages(3).eq(transformer3)) - assert(pipelineModel.getModel(estimator0).eq(model0)) - assert(pipelineModel.getModel(estimator2).eq(model2)) - intercept[NoSuchElementException] { - pipelineModel.getModel(mock[Estimator[MyModel]]) - } val output = pipelineModel.transform(dataset0) assert(output.eq(dataset4)) } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala index 9b31adecdcb1c..03af4ecd7a7e0 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala @@ -267,8 +267,8 @@ private[ml] object DecisionTreeClassifierSuite extends FunSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) val newTree = dt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldTreeAsNew = DecisionTreeClassificationModel.fromOld(oldTree, newTree.parent, - newTree.fittingParamMap, categoricalFeatures) + val oldTreeAsNew = DecisionTreeClassificationModel.fromOld( + oldTree, newTree.parent, categoricalFeatures) TreeTests.checkEqual(oldTreeAsNew, newTree) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala index e6ccc2c93cba8..16c758b82c7cd 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala @@ -129,8 +129,8 @@ private object GBTClassifierSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 2) val newModel = gbt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = GBTClassificationModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = GBTClassificationModel.fromOld( + oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index 35d8c2e16c6cd..6dd1fdf05514e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -74,9 +74,10 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { .setThreshold(0.6) .setProbabilityCol("myProbability") val model = lr.fit(dataset) - assert(model.fittingParamMap.get(lr.maxIter) === Some(10)) - assert(model.fittingParamMap.get(lr.regParam) === Some(1.0)) - assert(model.fittingParamMap.get(lr.threshold) === Some(0.6)) + val parent = model.parent + assert(parent.getMaxIter === 10) + assert(parent.getRegParam === 1.0) + assert(parent.getThreshold === 0.6) assert(model.getThreshold === 0.6) // Modify model params, and check that the params worked. @@ -99,9 +100,10 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { // Call fit() with new params, and check as many params as we can. val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4, lr.probabilityCol -> "theProb") - assert(model2.fittingParamMap.get(lr.maxIter).get === 5) - assert(model2.fittingParamMap.get(lr.regParam).get === 0.1) - assert(model2.fittingParamMap.get(lr.threshold).get === 0.4) + val parent2 = model2.parent + assert(parent2.getMaxIter === 5) + assert(parent2.getRegParam === 0.1) + assert(parent2.getThreshold === 0.4) assert(model2.getThreshold === 0.4) assert(model2.getProbabilityCol == "theProb") } @@ -117,7 +119,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { val results = model.transform(dataset) // Compare rawPrediction with probability - results.select("rawPrediction", "probability").collect().map { + results.select("rawPrediction", "probability").collect().foreach { case Row(raw: Vector, prob: Vector) => assert(raw.size === 2) assert(prob.size === 2) @@ -127,7 +129,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { } // Compare prediction with probability - results.select("prediction", "probability").collect().map { + results.select("prediction", "probability").collect().foreach { case Row(pred: Double, prob: Vector) => val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2 assert(pred == predFromProb) diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala index ed41a9664f94f..c41def9330504 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala @@ -159,8 +159,8 @@ private object RandomForestClassifierSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) val newModel = rf.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = RandomForestClassificationModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = RandomForestClassificationModel.fromOld( + oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala new file mode 100644 index 0000000000000..caf1b759593f3 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala @@ -0,0 +1,69 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{DataFrame, Row, SQLContext} + + +class BinarizerSuite extends FunSuite with MLlibTestSparkContext { + + @transient var data: Array[Double] = _ + @transient var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + data = Array(0.1, -0.5, 0.2, -0.3, 0.8, 0.7, -0.1, -0.4) + } + + test("Binarize continuous features with default parameter") { + val defaultBinarized: Array[Double] = data.map(x => if (x > 0.0) 1.0 else 0.0) + val dataFrame: DataFrame = sqlContext.createDataFrame( + data.zip(defaultBinarized)).toDF("feature", "expected") + + val binarizer: Binarizer = new Binarizer() + .setInputCol("feature") + .setOutputCol("binarized_feature") + + binarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach { + case Row(x: Double, y: Double) => + assert(x === y, "The feature value is not correct after binarization.") + } + } + + test("Binarize continuous features with setter") { + val threshold: Double = 0.2 + val thresholdBinarized: Array[Double] = data.map(x => if (x > threshold) 1.0 else 0.0) + val dataFrame: DataFrame = sqlContext.createDataFrame( + data.zip(thresholdBinarized)).toDF("feature", "expected") + + val binarizer: Binarizer = new Binarizer() + .setInputCol("feature") + .setOutputCol("binarized_feature") + .setThreshold(threshold) + + binarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach { + case Row(x: Double, y: Double) => + assert(x === y, "The feature value is not correct after binarization.") + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala new file mode 100644 index 0000000000000..92ec407b98d69 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala @@ -0,0 +1,80 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.{DataFrame, SQLContext} + + +class OneHotEncoderSuite extends FunSuite with MLlibTestSparkContext { + private var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + } + + def stringIndexed(): DataFrame = { + val data = sc.parallelize(Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")), 2) + val df = sqlContext.createDataFrame(data).toDF("id", "label") + val indexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("labelIndex") + .fit(df) + indexer.transform(df) + } + + test("OneHotEncoder includeFirst = true") { + val transformed = stringIndexed() + val encoder = new OneHotEncoder() + .setInputCol("labelIndex") + .setOutputCol("labelVec") + val encoded = encoder.transform(transformed) + + val output = encoded.select("id", "labelVec").map { r => + val vec = r.get(1).asInstanceOf[Vector] + (r.getInt(0), vec(0), vec(1), vec(2)) + }.collect().toSet + // a -> 0, b -> 2, c -> 1 + val expected = Set((0, 1.0, 0.0, 0.0), (1, 0.0, 0.0, 1.0), (2, 0.0, 1.0, 0.0), + (3, 1.0, 0.0, 0.0), (4, 1.0, 0.0, 0.0), (5, 0.0, 1.0, 0.0)) + assert(output === expected) + } + + test("OneHotEncoder includeFirst = false") { + val transformed = stringIndexed() + val encoder = new OneHotEncoder() + .setIncludeFirst(false) + .setInputCol("labelIndex") + .setOutputCol("labelVec") + val encoded = encoder.transform(transformed) + + val output = encoded.select("id", "labelVec").map { r => + val vec = r.get(1).asInstanceOf[Vector] + (r.getInt(0), vec(0), vec(1)) + }.collect().toSet + // a -> 0, b -> 2, c -> 1 + val expected = Set((0, 0.0, 0.0), (1, 0.0, 1.0), (2, 1.0, 0.0), + (3, 0.0, 0.0), (4, 0.0, 0.0), (5, 1.0, 0.0)) + assert(output === expected) + } + +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala index 1b261b2643854..38dc83b1241cf 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala @@ -23,7 +23,6 @@ import org.scalatest.FunSuite import org.apache.spark.SparkException import org.apache.spark.ml.attribute._ -import org.apache.spark.ml.util.TestingUtils import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD @@ -111,8 +110,8 @@ class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { val model = vectorIndexer.fit(densePoints1) // vectors of length 3 model.transform(densePoints1) // should work model.transform(sparsePoints1) // should work - intercept[IllegalArgumentException] { - model.transform(densePoints2) + intercept[SparkException] { + model.transform(densePoints2).collect() println("Did not throw error when fit, transform were called on vectors of different lengths") } intercept[SparkException] { @@ -245,8 +244,6 @@ class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { // TODO: Once input features marked as categorical are handled correctly, check that here. } } - // Check that non-ML metadata are preserved. - TestingUtils.testPreserveMetadata(densePoints1WithMeta, model, "features", "indexed") } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala new file mode 100644 index 0000000000000..03ba86670d453 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{Row, SQLContext} + +class Word2VecSuite extends FunSuite with MLlibTestSparkContext { + + test("Word2Vec") { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + val sentence = "a b " * 100 + "a c " * 10 + val numOfWords = sentence.split(" ").size + val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" ")) + + val codes = Map( + "a" -> Array(-0.2811822295188904,-0.6356269121170044,-0.3020961284637451), + "b" -> Array(1.0309048891067505,-1.29472815990448,0.22276712954044342), + "c" -> Array(-0.08456747233867645,0.5137411952018738,0.11731560528278351) + ) + + val expected = doc.map { sentence => + Vectors.dense(sentence.map(codes.apply).reduce((word1, word2) => + word1.zip(word2).map { case (v1, v2) => v1 + v2 } + ).map(_ / numOfWords)) + } + + val docDF = doc.zip(expected).toDF("text", "expected") + + val model = new Word2Vec() + .setVectorSize(3) + .setInputCol("text") + .setOutputCol("result") + .fit(docDF) + + model.transform(docDF).select("result", "expected").collect().foreach { + case Row(vector1: Vector, vector2: Vector) => + assert(vector1 ~== vector2 absTol 1E-5, "Transformed vector is different with expected.") + } + } +} + diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala index 88ea679eeaad5..6056e7d3f6ff8 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala @@ -26,14 +26,22 @@ class ParamsSuite extends FunSuite { import solver.{maxIter, inputCol} assert(maxIter.name === "maxIter") - assert(maxIter.doc === "max number of iterations") + assert(maxIter.doc === "max number of iterations (>= 0)") assert(maxIter.parent.eq(solver)) - assert(maxIter.toString === "maxIter: max number of iterations (default: 10)") + assert(maxIter.toString === "maxIter: max number of iterations (>= 0) (default: 10)") + assert(!maxIter.isValid(-1)) + assert(maxIter.isValid(0)) + assert(maxIter.isValid(1)) solver.setMaxIter(5) - assert(maxIter.toString === "maxIter: max number of iterations (default: 10, current: 5)") + assert(maxIter.toString === + "maxIter: max number of iterations (>= 0) (default: 10, current: 5)") assert(inputCol.toString === "inputCol: input column name (undefined)") + + intercept[IllegalArgumentException] { + solver.setMaxIter(-1) + } } test("param pair") { @@ -47,6 +55,9 @@ class ParamsSuite extends FunSuite { assert(pair.param.eq(maxIter)) assert(pair.value === 5) } + intercept[IllegalArgumentException] { + val pair = maxIter -> -1 + } } test("param map") { @@ -59,6 +70,9 @@ class ParamsSuite extends FunSuite { map0.put(maxIter, 10) assert(map0.contains(maxIter)) assert(map0(maxIter) === 10) + intercept[IllegalArgumentException] { + map0.put(maxIter, -1) + } assert(!map0.contains(inputCol)) intercept[NoSuchElementException] { @@ -108,28 +122,78 @@ class ParamsSuite extends FunSuite { assert(solver.getParam("inputCol").eq(inputCol)) assert(solver.getParam("maxIter").eq(maxIter)) + assert(solver.hasParam("inputCol")) + assert(!solver.hasParam("abc")) intercept[NoSuchElementException] { solver.getParam("abc") } intercept[IllegalArgumentException] { - solver.validate() + solver.validateParams() } - solver.validate(ParamMap(inputCol -> "input")) + solver.validateParams(ParamMap(inputCol -> "input")) solver.setInputCol("input") assert(solver.isSet(inputCol)) assert(solver.isDefined(inputCol)) assert(solver.getInputCol === "input") - solver.validate() + solver.validateParams() intercept[IllegalArgumentException] { - solver.validate(ParamMap(maxIter -> -10)) + ParamMap(maxIter -> -10) } - solver.setMaxIter(-10) intercept[IllegalArgumentException] { - solver.validate() + solver.setMaxIter(-10) } solver.clearMaxIter() assert(!solver.isSet(maxIter)) + + val copied = solver.copy(ParamMap(solver.maxIter -> 50)) + assert(copied.uid !== solver.uid) + assert(copied.getInputCol === solver.getInputCol) + assert(copied.getMaxIter === 50) + } + + test("ParamValidate") { + val alwaysTrue = ParamValidators.alwaysTrue[Int] + assert(alwaysTrue(1)) + + val gt1Int = ParamValidators.gt[Int](1) + assert(!gt1Int(1) && gt1Int(2)) + val gt1Double = ParamValidators.gt[Double](1) + assert(!gt1Double(1.0) && gt1Double(1.1)) + + val gtEq1Int = ParamValidators.gtEq[Int](1) + assert(!gtEq1Int(0) && gtEq1Int(1)) + val gtEq1Double = ParamValidators.gtEq[Double](1) + assert(!gtEq1Double(0.9) && gtEq1Double(1.0)) + + val lt1Int = ParamValidators.lt[Int](1) + assert(lt1Int(0) && !lt1Int(1)) + val lt1Double = ParamValidators.lt[Double](1) + assert(lt1Double(0.9) && !lt1Double(1.0)) + + val ltEq1Int = ParamValidators.ltEq[Int](1) + assert(ltEq1Int(1) && !ltEq1Int(2)) + val ltEq1Double = ParamValidators.ltEq[Double](1) + assert(ltEq1Double(1.0) && !ltEq1Double(1.1)) + + val inRange02IntInclusive = ParamValidators.inRange[Int](0, 2) + assert(inRange02IntInclusive(0) && inRange02IntInclusive(1) && inRange02IntInclusive(2) && + !inRange02IntInclusive(-1) && !inRange02IntInclusive(3)) + val inRange02IntExclusive = + ParamValidators.inRange[Int](0, 2, lowerInclusive = false, upperInclusive = false) + assert(!inRange02IntExclusive(0) && inRange02IntExclusive(1) && !inRange02IntExclusive(2)) + + val inRange02DoubleInclusive = ParamValidators.inRange[Double](0, 2) + assert(inRange02DoubleInclusive(0) && inRange02DoubleInclusive(1) && + inRange02DoubleInclusive(2) && + !inRange02DoubleInclusive(-0.1) && !inRange02DoubleInclusive(2.1)) + val inRange02DoubleExclusive = + ParamValidators.inRange[Double](0, 2, lowerInclusive = false, upperInclusive = false) + assert(!inRange02DoubleExclusive(0) && inRange02DoubleExclusive(1) && + !inRange02DoubleExclusive(2)) + + val inArray = ParamValidators.inArray[Int](Array(1, 2)) + assert(inArray(1) && inArray(2) && !inArray(0)) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala index 641b64b42a5e7..dc16073640407 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala @@ -23,15 +23,19 @@ import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter} class TestParams extends Params with HasMaxIter with HasInputCol { def setMaxIter(value: Int): this.type = { set(maxIter, value); this } + def setInputCol(value: String): this.type = { set(inputCol, value); this } setDefault(maxIter -> 10) - override def validate(paramMap: ParamMap): Unit = { - val m = extractParamMap(paramMap) - require(m(maxIter) >= 0) - require(m.contains(inputCol)) + def clearMaxIter(): this.type = clear(maxIter) + + override def validateParams(): Unit = { + super.validateParams() + require(isDefined(inputCol)) } - def clearMaxIter(): this.type = clear(maxIter) + override def copy(extra: ParamMap): TestParams = { + super.copy(extra).asInstanceOf[TestParams] + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala index c87a171b4b229..5aa81b44ddaf9 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala @@ -84,8 +84,8 @@ private[ml] object DecisionTreeRegressorSuite extends FunSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newTree = dt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldTreeAsNew = DecisionTreeRegressionModel.fromOld(oldTree, newTree.parent, - newTree.fittingParamMap, categoricalFeatures) + val oldTreeAsNew = DecisionTreeRegressionModel.fromOld( + oldTree, newTree.parent, categoricalFeatures) TreeTests.checkEqual(oldTreeAsNew, newTree) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala index 4aec36948ac92..25b36ab08b67c 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala @@ -130,8 +130,7 @@ private object GBTRegressorSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newModel = gbt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala index c6dc1cc29b6ff..45f09f4fdab81 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala @@ -115,8 +115,8 @@ private object RandomForestRegressorSuite extends FunSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newModel = rf.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = RandomForestRegressionModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = RandomForestRegressionModel.fromOld( + oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala index 761ea821ef7c6..05313d440fbf6 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala @@ -49,8 +49,8 @@ class CrossValidatorSuite extends FunSuite with MLlibTestSparkContext { .setEvaluator(eval) .setNumFolds(3) val cvModel = cv.fit(dataset) - val bestParamMap = cvModel.bestModel.fittingParamMap - assert(bestParamMap(lr.regParam) === 0.001) - assert(bestParamMap(lr.maxIter) === 10) + val parent = cvModel.bestModel.parent.asInstanceOf[LogisticRegression] + assert(parent.getRegParam === 0.001) + assert(parent.getMaxIter === 10) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala b/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala deleted file mode 100644 index c44cb61b34171..0000000000000 --- a/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.ml.util - -import org.apache.spark.ml.Transformer -import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.types.MetadataBuilder -import org.scalatest.FunSuite - -private[ml] object TestingUtils extends FunSuite { - - /** - * Test whether unrelated metadata are preserved for this transformer. - * This attaches extra metadata to a column, transforms the column, and check to ensure the - * extra metadata have not changed. - * @param data Input dataset - * @param transformer Transformer to test - * @param inputCol Unique input column for Transformer. This must be the ONLY input column. - * @param outputCol Output column to test for metadata presence. - */ - def testPreserveMetadata( - data: DataFrame, - transformer: Transformer, - inputCol: String, - outputCol: String): Unit = { - // Create some fake metadata - val origMetadata = data.schema(inputCol).metadata - val metaKey = "__testPreserveMetadata__fake_key" - val metaValue = 12345 - assert(!origMetadata.contains(metaKey), - s"Unit test with testPreserveMetadata will fail since metadata key was present: $metaKey") - val newMetadata = - new MetadataBuilder().withMetadata(origMetadata).putLong(metaKey, metaValue).build() - // Add metadata to the inputCol - val withMetadata = data.select(data(inputCol).as(inputCol, newMetadata)) - // Transform, and ensure extra metadata was not affected - val transformed = transformer.transform(withMetadata) - val transMetadata = transformed.schema(outputCol).metadata - assert(transMetadata.contains(metaKey), - "Unit test with testPreserveMetadata failed; extra metadata key was not present.") - assert(transMetadata.getLong(metaKey) === metaValue, - "Unit test with testPreserveMetadata failed; extra metadata value was wrong." + - s" Expected $metaValue but found ${transMetadata.getLong(metaKey)}") - } -} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala index db8ed62fa46ce..a629dba8a426f 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala @@ -19,7 +19,7 @@ package org.apache.spark.mllib.api.python import org.scalatest.FunSuite -import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Vectors} +import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Vectors, SparseMatrix} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.recommendation.Rating @@ -77,6 +77,16 @@ class PythonMLLibAPISuite extends FunSuite { val emptyMatrix = Matrices.dense(0, 0, empty) val ne = SerDe.loads(SerDe.dumps(emptyMatrix)).asInstanceOf[DenseMatrix] assert(emptyMatrix == ne) + + val sm = new SparseMatrix(3, 2, Array(0, 1, 3), Array(1, 0, 2), Array(0.9, 1.2, 3.4)) + val nsm = SerDe.loads(SerDe.dumps(sm)).asInstanceOf[SparseMatrix] + assert(sm.toArray === nsm.toArray) + + val smt = new SparseMatrix( + 3, 3, Array(0, 2, 3, 5), Array(0, 2, 1, 0, 2), Array(0.9, 1.2, 3.4, 5.7, 8.9), + isTransposed=true) + val nsmt = SerDe.loads(SerDe.dumps(smt)).asInstanceOf[SparseMatrix] + assert(smt.toArray === nsmt.toArray) } test("pickle rating") { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala index 41ec794146c69..2dcc881f5abd2 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.mllib.clustering +import breeze.linalg.{DenseMatrix => BDM} + import org.scalatest.FunSuite import org.apache.spark.mllib.linalg.{Vector, DenseMatrix, Matrix, Vectors} @@ -37,7 +39,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { // Check: describeTopics() with all terms val fullTopicSummary = model.describeTopics() - assert(fullTopicSummary.size === tinyK) + assert(fullTopicSummary.length === tinyK) fullTopicSummary.zip(tinyTopicDescription).foreach { case ((algTerms, algTermWeights), (terms, termWeights)) => assert(algTerms === terms) @@ -54,7 +56,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { } } - test("running and DistributedLDAModel") { + test("running and DistributedLDAModel with default Optimizer (EM)") { val k = 3 val topicSmoothing = 1.2 val termSmoothing = 1.2 @@ -99,7 +101,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { // Check: per-doc topic distributions val topicDistributions = model.topicDistributions.collect() // Ensure all documents are covered. - assert(topicDistributions.size === tinyCorpus.size) + assert(topicDistributions.length === tinyCorpus.length) assert(tinyCorpus.map(_._1).toSet === topicDistributions.map(_._1).toSet) // Ensure we have proper distributions topicDistributions.foreach { case (docId, topicDistribution) => @@ -131,6 +133,87 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { assert(lda.getBeta === 3.0) assert(lda.getTopicConcentration === 3.0) } + + test("OnlineLDAOptimizer initialization") { + val lda = new LDA().setK(2) + val corpus = sc.parallelize(tinyCorpus, 2) + val op = new OnlineLDAOptimizer().initialize(corpus, lda) + op.setKappa(0.9876).setMiniBatchFraction(0.123).setTau_0(567) + assert(op.getAlpha == 0.5) // default 1.0 / k + assert(op.getEta == 0.5) // default 1.0 / k + assert(op.getKappa == 0.9876) + assert(op.getMiniBatchFraction == 0.123) + assert(op.getTau_0 == 567) + } + + test("OnlineLDAOptimizer one iteration") { + // run OnlineLDAOptimizer for 1 iteration to verify it's consistency with Blei-lab, + // [[https://github.com/Blei-Lab/onlineldavb]] + val k = 2 + val vocabSize = 6 + + def docs: Array[(Long, Vector)] = Array( + Vectors.sparse(vocabSize, Array(0, 1, 2), Array(1, 1, 1)), // apple, orange, banana + Vectors.sparse(vocabSize, Array(3, 4, 5), Array(1, 1, 1)) // tiger, cat, dog + ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) } + val corpus = sc.parallelize(docs, 2) + + // Set GammaShape large to avoid the stochastic impact. + val op = new OnlineLDAOptimizer().setTau_0(1024).setKappa(0.51).setGammaShape(1e40) + .setMiniBatchFraction(1) + val lda = new LDA().setK(k).setMaxIterations(1).setOptimizer(op).setSeed(12345) + + val state = op.initialize(corpus, lda) + // override lambda to simulate an intermediate state + // [[ 1.1 1.2 1.3 0.9 0.8 0.7] + // [ 0.9 0.8 0.7 1.1 1.2 1.3]] + op.setLambda(new BDM[Double](k, vocabSize, + Array(1.1, 0.9, 1.2, 0.8, 1.3, 0.7, 0.9, 1.1, 0.8, 1.2, 0.7, 1.3))) + + // run for one iteration + state.submitMiniBatch(corpus) + + // verify the result, Note this generate the identical result as + // [[https://github.com/Blei-Lab/onlineldavb]] + val topic1 = op.getLambda(0, ::).inner.toArray.map("%.4f".format(_)).mkString(", ") + val topic2 = op.getLambda(1, ::).inner.toArray.map("%.4f".format(_)).mkString(", ") + assert("1.1101, 1.2076, 1.3050, 0.8899, 0.7924, 0.6950" == topic1) + assert("0.8899, 0.7924, 0.6950, 1.1101, 1.2076, 1.3050" == topic2) + } + + test("OnlineLDAOptimizer with toy data") { + def toydata: Array[(Long, Vector)] = Array( + Vectors.sparse(6, Array(0, 1), Array(1, 1)), + Vectors.sparse(6, Array(1, 2), Array(1, 1)), + Vectors.sparse(6, Array(0, 2), Array(1, 1)), + Vectors.sparse(6, Array(3, 4), Array(1, 1)), + Vectors.sparse(6, Array(3, 5), Array(1, 1)), + Vectors.sparse(6, Array(4, 5), Array(1, 1)) + ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) } + + val docs = sc.parallelize(toydata) + val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau_0(1024).setKappa(0.51) + .setGammaShape(1e10) + val lda = new LDA().setK(2) + .setDocConcentration(0.01) + .setTopicConcentration(0.01) + .setMaxIterations(100) + .setOptimizer(op) + .setSeed(12345) + + val ldaModel = lda.run(docs) + val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 10) + val topics = topicIndices.map { case (terms, termWeights) => + terms.zip(termWeights) + } + + // check distribution for each topic, typical distribution is (0.3, 0.3, 0.3, 0.02, 0.02, 0.02) + topics.foreach { topic => + val smalls = topic.filter(t => t._2 < 0.1).map(_._2) + assert(smalls.length == 3 && smalls.sum < 0.2) + } + } + } private[clustering] object LDASuite { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala new file mode 100644 index 0000000000000..0b646cf1ce6c4 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala @@ -0,0 +1,84 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.dmg.pmml.RegressionModel +import org.dmg.pmml.RegressionNormalizationMethodType +import org.scalatest.FunSuite + +import org.apache.spark.mllib.classification.LogisticRegressionModel +import org.apache.spark.mllib.classification.SVMModel +import org.apache.spark.mllib.util.LinearDataGenerator + +class BinaryClassificationPMMLModelExportSuite extends FunSuite { + + test("logistic regression PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val logisticRegressionModel = + new LogisticRegressionModel(linearInput(0).features, linearInput(0).label) + + val logisticModelExport = PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel) + + // assert that the PMML format is as expected + assert(logisticModelExport.isInstanceOf[PMMLModelExport]) + val pmml = logisticModelExport.asInstanceOf[PMMLModelExport].getPmml + assert(pmml.getHeader.getDescription === "logistic regression") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === logisticRegressionModel.weights.size + 1) + // This verify that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table (for target category 1) + // with the same number of predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getTargetCategory === "1") + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === logisticRegressionModel.weights.size) + // verify if there is a second table with target category 0 and no predictors + assert(pmmlRegressionModel.getRegressionTables.get(1).getTargetCategory === "0") + assert(pmmlRegressionModel.getRegressionTables.get(1).getNumericPredictors.size === 0) + // ensure logistic regression has normalization method set to LOGIT + assert(pmmlRegressionModel.getNormalizationMethod() == RegressionNormalizationMethodType.LOGIT) + } + + test("linear SVM PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label) + + val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel) + + // assert that the PMML format is as expected + assert(svmModelExport.isInstanceOf[PMMLModelExport]) + val pmml = svmModelExport.getPmml + assert(pmml.getHeader.getDescription + === "linear SVM") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === svmModel.weights.size + 1) + // This verify that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table (for target category 1) + // with the same number of predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getTargetCategory === "1") + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === svmModel.weights.size) + // verify if there is a second table with target category 0 and no predictors + assert(pmmlRegressionModel.getRegressionTables.get(1).getTargetCategory === "0") + assert(pmmlRegressionModel.getRegressionTables.get(1).getNumericPredictors.size === 0) + // ensure linear SVM has normalization method set to NONE + assert(pmmlRegressionModel.getNormalizationMethod() == RegressionNormalizationMethodType.NONE) + } + +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala new file mode 100644 index 0000000000000..f9afbd888dfc5 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala @@ -0,0 +1,84 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.dmg.pmml.RegressionModel +import org.scalatest.FunSuite + +import org.apache.spark.mllib.regression.{LassoModel, LinearRegressionModel, RidgeRegressionModel} +import org.apache.spark.mllib.util.LinearDataGenerator + +class GeneralizedLinearPMMLModelExportSuite extends FunSuite { + + test("linear regression PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val linearRegressionModel = + new LinearRegressionModel(linearInput(0).features, linearInput(0).label) + val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel) + // assert that the PMML format is as expected + assert(linearModelExport.isInstanceOf[PMMLModelExport]) + val pmml = linearModelExport.getPmml + assert(pmml.getHeader.getDescription === "linear regression") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === linearRegressionModel.weights.size + 1) + // This verifies that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table with the same number of + // predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === linearRegressionModel.weights.size) + } + + test("ridge regression PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val ridgeRegressionModel = + new RidgeRegressionModel(linearInput(0).features, linearInput(0).label) + val ridgeModelExport = PMMLModelExportFactory.createPMMLModelExport(ridgeRegressionModel) + // assert that the PMML format is as expected + assert(ridgeModelExport.isInstanceOf[PMMLModelExport]) + val pmml = ridgeModelExport.getPmml + assert(pmml.getHeader.getDescription === "ridge regression") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === ridgeRegressionModel.weights.size + 1) + // This verify that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table with the same number of + // predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === ridgeRegressionModel.weights.size) + } + + test("lasso PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label) + val lassoModelExport = PMMLModelExportFactory.createPMMLModelExport(lassoModel) + // assert that the PMML format is as expected + assert(lassoModelExport.isInstanceOf[PMMLModelExport]) + val pmml = lassoModelExport.getPmml + assert(pmml.getHeader.getDescription === "lasso regression") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === lassoModel.weights.size + 1) + // This verify that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table with the same number of + // predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === lassoModel.weights.size) + } + +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala new file mode 100644 index 0000000000000..b985d0446d7b0 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala @@ -0,0 +1,49 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.dmg.pmml.ClusteringModel +import org.scalatest.FunSuite + +import org.apache.spark.mllib.clustering.KMeansModel +import org.apache.spark.mllib.linalg.Vectors + +class KMeansPMMLModelExportSuite extends FunSuite { + + test("KMeansPMMLModelExport generate PMML format") { + val clusterCenters = Array( + Vectors.dense(1.0, 2.0, 6.0), + Vectors.dense(1.0, 3.0, 0.0), + Vectors.dense(1.0, 4.0, 6.0)) + val kmeansModel = new KMeansModel(clusterCenters) + + val modelExport = PMMLModelExportFactory.createPMMLModelExport(kmeansModel) + + // assert that the PMML format is as expected + assert(modelExport.isInstanceOf[PMMLModelExport]) + val pmml = modelExport.asInstanceOf[PMMLModelExport].getPmml + assert(pmml.getHeader.getDescription === "k-means clustering") + // check that the number of fields match the single vector size + assert(pmml.getDataDictionary.getNumberOfFields === clusterCenters(0).size) + // This verify that there is a model attached to the pmml object and the model is a clustering + // one. It also verifies that the pmml model has the same number of clusters of the spark model. + val pmmlClusteringModel = pmml.getModels.get(0).asInstanceOf[ClusteringModel] + assert(pmmlClusteringModel.getNumberOfClusters === clusterCenters.length) + } + +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala new file mode 100644 index 0000000000000..f28a4ac8ad01f --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala @@ -0,0 +1,95 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.classification.{LogisticRegressionModel, SVMModel} +import org.apache.spark.mllib.clustering.KMeansModel +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.{LassoModel, LinearRegressionModel, RidgeRegressionModel} +import org.apache.spark.mllib.util.LinearDataGenerator + +class PMMLModelExportFactorySuite extends FunSuite { + + test("PMMLModelExportFactory create KMeansPMMLModelExport when passing a KMeansModel") { + val clusterCenters = Array( + Vectors.dense(1.0, 2.0, 6.0), + Vectors.dense(1.0, 3.0, 0.0), + Vectors.dense(1.0, 4.0, 6.0)) + val kmeansModel = new KMeansModel(clusterCenters) + + val modelExport = PMMLModelExportFactory.createPMMLModelExport(kmeansModel) + + assert(modelExport.isInstanceOf[KMeansPMMLModelExport]) + } + + test("PMMLModelExportFactory create GeneralizedLinearPMMLModelExport when passing a " + + "LinearRegressionModel, RidgeRegressionModel or LassoModel") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + + val linearRegressionModel = + new LinearRegressionModel(linearInput(0).features, linearInput(0).label) + val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel) + assert(linearModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport]) + + val ridgeRegressionModel = + new RidgeRegressionModel(linearInput(0).features, linearInput(0).label) + val ridgeModelExport = PMMLModelExportFactory.createPMMLModelExport(ridgeRegressionModel) + assert(ridgeModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport]) + + val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label) + val lassoModelExport = PMMLModelExportFactory.createPMMLModelExport(lassoModel) + assert(lassoModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport]) + } + + test("PMMLModelExportFactory create BinaryClassificationPMMLModelExport " + + "when passing a LogisticRegressionModel or SVMModel") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + + val logisticRegressionModel = + new LogisticRegressionModel(linearInput(0).features, linearInput(0).label) + val logisticRegressionModelExport = + PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel) + assert(logisticRegressionModelExport.isInstanceOf[BinaryClassificationPMMLModelExport]) + + val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label) + val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel) + assert(svmModelExport.isInstanceOf[BinaryClassificationPMMLModelExport]) + } + + test("PMMLModelExportFactory throw IllegalArgumentException " + + "when passing a Multinomial Logistic Regression") { + /** 3 classes, 2 features */ + val multiclassLogisticRegressionModel = new LogisticRegressionModel( + weights = Vectors.dense(0.1, 0.2, 0.3, 0.4), intercept = 1.0, + numFeatures = 2, numClasses = 3) + + intercept[IllegalArgumentException] { + PMMLModelExportFactory.createPMMLModelExport(multiclassLogisticRegressionModel) + } + } + + test("PMMLModelExportFactory throw IllegalArgumentException when passing an unsupported model") { + val invalidModel = new Object + + intercept[IllegalArgumentException] { + PMMLModelExportFactory.createPMMLModelExport(invalidModel) + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala index 1ac7c12c4e8e6..cb8fe4dba96f5 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala @@ -24,13 +24,13 @@ import org.apache.spark.mllib.rdd.MLPairRDDFunctions._ class MLPairRDDFunctionsSuite extends FunSuite with MLlibTestSparkContext { test("topByKey") { - val topMap = sc.parallelize(Array((1, 1), (1, 2), (3, 2), (3, 7), (3, 5), (5, 1), (5, 3)), 2) + val topMap = sc.parallelize(Array((1, 1), (1, 2), (3, 2), (3, 7), (5, 1), (3, 5)), 2) .topByKey(2) .collectAsMap() assert(topMap.size === 3) assert(topMap(1) === Array(2, 1)) assert(topMap(3) === Array(7, 5)) - assert(topMap(5) === Array(3, 1)) + assert(topMap(5) === Array(1)) } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala index 9801e87576744..2c92866f3893d 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala @@ -72,4 +72,24 @@ class MatrixFactorizationModelSuite extends FunSuite with MLlibTestSparkContext Utils.deleteRecursively(tempDir) } } + + test("batch predict API recommendProductsForUsers") { + val model = new MatrixFactorizationModel(rank, userFeatures, prodFeatures) + val topK = 10 + val recommendations = model.recommendProductsForUsers(topK).collectAsMap() + + assert(recommendations(0)(0).rating ~== 17.0 relTol 1e-14) + assert(recommendations(1)(0).rating ~== 39.0 relTol 1e-14) + } + + test("batch predict API recommendUsersForProducts") { + val model = new MatrixFactorizationModel(rank, userFeatures, prodFeatures) + val topK = 10 + val recommendations = model.recommendUsersForProducts(topK).collectAsMap() + + assert(recommendations(2)(0).user == 1) + assert(recommendations(2)(0).rating ~== 39.0 relTol 1e-14) + assert(recommendations(2)(1).user == 0) + assert(recommendations(2)(1).rating ~== 17.0 relTol 1e-14) + } } diff --git a/network/common/src/main/java/org/apache/spark/network/TransportContext.java b/network/common/src/main/java/org/apache/spark/network/TransportContext.java index 3fe69b1bd8851..b8d073fa16b4b 100644 --- a/network/common/src/main/java/org/apache/spark/network/TransportContext.java +++ b/network/common/src/main/java/org/apache/spark/network/TransportContext.java @@ -36,6 +36,7 @@ import org.apache.spark.network.server.TransportChannelHandler; import org.apache.spark.network.server.TransportRequestHandler; import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; import org.apache.spark.network.util.NettyUtils; import org.apache.spark.network.util.TransportConf; @@ -82,13 +83,21 @@ public TransportClientFactory createClientFactory() { } /** Create a server which will attempt to bind to a specific port. */ - public TransportServer createServer(int port) { - return new TransportServer(this, port); + public TransportServer createServer(int port, List bootstraps) { + return new TransportServer(this, port, rpcHandler, bootstraps); } /** Creates a new server, binding to any available ephemeral port. */ + public TransportServer createServer(List bootstraps) { + return createServer(0, bootstraps); + } + public TransportServer createServer() { - return new TransportServer(this, 0); + return createServer(0, Lists.newArrayList()); + } + + public TransportChannelHandler initializePipeline(SocketChannel channel) { + return initializePipeline(channel, rpcHandler); } /** @@ -96,13 +105,18 @@ public TransportServer createServer() { * has a {@link org.apache.spark.network.server.TransportChannelHandler} to handle request or * response messages. * + * @param channel The channel to initialize. + * @param channelRpcHandler The RPC handler to use for the channel. + * * @return Returns the created TransportChannelHandler, which includes a TransportClient that can * be used to communicate on this channel. The TransportClient is directly associated with a * ChannelHandler to ensure all users of the same channel get the same TransportClient object. */ - public TransportChannelHandler initializePipeline(SocketChannel channel) { + public TransportChannelHandler initializePipeline( + SocketChannel channel, + RpcHandler channelRpcHandler) { try { - TransportChannelHandler channelHandler = createChannelHandler(channel); + TransportChannelHandler channelHandler = createChannelHandler(channel, channelRpcHandler); channel.pipeline() .addLast("encoder", encoder) .addLast("frameDecoder", NettyUtils.createFrameDecoder()) @@ -123,7 +137,7 @@ public TransportChannelHandler initializePipeline(SocketChannel channel) { * ResponseMessages. The channel is expected to have been successfully created, though certain * properties (such as the remoteAddress()) may not be available yet. */ - private TransportChannelHandler createChannelHandler(Channel channel) { + private TransportChannelHandler createChannelHandler(Channel channel, RpcHandler rpcHandler) { TransportResponseHandler responseHandler = new TransportResponseHandler(channel); TransportClient client = new TransportClient(channel, responseHandler); TransportRequestHandler requestHandler = new TransportRequestHandler(channel, client, diff --git a/network/common/src/main/java/org/apache/spark/network/client/TransportClientBootstrap.java b/network/common/src/main/java/org/apache/spark/network/client/TransportClientBootstrap.java index 65e8020e34121..eaae2ee043c5a 100644 --- a/network/common/src/main/java/org/apache/spark/network/client/TransportClientBootstrap.java +++ b/network/common/src/main/java/org/apache/spark/network/client/TransportClientBootstrap.java @@ -17,6 +17,8 @@ package org.apache.spark.network.client; +import io.netty.channel.Channel; + /** * A bootstrap which is executed on a TransportClient before it is returned to the user. * This enables an initial exchange of information (e.g., SASL authentication tokens) on a once-per- @@ -28,5 +30,5 @@ */ public interface TransportClientBootstrap { /** Performs the bootstrapping operation, throwing an exception on failure. */ - public void doBootstrap(TransportClient client) throws RuntimeException; + void doBootstrap(TransportClient client, Channel channel) throws RuntimeException; } diff --git a/network/common/src/main/java/org/apache/spark/network/client/TransportClientFactory.java b/network/common/src/main/java/org/apache/spark/network/client/TransportClientFactory.java index d26b9b4d6055f..4952ffb44bb8b 100644 --- a/network/common/src/main/java/org/apache/spark/network/client/TransportClientFactory.java +++ b/network/common/src/main/java/org/apache/spark/network/client/TransportClientFactory.java @@ -172,12 +172,14 @@ private TransportClient createClient(InetSocketAddress address) throws IOExcepti .option(ChannelOption.ALLOCATOR, pooledAllocator); final AtomicReference clientRef = new AtomicReference(); + final AtomicReference channelRef = new AtomicReference(); bootstrap.handler(new ChannelInitializer() { @Override public void initChannel(SocketChannel ch) { TransportChannelHandler clientHandler = context.initializePipeline(ch); clientRef.set(clientHandler.getClient()); + channelRef.set(ch); } }); @@ -192,6 +194,7 @@ public void initChannel(SocketChannel ch) { } TransportClient client = clientRef.get(); + Channel channel = channelRef.get(); assert client != null : "Channel future completed successfully with null client"; // Execute any client bootstraps synchronously before marking the Client as successful. @@ -199,7 +202,7 @@ public void initChannel(SocketChannel ch) { logger.debug("Connection to {} successful, running bootstraps...", address); try { for (TransportClientBootstrap clientBootstrap : clientBootstraps) { - clientBootstrap.doBootstrap(client); + clientBootstrap.doBootstrap(client, channel); } } catch (Exception e) { // catch non-RuntimeExceptions too as bootstrap may be written in Scala long bootstrapTimeMs = (System.nanoTime() - preBootstrap) / 1000000; diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java index 33aa1344345ff..185ba2ef3bb1f 100644 --- a/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java @@ -17,8 +17,12 @@ package org.apache.spark.network.sasl; +import javax.security.sasl.Sasl; +import javax.security.sasl.SaslException; + import io.netty.buffer.ByteBuf; import io.netty.buffer.Unpooled; +import io.netty.channel.Channel; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -33,14 +37,24 @@ public class SaslClientBootstrap implements TransportClientBootstrap { private final Logger logger = LoggerFactory.getLogger(SaslClientBootstrap.class); + private final boolean encrypt; private final TransportConf conf; private final String appId; private final SecretKeyHolder secretKeyHolder; public SaslClientBootstrap(TransportConf conf, String appId, SecretKeyHolder secretKeyHolder) { + this(conf, appId, secretKeyHolder, false); + } + + public SaslClientBootstrap( + TransportConf conf, + String appId, + SecretKeyHolder secretKeyHolder, + boolean encrypt) { this.conf = conf; this.appId = appId; this.secretKeyHolder = secretKeyHolder; + this.encrypt = encrypt; } /** @@ -49,8 +63,8 @@ public SaslClientBootstrap(TransportConf conf, String appId, SecretKeyHolder sec * due to mismatch. */ @Override - public void doBootstrap(TransportClient client) { - SparkSaslClient saslClient = new SparkSaslClient(appId, secretKeyHolder); + public void doBootstrap(TransportClient client, Channel channel) { + SparkSaslClient saslClient = new SparkSaslClient(appId, secretKeyHolder, encrypt); try { byte[] payload = saslClient.firstToken(); @@ -62,13 +76,26 @@ public void doBootstrap(TransportClient client) { byte[] response = client.sendRpcSync(buf.array(), conf.saslRTTimeoutMs()); payload = saslClient.response(response); } + + if (encrypt) { + if (!SparkSaslServer.QOP_AUTH_CONF.equals(saslClient.getNegotiatedProperty(Sasl.QOP))) { + throw new RuntimeException( + new SaslException("Encryption requests by negotiated non-encrypted connection.")); + } + SaslEncryption.addToChannel(channel, saslClient, conf.maxSaslEncryptedBlockSize()); + saslClient = null; + logger.debug("Channel {} configured for SASL encryption.", client); + } } finally { - try { - // Once authentication is complete, the server will trust all remaining communication. - saslClient.dispose(); - } catch (RuntimeException e) { - logger.error("Error while disposing SASL client", e); + if (saslClient != null) { + try { + // Once authentication is complete, the server will trust all remaining communication. + saslClient.dispose(); + } catch (RuntimeException e) { + logger.error("Error while disposing SASL client", e); + } } } } + } diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java new file mode 100644 index 0000000000000..127335e4d35fb --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java @@ -0,0 +1,291 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.sasl; + +import java.io.IOException; +import java.nio.ByteBuffer; +import java.nio.channels.WritableByteChannel; +import java.util.List; + +import com.google.common.annotations.VisibleForTesting; +import com.google.common.base.Preconditions; +import io.netty.buffer.ByteBuf; +import io.netty.buffer.Unpooled; +import io.netty.channel.Channel; +import io.netty.channel.ChannelHandlerContext; +import io.netty.channel.ChannelOutboundHandlerAdapter; +import io.netty.channel.ChannelPromise; +import io.netty.channel.FileRegion; +import io.netty.handler.codec.MessageToMessageDecoder; +import io.netty.util.AbstractReferenceCounted; +import io.netty.util.ReferenceCountUtil; + +import org.apache.spark.network.util.ByteArrayWritableChannel; +import org.apache.spark.network.util.NettyUtils; + +/** + * Provides SASL-based encription for transport channels. The single method exposed by this + * class installs the needed channel handlers on a connected channel. + */ +class SaslEncryption { + + @VisibleForTesting + static final String ENCRYPTION_HANDLER_NAME = "saslEncryption"; + + /** + * Adds channel handlers that perform encryption / decryption of data using SASL. + * + * @param channel The channel. + * @param backend The SASL backend. + * @param maxOutboundBlockSize Max size in bytes of outgoing encrypted blocks, to control + * memory usage. + */ + static void addToChannel( + Channel channel, + SaslEncryptionBackend backend, + int maxOutboundBlockSize) { + channel.pipeline() + .addFirst(ENCRYPTION_HANDLER_NAME, new EncryptionHandler(backend, maxOutboundBlockSize)) + .addFirst("saslDecryption", new DecryptionHandler(backend)) + .addFirst("saslFrameDecoder", NettyUtils.createFrameDecoder()); + } + + private static class EncryptionHandler extends ChannelOutboundHandlerAdapter { + + private final int maxOutboundBlockSize; + private final SaslEncryptionBackend backend; + + EncryptionHandler(SaslEncryptionBackend backend, int maxOutboundBlockSize) { + this.backend = backend; + this.maxOutboundBlockSize = maxOutboundBlockSize; + } + + /** + * Wrap the incoming message in an implementation that will perform encryption lazily. This is + * needed to guarantee ordering of the outgoing encrypted packets - they need to be decrypted in + * the same order, and netty doesn't have an atomic ChannelHandlerContext.write() API, so it + * does not guarantee any ordering. + */ + @Override + public void write(ChannelHandlerContext ctx, Object msg, ChannelPromise promise) + throws Exception { + + ctx.write(new EncryptedMessage(backend, msg, maxOutboundBlockSize), promise); + } + + @Override + public void handlerRemoved(ChannelHandlerContext ctx) throws Exception { + try { + backend.dispose(); + } finally { + super.handlerRemoved(ctx); + } + } + + } + + private static class DecryptionHandler extends MessageToMessageDecoder { + + private final SaslEncryptionBackend backend; + + DecryptionHandler(SaslEncryptionBackend backend) { + this.backend = backend; + } + + @Override + protected void decode(ChannelHandlerContext ctx, ByteBuf msg, List out) + throws Exception { + + byte[] data; + int offset; + int length = msg.readableBytes(); + if (msg.hasArray()) { + data = msg.array(); + offset = msg.arrayOffset(); + msg.skipBytes(length); + } else { + data = new byte[length]; + msg.readBytes(data); + offset = 0; + } + + out.add(Unpooled.wrappedBuffer(backend.unwrap(data, offset, length))); + } + + } + + @VisibleForTesting + static class EncryptedMessage extends AbstractReferenceCounted implements FileRegion { + + private final SaslEncryptionBackend backend; + private final boolean isByteBuf; + private final ByteBuf buf; + private final FileRegion region; + + /** + * A channel used to buffer input data for encryption. The channel has an upper size bound + * so that if the input is larger than the allowed buffer, it will be broken into multiple + * chunks. + */ + private final ByteArrayWritableChannel byteChannel; + + private ByteBuf currentHeader; + private ByteBuffer currentChunk; + private long currentChunkSize; + private long currentReportedBytes; + private long unencryptedChunkSize; + private long transferred; + + EncryptedMessage(SaslEncryptionBackend backend, Object msg, int maxOutboundBlockSize) { + Preconditions.checkArgument(msg instanceof ByteBuf || msg instanceof FileRegion, + "Unrecognized message type: %s", msg.getClass().getName()); + this.backend = backend; + this.isByteBuf = msg instanceof ByteBuf; + this.buf = isByteBuf ? (ByteBuf) msg : null; + this.region = isByteBuf ? null : (FileRegion) msg; + this.byteChannel = new ByteArrayWritableChannel(maxOutboundBlockSize); + } + + /** + * Returns the size of the original (unencrypted) message. + * + * This makes assumptions about how netty treats FileRegion instances, because there's no way + * to know beforehand what will be the size of the encrypted message. Namely, it assumes + * that netty will try to transfer data from this message while + * transfered() < count(). So these two methods return, technically, wrong data, + * but netty doesn't know better. + */ + @Override + public long count() { + return isByteBuf ? buf.readableBytes() : region.count(); + } + + @Override + public long position() { + return 0; + } + + /** + * Returns an approximation of the amount of data transferred. See {@link #count()}. + */ + @Override + public long transfered() { + return transferred; + } + + /** + * Transfers data from the original message to the channel, encrypting it in the process. + * + * This method also breaks down the original message into smaller chunks when needed. This + * is done to keep memory usage under control. This avoids having to copy the whole message + * data into memory at once, and can avoid ballooning memory usage when transferring large + * messages such as shuffle blocks. + * + * The {@link #transfered()} counter also behaves a little funny, in that it won't go forward + * until a whole chunk has been written. This is done because the code can't use the actual + * number of bytes written to the channel as the transferred count (see {@link #count()}). + * Instead, once an encrypted chunk is written to the output (including its header), the + * size of the original block will be added to the {@link #transfered()} amount. + */ + @Override + public long transferTo(final WritableByteChannel target, final long position) + throws IOException { + + Preconditions.checkArgument(position == transfered(), "Invalid position."); + + long reportedWritten = 0L; + long actuallyWritten = 0L; + do { + if (currentChunk == null) { + nextChunk(); + } + + if (currentHeader.readableBytes() > 0) { + int bytesWritten = target.write(currentHeader.nioBuffer()); + currentHeader.skipBytes(bytesWritten); + actuallyWritten += bytesWritten; + if (currentHeader.readableBytes() > 0) { + // Break out of loop if there are still header bytes left to write. + break; + } + } + + actuallyWritten += target.write(currentChunk); + if (!currentChunk.hasRemaining()) { + // Only update the count of written bytes once a full chunk has been written. + // See method javadoc. + long chunkBytesRemaining = unencryptedChunkSize - currentReportedBytes; + reportedWritten += chunkBytesRemaining; + transferred += chunkBytesRemaining; + currentHeader.release(); + currentHeader = null; + currentChunk = null; + currentChunkSize = 0; + currentReportedBytes = 0; + } + } while (currentChunk == null && transfered() + reportedWritten < count()); + + // Returning 0 triggers a backoff mechanism in netty which may harm performance. Instead, + // we return 1 until we can (i.e. until the reported count would actually match the size + // of the current chunk), at which point we resort to returning 0 so that the counts still + // match, at the cost of some performance. That situation should be rare, though. + if (reportedWritten != 0L) { + return reportedWritten; + } + + if (actuallyWritten > 0 && currentReportedBytes < currentChunkSize - 1) { + transferred += 1L; + currentReportedBytes += 1L; + return 1L; + } + + return 0L; + } + + private void nextChunk() throws IOException { + byteChannel.reset(); + if (isByteBuf) { + int copied = byteChannel.write(buf.nioBuffer()); + buf.skipBytes(copied); + } else { + region.transferTo(byteChannel, region.transfered()); + } + + byte[] encrypted = backend.wrap(byteChannel.getData(), 0, byteChannel.length()); + this.currentChunk = ByteBuffer.wrap(encrypted); + this.currentChunkSize = encrypted.length; + this.currentHeader = Unpooled.copyLong(8 + currentChunkSize); + this.unencryptedChunkSize = byteChannel.length(); + } + + @Override + protected void deallocate() { + if (currentHeader != null) { + currentHeader.release(); + } + if (buf != null) { + buf.release(); + } + if (region != null) { + region.release(); + } + } + + } + +} diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryptionBackend.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryptionBackend.java new file mode 100644 index 0000000000000..89b78bc7e1df1 --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryptionBackend.java @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.sasl; + +import javax.security.sasl.SaslException; + +interface SaslEncryptionBackend { + + /** Disposes of resources used by the backend. */ + void dispose(); + + /** Encrypt data. */ + byte[] wrap(byte[] data, int offset, int len) throws SaslException; + + /** Decrypt data. */ + byte[] unwrap(byte[] data, int offset, int len) throws SaslException; + +} diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java index 026cbd260d16c..be6165caf3c74 100644 --- a/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java @@ -17,10 +17,10 @@ package org.apache.spark.network.sasl; -import java.util.concurrent.ConcurrentMap; +import javax.security.sasl.Sasl; -import com.google.common.collect.Maps; import io.netty.buffer.Unpooled; +import io.netty.channel.Channel; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -28,6 +28,7 @@ import org.apache.spark.network.client.TransportClient; import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.StreamManager; +import org.apache.spark.network.util.TransportConf; /** * RPC Handler which performs SASL authentication before delegating to a child RPC handler. @@ -37,8 +38,14 @@ * Note that the authentication process consists of multiple challenge-response pairs, each of * which are individual RPCs. */ -public class SaslRpcHandler extends RpcHandler { - private final Logger logger = LoggerFactory.getLogger(SaslRpcHandler.class); +class SaslRpcHandler extends RpcHandler { + private static final Logger logger = LoggerFactory.getLogger(SaslRpcHandler.class); + + /** Transport configuration. */ + private final TransportConf conf; + + /** The client channel. */ + private final Channel channel; /** RpcHandler we will delegate to for authenticated connections. */ private final RpcHandler delegate; @@ -46,19 +53,25 @@ public class SaslRpcHandler extends RpcHandler { /** Class which provides secret keys which are shared by server and client on a per-app basis. */ private final SecretKeyHolder secretKeyHolder; - /** Maps each channel to its SASL authentication state. */ - private final ConcurrentMap channelAuthenticationMap; + private SparkSaslServer saslServer; + private boolean isComplete; - public SaslRpcHandler(RpcHandler delegate, SecretKeyHolder secretKeyHolder) { + SaslRpcHandler( + TransportConf conf, + Channel channel, + RpcHandler delegate, + SecretKeyHolder secretKeyHolder) { + this.conf = conf; + this.channel = channel; this.delegate = delegate; this.secretKeyHolder = secretKeyHolder; - this.channelAuthenticationMap = Maps.newConcurrentMap(); + this.saslServer = null; + this.isComplete = false; } @Override public void receive(TransportClient client, byte[] message, RpcResponseCallback callback) { - SparkSaslServer saslServer = channelAuthenticationMap.get(client); - if (saslServer != null && saslServer.isComplete()) { + if (isComplete) { // Authentication complete, delegate to base handler. delegate.receive(client, message, callback); return; @@ -68,15 +81,30 @@ public void receive(TransportClient client, byte[] message, RpcResponseCallback if (saslServer == null) { // First message in the handshake, setup the necessary state. - saslServer = new SparkSaslServer(saslMessage.appId, secretKeyHolder); - channelAuthenticationMap.put(client, saslServer); + saslServer = new SparkSaslServer(saslMessage.appId, secretKeyHolder, + conf.saslServerAlwaysEncrypt()); } byte[] response = saslServer.response(saslMessage.payload); + callback.onSuccess(response); + + // Setup encryption after the SASL response is sent, otherwise the client can't parse the + // response. It's ok to change the channel pipeline here since we are processing an incoming + // message, so the pipeline is busy and no new incoming messages will be fed to it before this + // method returns. This assumes that the code ensures, through other means, that no outbound + // messages are being written to the channel while negotiation is still going on. if (saslServer.isComplete()) { logger.debug("SASL authentication successful for channel {}", client); + isComplete = true; + if (SparkSaslServer.QOP_AUTH_CONF.equals(saslServer.getNegotiatedProperty(Sasl.QOP))) { + logger.debug("Enabling encryption for channel {}", client); + SaslEncryption.addToChannel(channel, saslServer, conf.maxSaslEncryptedBlockSize()); + saslServer = null; + } else { + saslServer.dispose(); + saslServer = null; + } } - callback.onSuccess(response); } @Override @@ -86,9 +114,9 @@ public StreamManager getStreamManager() { @Override public void connectionTerminated(TransportClient client) { - SparkSaslServer saslServer = channelAuthenticationMap.remove(client); if (saslServer != null) { saslServer.dispose(); } } + } diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslServerBootstrap.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslServerBootstrap.java new file mode 100644 index 0000000000000..f2f983856f444 --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslServerBootstrap.java @@ -0,0 +1,49 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.sasl; + +import io.netty.channel.Channel; + +import org.apache.spark.network.server.RpcHandler; +import org.apache.spark.network.server.TransportServerBootstrap; +import org.apache.spark.network.util.TransportConf; + +/** + * A bootstrap which is executed on a TransportServer's client channel once a client connects + * to the server. This allows customizing the client channel to allow for things such as SASL + * authentication. + */ +public class SaslServerBootstrap implements TransportServerBootstrap { + + private final TransportConf conf; + private final SecretKeyHolder secretKeyHolder; + + public SaslServerBootstrap(TransportConf conf, SecretKeyHolder secretKeyHolder) { + this.conf = conf; + this.secretKeyHolder = secretKeyHolder; + } + + /** + * Wrap the given application handler in a SaslRpcHandler that will handle the initial SASL + * negotiation. + */ + public RpcHandler doBootstrap(Channel channel, RpcHandler rpcHandler) { + return new SaslRpcHandler(conf, channel, rpcHandler, secretKeyHolder); + } + +} diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java index 9abad1f30a259..94685e91b862e 100644 --- a/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java @@ -17,6 +17,8 @@ package org.apache.spark.network.sasl; +import java.io.IOException; +import java.util.Map; import javax.security.auth.callback.Callback; import javax.security.auth.callback.CallbackHandler; import javax.security.auth.callback.NameCallback; @@ -27,9 +29,9 @@ import javax.security.sasl.Sasl; import javax.security.sasl.SaslClient; import javax.security.sasl.SaslException; -import java.io.IOException; import com.google.common.base.Throwables; +import com.google.common.collect.ImmutableMap; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -40,19 +42,25 @@ * initial state to the "authenticated" state. This client initializes the protocol via a * firstToken, which is then followed by a set of challenges and responses. */ -public class SparkSaslClient { +public class SparkSaslClient implements SaslEncryptionBackend { private final Logger logger = LoggerFactory.getLogger(SparkSaslClient.class); private final String secretKeyId; private final SecretKeyHolder secretKeyHolder; + private final String expectedQop; private SaslClient saslClient; - public SparkSaslClient(String secretKeyId, SecretKeyHolder secretKeyHolder) { + public SparkSaslClient(String secretKeyId, SecretKeyHolder secretKeyHolder, boolean encrypt) { this.secretKeyId = secretKeyId; this.secretKeyHolder = secretKeyHolder; + this.expectedQop = encrypt ? QOP_AUTH_CONF : QOP_AUTH; + + Map saslProps = ImmutableMap.builder() + .put(Sasl.QOP, expectedQop) + .build(); try { this.saslClient = Sasl.createSaslClient(new String[] { DIGEST }, null, null, DEFAULT_REALM, - SASL_PROPS, new ClientCallbackHandler()); + saslProps, new ClientCallbackHandler()); } catch (SaslException e) { throw Throwables.propagate(e); } @@ -76,6 +84,11 @@ public synchronized boolean isComplete() { return saslClient != null && saslClient.isComplete(); } + /** Returns the value of a negotiated property. */ + public Object getNegotiatedProperty(String name) { + return saslClient.getNegotiatedProperty(name); + } + /** * Respond to server's SASL token. * @param token contains server's SASL token @@ -93,6 +106,7 @@ public synchronized byte[] response(byte[] token) { * Disposes of any system resources or security-sensitive information the * SaslClient might be using. */ + @Override public synchronized void dispose() { if (saslClient != null) { try { @@ -134,4 +148,15 @@ public void handle(Callback[] callbacks) throws IOException, UnsupportedCallback } } } + + @Override + public byte[] wrap(byte[] data, int offset, int len) throws SaslException { + return saslClient.wrap(data, offset, len); + } + + @Override + public byte[] unwrap(byte[] data, int offset, int len) throws SaslException { + return saslClient.unwrap(data, offset, len); + } + } diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java index e87b17ead1e1a..431cb67a2ae0b 100644 --- a/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java @@ -44,7 +44,7 @@ * initial state to the "authenticated" state. (It is not a server in the sense of accepting * connections on some socket.) */ -public class SparkSaslServer { +public class SparkSaslServer implements SaslEncryptionBackend { private final Logger logger = LoggerFactory.getLogger(SparkSaslServer.class); /** @@ -60,26 +60,37 @@ public class SparkSaslServer { static final String DIGEST = "DIGEST-MD5"; /** - * The quality of protection is just "auth". This means that we are doing - * authentication only, we are not supporting integrity or privacy protection of the - * communication channel after authentication. This could be changed to be configurable - * in the future. + * Quality of protection value that includes encryption. */ - static final Map SASL_PROPS = ImmutableMap.builder() - .put(Sasl.QOP, "auth") - .put(Sasl.SERVER_AUTH, "true") - .build(); + static final String QOP_AUTH_CONF = "auth-conf"; + + /** + * Quality of protection value that does not include encryption. + */ + static final String QOP_AUTH = "auth"; /** Identifier for a certain secret key within the secretKeyHolder. */ private final String secretKeyId; private final SecretKeyHolder secretKeyHolder; private SaslServer saslServer; - public SparkSaslServer(String secretKeyId, SecretKeyHolder secretKeyHolder) { + public SparkSaslServer( + String secretKeyId, + SecretKeyHolder secretKeyHolder, + boolean alwaysEncrypt) { this.secretKeyId = secretKeyId; this.secretKeyHolder = secretKeyHolder; + + // Sasl.QOP is a comma-separated list of supported values. The value that allows encryption + // is listed first since it's preferred over the non-encrypted one (if the client also + // lists both in the request). + String qop = alwaysEncrypt ? QOP_AUTH_CONF : String.format("%s,%s", QOP_AUTH_CONF, QOP_AUTH); + Map saslProps = ImmutableMap.builder() + .put(Sasl.SERVER_AUTH, "true") + .put(Sasl.QOP, qop) + .build(); try { - this.saslServer = Sasl.createSaslServer(DIGEST, null, DEFAULT_REALM, SASL_PROPS, + this.saslServer = Sasl.createSaslServer(DIGEST, null, DEFAULT_REALM, saslProps, new DigestCallbackHandler()); } catch (SaslException e) { throw Throwables.propagate(e); @@ -93,6 +104,11 @@ public synchronized boolean isComplete() { return saslServer != null && saslServer.isComplete(); } + /** Returns the value of a negotiated property. */ + public Object getNegotiatedProperty(String name) { + return saslServer.getNegotiatedProperty(name); + } + /** * Used to respond to server SASL tokens. * @param token Server's SASL token @@ -110,6 +126,7 @@ public synchronized byte[] response(byte[] token) { * Disposes of any system resources or security-sensitive information the * SaslServer might be using. */ + @Override public synchronized void dispose() { if (saslServer != null) { try { @@ -122,6 +139,16 @@ public synchronized void dispose() { } } + @Override + public byte[] wrap(byte[] data, int offset, int len) throws SaslException { + return saslServer.wrap(data, offset, len); + } + + @Override + public byte[] unwrap(byte[] data, int offset, int len) throws SaslException { + return saslServer.unwrap(data, offset, len); + } + /** * Implementation of javax.security.auth.callback.CallbackHandler for SASL DIGEST-MD5 mechanism. */ diff --git a/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java b/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java index a6d390e13f396..c95e64e8e2cda 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java +++ b/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java @@ -20,14 +20,18 @@ import java.util.Iterator; import java.util.Map; import java.util.Random; +import java.util.Set; import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.atomic.AtomicLong; +import io.netty.channel.Channel; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.apache.spark.network.buffer.ManagedBuffer; +import com.google.common.base.Preconditions; + /** * StreamManager which allows registration of an Iterator<ManagedBuffer>, which are individually * fetched as chunks by the client. Each registered buffer is one chunk. @@ -36,18 +40,21 @@ public class OneForOneStreamManager extends StreamManager { private final Logger logger = LoggerFactory.getLogger(OneForOneStreamManager.class); private final AtomicLong nextStreamId; - private final Map streams; + private final ConcurrentHashMap streams; /** State of a single stream. */ private static class StreamState { final Iterator buffers; + // The channel associated to the stream + Channel associatedChannel = null; + // Used to keep track of the index of the buffer that the user has retrieved, just to ensure // that the caller only requests each chunk one at a time, in order. int curChunk = 0; StreamState(Iterator buffers) { - this.buffers = buffers; + this.buffers = Preconditions.checkNotNull(buffers); } } @@ -58,6 +65,13 @@ public OneForOneStreamManager() { streams = new ConcurrentHashMap(); } + @Override + public void registerChannel(Channel channel, long streamId) { + if (streams.containsKey(streamId)) { + streams.get(streamId).associatedChannel = channel; + } + } + @Override public ManagedBuffer getChunk(long streamId, int chunkIndex) { StreamState state = streams.get(streamId); @@ -80,12 +94,17 @@ public ManagedBuffer getChunk(long streamId, int chunkIndex) { } @Override - public void connectionTerminated(long streamId) { - // Release all remaining buffers. - StreamState state = streams.remove(streamId); - if (state != null && state.buffers != null) { - while (state.buffers.hasNext()) { - state.buffers.next().release(); + public void connectionTerminated(Channel channel) { + // Close all streams which have been associated with the channel. + for (Map.Entry entry: streams.entrySet()) { + StreamState state = entry.getValue(); + if (state.associatedChannel == channel) { + streams.remove(entry.getKey()); + + // Release all remaining buffers. + while (state.buffers.hasNext()) { + state.buffers.next().release(); + } } } } diff --git a/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java b/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java index 5a9a14a180c10..929f789bf9d24 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java +++ b/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java @@ -17,6 +17,8 @@ package org.apache.spark.network.server; +import io.netty.channel.Channel; + import org.apache.spark.network.buffer.ManagedBuffer; /** @@ -44,9 +46,18 @@ public abstract class StreamManager { public abstract ManagedBuffer getChunk(long streamId, int chunkIndex); /** - * Indicates that the TCP connection that was tied to the given stream has been terminated. After - * this occurs, we are guaranteed not to read from the stream again, so any state can be cleaned - * up. + * Associates a stream with a single client connection, which is guaranteed to be the only reader + * of the stream. The getChunk() method will be called serially on this connection and once the + * connection is closed, the stream will never be used again, enabling cleanup. + * + * This must be called before the first getChunk() on the stream, but it may be invoked multiple + * times with the same channel and stream id. + */ + public void registerChannel(Channel channel, long streamId) { } + + /** + * Indicates that the given channel has been terminated. After this occurs, we are guaranteed not + * to read from the associated streams again, so any state can be cleaned up. */ - public void connectionTerminated(long streamId) { } + public void connectionTerminated(Channel channel) { } } diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java b/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java index 1580180cc17e9..e5159ab56d0d4 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java +++ b/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java @@ -17,10 +17,7 @@ package org.apache.spark.network.server; -import java.util.Set; - import com.google.common.base.Throwables; -import com.google.common.collect.Sets; import io.netty.channel.Channel; import io.netty.channel.ChannelFuture; import io.netty.channel.ChannelFutureListener; @@ -62,9 +59,6 @@ public class TransportRequestHandler extends MessageHandler { /** Returns each chunk part of a stream. */ private final StreamManager streamManager; - /** List of all stream ids that have been read on this handler, used for cleanup. */ - private final Set streamIds; - public TransportRequestHandler( Channel channel, TransportClient reverseClient, @@ -73,7 +67,6 @@ public TransportRequestHandler( this.reverseClient = reverseClient; this.rpcHandler = rpcHandler; this.streamManager = rpcHandler.getStreamManager(); - this.streamIds = Sets.newHashSet(); } @Override @@ -82,10 +75,7 @@ public void exceptionCaught(Throwable cause) { @Override public void channelUnregistered() { - // Inform the StreamManager that these streams will no longer be read from. - for (long streamId : streamIds) { - streamManager.connectionTerminated(streamId); - } + streamManager.connectionTerminated(channel); rpcHandler.connectionTerminated(reverseClient); } @@ -102,12 +92,12 @@ public void handle(RequestMessage request) { private void processFetchRequest(final ChunkFetchRequest req) { final String client = NettyUtils.getRemoteAddress(channel); - streamIds.add(req.streamChunkId.streamId); logger.trace("Received req from {} to fetch block {}", client, req.streamChunkId); ManagedBuffer buf; try { + streamManager.registerChannel(channel, req.streamChunkId.streamId); buf = streamManager.getChunk(req.streamChunkId.streamId, req.streamChunkId.chunkIndex); } catch (Exception e) { logger.error(String.format( diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java b/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java index b7ce8541e565e..941ef95772e16 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java +++ b/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java @@ -19,8 +19,11 @@ import java.io.Closeable; import java.net.InetSocketAddress; +import java.util.List; import java.util.concurrent.TimeUnit; +import com.google.common.base.Preconditions; +import com.google.common.collect.Lists; import io.netty.bootstrap.ServerBootstrap; import io.netty.buffer.PooledByteBufAllocator; import io.netty.channel.ChannelFuture; @@ -44,15 +47,23 @@ public class TransportServer implements Closeable { private final TransportContext context; private final TransportConf conf; + private final RpcHandler appRpcHandler; + private final List bootstraps; private ServerBootstrap bootstrap; private ChannelFuture channelFuture; private int port = -1; /** Creates a TransportServer that binds to the given port, or to any available if 0. */ - public TransportServer(TransportContext context, int portToBind) { + public TransportServer( + TransportContext context, + int portToBind, + RpcHandler appRpcHandler, + List bootstraps) { this.context = context; this.conf = context.getConf(); + this.appRpcHandler = appRpcHandler; + this.bootstraps = Lists.newArrayList(Preconditions.checkNotNull(bootstraps)); init(portToBind); } @@ -95,7 +106,11 @@ private void init(int portToBind) { bootstrap.childHandler(new ChannelInitializer() { @Override protected void initChannel(SocketChannel ch) throws Exception { - context.initializePipeline(ch); + RpcHandler rpcHandler = appRpcHandler; + for (TransportServerBootstrap bootstrap : bootstraps) { + rpcHandler = bootstrap.doBootstrap(ch, rpcHandler); + } + context.initializePipeline(ch, rpcHandler); } }); diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportServerBootstrap.java b/network/common/src/main/java/org/apache/spark/network/server/TransportServerBootstrap.java new file mode 100644 index 0000000000000..05803ab1bb059 --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/server/TransportServerBootstrap.java @@ -0,0 +1,36 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.server; + +import io.netty.channel.Channel; + +/** + * A bootstrap which is executed on a TransportServer's client channel once a client connects + * to the server. This allows customizing the client channel to allow for things such as SASL + * authentication. + */ +public interface TransportServerBootstrap { + /** + * Customizes the channel to include new features, if needed. + * + * @param channel The connected channel opened by the client. + * @param rpcHandler The RPC handler for the server. + * @return The RPC handler to use for the channel. + */ + RpcHandler doBootstrap(Channel channel, RpcHandler rpcHandler); +} diff --git a/network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java b/network/common/src/main/java/org/apache/spark/network/util/ByteArrayWritableChannel.java similarity index 70% rename from network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java rename to network/common/src/main/java/org/apache/spark/network/util/ByteArrayWritableChannel.java index b525ed69fc9fb..b1415720045e2 100644 --- a/network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java +++ b/network/common/src/main/java/org/apache/spark/network/util/ByteArrayWritableChannel.java @@ -15,11 +15,14 @@ * limitations under the License. */ -package org.apache.spark.network; +package org.apache.spark.network.util; import java.nio.ByteBuffer; import java.nio.channels.WritableByteChannel; +/** + * A writable channel that stores the written data in a byte array in memory. + */ public class ByteArrayWritableChannel implements WritableByteChannel { private final byte[] data; @@ -27,19 +30,30 @@ public class ByteArrayWritableChannel implements WritableByteChannel { public ByteArrayWritableChannel(int size) { this.data = new byte[size]; - this.offset = 0; } public byte[] getData() { return data; } + public int length() { + return offset; + } + + /** Resets the channel so that writing to it will overwrite the existing buffer. */ + public void reset() { + offset = 0; + } + + /** + * Reads from the given buffer into the internal byte array. + */ @Override public int write(ByteBuffer src) { - int available = src.remaining(); - src.get(data, offset, available); - offset += available; - return available; + int toTransfer = Math.min(src.remaining(), data.length - offset); + src.get(data, offset, toTransfer); + offset += toTransfer; + return toTransfer; } @Override diff --git a/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java b/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java index 0aef7f1987315..3b2eff377955a 100644 --- a/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java +++ b/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java @@ -17,6 +17,8 @@ package org.apache.spark.network.util; +import com.google.common.primitives.Ints; + /** * A central location that tracks all the settings we expose to users. */ @@ -112,4 +114,20 @@ public boolean lazyFileDescriptor() { public int portMaxRetries() { return conf.getInt("spark.port.maxRetries", 16); } + + /** + * Maximum number of bytes to be encrypted at a time when SASL encryption is enabled. + */ + public int maxSaslEncryptedBlockSize() { + return Ints.checkedCast(JavaUtils.byteStringAsBytes( + conf.get("spark.network.sasl.maxEncryptedBlockSize", "64k"))); + } + + /** + * Whether the server should enforce encryption on SASL-authenticated connections. + */ + public boolean saslServerAlwaysEncrypt() { + return conf.getBoolean("spark.network.sasl.serverAlwaysEncrypt", false); + } + } diff --git a/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java b/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java index 860dd6d9b3915..d500bc3c98a78 100644 --- a/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java +++ b/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java @@ -39,6 +39,7 @@ import org.apache.spark.network.protocol.RpcRequest; import org.apache.spark.network.protocol.RpcResponse; import org.apache.spark.network.protocol.StreamChunkId; +import org.apache.spark.network.util.ByteArrayWritableChannel; import org.apache.spark.network.util.NettyUtils; public class ProtocolSuite { diff --git a/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java b/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java index ff985096d72d5..6c98e733b462f 100644 --- a/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java +++ b/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java @@ -29,7 +29,7 @@ import static org.junit.Assert.*; -import org.apache.spark.network.ByteArrayWritableChannel; +import org.apache.spark.network.util.ByteArrayWritableChannel; public class MessageWithHeaderSuite { diff --git a/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java index 23b4e06f064e1..be6632bb8cf49 100644 --- a/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java +++ b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java @@ -17,12 +17,47 @@ package org.apache.spark.network.sasl; -import static org.junit.Assert.assertFalse; -import static org.junit.Assert.assertTrue; -import static org.junit.Assert.fail; +import static com.google.common.base.Charsets.UTF_8; +import static org.junit.Assert.*; +import static org.mockito.Mockito.*; +import java.io.File; +import java.util.Arrays; +import java.util.List; +import java.util.Random; +import java.util.concurrent.TimeoutException; +import java.util.concurrent.TimeUnit; +import java.util.concurrent.atomic.AtomicReference; +import javax.security.sasl.SaslException; + +import com.google.common.collect.Lists; +import com.google.common.io.ByteStreams; +import com.google.common.io.Files; +import io.netty.buffer.ByteBuf; +import io.netty.buffer.Unpooled; +import io.netty.channel.Channel; +import io.netty.channel.ChannelHandlerContext; +import io.netty.channel.ChannelOutboundHandlerAdapter; +import io.netty.channel.ChannelPromise; import org.junit.Test; +import org.mockito.invocation.InvocationOnMock; +import org.mockito.stubbing.Answer; +import org.apache.spark.network.TestUtils; +import org.apache.spark.network.TransportContext; +import org.apache.spark.network.buffer.FileSegmentManagedBuffer; +import org.apache.spark.network.buffer.ManagedBuffer; +import org.apache.spark.network.client.ChunkReceivedCallback; +import org.apache.spark.network.client.RpcResponseCallback; +import org.apache.spark.network.client.TransportClient; +import org.apache.spark.network.client.TransportClientBootstrap; +import org.apache.spark.network.server.RpcHandler; +import org.apache.spark.network.server.StreamManager; +import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; +import org.apache.spark.network.util.ByteArrayWritableChannel; +import org.apache.spark.network.util.SystemPropertyConfigProvider; +import org.apache.spark.network.util.TransportConf; /** * Jointly tests SparkSaslClient and SparkSaslServer, as both are black boxes. @@ -44,8 +79,8 @@ public String getSecretKey(String appId) { @Test public void testMatching() { - SparkSaslClient client = new SparkSaslClient("shared-secret", secretKeyHolder); - SparkSaslServer server = new SparkSaslServer("shared-secret", secretKeyHolder); + SparkSaslClient client = new SparkSaslClient("shared-secret", secretKeyHolder, false); + SparkSaslServer server = new SparkSaslServer("shared-secret", secretKeyHolder, false); assertFalse(client.isComplete()); assertFalse(server.isComplete()); @@ -64,11 +99,10 @@ public void testMatching() { assertFalse(client.isComplete()); } - @Test public void testNonMatching() { - SparkSaslClient client = new SparkSaslClient("my-secret", secretKeyHolder); - SparkSaslServer server = new SparkSaslServer("your-secret", secretKeyHolder); + SparkSaslClient client = new SparkSaslClient("my-secret", secretKeyHolder, false); + SparkSaslServer server = new SparkSaslServer("your-secret", secretKeyHolder, false); assertFalse(client.isComplete()); assertFalse(server.isComplete()); @@ -86,4 +120,312 @@ public void testNonMatching() { assertFalse(server.isComplete()); } } + + @Test + public void testSaslAuthentication() throws Exception { + testBasicSasl(false); + } + + @Test + public void testSaslEncryption() throws Exception { + testBasicSasl(true); + } + + private void testBasicSasl(boolean encrypt) throws Exception { + RpcHandler rpcHandler = mock(RpcHandler.class); + doAnswer(new Answer() { + @Override + public Void answer(InvocationOnMock invocation) { + byte[] message = (byte[]) invocation.getArguments()[1]; + RpcResponseCallback cb = (RpcResponseCallback) invocation.getArguments()[2]; + assertEquals("Ping", new String(message, UTF_8)); + cb.onSuccess("Pong".getBytes(UTF_8)); + return null; + } + }) + .when(rpcHandler) + .receive(any(TransportClient.class), any(byte[].class), any(RpcResponseCallback.class)); + + SaslTestCtx ctx = new SaslTestCtx(rpcHandler, encrypt, false); + try { + byte[] response = ctx.client.sendRpcSync("Ping".getBytes(UTF_8), TimeUnit.SECONDS.toMillis(10)); + assertEquals("Pong", new String(response, UTF_8)); + } finally { + ctx.close(); + } + } + + @Test + public void testEncryptedMessage() throws Exception { + SaslEncryptionBackend backend = mock(SaslEncryptionBackend.class); + byte[] data = new byte[1024]; + new Random().nextBytes(data); + when(backend.wrap(any(byte[].class), anyInt(), anyInt())).thenReturn(data); + + ByteBuf msg = Unpooled.buffer(); + try { + msg.writeBytes(data); + + // Create a channel with a really small buffer compared to the data. This means that on each + // call, the outbound data will not be fully written, so the write() method should return a + // dummy count to keep the channel alive when possible. + ByteArrayWritableChannel channel = new ByteArrayWritableChannel(32); + + SaslEncryption.EncryptedMessage emsg = + new SaslEncryption.EncryptedMessage(backend, msg, 1024); + long count = emsg.transferTo(channel, emsg.transfered()); + assertTrue(count < data.length); + assertTrue(count > 0); + + // Here, the output buffer is full so nothing should be transferred. + assertEquals(0, emsg.transferTo(channel, emsg.transfered())); + + // Now there's room in the buffer, but not enough to transfer all the remaining data, + // so the dummy count should be returned. + channel.reset(); + assertEquals(1, emsg.transferTo(channel, emsg.transfered())); + + // Eventually, the whole message should be transferred. + for (int i = 0; i < data.length / 32 - 2; i++) { + channel.reset(); + assertEquals(1, emsg.transferTo(channel, emsg.transfered())); + } + + channel.reset(); + count = emsg.transferTo(channel, emsg.transfered()); + assertTrue("Unexpected count: " + count, count > 1 && count < data.length); + assertEquals(data.length, emsg.transfered()); + } finally { + msg.release(); + } + } + + @Test + public void testEncryptedMessageChunking() throws Exception { + File file = File.createTempFile("sasltest", ".txt"); + try { + TransportConf conf = new TransportConf(new SystemPropertyConfigProvider()); + + byte[] data = new byte[8 * 1024]; + new Random().nextBytes(data); + Files.write(data, file); + + SaslEncryptionBackend backend = mock(SaslEncryptionBackend.class); + // It doesn't really matter what we return here, as long as it's not null. + when(backend.wrap(any(byte[].class), anyInt(), anyInt())).thenReturn(data); + + FileSegmentManagedBuffer msg = new FileSegmentManagedBuffer(conf, file, 0, file.length()); + SaslEncryption.EncryptedMessage emsg = + new SaslEncryption.EncryptedMessage(backend, msg.convertToNetty(), data.length / 8); + + ByteArrayWritableChannel channel = new ByteArrayWritableChannel(data.length); + while (emsg.transfered() < emsg.count()) { + channel.reset(); + emsg.transferTo(channel, emsg.transfered()); + } + + verify(backend, times(8)).wrap(any(byte[].class), anyInt(), anyInt()); + } finally { + file.delete(); + } + } + + @Test + public void testFileRegionEncryption() throws Exception { + final String blockSizeConf = "spark.network.sasl.maxEncryptedBlockSize"; + System.setProperty(blockSizeConf, "1k"); + + final AtomicReference response = new AtomicReference(); + final File file = File.createTempFile("sasltest", ".txt"); + SaslTestCtx ctx = null; + try { + final TransportConf conf = new TransportConf(new SystemPropertyConfigProvider()); + StreamManager sm = mock(StreamManager.class); + when(sm.getChunk(anyLong(), anyInt())).thenAnswer(new Answer() { + @Override + public ManagedBuffer answer(InvocationOnMock invocation) { + return new FileSegmentManagedBuffer(conf, file, 0, file.length()); + } + }); + + RpcHandler rpcHandler = mock(RpcHandler.class); + when(rpcHandler.getStreamManager()).thenReturn(sm); + + byte[] data = new byte[8 * 1024]; + new Random().nextBytes(data); + Files.write(data, file); + + ctx = new SaslTestCtx(rpcHandler, true, false); + + final Object lock = new Object(); + + ChunkReceivedCallback callback = mock(ChunkReceivedCallback.class); + doAnswer(new Answer() { + @Override + public Void answer(InvocationOnMock invocation) { + response.set((ManagedBuffer) invocation.getArguments()[1]); + response.get().retain(); + synchronized (lock) { + lock.notifyAll(); + } + return null; + } + }).when(callback).onSuccess(anyInt(), any(ManagedBuffer.class)); + + synchronized (lock) { + ctx.client.fetchChunk(0, 0, callback); + lock.wait(10 * 1000); + } + + verify(callback, times(1)).onSuccess(anyInt(), any(ManagedBuffer.class)); + verify(callback, never()).onFailure(anyInt(), any(Throwable.class)); + + byte[] received = ByteStreams.toByteArray(response.get().createInputStream()); + assertTrue(Arrays.equals(data, received)); + } finally { + file.delete(); + if (ctx != null) { + ctx.close(); + } + if (response.get() != null) { + response.get().release(); + } + System.clearProperty(blockSizeConf); + } + } + + @Test + public void testServerAlwaysEncrypt() throws Exception { + final String alwaysEncryptConfName = "spark.network.sasl.serverAlwaysEncrypt"; + System.setProperty(alwaysEncryptConfName, "true"); + + SaslTestCtx ctx = null; + try { + ctx = new SaslTestCtx(mock(RpcHandler.class), false, false); + fail("Should have failed to connect without encryption."); + } catch (Exception e) { + assertTrue(e.getCause() instanceof SaslException); + } finally { + if (ctx != null) { + ctx.close(); + } + System.clearProperty(alwaysEncryptConfName); + } + } + + @Test + public void testDataEncryptionIsActuallyEnabled() throws Exception { + // This test sets up an encrypted connection but then, using a client bootstrap, removes + // the encryption handler from the client side. This should cause the server to not be + // able to understand RPCs sent to it and thus close the connection. + SaslTestCtx ctx = null; + try { + ctx = new SaslTestCtx(mock(RpcHandler.class), true, true); + ctx.client.sendRpcSync("Ping".getBytes(UTF_8), TimeUnit.SECONDS.toMillis(10)); + fail("Should have failed to send RPC to server."); + } catch (Exception e) { + assertFalse(e.getCause() instanceof TimeoutException); + } finally { + if (ctx != null) { + ctx.close(); + } + } + } + + private static class SaslTestCtx { + + final TransportClient client; + final TransportServer server; + + private final boolean encrypt; + private final boolean disableClientEncryption; + private final EncryptionCheckerBootstrap checker; + + SaslTestCtx( + RpcHandler rpcHandler, + boolean encrypt, + boolean disableClientEncryption) + throws Exception { + + TransportConf conf = new TransportConf(new SystemPropertyConfigProvider()); + + SecretKeyHolder keyHolder = mock(SecretKeyHolder.class); + when(keyHolder.getSaslUser(anyString())).thenReturn("user"); + when(keyHolder.getSecretKey(anyString())).thenReturn("secret"); + + TransportContext ctx = new TransportContext(conf, rpcHandler); + + this.checker = new EncryptionCheckerBootstrap(); + this.server = ctx.createServer(Arrays.asList(new SaslServerBootstrap(conf, keyHolder), + checker)); + + try { + List clientBootstraps = Lists.newArrayList(); + clientBootstraps.add(new SaslClientBootstrap(conf, "user", keyHolder, encrypt)); + if (disableClientEncryption) { + clientBootstraps.add(new EncryptionDisablerBootstrap()); + } + + this.client = ctx.createClientFactory(clientBootstraps) + .createClient(TestUtils.getLocalHost(), server.getPort()); + } catch (Exception e) { + close(); + throw e; + } + + this.encrypt = encrypt; + this.disableClientEncryption = disableClientEncryption; + } + + void close() { + if (!disableClientEncryption) { + assertEquals(encrypt, checker.foundEncryptionHandler); + } + if (client != null) { + client.close(); + } + if (server != null) { + server.close(); + } + } + + } + + private static class EncryptionCheckerBootstrap extends ChannelOutboundHandlerAdapter + implements TransportServerBootstrap { + + boolean foundEncryptionHandler; + + @Override + public void write(ChannelHandlerContext ctx, Object msg, ChannelPromise promise) + throws Exception { + if (!foundEncryptionHandler) { + foundEncryptionHandler = + ctx.channel().pipeline().get(SaslEncryption.ENCRYPTION_HANDLER_NAME) != null; + } + ctx.write(msg, promise); + } + + @Override + public void handlerRemoved(ChannelHandlerContext ctx) throws Exception { + super.handlerRemoved(ctx); + } + + @Override + public RpcHandler doBootstrap(Channel channel, RpcHandler rpcHandler) { + channel.pipeline().addFirst("encryptionChecker", this); + return rpcHandler; + } + + } + + private static class EncryptionDisablerBootstrap implements TransportClientBootstrap { + + @Override + public void doBootstrap(TransportClient client, Channel channel) { + channel.pipeline().remove(SaslEncryption.ENCRYPTION_HANDLER_NAME); + } + + } + } diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java index 6e8018b723dc6..612bce571a493 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java @@ -20,6 +20,7 @@ import java.io.IOException; import java.util.List; +import com.google.common.base.Preconditions; import com.google.common.collect.Lists; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -46,6 +47,7 @@ public class ExternalShuffleClient extends ShuffleClient { private final TransportConf conf; private final boolean saslEnabled; + private final boolean saslEncryptionEnabled; private final SecretKeyHolder secretKeyHolder; private TransportClientFactory clientFactory; @@ -58,10 +60,15 @@ public class ExternalShuffleClient extends ShuffleClient { public ExternalShuffleClient( TransportConf conf, SecretKeyHolder secretKeyHolder, - boolean saslEnabled) { + boolean saslEnabled, + boolean saslEncryptionEnabled) { + Preconditions.checkArgument( + !saslEncryptionEnabled || saslEnabled, + "SASL encryption can only be enabled if SASL is also enabled."); this.conf = conf; this.secretKeyHolder = secretKeyHolder; this.saslEnabled = saslEnabled; + this.saslEncryptionEnabled = saslEncryptionEnabled; } @Override @@ -70,7 +77,7 @@ public void init(String appId) { TransportContext context = new TransportContext(conf, new NoOpRpcHandler()); List bootstraps = Lists.newArrayList(); if (saslEnabled) { - bootstraps.add(new SaslClientBootstrap(conf, appId, secretKeyHolder)); + bootstraps.add(new SaslClientBootstrap(conf, appId, secretKeyHolder, saslEncryptionEnabled)); } clientFactory = context.createClientFactory(bootstraps); } diff --git a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java index d25283e46ef96..382f613ecbb1b 100644 --- a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java +++ b/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java @@ -18,6 +18,7 @@ package org.apache.spark.network.sasl; import java.io.IOException; +import java.util.Arrays; import com.google.common.collect.Lists; import org.junit.After; @@ -37,6 +38,7 @@ import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.StreamManager; import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler; import org.apache.spark.network.util.SystemPropertyConfigProvider; import org.apache.spark.network.util.TransportConf; @@ -72,10 +74,11 @@ public String getSecretKey(String appId) { @BeforeClass public static void beforeAll() throws IOException { SecretKeyHolder secretKeyHolder = new TestSecretKeyHolder("good-key"); - SaslRpcHandler handler = new SaslRpcHandler(new TestRpcHandler(), secretKeyHolder); conf = new TransportConf(new SystemPropertyConfigProvider()); - context = new TransportContext(conf, handler); - server = context.createServer(); + context = new TransportContext(conf, new TestRpcHandler()); + + TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf, secretKeyHolder); + server = context.createServer(Arrays.asList(bootstrap)); } diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java index 02c10bcb7b261..39aa49911d9cb 100644 --- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java +++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java @@ -136,7 +136,7 @@ private FetchResult fetchBlocks(String execId, String[] blockIds, int port) thro final Semaphore requestsRemaining = new Semaphore(0); - ExternalShuffleClient client = new ExternalShuffleClient(conf, null, false); + ExternalShuffleClient client = new ExternalShuffleClient(conf, null, false, false); client.init(APP_ID); client.fetchBlocks(TestUtils.getLocalHost(), port, execId, blockIds, new BlockFetchingListener() { @@ -274,7 +274,7 @@ public void testFetchNoServer() throws Exception { private void registerExecutor(String executorId, ExecutorShuffleInfo executorInfo) throws IOException { - ExternalShuffleClient client = new ExternalShuffleClient(conf, null, false); + ExternalShuffleClient client = new ExternalShuffleClient(conf, null, false, false); client.init(APP_ID); client.registerWithShuffleServer(TestUtils.getLocalHost(), server.getPort(), executorId, executorInfo); diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java index 759a12910c94d..d4ec1956c1e29 100644 --- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java +++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java @@ -18,6 +18,7 @@ package org.apache.spark.network.shuffle; import java.io.IOException; +import java.util.Arrays; import org.junit.After; import org.junit.Before; @@ -27,10 +28,11 @@ import org.apache.spark.network.TestUtils; import org.apache.spark.network.TransportContext; -import org.apache.spark.network.sasl.SaslRpcHandler; +import org.apache.spark.network.sasl.SaslServerBootstrap; import org.apache.spark.network.sasl.SecretKeyHolder; import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo; import org.apache.spark.network.util.SystemPropertyConfigProvider; import org.apache.spark.network.util.TransportConf; @@ -42,10 +44,10 @@ public class ExternalShuffleSecuritySuite { @Before public void beforeEach() { - RpcHandler handler = new SaslRpcHandler(new ExternalShuffleBlockHandler(conf), - new TestSecretKeyHolder("my-app-id", "secret")); - TransportContext context = new TransportContext(conf, handler); - this.server = context.createServer(); + TransportContext context = new TransportContext(conf, new ExternalShuffleBlockHandler(conf)); + TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf, + new TestSecretKeyHolder("my-app-id", "secret")); + this.server = context.createServer(Arrays.asList(bootstrap)); } @After @@ -58,13 +60,13 @@ public void afterEach() { @Test public void testValid() throws IOException { - validate("my-app-id", "secret"); + validate("my-app-id", "secret", false); } @Test public void testBadAppId() { try { - validate("wrong-app-id", "secret"); + validate("wrong-app-id", "secret", false); } catch (Exception e) { assertTrue(e.getMessage(), e.getMessage().contains("Wrong appId!")); } @@ -73,16 +75,21 @@ public void testBadAppId() { @Test public void testBadSecret() { try { - validate("my-app-id", "bad-secret"); + validate("my-app-id", "bad-secret", false); } catch (Exception e) { assertTrue(e.getMessage(), e.getMessage().contains("Mismatched response")); } } + @Test + public void testEncryption() throws IOException { + validate("my-app-id", "secret", true); + } + /** Creates an ExternalShuffleClient and attempts to register with the server. */ - private void validate(String appId, String secretKey) throws IOException { + private void validate(String appId, String secretKey, boolean encrypt) throws IOException { ExternalShuffleClient client = - new ExternalShuffleClient(conf, new TestSecretKeyHolder(appId, secretKey), true); + new ExternalShuffleClient(conf, new TestSecretKeyHolder(appId, secretKey), true, encrypt); client.init(appId); // Registration either succeeds or throws an exception. client.registerWithShuffleServer(TestUtils.getLocalHost(), server.getPort(), "exec0", diff --git a/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java b/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java index 63b21222e7b77..463f99ef3352d 100644 --- a/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java +++ b/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java @@ -17,9 +17,10 @@ package org.apache.spark.network.yarn; -import java.lang.Override; import java.nio.ByteBuffer; +import java.util.List; +import com.google.common.collect.Lists; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.yarn.api.records.ApplicationId; import org.apache.hadoop.yarn.api.records.ContainerId; @@ -32,10 +33,11 @@ import org.slf4j.LoggerFactory; import org.apache.spark.network.TransportContext; -import org.apache.spark.network.sasl.SaslRpcHandler; +import org.apache.spark.network.sasl.SaslServerBootstrap; import org.apache.spark.network.sasl.ShuffleSecretManager; import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler; import org.apache.spark.network.util.TransportConf; import org.apache.spark.network.yarn.util.HadoopConfigProvider; @@ -103,16 +105,17 @@ protected void serviceInit(Configuration conf) { // special RPC handler that filters out unauthenticated fetch requests boolean authEnabled = conf.getBoolean(SPARK_AUTHENTICATE_KEY, DEFAULT_SPARK_AUTHENTICATE); blockHandler = new ExternalShuffleBlockHandler(transportConf); - RpcHandler rpcHandler = blockHandler; + + List bootstraps = Lists.newArrayList(); if (authEnabled) { secretManager = new ShuffleSecretManager(); - rpcHandler = new SaslRpcHandler(rpcHandler, secretManager); + bootstraps.add(new SaslServerBootstrap(transportConf, secretManager)); } int port = conf.getInt( SPARK_SHUFFLE_SERVICE_PORT_KEY, DEFAULT_SPARK_SHUFFLE_SERVICE_PORT); - TransportContext transportContext = new TransportContext(transportConf, rpcHandler); - shuffleServer = transportContext.createServer(port); + TransportContext transportContext = new TransportContext(transportConf, blockHandler); + shuffleServer = transportContext.createServer(port, bootstraps); String authEnabledString = authEnabled ? "enabled" : "not enabled"; logger.info("Started YARN shuffle service for Spark on port {}. " + "Authentication is {}.", port, authEnabledString); diff --git a/pom.xml b/pom.xml index 928f5d0f5efad..12ad7c5be4e40 100644 --- a/pom.xml +++ b/pom.xml @@ -97,6 +97,7 @@ sql/catalyst sql/core sql/hive + unsafe assembly external/twitter external/flume @@ -116,7 +117,7 @@ 1.6 spark 2.0.1 - 0.21.0 + 0.21.1 shaded-protobuf 1.7.10 1.2.17 @@ -415,6 +416,11 @@ commons-math3 ${commons.math3.version} + + org.apache.ivy + ivy + ${ivy.version} + com.google.code.findbugs jsr305 @@ -1215,6 +1221,7 @@ false false true + true false @@ -1612,20 +1619,6 @@ http://hadoop.apache.org/docs/ra.b.c/hadoop-project-dist/hadoop-common/dependency-analysis.html --> - - hadoop-0.23 - - - - org.apache.avro - avro - - - - 0.23.10 - - - hadoop-2.2 diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 3beafa158eb97..bf343d4b7e40b 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -88,6 +88,22 @@ object MimaExcludes { "org.apache.spark.mllib.linalg.Vector.toSparse"), ProblemFilters.exclude[MissingMethodProblem]( "org.apache.spark.mllib.linalg.Vector.numActives") + ) ++ Seq( + // This `protected[sql]` method was removed in 1.3.1 + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.sql.SQLContext.checkAnalysis"), + // This `private[sql]` class was removed in 1.4.0: + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.execution.AddExchange"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.execution.AddExchange$"), + // These test support classes were moved out of src/main and into src/test: + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetTestData"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetTestData$"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.TestGroupWriteSupport") ) case v if v.startsWith("1.3") => diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 09b4976d10c26..b4431c7ee05b6 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -34,11 +34,11 @@ object BuildCommons { val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, sql, networkCommon, networkShuffle, streaming, streamingFlumeSink, streamingFlume, streamingKafka, - streamingMqtt, streamingTwitter, streamingZeromq, launcher) = + streamingMqtt, streamingTwitter, streamingZeromq, launcher, unsafe) = Seq("bagel", "catalyst", "core", "graphx", "hive", "hive-thriftserver", "mllib", "repl", "sql", "network-common", "network-shuffle", "streaming", "streaming-flume-sink", "streaming-flume", "streaming-kafka", "streaming-mqtt", "streaming-twitter", - "streaming-zeromq", "launcher").map(ProjectRef(buildLocation, _)) + "streaming-zeromq", "launcher", "unsafe").map(ProjectRef(buildLocation, _)) val optionallyEnabledProjects@Seq(yarn, yarnStable, java8Tests, sparkGangliaLgpl, sparkKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl", @@ -156,13 +156,15 @@ object SparkBuild extends PomBuild { /* Enable tests settings for all projects except examples, assembly and tools */ (allProjects ++ optionallyEnabledProjects).foreach(enable(TestSettings.settings)) - // TODO: Add Sql to mima checks - // TODO: remove launcher from this list after 1.3. - allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl, - networkCommon, networkShuffle, networkYarn, launcher).contains(x)).foreach { + // TODO: remove launcher from this list after 1.4.0 + allProjects.filterNot(x => Seq(spark, hive, hiveThriftServer, catalyst, repl, + networkCommon, networkShuffle, networkYarn, launcher, unsafe).contains(x)).foreach { x => enable(MimaBuild.mimaSettings(sparkHome, x))(x) } + /* Unsafe settings */ + enable(Unsafe.settings)(unsafe) + /* Enable Assembly for all assembly projects */ assemblyProjects.foreach(enable(Assembly.settings)) @@ -216,6 +218,13 @@ object SparkBuild extends PomBuild { } +object Unsafe { + lazy val settings = Seq( + // This option is needed to suppress warnings from sun.misc.Unsafe usage + javacOptions in Compile += "-XDignore.symbol.file" + ) +} + object Flume { lazy val settings = sbtavro.SbtAvro.avroSettings } @@ -424,6 +433,7 @@ object Unidoc { .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/network"))) .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/shuffle"))) .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/executor"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/unsafe"))) .map(_.filterNot(_.getCanonicalPath.contains("python"))) .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/util/collection"))) .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/sql/catalyst"))) @@ -467,7 +477,7 @@ object Unidoc { "mllib.evaluation", "mllib.feature", "mllib.random", "mllib.stat.correlation", "mllib.stat.test", "mllib.tree.impl", "mllib.tree.loss", "ml", "ml.attribute", "ml.classification", "ml.evaluation", "ml.feature", "ml.param", - "ml.tuning" + "ml.recommendation", "ml.regression", "ml.tuning" ), "-group", "Spark SQL", packageList("sql.api.java", "sql.api.java.types", "sql.hive.api.java"), "-noqualifier", "java.lang" @@ -496,6 +506,7 @@ object TestSettings { javaOptions in Test += "-Dspark.ui.enabled=false", javaOptions in Test += "-Dspark.ui.showConsoleProgress=false", javaOptions in Test += "-Dspark.driver.allowMultipleContexts=true", + javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true", javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true", javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark") .map { case (k,v) => s"-D$k=$v" }.toSeq, diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst index 4da6d4a74a299..220ea595df903 100644 --- a/python/docs/pyspark.ml.rst +++ b/python/docs/pyspark.ml.rst @@ -24,3 +24,19 @@ pyspark.ml.classification module :members: :undoc-members: :inherited-members: + +pyspark.ml.tuning module +-------------------------------- + +.. automodule:: pyspark.ml.tuning + :members: + :undoc-members: + :inherited-members: + +pyspark.ml.evaluation module +-------------------------------- + +.. automodule:: pyspark.ml.evaluation + :members: + :undoc-members: + :inherited-members: diff --git a/python/pyspark/context.py b/python/pyspark/context.py index b006120eb266d..31992795a9e45 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -267,6 +267,13 @@ def __exit__(self, type, value, trace): """ self.stop() + def setLogLevel(self, logLevel): + """ + Control our logLevel. This overrides any user-defined log settings. + Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN + """ + self._jsc.setLogLevel(logLevel) + @classmethod def setSystemProperty(cls, key, value): """ diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py new file mode 100644 index 0000000000000..02020ebff94c2 --- /dev/null +++ b/python/pyspark/ml/evaluation.py @@ -0,0 +1,107 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from pyspark.ml.wrapper import JavaEvaluator +from pyspark.ml.param import Param, Params +from pyspark.ml.param.shared import HasLabelCol, HasRawPredictionCol +from pyspark.ml.util import keyword_only +from pyspark.mllib.common import inherit_doc + +__all__ = ['BinaryClassificationEvaluator'] + + +@inherit_doc +class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol): + """ + Evaluator for binary classification, which expects two input + columns: rawPrediction and label. + + >>> from pyspark.mllib.linalg import Vectors + >>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]), + ... [(0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)]) + >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"]) + ... + >>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw") + >>> evaluator.evaluate(dataset) + 0.70... + >>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"}) + 0.83... + """ + + _java_class = "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator" + + # a placeholder to make it appear in the generated doc + metricName = Param(Params._dummy(), "metricName", + "metric name in evaluation (areaUnderROC|areaUnderPR)") + + @keyword_only + def __init__(self, rawPredictionCol="rawPrediction", labelCol="label", + metricName="areaUnderROC"): + """ + __init__(self, rawPredictionCol="rawPrediction", labelCol="label", \ + metricName="areaUnderROC") + """ + super(BinaryClassificationEvaluator, self).__init__() + #: param for metric name in evaluation (areaUnderROC|areaUnderPR) + self.metricName = Param(self, "metricName", + "metric name in evaluation (areaUnderROC|areaUnderPR)") + self._setDefault(rawPredictionCol="rawPrediction", labelCol="label", + metricName="areaUnderROC") + kwargs = self.__init__._input_kwargs + self._set(**kwargs) + + def setMetricName(self, value): + """ + Sets the value of :py:attr:`metricName`. + """ + self.paramMap[self.metricName] = value + return self + + def getMetricName(self): + """ + Gets the value of metricName or its default value. + """ + return self.getOrDefault(self.metricName) + + @keyword_only + def setParams(self, rawPredictionCol="rawPrediction", labelCol="label", + metricName="areaUnderROC"): + """ + setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \ + metricName="areaUnderROC") + Sets params for binary classification evaluator. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + +if __name__ == "__main__": + import doctest + from pyspark.context import SparkContext + from pyspark.sql import SQLContext + globs = globals().copy() + # The small batch size here ensures that we see multiple batches, + # even in these small test examples: + sc = SparkContext("local[2]", "ml.evaluation tests") + sqlContext = SQLContext(sc) + globs['sc'] = sc + globs['sqlContext'] = sqlContext + (failure_count, test_count) = doctest.testmod( + globs=globs, optionflags=doctest.ELLIPSIS) + sc.stop() + if failure_count: + exit(-1) diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py index 6a3192465d66d..c71c823db2c81 100644 --- a/python/pyspark/ml/param/_shared_params_code_gen.py +++ b/python/pyspark/ml/param/_shared_params_code_gen.py @@ -93,6 +93,7 @@ def get$Name(self): ("featuresCol", "features column name", "'features'"), ("labelCol", "label column name", "'label'"), ("predictionCol", "prediction column name", "'prediction'"), + ("rawPredictionCol", "raw prediction column name", "'rawPrediction'"), ("inputCol", "input column name", None), ("outputCol", "output column name", None), ("numFeatures", "number of features", None)] diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index 13b6749998ad0..4f243844f8caa 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -165,6 +165,35 @@ def getPredictionCol(self): return self.getOrDefault(self.predictionCol) +class HasRawPredictionCol(Params): + """ + Mixin for param rawPredictionCol: raw prediction column name. + """ + + # a placeholder to make it appear in the generated doc + rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction column name") + + def __init__(self): + super(HasRawPredictionCol, self).__init__() + #: param for raw prediction column name + self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction column name") + if 'rawPrediction' is not None: + self._setDefault(rawPredictionCol='rawPrediction') + + def setRawPredictionCol(self, value): + """ + Sets the value of :py:attr:`rawPredictionCol`. + """ + self.paramMap[self.rawPredictionCol] = value + return self + + def getRawPredictionCol(self): + """ + Gets the value of rawPredictionCol or its default value. + """ + return self.getOrDefault(self.rawPredictionCol) + + class HasInputCol(Params): """ Mixin for param inputCol: input column name. diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py index 7c1ec3026da6f..c1b2077c985cf 100644 --- a/python/pyspark/ml/pipeline.py +++ b/python/pyspark/ml/pipeline.py @@ -22,7 +22,7 @@ from pyspark.mllib.common import inherit_doc -__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel'] +__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel', 'Evaluator', 'Model'] @inherit_doc @@ -70,6 +70,15 @@ def transform(self, dataset, params={}): raise NotImplementedError() +@inherit_doc +class Model(Transformer): + """ + Abstract class for models that are fitted by estimators. + """ + + __metaclass__ = ABCMeta + + @inherit_doc class Pipeline(Estimator): """ @@ -154,7 +163,7 @@ def fit(self, dataset, params={}): @inherit_doc -class PipelineModel(Transformer): +class PipelineModel(Model): """ Represents a compiled pipeline with transformers and fitted models. """ @@ -168,3 +177,24 @@ def transform(self, dataset, params={}): for t in self.transformers: dataset = t.transform(dataset, paramMap) return dataset + + +class Evaluator(object): + """ + Base class for evaluators that compute metrics from predictions. + """ + + __metaclass__ = ABCMeta + + @abstractmethod + def evaluate(self, dataset, params={}): + """ + Evaluates the output. + + :param dataset: a dataset that contains labels/observations and + predictions + :param params: an optional param map that overrides embedded + params + :return: metric + """ + raise NotImplementedError() diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py new file mode 100644 index 0000000000000..1e04c37fca53e --- /dev/null +++ b/python/pyspark/ml/tuning.py @@ -0,0 +1,263 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import itertools +import numpy as np + +from pyspark.ml.param import Params, Param +from pyspark.ml import Estimator, Model +from pyspark.ml.util import keyword_only +from pyspark.sql.functions import rand + +__all__ = ['ParamGridBuilder', 'CrossValidator', 'CrossValidatorModel'] + + +class ParamGridBuilder(object): + """ + Builder for a param grid used in grid search-based model selection. + + >>> from classification import LogisticRegression + >>> lr = LogisticRegression() + >>> output = ParamGridBuilder().baseOn({lr.labelCol: 'l'}) \ + .baseOn([lr.predictionCol, 'p']) \ + .addGrid(lr.regParam, [1.0, 2.0, 3.0]) \ + .addGrid(lr.maxIter, [1, 5]) \ + .addGrid(lr.featuresCol, ['f']) \ + .build() + >>> expected = [ \ +{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}] + >>> len(output) == len(expected) + True + >>> all([m in expected for m in output]) + True + """ + + def __init__(self): + self._param_grid = {} + + def addGrid(self, param, values): + """ + Sets the given parameters in this grid to fixed values. + """ + self._param_grid[param] = values + + return self + + def baseOn(self, *args): + """ + Sets the given parameters in this grid to fixed values. + Accepts either a parameter dictionary or a list of (parameter, value) pairs. + """ + if isinstance(args[0], dict): + self.baseOn(*args[0].items()) + else: + for (param, value) in args: + self.addGrid(param, [value]) + + return self + + def build(self): + """ + Builds and returns all combinations of parameters specified + by the param grid. + """ + keys = self._param_grid.keys() + grid_values = self._param_grid.values() + return [dict(zip(keys, prod)) for prod in itertools.product(*grid_values)] + + +class CrossValidator(Estimator): + """ + K-fold cross validation. + + >>> from pyspark.ml.classification import LogisticRegression + >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator + >>> from pyspark.mllib.linalg import Vectors + >>> dataset = sqlContext.createDataFrame( + ... [(Vectors.dense([0.0, 1.0]), 0.0), + ... (Vectors.dense([1.0, 2.0]), 1.0), + ... (Vectors.dense([0.55, 3.0]), 0.0), + ... (Vectors.dense([0.45, 4.0]), 1.0), + ... (Vectors.dense([0.51, 5.0]), 1.0)] * 10, + ... ["features", "label"]) + >>> lr = LogisticRegression() + >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1, 5]).build() + >>> evaluator = BinaryClassificationEvaluator() + >>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator) + >>> # SPARK-7432: The following test is flaky. + >>> # cvModel = cv.fit(dataset) + >>> # expected = lr.fit(dataset, {lr.maxIter: 5}).transform(dataset) + >>> # cvModel.transform(dataset).collect() == expected.collect() + """ + + # a placeholder to make it appear in the generated doc + estimator = Param(Params._dummy(), "estimator", "estimator to be cross-validated") + + # a placeholder to make it appear in the generated doc + estimatorParamMaps = Param(Params._dummy(), "estimatorParamMaps", "estimator param maps") + + # a placeholder to make it appear in the generated doc + evaluator = Param( + Params._dummy(), "evaluator", + "evaluator used to select hyper-parameters that maximize the cross-validated metric") + + # a placeholder to make it appear in the generated doc + numFolds = Param(Params._dummy(), "numFolds", "number of folds for cross validation") + + @keyword_only + def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3): + """ + __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3) + """ + super(CrossValidator, self).__init__() + #: param for estimator to be cross-validated + self.estimator = Param(self, "estimator", "estimator to be cross-validated") + #: param for estimator param maps + self.estimatorParamMaps = Param(self, "estimatorParamMaps", "estimator param maps") + #: param for the evaluator used to select hyper-parameters that + #: maximize the cross-validated metric + self.evaluator = Param( + self, "evaluator", + "evaluator used to select hyper-parameters that maximize the cross-validated metric") + #: param for number of folds for cross validation + self.numFolds = Param(self, "numFolds", "number of folds for cross validation") + self._setDefault(numFolds=3) + kwargs = self.__init__._input_kwargs + self._set(**kwargs) + + @keyword_only + def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3): + """ + setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3): + Sets params for cross validator. + """ + kwargs = self.setParams._input_kwargs + return self._set(**kwargs) + + def setEstimator(self, value): + """ + Sets the value of :py:attr:`estimator`. + """ + self.paramMap[self.estimator] = value + return self + + def getEstimator(self): + """ + Gets the value of estimator or its default value. + """ + return self.getOrDefault(self.estimator) + + def setEstimatorParamMaps(self, value): + """ + Sets the value of :py:attr:`estimatorParamMaps`. + """ + self.paramMap[self.estimatorParamMaps] = value + return self + + def getEstimatorParamMaps(self): + """ + Gets the value of estimatorParamMaps or its default value. + """ + return self.getOrDefault(self.estimatorParamMaps) + + def setEvaluator(self, value): + """ + Sets the value of :py:attr:`evaluator`. + """ + self.paramMap[self.evaluator] = value + return self + + def getEvaluator(self): + """ + Gets the value of evaluator or its default value. + """ + return self.getOrDefault(self.evaluator) + + def setNumFolds(self, value): + """ + Sets the value of :py:attr:`numFolds`. + """ + self.paramMap[self.numFolds] = value + return self + + def getNumFolds(self): + """ + Gets the value of numFolds or its default value. + """ + return self.getOrDefault(self.numFolds) + + def fit(self, dataset, params={}): + paramMap = self.extractParamMap(params) + est = paramMap[self.estimator] + epm = paramMap[self.estimatorParamMaps] + numModels = len(epm) + eva = paramMap[self.evaluator] + nFolds = paramMap[self.numFolds] + h = 1.0 / nFolds + randCol = self.uid + "_rand" + df = dataset.select("*", rand(0).alias(randCol)) + metrics = np.zeros(numModels) + for i in range(nFolds): + validateLB = i * h + validateUB = (i + 1) * h + condition = (df[randCol] >= validateLB) & (df[randCol] < validateUB) + validation = df.filter(condition) + train = df.filter(~condition) + for j in range(numModels): + model = est.fit(train, epm[j]) + # TODO: duplicate evaluator to take extra params from input + metric = eva.evaluate(model.transform(validation, epm[j])) + metrics[j] += metric + bestIndex = np.argmax(metrics) + bestModel = est.fit(dataset, epm[bestIndex]) + return CrossValidatorModel(bestModel) + + +class CrossValidatorModel(Model): + """ + Model from k-fold cross validation. + """ + + def __init__(self, bestModel): + #: best model from cross validation + self.bestModel = bestModel + + def transform(self, dataset, params={}): + return self.bestModel.transform(dataset, params) + + +if __name__ == "__main__": + import doctest + from pyspark.context import SparkContext + from pyspark.sql import SQLContext + globs = globals().copy() + # The small batch size here ensures that we see multiple batches, + # even in these small test examples: + sc = SparkContext("local[2]", "ml.tuning tests") + sqlContext = SQLContext(sc) + globs['sc'] = sc + globs['sqlContext'] = sqlContext + (failure_count, test_count) = doctest.testmod( + globs=globs, optionflags=doctest.ELLIPSIS) + sc.stop() + if failure_count: + exit(-1) diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py index 394f23c5e9b12..0634254bbd5cf 100644 --- a/python/pyspark/ml/wrapper.py +++ b/python/pyspark/ml/wrapper.py @@ -20,7 +20,7 @@ from pyspark import SparkContext from pyspark.sql import DataFrame from pyspark.ml.param import Params -from pyspark.ml.pipeline import Estimator, Transformer +from pyspark.ml.pipeline import Estimator, Transformer, Evaluator, Model from pyspark.mllib.common import inherit_doc @@ -133,7 +133,7 @@ def transform(self, dataset, params={}): @inherit_doc -class JavaModel(JavaTransformer): +class JavaModel(Model, JavaTransformer): """ Base class for :py:class:`Model`s that wrap Java/Scala implementations. @@ -147,3 +147,18 @@ def __init__(self, java_model): def _java_obj(self): return self._java_model + + +@inherit_doc +class JavaEvaluator(Evaluator, JavaWrapper): + """ + Base class for :py:class:`Evaluator`s that wrap Java/Scala + implementations. + """ + + __metaclass__ = ABCMeta + + def evaluate(self, dataset, params={}): + java_obj = self._java_obj() + self._transfer_params_to_java(params, java_obj) + return java_obj.evaluate(dataset._jdf, self._empty_java_param_map()) diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index abbb7cf60eece..04e67158514f5 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -40,11 +40,16 @@ class KMeansModel(Saveable, Loader): >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2) >>> model = KMeans.train( - ... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random") + ... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random", + ... seed=50, initializationSteps=5, epsilon=1e-4) >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0])) True >>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0])) True + >>> model.k + 2 + >>> model.computeCost(sc.parallelize(data)) + 2.0000000000000004 >>> model = KMeans.train(sc.parallelize(data), 2) >>> sparse_data = [ ... SparseVector(3, {1: 1.0}), @@ -52,7 +57,8 @@ class KMeansModel(Saveable, Loader): ... SparseVector(3, {2: 1.0}), ... SparseVector(3, {2: 1.1}) ... ] - >>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||") + >>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||", + ... seed=50, initializationSteps=5, epsilon=1e-4) >>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.])) True >>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1])) @@ -83,6 +89,11 @@ def clusterCenters(self): """Get the cluster centers, represented as a list of NumPy arrays.""" return self.centers + @property + def k(self): + """Total number of clusters.""" + return len(self.centers) + def predict(self, x): """Find the cluster to which x belongs in this model.""" best = 0 @@ -95,6 +106,15 @@ def predict(self, x): best_distance = distance return best + def computeCost(self, rdd): + """ + Return the K-means cost (sum of squared distances of points to + their nearest center) for this model on the given data. + """ + cost = callMLlibFunc("computeCostKmeansModel", rdd.map(_convert_to_vector), + [_convert_to_vector(c) for c in self.centers]) + return cost + def save(self, sc, path): java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers]) java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers) @@ -109,10 +129,11 @@ def load(cls, sc, path): class KMeans(object): @classmethod - def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||", seed=None): + def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||", + seed=None, initializationSteps=5, epsilon=1e-4): """Train a k-means clustering model.""" model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations, - runs, initializationMode, seed) + runs, initializationMode, seed, initializationSteps, epsilon) centers = callJavaFunc(rdd.context, model.clusterCenters) return KMeansModel([c.toArray() for c in centers]) diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index a57c0b3ae0d00..9f3b0baf9f19f 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -755,7 +755,7 @@ def __reduce__(self): return SparseMatrix, ( self.numRows, self.numCols, self.colPtrs.tostring(), self.rowIndices.tostring(), self.values.tostring(), - self.isTransposed) + int(self.isTransposed)) def __getitem__(self, indices): i, j = indices @@ -801,7 +801,7 @@ def toDense(self): # TODO: More efficient implementation: def __eq__(self, other): - return np.all(self.toArray == other.toArray) + return np.all(self.toArray() == other.toArray()) class Matrices(object): diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index 4b7d17d64e947..9c4647ddfdcfd 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -65,6 +65,13 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): >>> model.userFeatures().collect() [(1, array('d', [...])), (2, array('d', [...]))] + >>> model.recommendUsers(1, 2) + [Rating(user=2, product=1, rating=1.9...), Rating(user=1, product=1, rating=1.0...)] + >>> model.recommendProducts(1, 2) + [Rating(user=1, product=2, rating=1.9...), Rating(user=1, product=1, rating=1.0...)] + >>> model.rank + 4 + >>> first_user = model.userFeatures().take(1)[0] >>> latents = first_user[1] >>> len(latents) == 4 @@ -105,9 +112,15 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): ... pass """ def predict(self, user, product): + """ + Predicts rating for the given user and product. + """ return self._java_model.predict(int(user), int(product)) def predictAll(self, user_product): + """ + Returns a list of predicted ratings for input user and product pairs. + """ assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)" first = user_product.first() assert len(first) == 2, "user_product should be RDD of (user, product)" @@ -115,11 +128,37 @@ def predictAll(self, user_product): return self.call("predict", user_product) def userFeatures(self): + """ + Returns a paired RDD, where the first element is the user and the + second is an array of features corresponding to that user. + """ return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v)) def productFeatures(self): + """ + Returns a paired RDD, where the first element is the product and the + second is an array of features corresponding to that product. + """ return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v)) + def recommendUsers(self, product, num): + """ + Recommends the top "num" number of users for a given product and returns a list + of Rating objects sorted by the predicted rating in descending order. + """ + return list(self.call("recommendUsers", product, num)) + + def recommendProducts(self, user, num): + """ + Recommends the top "num" number of products for a given user and returns a list + of Rating objects sorted by the predicted rating in descending order. + """ + return list(self.call("recommendProducts", user, num)) + + @property + def rank(self): + return self.call("rank") + @classmethod def load(cls, sc, path): model = cls._load_java(sc, path) diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 4bc6351bdf02f..41bde2ce3e60b 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -18,14 +18,16 @@ import numpy as np from numpy import array +from pyspark import RDD from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py, inherit_doc -from pyspark.mllib.linalg import SparseVector, _convert_to_vector +from pyspark.mllib.linalg import SparseVector, Vectors, _convert_to_vector from pyspark.mllib.util import Saveable, Loader __all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'LinearRegressionWithSGD', 'RidgeRegressionModel', 'RidgeRegressionWithSGD', - 'LassoModel', 'LassoWithSGD'] + 'LassoModel', 'LassoWithSGD', 'IsotonicRegressionModel', + 'IsotonicRegression'] class LabeledPoint(object): @@ -396,6 +398,73 @@ def train(rdd, i): return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights) +class IsotonicRegressionModel(Saveable, Loader): + + """Regression model for isotonic regression. + + >>> data = [(1, 0, 1), (2, 1, 1), (3, 2, 1), (1, 3, 1), (6, 4, 1), (17, 5, 1), (16, 6, 1)] + >>> irm = IsotonicRegression.train(sc.parallelize(data)) + >>> irm.predict(3) + 2.0 + >>> irm.predict(5) + 16.5 + >>> irm.predict(sc.parallelize([3, 5])).collect() + [2.0, 16.5] + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> irm.save(sc, path) + >>> sameModel = IsotonicRegressionModel.load(sc, path) + >>> sameModel.predict(3) + 2.0 + >>> sameModel.predict(5) + 16.5 + >>> try: + ... os.removedirs(path) + ... except OSError: + ... pass + """ + + def __init__(self, boundaries, predictions, isotonic): + self.boundaries = boundaries + self.predictions = predictions + self.isotonic = isotonic + + def predict(self, x): + if isinstance(x, RDD): + return x.map(lambda v: self.predict(v)) + return np.interp(x, self.boundaries, self.predictions) + + def save(self, sc, path): + java_boundaries = _py2java(sc, self.boundaries.tolist()) + java_predictions = _py2java(sc, self.predictions.tolist()) + java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel( + java_boundaries, java_predictions, self.isotonic) + java_model.save(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel.load( + sc._jsc.sc(), path) + py_boundaries = _java2py(sc, java_model.boundaryVector()).toArray() + py_predictions = _java2py(sc, java_model.predictionVector()).toArray() + return IsotonicRegressionModel(py_boundaries, py_predictions, java_model.isotonic) + + +class IsotonicRegression(object): + """ + Run IsotonicRegression algorithm to obtain isotonic regression model. + + :param data: RDD of (label, feature, weight) tuples. + :param isotonic: Whether this is isotonic or antitonic. + """ + @classmethod + def train(cls, data, isotonic=True): + """Train a isotonic regression model on the given data.""" + boundaries, predictions = callMLlibFunc("trainIsotonicRegressionModel", + data.map(_convert_to_vector), bool(isotonic)) + return IsotonicRegressionModel(boundaries.toArray(), predictions.toArray(), isotonic) + + def _test(): import doctest from pyspark import SparkContext diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 1b008b93bc137..d05cfe2af04b2 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -92,6 +92,9 @@ def test_serialize(self): self._test_serialize(SparseVector(4, {1: 1, 3: 2})) self._test_serialize(SparseVector(3, {})) self._test_serialize(DenseMatrix(2, 3, range(6))) + sm1 = SparseMatrix( + 3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0]) + self._test_serialize(sm1) def test_dot(self): sv = SparseVector(4, {1: 1, 3: 2}) @@ -233,7 +236,8 @@ def test_kmeans(self): [1.1, 0], [1.2, 0], ] - clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||") + clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||", + initializationSteps=7, epsilon=1e-4) self.assertEquals(clusters.predict(data[0]), clusters.predict(data[1])) self.assertEquals(clusters.predict(data[2]), clusters.predict(data[3])) @@ -243,9 +247,11 @@ def test_kmeans_deterministic(self): Y = range(0, 100, 10) data = [[x, y] for x, y in zip(X, Y)] clusters1 = KMeans.train(self.sc.parallelize(data), - 3, initializationMode="k-means||", seed=42) + 3, initializationMode="k-means||", + seed=42, initializationSteps=7, epsilon=1e-4) clusters2 = KMeans.train(self.sc.parallelize(data), - 3, initializationMode="k-means||", seed=42) + 3, initializationMode="k-means||", + seed=42, initializationSteps=7, epsilon=1e-4) centers1 = clusters1.centers centers2 = clusters2.centers for c1, c2 in zip(centers1, centers2): diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py index 6d54b9e49ed10..b60b991dd4d8b 100644 --- a/python/pyspark/sql/__init__.py +++ b/python/pyspark/sql/__init__.py @@ -54,7 +54,9 @@ from pyspark.sql.types import Row from pyspark.sql.context import SQLContext, HiveContext from pyspark.sql.dataframe import DataFrame, GroupedData, Column, SchemaRDD, DataFrameNaFunctions +from pyspark.sql.dataframe import DataFrameStatFunctions __all__ = [ - 'SQLContext', 'HiveContext', 'DataFrame', 'GroupedData', 'Column', 'Row', 'DataFrameNaFunctions' + 'SQLContext', 'HiveContext', 'DataFrame', 'GroupedData', 'Column', 'Row', + 'DataFrameNaFunctions', 'DataFrameStatFunctions' ] diff --git a/python/pyspark/sql/_types.py b/python/pyspark/sql/_types.py index 95fb91ad43457..fd98e116d2cf1 100644 --- a/python/pyspark/sql/_types.py +++ b/python/pyspark/sql/_types.py @@ -652,7 +652,7 @@ def _python_to_sql_converter(dataType): if isinstance(dataType, StructType): names, types = zip(*[(f.name, f.dataType) for f in dataType.fields]) - converters = map(_python_to_sql_converter, types) + converters = [_python_to_sql_converter(t) for t in types] def converter(obj): if isinstance(obj, dict): diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 4759f5fe783ad..24f370543def4 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -34,7 +34,8 @@ from pyspark.sql.types import _create_cls, _parse_datatype_json_string -__all__ = ["DataFrame", "GroupedData", "Column", "SchemaRDD", "DataFrameNaFunctions"] +__all__ = ["DataFrame", "GroupedData", "Column", "SchemaRDD", "DataFrameNaFunctions", + "DataFrameStatFunctions"] class DataFrame(object): @@ -93,6 +94,12 @@ def na(self): """ return DataFrameNaFunctions(self) + @property + def stat(self): + """Returns a :class:`DataFrameStatFunctions` for statistic functions. + """ + return DataFrameStatFunctions(self) + @ignore_unicode_prefix def toJSON(self, use_unicode=True): """Converts a :class:`DataFrame` into a :class:`RDD` of string. @@ -237,7 +244,8 @@ def explain(self, extended=False): :param extended: boolean, default ``False``. If ``False``, prints only the physical plan. >>> df.explain() - PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at mapPartitions at SQLContext.scala:... + PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at applySchemaToPythonRDD at\ + NativeMethodAccessorImpl.java:... >>> df.explain(True) == Parsed Logical Plan == @@ -267,9 +275,12 @@ def show(self, n=20): >>> df DataFrame[age: int, name: string] >>> df.show() - age name - 2 Alice - 5 Bob + +---+-----+ + |age| name| + +---+-----+ + | 2|Alice| + | 5| Bob| + +---+-----+ """ print(self._jdf.showString(n)) @@ -425,7 +436,7 @@ def distinct(self): def sample(self, withReplacement, fraction, seed=None): """Returns a sampled subset of this :class:`DataFrame`. - >>> df.sample(False, 0.5, 97).count() + >>> df.sample(False, 0.5, 42).count() 1 """ assert fraction >= 0.0, "Negative fraction value: %s" % fraction @@ -433,6 +444,27 @@ def sample(self, withReplacement, fraction, seed=None): rdd = self._jdf.sample(withReplacement, fraction, long(seed)) return DataFrame(rdd, self.sql_ctx) + def randomSplit(self, weights, seed=None): + """Randomly splits this :class:`DataFrame` with the provided weights. + + :param weights: list of doubles as weights with which to split the DataFrame. Weights will + be normalized if they don't sum up to 1.0. + :param seed: The seed for sampling. + + >>> splits = df4.randomSplit([1.0, 2.0], 24) + >>> splits[0].count() + 1 + + >>> splits[1].count() + 3 + """ + for w in weights: + if w < 0.0: + raise ValueError("Weights must be positive. Found weight value: %s" % w) + seed = seed if seed is not None else random.randint(0, sys.maxsize) + rdd_array = self._jdf.randomSplit(_to_seq(self.sql_ctx._sc, weights), long(seed)) + return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array] + @property def dtypes(self): """Returns all column names and their data types as a list. @@ -562,12 +594,15 @@ def describe(self, *cols): given, this function computes statistics for all numerical columns. >>> df.describe().show() - summary age - count 2 - mean 3.5 - stddev 1.5 - min 2 - max 5 + +-------+---+ + |summary|age| + +-------+---+ + | count| 2| + | mean|3.5| + | stddev|1.5| + | min| 2| + | max| 5| + +-------+---+ """ jdf = self._jdf.describe(self._jseq(cols)) return DataFrame(jdf, self.sql_ctx) @@ -632,7 +667,8 @@ def __getattr__(self, name): [Row(age=2), Row(age=5)] """ if name not in self.columns: - raise AttributeError("No such column: %s" % name) + raise AttributeError( + "'%s' object has no attribute '%s'" % (self.__class__.__name__, name)) jc = self._jdf.apply(name) return Column(jc) @@ -771,12 +807,18 @@ def dropna(self, how='any', thresh=None, subset=None): :param subset: optional list of column names to consider. >>> df4.dropna().show() - age height name - 10 80 Alice + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 10| 80|Alice| + +---+------+-----+ >>> df4.na.drop().show() - age height name - 10 80 Alice + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 10| 80|Alice| + +---+------+-----+ """ if how is not None and how not in ['any', 'all']: raise ValueError("how ('" + how + "') should be 'any' or 'all'") @@ -807,25 +849,34 @@ def fillna(self, value, subset=None): then the non-string column is simply ignored. >>> df4.fillna(50).show() - age height name - 10 80 Alice - 5 50 Bob - 50 50 Tom - 50 50 null + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 10| 80|Alice| + | 5| 50| Bob| + | 50| 50| Tom| + | 50| 50| null| + +---+------+-----+ >>> df4.fillna({'age': 50, 'name': 'unknown'}).show() - age height name - 10 80 Alice - 5 null Bob - 50 null Tom - 50 null unknown + +---+------+-------+ + |age|height| name| + +---+------+-------+ + | 10| 80| Alice| + | 5| null| Bob| + | 50| null| Tom| + | 50| null|unknown| + +---+------+-------+ >>> df4.na.fill({'age': 50, 'name': 'unknown'}).show() - age height name - 10 80 Alice - 5 null Bob - 50 null Tom - 50 null unknown + +---+------+-------+ + |age|height| name| + +---+------+-------+ + | 10| 80| Alice| + | 5| null| Bob| + | 50| null| Tom| + | 50| null|unknown| + +---+------+-------+ """ if not isinstance(value, (float, int, long, basestring, dict)): raise ValueError("value should be a float, int, long, string, or dict") @@ -845,6 +896,82 @@ def fillna(self, value, subset=None): return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx) + def corr(self, col1, col2, method=None): + """ + Calculates the correlation of two columns of a DataFrame as a double value. Currently only + supports the Pearson Correlation Coefficient. + :func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases. + + :param col1: The name of the first column + :param col2: The name of the second column + :param method: The correlation method. Currently only supports "pearson" + """ + if not isinstance(col1, str): + raise ValueError("col1 should be a string.") + if not isinstance(col2, str): + raise ValueError("col2 should be a string.") + if not method: + method = "pearson" + if not method == "pearson": + raise ValueError("Currently only the calculation of the Pearson Correlation " + + "coefficient is supported.") + return self._jdf.stat().corr(col1, col2, method) + + def cov(self, col1, col2): + """ + Calculate the sample covariance for the given columns, specified by their names, as a + double value. :func:`DataFrame.cov` and :func:`DataFrameStatFunctions.cov` are aliases. + + :param col1: The name of the first column + :param col2: The name of the second column + """ + if not isinstance(col1, str): + raise ValueError("col1 should be a string.") + if not isinstance(col2, str): + raise ValueError("col2 should be a string.") + return self._jdf.stat().cov(col1, col2) + + def crosstab(self, col1, col2): + """ + Computes a pair-wise frequency table of the given columns. Also known as a contingency + table. The number of distinct values for each column should be less than 1e4. At most 1e6 + non-zero pair frequencies will be returned. + The first column of each row will be the distinct values of `col1` and the column names + will be the distinct values of `col2`. The name of the first column will be `$col1_$col2`. + Pairs that have no occurrences will have `null` as their counts. + :func:`DataFrame.crosstab` and :func:`DataFrameStatFunctions.crosstab` are aliases. + + :param col1: The name of the first column. Distinct items will make the first item of + each row. + :param col2: The name of the second column. Distinct items will make the column names + of the DataFrame. + """ + if not isinstance(col1, str): + raise ValueError("col1 should be a string.") + if not isinstance(col2, str): + raise ValueError("col2 should be a string.") + return DataFrame(self._jdf.stat().crosstab(col1, col2), self.sql_ctx) + + def freqItems(self, cols, support=None): + """ + Finding frequent items for columns, possibly with false positives. Using the + frequent element count algorithm described in + "http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou". + :func:`DataFrame.freqItems` and :func:`DataFrameStatFunctions.freqItems` are aliases. + + :param cols: Names of the columns to calculate frequent items for as a list or tuple of + strings. + :param support: The frequency with which to consider an item 'frequent'. Default is 1%. + The support must be greater than 1e-4. + """ + if isinstance(cols, tuple): + cols = list(cols) + if not isinstance(cols, list): + raise ValueError("cols must be a list or tuple of column names as strings.") + if not support: + support = 0.01 + return DataFrame(self._jdf.stat().freqItems(_to_seq(self._sc, cols), support), self.sql_ctx) + @ignore_unicode_prefix def withColumn(self, colName, col): """Returns a new :class:`DataFrame` by adding a column. @@ -1156,11 +1283,17 @@ def getItem(self, key): >>> df = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["l", "d"]) >>> df.select(df.l.getItem(0), df.d.getItem("key")).show() - l[0] d[key] - 1 value + +----+------+ + |l[0]|d[key]| + +----+------+ + | 1| value| + +----+------+ >>> df.select(df.l[0], df.d["key"]).show() - l[0] d[key] - 1 value + +----+------+ + |l[0]|d[key]| + +----+------+ + | 1| value| + +----+------+ """ return self[key] @@ -1170,11 +1303,17 @@ def getField(self, name): >>> from pyspark.sql import Row >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF() >>> df.select(df.r.getField("b")).show() - r.b - b + +---+ + |r.b| + +---+ + | b| + +---+ >>> df.select(df.r.a).show() - r.a - 1 + +---+ + |r.a| + +---+ + | 1| + +---+ """ return Column(self._jc.getField(name)) @@ -1266,6 +1405,13 @@ def cast(self, dataType): raise TypeError("unexpected type: %s" % type(dataType)) return Column(jc) + @ignore_unicode_prefix + def between(self, lowerBound, upperBound): + """ A boolean expression that is evaluated to true if the value of this + expression is between the given columns. + """ + return (self >= lowerBound) & (self <= upperBound) + def __repr__(self): return 'Column<%s>' % self._jc.toString().encode('utf8') @@ -1288,6 +1434,34 @@ def fill(self, value, subset=None): fill.__doc__ = DataFrame.fillna.__doc__ +class DataFrameStatFunctions(object): + """Functionality for statistic functions with :class:`DataFrame`. + """ + + def __init__(self, df): + self.df = df + + def corr(self, col1, col2, method=None): + return self.df.corr(col1, col2, method) + + corr.__doc__ = DataFrame.corr.__doc__ + + def cov(self, col1, col2): + return self.df.cov(col1, col2) + + cov.__doc__ = DataFrame.cov.__doc__ + + def crosstab(self, col1, col2): + return self.df.crosstab(col1, col2) + + crosstab.__doc__ = DataFrame.crosstab.__doc__ + + def freqItems(self, cols, support=None): + return self.df.freqItems(cols, support) + + freqItems.__doc__ = DataFrame.freqItems.__doc__ + + def _test(): import doctest from pyspark.context import SparkContext diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 7b86655d9c82f..692af868dd534 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -24,13 +24,20 @@ from itertools import imap as map from pyspark import SparkContext -from pyspark.rdd import _prepare_for_python_RDD +from pyspark.rdd import _prepare_for_python_RDD, ignore_unicode_prefix from pyspark.serializers import PickleSerializer, AutoBatchedSerializer from pyspark.sql.types import StringType from pyspark.sql.dataframe import Column, _to_java_column, _to_seq -__all__ = ['countDistinct', 'approxCountDistinct', 'udf'] +__all__ = [ + 'approxCountDistinct', + 'countDistinct', + 'monotonicallyIncreasingId', + 'rand', + 'randn', + 'sparkPartitionId', + 'udf'] def _create_function(name, doc=""): @@ -44,6 +51,19 @@ def _(col): return _ +def _create_binary_mathfunction(name, doc=""): + """ Create a binary mathfunction by name""" + def _(col1, col2): + sc = SparkContext._active_spark_context + # users might write ints for simplicity. This would throw an error on the JVM side. + jc = getattr(sc._jvm.functions, name)(col1._jc if isinstance(col1, Column) else float(col1), + col2._jc if isinstance(col2, Column) else float(col2)) + return Column(jc) + _.__name__ = name + _.__doc__ = doc + return _ + + _functions = { 'lit': 'Creates a :class:`Column` of literal value.', 'col': 'Returns a :class:`Column` based on the given column name.', @@ -54,7 +74,35 @@ def _(col): 'upper': 'Converts a string expression to upper case.', 'lower': 'Converts a string expression to upper case.', 'sqrt': 'Computes the square root of the specified float value.', - 'abs': 'Computes the absolutle value.', + 'abs': 'Computes the absolute value.', + + # unary math functions + 'acos': 'Computes the cosine inverse of the given value; the returned angle is in the range' + + '0.0 through pi.', + 'asin': 'Computes the sine inverse of the given value; the returned angle is in the range' + + '-pi/2 through pi/2.', + 'atan': 'Computes the tangent inverse of the given value.', + 'cbrt': 'Computes the cube-root of the given value.', + 'ceil': 'Computes the ceiling of the given value.', + 'cos': 'Computes the cosine of the given value.', + 'cosh': 'Computes the hyperbolic cosine of the given value.', + 'exp': 'Computes the exponential of the given value.', + 'expm1': 'Computes the exponential of the given value minus one.', + 'floor': 'Computes the floor of the given value.', + 'log': 'Computes the natural logarithm of the given value.', + 'log10': 'Computes the logarithm of the given value in Base 10.', + 'log1p': 'Computes the natural logarithm of the given value plus one.', + 'rint': 'Returns the double value that is closest in value to the argument and' + + ' is equal to a mathematical integer.', + 'signum': 'Computes the signum of the given value.', + 'sin': 'Computes the sine of the given value.', + 'sinh': 'Computes the hyperbolic sine of the given value.', + 'tan': 'Computes the tangent of the given value.', + 'tanh': 'Computes the hyperbolic tangent of the given value.', + 'toDegrees': 'Converts an angle measured in radians to an approximately equivalent angle ' + + 'measured in degrees.', + 'toRadians': 'Converts an angle measured in degrees to an approximately equivalent angle ' + + 'measured in radians.', 'max': 'Aggregate function: returns the maximum value of the expression in a group.', 'min': 'Aggregate function: returns the minimum value of the expression in a group.', @@ -67,14 +115,42 @@ def _(col): 'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.', } +# math functions that take two arguments as input +_binary_mathfunctions = { + 'atan2': 'Returns the angle theta from the conversion of rectangular coordinates (x, y) to' + + 'polar coordinates (r, theta).', + 'hypot': 'Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.', + 'pow': 'Returns the value of the first argument raised to the power of the second argument.' +} for _name, _doc in _functions.items(): globals()[_name] = _create_function(_name, _doc) +for _name, _doc in _binary_mathfunctions.items(): + globals()[_name] = _create_binary_mathfunction(_name, _doc) del _name, _doc __all__ += _functions.keys() +__all__ += _binary_mathfunctions.keys() __all__.sort() +def array(*cols): + """Creates a new array column. + + :param cols: list of column names (string) or list of :class:`Column` expressions that have + the same data type. + + >>> df.select(array('age', 'age').alias("arr")).collect() + [Row(arr=[2, 2]), Row(arr=[5, 5])] + >>> df.select(array([df.age, df.age]).alias("arr")).collect() + [Row(arr=[2, 2]), Row(arr=[5, 5])] + """ + sc = SparkContext._active_spark_context + if len(cols) == 1 and isinstance(cols[0], (list, set)): + cols = cols[0] + jc = sc._jvm.functions.array(_to_seq(sc, cols, _to_java_column)) + return Column(jc) + + def approxCountDistinct(col, rsd=None): """Returns a new :class:`Column` for approximate distinct count of ``col``. @@ -123,6 +199,28 @@ def monotonicallyIncreasingId(): return Column(sc._jvm.functions.monotonicallyIncreasingId()) +def rand(seed=None): + """Generates a random column with i.i.d. samples from U[0.0, 1.0]. + """ + sc = SparkContext._active_spark_context + if seed: + jc = sc._jvm.functions.rand(seed) + else: + jc = sc._jvm.functions.rand() + return Column(jc) + + +def randn(seed=None): + """Generates a column with i.i.d. samples from the standard normal distribution. + """ + sc = SparkContext._active_spark_context + if seed: + jc = sc._jvm.functions.randn(seed) + else: + jc = sc._jvm.functions.randn() + return Column(jc) + + def sparkPartitionId(): """A column for partition ID of the Spark task. @@ -135,6 +233,25 @@ def sparkPartitionId(): return Column(sc._jvm.functions.sparkPartitionId()) +@ignore_unicode_prefix +def struct(*cols): + """Creates a new struct column. + + :param cols: list of column names (string) or list of :class:`Column` expressions + that are named or aliased. + + >>> df.select(struct('age', 'name').alias("struct")).collect() + [Row(struct=Row(age=2, name=u'Alice')), Row(struct=Row(age=5, name=u'Bob'))] + >>> df.select(struct([df.age, df.name]).alias("struct")).collect() + [Row(struct=Row(age=2, name=u'Alice')), Row(struct=Row(age=5, name=u'Bob'))] + """ + sc = SparkContext._active_spark_context + if len(cols) == 1 and isinstance(cols[0], (list, set)): + cols = cols[0] + jc = sc._jvm.functions.struct(_to_seq(sc, cols, _to_java_column)) + return Column(jc) + + class UserDefinedFunction(object): """ User defined function in Python diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index fe43c374f1cb1..b232f3a965526 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -375,6 +375,13 @@ def test_column_select(self): self.assertEqual(self.testData, df.select(df.key, df.value).collect()) self.assertEqual([Row(value='1')], df.where(df.key == 1).select(df.value).collect()) + def test_freqItems(self): + vals = [Row(a=1, b=-2.0) if i % 2 == 0 else Row(a=i, b=i * 1.0) for i in range(100)] + df = self.sc.parallelize(vals).toDF() + items = df.stat.freqItems(("a", "b"), 0.4).collect()[0] + self.assertTrue(1 in items[0]) + self.assertTrue(-2.0 in items[1]) + def test_aggregator(self): df = self.df g = df.groupBy() @@ -387,6 +394,73 @@ def test_aggregator(self): self.assertTrue(95 < g.agg(functions.approxCountDistinct(df.key)).first()[0]) self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0]) + def test_corr(self): + import math + df = self.sc.parallelize([Row(a=i, b=math.sqrt(i)) for i in range(10)]).toDF() + corr = df.stat.corr("a", "b") + self.assertTrue(abs(corr - 0.95734012) < 1e-6) + + def test_cov(self): + df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF() + cov = df.stat.cov("a", "b") + self.assertTrue(abs(cov - 55.0 / 3) < 1e-6) + + def test_crosstab(self): + df = self.sc.parallelize([Row(a=i % 3, b=i % 2) for i in range(1, 7)]).toDF() + ct = df.stat.crosstab("a", "b").collect() + ct = sorted(ct, key=lambda x: x[0]) + for i, row in enumerate(ct): + self.assertEqual(row[0], str(i)) + self.assertTrue(row[1], 1) + self.assertTrue(row[2], 1) + + def test_math_functions(self): + df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF() + from pyspark.sql import functions + import math + + def get_values(l): + return [j[0] for j in l] + + def assert_close(a, b): + c = get_values(b) + diff = [abs(v - c[k]) < 1e-6 for k, v in enumerate(a)] + return sum(diff) == len(a) + assert_close([math.cos(i) for i in range(10)], + df.select(functions.cos(df.a)).collect()) + assert_close([math.cos(i) for i in range(10)], + df.select(functions.cos("a")).collect()) + assert_close([math.sin(i) for i in range(10)], + df.select(functions.sin(df.a)).collect()) + assert_close([math.sin(i) for i in range(10)], + df.select(functions.sin(df['a'])).collect()) + assert_close([math.pow(i, 2 * i) for i in range(10)], + df.select(functions.pow(df.a, df.b)).collect()) + assert_close([math.pow(i, 2) for i in range(10)], + df.select(functions.pow(df.a, 2)).collect()) + assert_close([math.pow(i, 2) for i in range(10)], + df.select(functions.pow(df.a, 2.0)).collect()) + assert_close([math.hypot(i, 2 * i) for i in range(10)], + df.select(functions.hypot(df.a, df.b)).collect()) + + def test_rand_functions(self): + df = self.df + from pyspark.sql import functions + rnd = df.select('key', functions.rand()).collect() + for row in rnd: + assert row[1] >= 0.0 and row[1] <= 1.0, "got: %s" % row[1] + rndn = df.select('key', functions.randn(5)).collect() + for row in rndn: + assert row[1] >= -4.0 and row[1] <= 4.0, "got: %s" % row[1] + + def test_between_function(self): + df = self.sc.parallelize([ + Row(a=1, b=2, c=3), + Row(a=2, b=1, c=3), + Row(a=4, b=1, c=4)]).toDF() + self.assertEqual([Row(a=2, b=1, c=3), Row(a=4, b=1, c=4)], + df.filter(df.a.between(df.b, df.c)).collect()) + def test_save_and_load(self): df = self.df tmpPath = tempfile.mkdtemp() diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 7c06c203455d9..33ea8c9293d74 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -606,7 +606,6 @@ def _validateRddResult(self, sendData, rdd): result = {} for i in rdd.map(lambda x: x[1]).collect(): result[i] = result.get(i, 0) + 1 - self.assertEqual(sendData, result) def test_kafka_stream(self): @@ -616,6 +615,7 @@ def test_kafka_stream(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(), "test-streaming-consumer", {topic: 1}, @@ -631,6 +631,7 @@ def test_kafka_direct_stream(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams) self._validateStreamResult(sendData, stream) @@ -645,6 +646,7 @@ def test_kafka_direct_stream_from_offset(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams, fromOffsets) self._validateStreamResult(sendData, stream) @@ -659,7 +661,7 @@ def test_kafka_rdd(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) - + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges) self._validateRddResult(sendData, rdd) @@ -675,7 +677,7 @@ def test_kafka_rdd_with_leaders(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) - + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges, leaders) self._validateRddResult(sendData, rdd) diff --git a/python/run-tests b/python/run-tests index 88b63b84fdc27..f9ca26467f17e 100755 --- a/python/run-tests +++ b/python/run-tests @@ -98,7 +98,9 @@ function run_ml_tests() { echo "Run ml tests ..." run_test "pyspark/ml/feature.py" run_test "pyspark/ml/classification.py" + run_test "pyspark/ml/tuning.py" run_test "pyspark/ml/tests.py" + run_test "pyspark/ml/evaluation.py" } function run_streaming_tests() { diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml index 3dea2ee76542f..5c322d032d474 100644 --- a/sql/catalyst/pom.xml +++ b/sql/catalyst/pom.xml @@ -50,6 +50,11 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-unsafe_${scala.binary.version} + ${project.version} + org.scalacheck scalacheck_${scala.binary.version} diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java new file mode 100644 index 0000000000000..299ff3728a6d9 --- /dev/null +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java @@ -0,0 +1,259 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions; + +import java.util.Arrays; +import java.util.Iterator; + +import org.apache.spark.sql.Row; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import org.apache.spark.unsafe.PlatformDependent; +import org.apache.spark.unsafe.map.BytesToBytesMap; +import org.apache.spark.unsafe.memory.MemoryLocation; +import org.apache.spark.unsafe.memory.TaskMemoryManager; + +/** + * Unsafe-based HashMap for performing aggregations where the aggregated values are fixed-width. + * + * This map supports a maximum of 2 billion keys. + */ +public final class UnsafeFixedWidthAggregationMap { + + /** + * An empty aggregation buffer, encoded in UnsafeRow format. When inserting a new key into the + * map, we copy this buffer and use it as the value. + */ + private final long[] emptyAggregationBuffer; + + private final StructType aggregationBufferSchema; + + private final StructType groupingKeySchema; + + /** + * Encodes grouping keys as UnsafeRows. + */ + private final UnsafeRowConverter groupingKeyToUnsafeRowConverter; + + /** + * A hashmap which maps from opaque bytearray keys to bytearray values. + */ + private final BytesToBytesMap map; + + /** + * Re-used pointer to the current aggregation buffer + */ + private final UnsafeRow currentAggregationBuffer = new UnsafeRow(); + + /** + * Scratch space that is used when encoding grouping keys into UnsafeRow format. + * + * By default, this is a 1MB array, but it will grow as necessary in case larger keys are + * encountered. + */ + private long[] groupingKeyConversionScratchSpace = new long[1024 / 8]; + + private final boolean enablePerfMetrics; + + /** + * @return true if UnsafeFixedWidthAggregationMap supports grouping keys with the given schema, + * false otherwise. + */ + public static boolean supportsGroupKeySchema(StructType schema) { + for (StructField field: schema.fields()) { + if (!UnsafeRow.readableFieldTypes.contains(field.dataType())) { + return false; + } + } + return true; + } + + /** + * @return true if UnsafeFixedWidthAggregationMap supports aggregation buffers with the given + * schema, false otherwise. + */ + public static boolean supportsAggregationBufferSchema(StructType schema) { + for (StructField field: schema.fields()) { + if (!UnsafeRow.settableFieldTypes.contains(field.dataType())) { + return false; + } + } + return true; + } + + /** + * Create a new UnsafeFixedWidthAggregationMap. + * + * @param emptyAggregationBuffer the default value for new keys (a "zero" of the agg. function) + * @param aggregationBufferSchema the schema of the aggregation buffer, used for row conversion. + * @param groupingKeySchema the schema of the grouping key, used for row conversion. + * @param memoryManager the memory manager used to allocate our Unsafe memory structures. + * @param initialCapacity the initial capacity of the map (a sizing hint to avoid re-hashing). + * @param enablePerfMetrics if true, performance metrics will be recorded (has minor perf impact) + */ + public UnsafeFixedWidthAggregationMap( + Row emptyAggregationBuffer, + StructType aggregationBufferSchema, + StructType groupingKeySchema, + TaskMemoryManager memoryManager, + int initialCapacity, + boolean enablePerfMetrics) { + this.emptyAggregationBuffer = + convertToUnsafeRow(emptyAggregationBuffer, aggregationBufferSchema); + this.aggregationBufferSchema = aggregationBufferSchema; + this.groupingKeyToUnsafeRowConverter = new UnsafeRowConverter(groupingKeySchema); + this.groupingKeySchema = groupingKeySchema; + this.map = new BytesToBytesMap(memoryManager, initialCapacity, enablePerfMetrics); + this.enablePerfMetrics = enablePerfMetrics; + } + + /** + * Convert a Java object row into an UnsafeRow, allocating it into a new long array. + */ + private static long[] convertToUnsafeRow(Row javaRow, StructType schema) { + final UnsafeRowConverter converter = new UnsafeRowConverter(schema); + final long[] unsafeRow = new long[converter.getSizeRequirement(javaRow)]; + final long writtenLength = + converter.writeRow(javaRow, unsafeRow, PlatformDependent.LONG_ARRAY_OFFSET); + assert (writtenLength == unsafeRow.length): "Size requirement calculation was wrong!"; + return unsafeRow; + } + + /** + * Return the aggregation buffer for the current group. For efficiency, all calls to this method + * return the same object. + */ + public UnsafeRow getAggregationBuffer(Row groupingKey) { + final int groupingKeySize = groupingKeyToUnsafeRowConverter.getSizeRequirement(groupingKey); + // Make sure that the buffer is large enough to hold the key. If it's not, grow it: + if (groupingKeySize > groupingKeyConversionScratchSpace.length) { + // This new array will be initially zero, so there's no need to zero it out here + groupingKeyConversionScratchSpace = new long[groupingKeySize]; + } else { + // Zero out the buffer that's used to hold the current row. This is necessary in order + // to ensure that rows hash properly, since garbage data from the previous row could + // otherwise end up as padding in this row. As a performance optimization, we only zero out + // the portion of the buffer that we'll actually write to. + Arrays.fill(groupingKeyConversionScratchSpace, 0, groupingKeySize, 0); + } + final long actualGroupingKeySize = groupingKeyToUnsafeRowConverter.writeRow( + groupingKey, + groupingKeyConversionScratchSpace, + PlatformDependent.LONG_ARRAY_OFFSET); + assert (groupingKeySize == actualGroupingKeySize) : "Size requirement calculation was wrong!"; + + // Probe our map using the serialized key + final BytesToBytesMap.Location loc = map.lookup( + groupingKeyConversionScratchSpace, + PlatformDependent.LONG_ARRAY_OFFSET, + groupingKeySize); + if (!loc.isDefined()) { + // This is the first time that we've seen this grouping key, so we'll insert a copy of the + // empty aggregation buffer into the map: + loc.putNewKey( + groupingKeyConversionScratchSpace, + PlatformDependent.LONG_ARRAY_OFFSET, + groupingKeySize, + emptyAggregationBuffer, + PlatformDependent.LONG_ARRAY_OFFSET, + emptyAggregationBuffer.length + ); + } + + // Reset the pointer to point to the value that we just stored or looked up: + final MemoryLocation address = loc.getValueAddress(); + currentAggregationBuffer.pointTo( + address.getBaseObject(), + address.getBaseOffset(), + aggregationBufferSchema.length(), + aggregationBufferSchema + ); + return currentAggregationBuffer; + } + + /** + * Mutable pair object returned by {@link UnsafeFixedWidthAggregationMap#iterator()}. + */ + public static class MapEntry { + private MapEntry() { }; + public final UnsafeRow key = new UnsafeRow(); + public final UnsafeRow value = new UnsafeRow(); + } + + /** + * Returns an iterator over the keys and values in this map. + * + * For efficiency, each call returns the same object. + */ + public Iterator iterator() { + return new Iterator() { + + private final MapEntry entry = new MapEntry(); + private final Iterator mapLocationIterator = map.iterator(); + + @Override + public boolean hasNext() { + return mapLocationIterator.hasNext(); + } + + @Override + public MapEntry next() { + final BytesToBytesMap.Location loc = mapLocationIterator.next(); + final MemoryLocation keyAddress = loc.getKeyAddress(); + final MemoryLocation valueAddress = loc.getValueAddress(); + entry.key.pointTo( + keyAddress.getBaseObject(), + keyAddress.getBaseOffset(), + groupingKeySchema.length(), + groupingKeySchema + ); + entry.value.pointTo( + valueAddress.getBaseObject(), + valueAddress.getBaseOffset(), + aggregationBufferSchema.length(), + aggregationBufferSchema + ); + return entry; + } + + @Override + public void remove() { + throw new UnsupportedOperationException(); + } + }; + } + + /** + * Free the unsafe memory associated with this map. + */ + public void free() { + map.free(); + } + + @SuppressWarnings("UseOfSystemOutOrSystemErr") + public void printPerfMetrics() { + if (!enablePerfMetrics) { + throw new IllegalStateException("Perf metrics not enabled"); + } + System.out.println("Average probes per lookup: " + map.getAverageProbesPerLookup()); + System.out.println("Number of hash collisions: " + map.getNumHashCollisions()); + System.out.println("Time spent resizing (ns): " + map.getTimeSpentResizingNs()); + System.out.println("Total memory consumption (bytes): " + map.getTotalMemoryConsumption()); + } + +} diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java new file mode 100644 index 0000000000000..bb546b3086b33 --- /dev/null +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java @@ -0,0 +1,435 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions; + +import scala.collection.Map; +import scala.collection.Seq; +import scala.collection.mutable.ArraySeq; + +import javax.annotation.Nullable; +import java.math.BigDecimal; +import java.sql.Date; +import java.util.*; + +import org.apache.spark.sql.Row; +import org.apache.spark.sql.types.DataType; +import static org.apache.spark.sql.types.DataTypes.*; +import org.apache.spark.sql.types.StructType; +import org.apache.spark.sql.types.UTF8String; +import org.apache.spark.unsafe.PlatformDependent; +import org.apache.spark.unsafe.bitset.BitSetMethods; + +/** + * An Unsafe implementation of Row which is backed by raw memory instead of Java objects. + * + * Each tuple has three parts: [null bit set] [values] [variable length portion] + * + * The bit set is used for null tracking and is aligned to 8-byte word boundaries. It stores + * one bit per field. + * + * In the `values` region, we store one 8-byte word per field. For fields that hold fixed-length + * primitive types, such as long, double, or int, we store the value directly in the word. For + * fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the + * base address of the row) that points to the beginning of the variable-length field. + * + * Instances of `UnsafeRow` act as pointers to row data stored in this format. + */ +public final class UnsafeRow implements MutableRow { + + private Object baseObject; + private long baseOffset; + + Object getBaseObject() { return baseObject; } + long getBaseOffset() { return baseOffset; } + + /** The number of fields in this row, used for calculating the bitset width (and in assertions) */ + private int numFields; + + /** The width of the null tracking bit set, in bytes */ + private int bitSetWidthInBytes; + /** + * This optional schema is required if you want to call generic get() and set() methods on + * this UnsafeRow, but is optional if callers will only use type-specific getTYPE() and setTYPE() + * methods. This should be removed after the planned InternalRow / Row split; right now, it's only + * needed by the generic get() method, which is only called internally by code that accesses + * UTF8String-typed columns. + */ + @Nullable + private StructType schema; + + private long getFieldOffset(int ordinal) { + return baseOffset + bitSetWidthInBytes + ordinal * 8L; + } + + public static int calculateBitSetWidthInBytes(int numFields) { + return ((numFields / 64) + (numFields % 64 == 0 ? 0 : 1)) * 8; + } + + /** + * Field types that can be updated in place in UnsafeRows (e.g. we support set() for these types) + */ + public static final Set settableFieldTypes; + + /** + * Fields types can be read(but not set (e.g. set() will throw UnsupportedOperationException). + */ + public static final Set readableFieldTypes; + + static { + settableFieldTypes = Collections.unmodifiableSet( + new HashSet( + Arrays.asList(new DataType[] { + NullType, + BooleanType, + ByteType, + ShortType, + IntegerType, + LongType, + FloatType, + DoubleType + }))); + + // We support get() on a superset of the types for which we support set(): + final Set _readableFieldTypes = new HashSet( + Arrays.asList(new DataType[]{ + StringType + })); + _readableFieldTypes.addAll(settableFieldTypes); + readableFieldTypes = Collections.unmodifiableSet(_readableFieldTypes); + } + + /** + * Construct a new UnsafeRow. The resulting row won't be usable until `pointTo()` has been called, + * since the value returned by this constructor is equivalent to a null pointer. + */ + public UnsafeRow() { } + + /** + * Update this UnsafeRow to point to different backing data. + * + * @param baseObject the base object + * @param baseOffset the offset within the base object + * @param numFields the number of fields in this row + * @param schema an optional schema; this is necessary if you want to call generic get() or set() + * methods on this row, but is optional if the caller will only use type-specific + * getTYPE() and setTYPE() methods. + */ + public void pointTo( + Object baseObject, + long baseOffset, + int numFields, + @Nullable StructType schema) { + assert numFields >= 0 : "numFields should >= 0"; + assert schema == null || schema.fields().length == numFields; + this.bitSetWidthInBytes = calculateBitSetWidthInBytes(numFields); + this.baseObject = baseObject; + this.baseOffset = baseOffset; + this.numFields = numFields; + this.schema = schema; + } + + private void assertIndexIsValid(int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + assert index < numFields : "index (" + index + ") should < " + numFields; + } + + @Override + public void setNullAt(int i) { + assertIndexIsValid(i); + BitSetMethods.set(baseObject, baseOffset, i); + // To preserve row equality, zero out the value when setting the column to null. + // Since this row does does not currently support updates to variable-length values, we don't + // have to worry about zeroing out that data. + PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(i), 0); + } + + private void setNotNullAt(int i) { + assertIndexIsValid(i); + BitSetMethods.unset(baseObject, baseOffset, i); + } + + @Override + public void update(int ordinal, Object value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setInt(int ordinal, int value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putInt(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setLong(int ordinal, long value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setDouble(int ordinal, double value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putDouble(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setBoolean(int ordinal, boolean value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putBoolean(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setShort(int ordinal, short value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putShort(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setByte(int ordinal, byte value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putByte(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setFloat(int ordinal, float value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setString(int ordinal, String value) { + throw new UnsupportedOperationException(); + } + + @Override + public int size() { + return numFields; + } + + @Override + public int length() { + return size(); + } + + @Override + public StructType schema() { + return schema; + } + + @Override + public Object apply(int i) { + return get(i); + } + + @Override + public Object get(int i) { + assertIndexIsValid(i); + assert (schema != null) : "Schema must be defined when calling generic get() method"; + final DataType dataType = schema.fields()[i].dataType(); + // UnsafeRow is only designed to be invoked by internal code, which only invokes this generic + // get() method when trying to access UTF8String-typed columns. If we refactor the codebase to + // separate the internal and external row interfaces, then internal code can fetch strings via + // a new getUTF8String() method and we'll be able to remove this method. + if (isNullAt(i)) { + return null; + } else if (dataType == StringType) { + return getUTF8String(i); + } else { + throw new UnsupportedOperationException(); + } + } + + @Override + public boolean isNullAt(int i) { + assertIndexIsValid(i); + return BitSetMethods.isSet(baseObject, baseOffset, i); + } + + @Override + public boolean getBoolean(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getBoolean(baseObject, getFieldOffset(i)); + } + + @Override + public byte getByte(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getByte(baseObject, getFieldOffset(i)); + } + + @Override + public short getShort(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getShort(baseObject, getFieldOffset(i)); + } + + @Override + public int getInt(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getInt(baseObject, getFieldOffset(i)); + } + + @Override + public long getLong(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getLong(baseObject, getFieldOffset(i)); + } + + @Override + public float getFloat(int i) { + assertIndexIsValid(i); + if (isNullAt(i)) { + return Float.NaN; + } else { + return PlatformDependent.UNSAFE.getFloat(baseObject, getFieldOffset(i)); + } + } + + @Override + public double getDouble(int i) { + assertIndexIsValid(i); + if (isNullAt(i)) { + return Float.NaN; + } else { + return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(i)); + } + } + + public UTF8String getUTF8String(int i) { + assertIndexIsValid(i); + final UTF8String str = new UTF8String(); + final long offsetToStringSize = getLong(i); + final int stringSizeInBytes = + (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offsetToStringSize); + final byte[] strBytes = new byte[stringSizeInBytes]; + PlatformDependent.copyMemory( + baseObject, + baseOffset + offsetToStringSize + 8, // The `+ 8` is to skip past the size to get the data + strBytes, + PlatformDependent.BYTE_ARRAY_OFFSET, + stringSizeInBytes + ); + str.set(strBytes); + return str; + } + + @Override + public String getString(int i) { + return getUTF8String(i).toString(); + } + + @Override + public BigDecimal getDecimal(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Date getDate(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Seq getSeq(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public List getList(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Map getMap(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public scala.collection.immutable.Map getValuesMap(Seq fieldNames) { + throw new UnsupportedOperationException(); + } + + @Override + public java.util.Map getJavaMap(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Row getStruct(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public T getAs(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public T getAs(String fieldName) { + throw new UnsupportedOperationException(); + } + + @Override + public int fieldIndex(String name) { + throw new UnsupportedOperationException(); + } + + @Override + public Row copy() { + throw new UnsupportedOperationException(); + } + + @Override + public boolean anyNull() { + return BitSetMethods.anySet(baseObject, baseOffset, bitSetWidthInBytes); + } + + @Override + public Seq toSeq() { + final ArraySeq values = new ArraySeq(numFields); + for (int fieldNumber = 0; fieldNumber < numFields; fieldNumber++) { + values.update(fieldNumber, get(fieldNumber)); + } + return values; + } + + @Override + public String toString() { + return mkString("[", ",", "]"); + } + + @Override + public String mkString() { + return toSeq().mkString(); + } + + @Override + public String mkString(String sep) { + return toSeq().mkString(sep); + } + + @Override + public String mkString(String start, String sep, String end) { + return toSeq().mkString(start, sep, end); + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala index 1f3c02478bd68..2eb3e167baad5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala @@ -25,10 +25,6 @@ import scala.util.parsing.input.CharArrayReader.EofCh import org.apache.spark.sql.catalyst.plans.logical._ -private[sql] object KeywordNormalizer { - def apply(str: String): String = str.toLowerCase() -} - private[sql] abstract class AbstractSparkSQLParser extends StandardTokenParsers with PackratParsers { @@ -42,7 +38,7 @@ private[sql] abstract class AbstractSparkSQLParser } protected case class Keyword(str: String) { - def normalize: String = KeywordNormalizer(str) + def normalize: String = lexical.normalizeKeyword(str) def parser: Parser[String] = normalize } @@ -90,13 +86,16 @@ class SqlLexical extends StdLexical { reserved ++= keywords } + /* Normal the keyword string */ + def normalizeKeyword(str: String): String = str.toLowerCase + delimiters += ( "@", "*", "+", "-", "<", "=", "<>", "!=", "<=", ">=", ">", "/", "(", ")", ",", ";", "%", "{", "}", ":", "[", "]", ".", "&", "|", "^", "~", "<=>" ) protected override def processIdent(name: String) = { - val token = KeywordNormalizer(name) + val token = normalizeKeyword(name) if (reserved contains token) Keyword(token) else Identifier(name) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala new file mode 100644 index 0000000000000..977003493d471 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan + +/** + * Root class of SQL Parser Dialect, and we don't guarantee the binary + * compatibility for the future release, let's keep it as the internal + * interface for advanced user. + * + */ +@DeveloperApi +abstract class Dialect { + // this is the main function that will be implemented by sql parser. + def parse(sqlText: String): LogicalPlan +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index 0af969cc5cc67..1d3a2dc0d9bb0 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -365,6 +365,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { protected lazy val baseExpression: Parser[Expression] = ( "*" ^^^ UnresolvedStar(None) + | ident <~ "." ~ "*" ^^ { case tableName => UnresolvedStar(Option(tableName)) } | primary ) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 5e42b409dcc59..7b543b6c2aa42 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql.catalyst.analysis +import scala.collection.mutable.ArrayBuffer + import org.apache.spark.util.collection.OpenHashSet import org.apache.spark.sql.AnalysisException import org.apache.spark.sql.catalyst.expressions._ @@ -61,6 +63,7 @@ class Analyzer( ResolveGenerate :: ImplicitGenerate :: ResolveFunctions :: + ExtractWindowExpressions :: GlobalAggregates :: UnresolvedHavingClauseAttributes :: TrimGroupingAliases :: @@ -529,6 +532,203 @@ class Analyzer( makeGeneratorOutput(p.generator, p.generatorOutput), p.child) } } + + /** + * Extracts [[WindowExpression]]s from the projectList of a [[Project]] operator and + * aggregateExpressions of an [[Aggregate]] operator and creates individual [[Window]] + * operators for every distinct [[WindowSpecDefinition]]. + * + * This rule handles three cases: + * - A [[Project]] having [[WindowExpression]]s in its projectList; + * - An [[Aggregate]] having [[WindowExpression]]s in its aggregateExpressions. + * - An [[Filter]]->[[Aggregate]] pattern representing GROUP BY with a HAVING + * clause and the [[Aggregate]] has [[WindowExpression]]s in its aggregateExpressions. + * Note: If there is a GROUP BY clause in the query, aggregations and corresponding + * filters (expressions in the HAVING clause) should be evaluated before any + * [[WindowExpression]]. If a query has SELECT DISTINCT, the DISTINCT part should be + * evaluated after all [[WindowExpression]]s. + * + * For every case, the transformation works as follows: + * 1. For a list of [[Expression]]s (a projectList or an aggregateExpressions), partitions + * it two lists of [[Expression]]s, one for all [[WindowExpression]]s and another for + * all regular expressions. + * 2. For all [[WindowExpression]]s, groups them based on their [[WindowSpecDefinition]]s. + * 3. For every distinct [[WindowSpecDefinition]], creates a [[Window]] operator and inserts + * it into the plan tree. + */ + object ExtractWindowExpressions extends Rule[LogicalPlan] { + def hasWindowFunction(projectList: Seq[NamedExpression]): Boolean = + projectList.exists(hasWindowFunction) + + def hasWindowFunction(expr: NamedExpression): Boolean = { + expr.find { + case window: WindowExpression => true + case _ => false + }.isDefined + } + + /** + * From a Seq of [[NamedExpression]]s, extract window expressions and + * other regular expressions. + */ + def extract( + expressions: Seq[NamedExpression]): (Seq[NamedExpression], Seq[NamedExpression]) = { + // First, we simple partition the input expressions to two part, one having + // WindowExpressions and another one without WindowExpressions. + val (windowExpressions, regularExpressions) = expressions.partition(hasWindowFunction) + + // Then, we need to extract those regular expressions used in the WindowExpression. + // For example, when we have col1 - Sum(col2 + col3) OVER (PARTITION BY col4 ORDER BY col5), + // we need to make sure that col1 to col5 are all projected from the child of the Window + // operator. + val extractedExprBuffer = new ArrayBuffer[NamedExpression]() + def extractExpr(expr: Expression): Expression = expr match { + case ne: NamedExpression => + // If a named expression is not in regularExpressions, add extract it and replace it + // with an AttributeReference. + val missingExpr = + AttributeSet(Seq(expr)) -- (regularExpressions ++ extractedExprBuffer) + if (missingExpr.nonEmpty) { + extractedExprBuffer += ne + } + ne.toAttribute + case e: Expression if e.foldable => + e // No need to create an attribute reference if it will be evaluated as a Literal. + case e: Expression => + // For other expressions, we extract it and replace it with an AttributeReference (with + // an interal column name, e.g. "_w0"). + val withName = Alias(e, s"_w${extractedExprBuffer.length}")() + extractedExprBuffer += withName + withName.toAttribute + } + + // Now, we extract expressions from windowExpressions by using extractExpr. + val newWindowExpressions = windowExpressions.map { + _.transform { + // Extracts children expressions of a WindowFunction (input parameters of + // a WindowFunction). + case wf : WindowFunction => + val newChildren = wf.children.map(extractExpr(_)) + wf.withNewChildren(newChildren) + + // Extracts expressions from the partition spec and order spec. + case wsc @ WindowSpecDefinition(partitionSpec, orderSpec, _) => + val newPartitionSpec = partitionSpec.map(extractExpr(_)) + val newOrderSpec = orderSpec.map { so => + val newChild = extractExpr(so.child) + so.copy(child = newChild) + } + wsc.copy(partitionSpec = newPartitionSpec, orderSpec = newOrderSpec) + + // Extracts AggregateExpression. For example, for SUM(x) - Sum(y) OVER (...), + // we need to extract SUM(x). + case agg: AggregateExpression => + val withName = Alias(agg, s"_w${extractedExprBuffer.length}")() + extractedExprBuffer += withName + withName.toAttribute + }.asInstanceOf[NamedExpression] + } + + (newWindowExpressions, regularExpressions ++ extractedExprBuffer) + } + + /** + * Adds operators for Window Expressions. Every Window operator handles a single Window Spec. + */ + def addWindow(windowExpressions: Seq[NamedExpression], child: LogicalPlan): LogicalPlan = { + // First, we group window expressions based on their Window Spec. + val groupedWindowExpression = windowExpressions.groupBy { expr => + val windowExpression = expr.find { + case window: WindowExpression => true + case other => false + }.map(_.asInstanceOf[WindowExpression].windowSpec) + windowExpression.getOrElse( + failAnalysis(s"$windowExpressions does not have any WindowExpression.")) + }.toSeq + + // For every Window Spec, we add a Window operator and set currentChild as the child of it. + var currentChild = child + var i = 0 + while (i < groupedWindowExpression.size) { + val (windowSpec, windowExpressions) = groupedWindowExpression(i) + // Set currentChild to the newly created Window operator. + currentChild = Window(currentChild.output, windowExpressions, windowSpec, currentChild) + + // Move to next WindowExpression. + i += 1 + } + + // We return the top operator. + currentChild + } + + // We have to use transformDown at here to make sure the rule of + // "Aggregate with Having clause" will be triggered. + def apply(plan: LogicalPlan): LogicalPlan = plan transformDown { + // Lookup WindowSpecDefinitions. This rule works with unresolved children. + case WithWindowDefinition(windowDefinitions, child) => + child.transform { + case plan => plan.transformExpressions { + case UnresolvedWindowExpression(c, WindowSpecReference(windowName)) => + val errorMessage = + s"Window specification $windowName is not defined in the WINDOW clause." + val windowSpecDefinition = + windowDefinitions + .get(windowName) + .getOrElse(failAnalysis(errorMessage)) + WindowExpression(c, windowSpecDefinition) + } + } + + // Aggregate with Having clause. This rule works with an unresolved Aggregate because + // a resolved Aggregate will not have Window Functions. + case f @ Filter(condition, a @ Aggregate(groupingExprs, aggregateExprs, child)) + if child.resolved && + hasWindowFunction(aggregateExprs) && + !a.expressions.exists(!_.resolved) => + val (windowExpressions, aggregateExpressions) = extract(aggregateExprs) + // Create an Aggregate operator to evaluate aggregation functions. + val withAggregate = Aggregate(groupingExprs, aggregateExpressions, child) + // Add a Filter operator for conditions in the Having clause. + val withFilter = Filter(condition, withAggregate) + val withWindow = addWindow(windowExpressions, withFilter) + + // Finally, generate output columns according to the original projectList. + val finalProjectList = aggregateExprs.map (_.toAttribute) + Project(finalProjectList, withWindow) + + case p: LogicalPlan if !p.childrenResolved => p + + // Aggregate without Having clause. + case a @ Aggregate(groupingExprs, aggregateExprs, child) + if hasWindowFunction(aggregateExprs) && + !a.expressions.exists(!_.resolved) => + val (windowExpressions, aggregateExpressions) = extract(aggregateExprs) + // Create an Aggregate operator to evaluate aggregation functions. + val withAggregate = Aggregate(groupingExprs, aggregateExpressions, child) + // Add Window operators. + val withWindow = addWindow(windowExpressions, withAggregate) + + // Finally, generate output columns according to the original projectList. + val finalProjectList = aggregateExprs.map (_.toAttribute) + Project(finalProjectList, withWindow) + + // We only extract Window Expressions after all expressions of the Project + // have been resolved. + case p @ Project(projectList, child) + if hasWindowFunction(projectList) && !p.expressions.exists(!_.resolved) => + val (windowExpressions, regularExpressions) = extract(projectList) + // We add a project to get all needed expressions for window expressions from the child + // of the original Project operator. + val withProject = Project(regularExpressions, child) + // Add Window operators. + val withWindow = addWindow(windowExpressions, withProject) + + // Finally, generate output columns according to the original projectList. + val finalProjectList = projectList.map (_.toAttribute) + Project(finalProjectList, withWindow) + } + } } /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala index b2f8157a1a61f..18c24b651921a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala @@ -27,6 +27,8 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery} */ class NoSuchTableException extends Exception +class NoSuchDatabaseException extends Exception + /** * An interface for looking up relations by name. Used by an [[Analyzer]]. */ diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index 2381689e17525..c8288c6767004 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -70,6 +70,11 @@ trait CheckAnalysis { failAnalysis( s"invalid expression ${b.prettyString} " + s"between ${b.left.simpleString} and ${b.right.simpleString}") + + case w @ WindowExpression(windowFunction, windowSpec) if windowSpec.validate.nonEmpty => + // The window spec is not valid. + val reason = windowSpec.validate.get + failAnalysis(s"Window specification $windowSpec is not valid because $reason") } operator match { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala index 73c9a1c7afdad..873c75c525c3b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala @@ -26,7 +26,14 @@ object HiveTypeCoercion { // See https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types. // The conversion for integral and floating point types have a linear widening hierarchy: private val numericPrecedence = - Seq(ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, DecimalType.Unlimited) + IndexedSeq( + ByteType, + ShortType, + IntegerType, + LongType, + FloatType, + DoubleType, + DecimalType.Unlimited) /** * Find the tightest common type of two types that might be used in a binary expression. @@ -34,25 +41,21 @@ object HiveTypeCoercion { * with primitive types, because in that case the precision and scale of the result depends on * the operation. Those rules are implemented in [[HiveTypeCoercion.DecimalPrecision]]. */ - def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = { - val valueTypes = Seq(t1, t2).filter(t => t != NullType) - if (valueTypes.distinct.size > 1) { - // Promote numeric types to the highest of the two and all numeric types to unlimited decimal - if (numericPrecedence.contains(t1) && numericPrecedence.contains(t2)) { - Some(numericPrecedence.filter(t => t == t1 || t == t2).last) - } else if (t1.isInstanceOf[DecimalType] && t2.isInstanceOf[DecimalType]) { - // Fixed-precision decimals can up-cast into unlimited - if (t1 == DecimalType.Unlimited || t2 == DecimalType.Unlimited) { - Some(DecimalType.Unlimited) - } else { - None - } - } else { - None - } - } else { - Some(if (valueTypes.size == 0) NullType else valueTypes.head) - } + val findTightestCommonType: (DataType, DataType) => Option[DataType] = { + case (t1, t2) if t1 == t2 => Some(t1) + case (NullType, t1) => Some(t1) + case (t1, NullType) => Some(t1) + + // Promote numeric types to the highest of the two and all numeric types to unlimited decimal + case (t1, t2) if Seq(t1, t2).forall(numericPrecedence.contains) => + val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2) + Some(numericPrecedence(index)) + + // Fixed-precision decimals can up-cast into unlimited + case (DecimalType.Unlimited, _: DecimalType) => Some(DecimalType.Unlimited) + case (_: DecimalType, DecimalType.Unlimited) => Some(DecimalType.Unlimited) + + case _ => None } } @@ -69,6 +72,7 @@ trait HiveTypeCoercion { val typeCoercionRules = PropagateTypes :: ConvertNaNs :: + InConversion :: WidenTypes :: PromoteStrings :: DecimalPrecision :: @@ -239,37 +243,43 @@ trait HiveTypeCoercion { a.makeCopy(Array(a.left, Cast(a.right, DoubleType))) // we should cast all timestamp/date/string compare into string compare - case p: BinaryPredicate if p.left.dataType == StringType - && p.right.dataType == DateType => + case p: BinaryComparison if p.left.dataType == StringType && + p.right.dataType == DateType => p.makeCopy(Array(p.left, Cast(p.right, StringType))) - case p: BinaryPredicate if p.left.dataType == DateType - && p.right.dataType == StringType => + case p: BinaryComparison if p.left.dataType == DateType && + p.right.dataType == StringType => p.makeCopy(Array(Cast(p.left, StringType), p.right)) - case p: BinaryPredicate if p.left.dataType == StringType - && p.right.dataType == TimestampType => + case p: BinaryComparison if p.left.dataType == StringType && + p.right.dataType == TimestampType => p.makeCopy(Array(p.left, Cast(p.right, StringType))) - case p: BinaryPredicate if p.left.dataType == TimestampType - && p.right.dataType == StringType => + case p: BinaryComparison if p.left.dataType == TimestampType && + p.right.dataType == StringType => p.makeCopy(Array(Cast(p.left, StringType), p.right)) - case p: BinaryPredicate if p.left.dataType == TimestampType - && p.right.dataType == DateType => + case p: BinaryComparison if p.left.dataType == TimestampType && + p.right.dataType == DateType => p.makeCopy(Array(Cast(p.left, StringType), Cast(p.right, StringType))) - case p: BinaryPredicate if p.left.dataType == DateType - && p.right.dataType == TimestampType => + case p: BinaryComparison if p.left.dataType == DateType && + p.right.dataType == TimestampType => p.makeCopy(Array(Cast(p.left, StringType), Cast(p.right, StringType))) - case p: BinaryPredicate if p.left.dataType == StringType && p.right.dataType != StringType => + case p: BinaryComparison if p.left.dataType == StringType && + p.right.dataType != StringType => p.makeCopy(Array(Cast(p.left, DoubleType), p.right)) - case p: BinaryPredicate if p.left.dataType != StringType && p.right.dataType == StringType => + case p: BinaryComparison if p.left.dataType != StringType && + p.right.dataType == StringType => p.makeCopy(Array(p.left, Cast(p.right, DoubleType))) - case i @ In(a, b) if a.dataType == DateType && b.forall(_.dataType == StringType) => + case i @ In(a, b) if a.dataType == DateType && + b.forall(_.dataType == StringType) => i.makeCopy(Array(Cast(a, StringType), b)) - case i @ In(a, b) if a.dataType == TimestampType && b.forall(_.dataType == StringType) => + case i @ In(a, b) if a.dataType == TimestampType && + b.forall(_.dataType == StringType) => i.makeCopy(Array(Cast(a, StringType), b)) - case i @ In(a, b) if a.dataType == DateType && b.forall(_.dataType == TimestampType) => + case i @ In(a, b) if a.dataType == DateType && + b.forall(_.dataType == TimestampType) => i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType)))) - case i @ In(a, b) if a.dataType == TimestampType && b.forall(_.dataType == DateType) => + case i @ In(a, b) if a.dataType == TimestampType && + b.forall(_.dataType == DateType) => i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType)))) case Sum(e) if e.dataType == StringType => @@ -281,6 +291,16 @@ trait HiveTypeCoercion { } } + /** + * Convert all expressions in in() list to the left operator type + */ + object InConversion extends Rule[LogicalPlan] { + def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { + case i @ In(a, b) if b.exists(_.dataType != a.dataType) => + i.makeCopy(Array(a, b.map(Cast(_, a.dataType)))) + } + } + // scalastyle:off /** * Calculates and propagates precision for fixed-precision decimals. Hive has a number of @@ -420,19 +440,19 @@ trait HiveTypeCoercion { ) case LessThan(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => LessThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) case LessThanOrEqual(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => LessThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) case GreaterThan(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => GreaterThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) case GreaterThanOrEqual(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => GreaterThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) // Promote integers inside a binary expression with fixed-precision decimals to decimals, @@ -481,8 +501,8 @@ trait HiveTypeCoercion { // No need to change the EqualNullSafe operators, too case e: EqualNullSafe => e // Otherwise turn them to Byte types so that there exists and ordering. - case p: BinaryComparison - if p.left.dataType == BooleanType && p.right.dataType == BooleanType => + case p: BinaryComparison if p.left.dataType == BooleanType && + p.right.dataType == BooleanType => p.makeCopy(Array(Cast(p.left, ByteType), Cast(p.right, ByteType))) } } @@ -564,10 +584,6 @@ trait HiveTypeCoercion { case Sum(e @ TimestampType()) => Sum(Cast(e, DoubleType)) case Average(e @ TimestampType()) => Average(Cast(e, DoubleType)) - // Compatible with Hive - case Substring(e, start, len) if e.dataType != StringType => - Substring(Cast(e, StringType), start, len) - // Coalesce should return the first non-null value, which could be any column // from the list. So we need to make sure the return type is deterministic and // compatible with every child column. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 5d5aba9644ff7..fa6cc7a1a36cf 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -278,12 +278,6 @@ package object dsl { def sfilter[T1](arg1: Symbol)(udf: (T1) => Boolean): LogicalPlan = Filter(ScalaUdf(udf, BooleanType, Seq(UnresolvedAttribute(arg1.name))), logicalPlan) - def sample( - fraction: Double, - withReplacement: Boolean = true, - seed: Int = (math.random * 1000).toInt): LogicalPlan = - Sample(fraction, withReplacement, seed, logicalPlan) - // TODO specify the output column names def generate( generator: Generator, diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala index bdeb660b1ecb7..0fd4f9b374ee0 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala @@ -38,6 +38,8 @@ package object errors { } } + class DialectException(msg: String, cause: Throwable) extends Exception(msg, cause) + /** * Wraps any exceptions that are thrown while executing `f` in a * [[catalyst.errors.TreeNodeException TreeNodeException]], attaching the provided `tree`. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala index 2225621dbaabd..c6217f07c452d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala @@ -28,13 +28,21 @@ import org.apache.spark.sql.catalyst.trees * the layout of intermediate tuples, BindReferences should be run after all such transformations. */ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean) - extends Expression with trees.LeafNode[Expression] { + extends NamedExpression with trees.LeafNode[Expression] { type EvaluatedType = Any override def toString: String = s"input[$ordinal]" override def eval(input: Row): Any = input(ordinal) + + override def name: String = s"i[$ordinal]" + + override def toAttribute: Attribute = throw new UnsupportedOperationException + + override def qualifiers: Seq[String] = throw new UnsupportedOperationException + + override def exprId: ExprId = throw new UnsupportedOperationException } object BindReferences extends Logging { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala index 1d71c1b4b0c7c..4fd1bc4dd642d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala @@ -18,7 +18,6 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute -import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.trees.TreeNode import org.apache.spark.sql.types._ @@ -86,6 +85,8 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express override def foldable: Boolean = left.foldable && right.foldable + override def nullable: Boolean = left.nullable || right.nullable + override def toString: String = s"($left $symbol $right)" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala index c2866cd955409..8cae548279eb1 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala @@ -548,3 +548,97 @@ class JoinedRow5 extends Row { } } } + +/** + * JIT HACK: Replace with macros + */ +class JoinedRow6 extends Row { + private[this] var row1: Row = _ + private[this] var row2: Row = _ + + def this(left: Row, right: Row) = { + this() + row1 = left + row2 = right + } + + /** Updates this JoinedRow to used point at two new base rows. Returns itself. */ + def apply(r1: Row, r2: Row): Row = { + row1 = r1 + row2 = r2 + this + } + + /** Updates this JoinedRow by updating its left base row. Returns itself. */ + def withLeft(newLeft: Row): Row = { + row1 = newLeft + this + } + + /** Updates this JoinedRow by updating its right base row. Returns itself. */ + def withRight(newRight: Row): Row = { + row2 = newRight + this + } + + override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq + + override def length: Int = row1.length + row2.length + + override def apply(i: Int): Any = + if (i < row1.length) row1(i) else row2(i - row1.length) + + override def isNullAt(i: Int): Boolean = + if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) + + override def getInt(i: Int): Int = + if (i < row1.length) row1.getInt(i) else row2.getInt(i - row1.length) + + override def getLong(i: Int): Long = + if (i < row1.length) row1.getLong(i) else row2.getLong(i - row1.length) + + override def getDouble(i: Int): Double = + if (i < row1.length) row1.getDouble(i) else row2.getDouble(i - row1.length) + + override def getBoolean(i: Int): Boolean = + if (i < row1.length) row1.getBoolean(i) else row2.getBoolean(i - row1.length) + + override def getShort(i: Int): Short = + if (i < row1.length) row1.getShort(i) else row2.getShort(i - row1.length) + + override def getByte(i: Int): Byte = + if (i < row1.length) row1.getByte(i) else row2.getByte(i - row1.length) + + override def getFloat(i: Int): Float = + if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length) + + override def getString(i: Int): String = + if (i < row1.length) row1.getString(i) else row2.getString(i - row1.length) + + override def getAs[T](i: Int): T = + if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) + + override def copy(): Row = { + val totalSize = row1.length + row2.length + val copiedValues = new Array[Any](totalSize) + var i = 0 + while(i < totalSize) { + copiedValues(i) = apply(i) + i += 1 + } + new GenericRow(copiedValues) + } + + override def toString: String = { + // Make sure toString never throws NullPointerException. + if ((row1 eq null) && (row2 eq null)) { + "[ empty row ]" + } else if (row1 eq null) { + row2.mkString("[", ",", "]") + } else if (row2 eq null) { + row1.mkString("[", ",", "]") + } else { + mkString("[", ",", "]") + } + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala index 3475ed05f4454..aa4099e4d7bf9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala @@ -202,6 +202,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR case DoubleType => new MutableDouble case BooleanType => new MutableBoolean case LongType => new MutableLong + case DateType => new MutableInt // We use INT for DATE internally case _ => new MutableAny }.toArray) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala new file mode 100644 index 0000000000000..5b2c8572784bd --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala @@ -0,0 +1,223 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.PlatformDependent +import org.apache.spark.unsafe.array.ByteArrayMethods + +/** + * Converts Rows into UnsafeRow format. This class is NOT thread-safe. + * + * @param fieldTypes the data types of the row's columns. + */ +class UnsafeRowConverter(fieldTypes: Array[DataType]) { + + def this(schema: StructType) { + this(schema.fields.map(_.dataType)) + } + + /** Re-used pointer to the unsafe row being written */ + private[this] val unsafeRow = new UnsafeRow() + + /** Functions for encoding each column */ + private[this] val writers: Array[UnsafeColumnWriter] = { + fieldTypes.map(t => UnsafeColumnWriter.forType(t)) + } + + /** The size, in bytes, of the fixed-length portion of the row, including the null bitmap */ + private[this] val fixedLengthSize: Int = + (8 * fieldTypes.length) + UnsafeRow.calculateBitSetWidthInBytes(fieldTypes.length) + + /** + * Compute the amount of space, in bytes, required to encode the given row. + */ + def getSizeRequirement(row: Row): Int = { + var fieldNumber = 0 + var variableLengthFieldSize: Int = 0 + while (fieldNumber < writers.length) { + if (!row.isNullAt(fieldNumber)) { + variableLengthFieldSize += writers(fieldNumber).getSize(row, fieldNumber) + } + fieldNumber += 1 + } + fixedLengthSize + variableLengthFieldSize + } + + /** + * Convert the given row into UnsafeRow format. + * + * @param row the row to convert + * @param baseObject the base object of the destination address + * @param baseOffset the base offset of the destination address + * @return the number of bytes written. This should be equal to `getSizeRequirement(row)`. + */ + def writeRow(row: Row, baseObject: Object, baseOffset: Long): Long = { + unsafeRow.pointTo(baseObject, baseOffset, writers.length, null) + var fieldNumber = 0 + var appendCursor: Int = fixedLengthSize + while (fieldNumber < writers.length) { + if (row.isNullAt(fieldNumber)) { + unsafeRow.setNullAt(fieldNumber) + } else { + appendCursor += writers(fieldNumber).write(row, unsafeRow, fieldNumber, appendCursor) + } + fieldNumber += 1 + } + appendCursor + } + +} + +/** + * Function for writing a column into an UnsafeRow. + */ +private abstract class UnsafeColumnWriter { + /** + * Write a value into an UnsafeRow. + * + * @param source the row being converted + * @param target a pointer to the converted unsafe row + * @param column the column to write + * @param appendCursor the offset from the start of the unsafe row to the end of the row; + * used for calculating where variable-length data should be written + * @return the number of variable-length bytes written + */ + def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int + + /** + * Return the number of bytes that are needed to write this variable-length value. + */ + def getSize(source: Row, column: Int): Int +} + +private object UnsafeColumnWriter { + + def forType(dataType: DataType): UnsafeColumnWriter = { + dataType match { + case NullType => NullUnsafeColumnWriter + case BooleanType => BooleanUnsafeColumnWriter + case ByteType => ByteUnsafeColumnWriter + case ShortType => ShortUnsafeColumnWriter + case IntegerType => IntUnsafeColumnWriter + case LongType => LongUnsafeColumnWriter + case FloatType => FloatUnsafeColumnWriter + case DoubleType => DoubleUnsafeColumnWriter + case StringType => StringUnsafeColumnWriter + case t => + throw new UnsupportedOperationException(s"Do not know how to write columns of type $t") + } + } +} + +// ------------------------------------------------------------------------------------------------ + +private object NullUnsafeColumnWriter extends NullUnsafeColumnWriter +private object BooleanUnsafeColumnWriter extends BooleanUnsafeColumnWriter +private object ByteUnsafeColumnWriter extends ByteUnsafeColumnWriter +private object ShortUnsafeColumnWriter extends ShortUnsafeColumnWriter +private object IntUnsafeColumnWriter extends IntUnsafeColumnWriter +private object LongUnsafeColumnWriter extends LongUnsafeColumnWriter +private object FloatUnsafeColumnWriter extends FloatUnsafeColumnWriter +private object DoubleUnsafeColumnWriter extends DoubleUnsafeColumnWriter +private object StringUnsafeColumnWriter extends StringUnsafeColumnWriter + +private abstract class PrimitiveUnsafeColumnWriter extends UnsafeColumnWriter { + // Primitives don't write to the variable-length region: + def getSize(sourceRow: Row, column: Int): Int = 0 +} + +private class NullUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setNullAt(column) + 0 + } +} + +private class BooleanUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setBoolean(column, source.getBoolean(column)) + 0 + } +} + +private class ByteUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setByte(column, source.getByte(column)) + 0 + } +} + +private class ShortUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setShort(column, source.getShort(column)) + 0 + } +} + +private class IntUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setInt(column, source.getInt(column)) + 0 + } +} + +private class LongUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setLong(column, source.getLong(column)) + 0 + } +} + +private class FloatUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setFloat(column, source.getFloat(column)) + 0 + } +} + +private class DoubleUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setDouble(column, source.getDouble(column)) + 0 + } +} + +private class StringUnsafeColumnWriter private() extends UnsafeColumnWriter { + def getSize(source: Row, column: Int): Int = { + val numBytes = source.get(column).asInstanceOf[UTF8String].getBytes.length + 8 + ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes) + } + + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + val value = source.get(column).asInstanceOf[UTF8String] + val baseObject = target.getBaseObject + val baseOffset = target.getBaseOffset + val numBytes = value.getBytes.length + PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + appendCursor, numBytes) + PlatformDependent.copyMemory( + value.getBytes, + PlatformDependent.BYTE_ARRAY_OFFSET, + baseObject, + baseOffset + appendCursor + 8, + numBytes + ) + target.setLong(column, appendCursor) + 8 + ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes) + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala index 140ccd8d3796f..c7a37ad966df6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala @@ -74,14 +74,12 @@ abstract class BinaryArithmetic extends BinaryExpression { type EvaluatedType = Any - def nullable: Boolean = left.nullable || right.nullable - override lazy val resolved = left.resolved && right.resolved && left.dataType == right.dataType && !DecimalType.isFixed(left.dataType) - def dataType: DataType = { + override def dataType: DataType = { if (!resolved) { throw new UnresolvedException(this, s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index dbc92fb93e95e..d17af0e7ff87e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -672,6 +672,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin case DoubleType => ru.Literal(Constant(-1.toDouble)) case DecimalType() => q"org.apache.spark.sql.types.Decimal(-1)" case IntegerType => ru.Literal(Constant(-1)) + case DateType => ru.Literal(Constant(-1)) case _ => ru.Literal(Constant(null)) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala index 5b4d912a64f71..fcc06d3aa1036 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala @@ -65,12 +65,6 @@ abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String) } } -case class Pow(left: Expression, right: Expression) extends BinaryMathExpression(math.pow, "POWER") - -case class Hypot( - left: Expression, - right: Expression) extends BinaryMathExpression(math.hypot, "HYPOT") - case class Atan2( left: Expression, right: Expression) extends BinaryMathExpression(math.atan2, "ATAN2") { @@ -91,3 +85,9 @@ case class Atan2( } } } + +case class Hypot( + left: Expression, + right: Expression) extends BinaryMathExpression(math.hypot, "HYPOT") + +case class Pow(left: Expression, right: Expression) extends BinaryMathExpression(math.pow, "POWER") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala index 96cb77d487529..dc68469e060cb 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala @@ -25,27 +25,16 @@ import org.apache.spark.sql.types._ * input format, therefore these functions extend `ExpectsInputTypes`. * @param name The short name of the function */ -abstract class MathematicalExpression(name: String) +abstract class MathematicalExpression(f: Double => Double, name: String) extends UnaryExpression with Serializable with ExpectsInputTypes { self: Product => type EvaluatedType = Any + override def expectedChildTypes: Seq[DataType] = Seq(DoubleType) override def dataType: DataType = DoubleType override def foldable: Boolean = child.foldable override def nullable: Boolean = true override def toString: String = s"$name($child)" -} - -/** - * A unary expression specifically for math functions that take a `Double` as input and return - * a `Double`. - * @param f The math function. - * @param name The short name of the function - */ -abstract class MathematicalExpressionForDouble(f: Double => Double, name: String) - extends MathematicalExpression(name) { self: Product => - - override def expectedChildTypes: Seq[DataType] = Seq(DoubleType) override def eval(input: Row): Any = { val evalE = child.eval(input) @@ -58,111 +47,46 @@ abstract class MathematicalExpressionForDouble(f: Double => Double, name: String } } -/** - * A unary expression specifically for math functions that take an `Int` as input and return - * an `Int`. - * @param f The math function. - * @param name The short name of the function - */ -abstract class MathematicalExpressionForInt(f: Int => Int, name: String) - extends MathematicalExpression(name) { self: Product => +case class Acos(child: Expression) extends MathematicalExpression(math.acos, "ACOS") - override def dataType: DataType = IntegerType - override def expectedChildTypes: Seq[DataType] = Seq(IntegerType) +case class Asin(child: Expression) extends MathematicalExpression(math.asin, "ASIN") - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) null else f(evalE.asInstanceOf[Int]) - } -} +case class Atan(child: Expression) extends MathematicalExpression(math.atan, "ATAN") -/** - * A unary expression specifically for math functions that take a `Float` as input and return - * a `Float`. - * @param f The math function. - * @param name The short name of the function - */ -abstract class MathematicalExpressionForFloat(f: Float => Float, name: String) - extends MathematicalExpression(name) { self: Product => +case class Cbrt(child: Expression) extends MathematicalExpression(math.cbrt, "CBRT") - override def dataType: DataType = FloatType - override def expectedChildTypes: Seq[DataType] = Seq(FloatType) +case class Ceil(child: Expression) extends MathematicalExpression(math.ceil, "CEIL") - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) { - null - } else { - val result = f(evalE.asInstanceOf[Float]) - if (result.isNaN) null else result - } - } -} - -/** - * A unary expression specifically for math functions that take a `Long` as input and return - * a `Long`. - * @param f The math function. - * @param name The short name of the function - */ -abstract class MathematicalExpressionForLong(f: Long => Long, name: String) - extends MathematicalExpression(name) { self: Product => - - override def dataType: DataType = LongType - override def expectedChildTypes: Seq[DataType] = Seq(LongType) - - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) null else f(evalE.asInstanceOf[Long]) - } -} - -case class Sin(child: Expression) extends MathematicalExpressionForDouble(math.sin, "SIN") - -case class Asin(child: Expression) extends MathematicalExpressionForDouble(math.asin, "ASIN") - -case class Sinh(child: Expression) extends MathematicalExpressionForDouble(math.sinh, "SINH") - -case class Cos(child: Expression) extends MathematicalExpressionForDouble(math.cos, "COS") +case class Cos(child: Expression) extends MathematicalExpression(math.cos, "COS") -case class Acos(child: Expression) extends MathematicalExpressionForDouble(math.acos, "ACOS") +case class Cosh(child: Expression) extends MathematicalExpression(math.cosh, "COSH") -case class Cosh(child: Expression) extends MathematicalExpressionForDouble(math.cosh, "COSH") +case class Exp(child: Expression) extends MathematicalExpression(math.exp, "EXP") -case class Tan(child: Expression) extends MathematicalExpressionForDouble(math.tan, "TAN") +case class Expm1(child: Expression) extends MathematicalExpression(math.expm1, "EXPM1") -case class Atan(child: Expression) extends MathematicalExpressionForDouble(math.atan, "ATAN") +case class Floor(child: Expression) extends MathematicalExpression(math.floor, "FLOOR") -case class Tanh(child: Expression) extends MathematicalExpressionForDouble(math.tanh, "TANH") +case class Log(child: Expression) extends MathematicalExpression(math.log, "LOG") -case class Ceil(child: Expression) extends MathematicalExpressionForDouble(math.ceil, "CEIL") +case class Log10(child: Expression) extends MathematicalExpression(math.log10, "LOG10") -case class Floor(child: Expression) extends MathematicalExpressionForDouble(math.floor, "FLOOR") +case class Log1p(child: Expression) extends MathematicalExpression(math.log1p, "LOG1P") -case class Rint(child: Expression) extends MathematicalExpressionForDouble(math.rint, "ROUND") +case class Rint(child: Expression) extends MathematicalExpression(math.rint, "ROUND") -case class Cbrt(child: Expression) extends MathematicalExpressionForDouble(math.cbrt, "CBRT") +case class Signum(child: Expression) extends MathematicalExpression(math.signum, "SIGNUM") -case class Signum(child: Expression) extends MathematicalExpressionForDouble(math.signum, "SIGNUM") +case class Sin(child: Expression) extends MathematicalExpression(math.sin, "SIN") -case class ISignum(child: Expression) extends MathematicalExpressionForInt(math.signum, "ISIGNUM") +case class Sinh(child: Expression) extends MathematicalExpression(math.sinh, "SINH") -case class FSignum(child: Expression) extends MathematicalExpressionForFloat(math.signum, "FSIGNUM") +case class Tan(child: Expression) extends MathematicalExpression(math.tan, "TAN") -case class LSignum(child: Expression) extends MathematicalExpressionForLong(math.signum, "LSIGNUM") +case class Tanh(child: Expression) extends MathematicalExpression(math.tanh, "TANH") case class ToDegrees(child: Expression) - extends MathematicalExpressionForDouble(math.toDegrees, "DEGREES") + extends MathematicalExpression(math.toDegrees, "DEGREES") case class ToRadians(child: Expression) - extends MathematicalExpressionForDouble(math.toRadians, "RADIANS") - -case class Log(child: Expression) extends MathematicalExpressionForDouble(math.log, "LOG") - -case class Log10(child: Expression) extends MathematicalExpressionForDouble(math.log10, "LOG10") - -case class Log1p(child: Expression) extends MathematicalExpressionForDouble(math.log1p, "LOG1P") - -case class Exp(child: Expression) extends MathematicalExpressionForDouble(math.exp, "EXP") - -case class Expm1(child: Expression) extends MathematicalExpressionForDouble(math.expm1, "EXPM1") + extends MathematicalExpression(math.toRadians, "RADIANS") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala index afcb2ce8b9cb4..57ace2a14f77c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala @@ -171,6 +171,11 @@ case class AttributeReference( val exprId: ExprId = NamedExpression.newExprId, val qualifiers: Seq[String] = Nil) extends Attribute with trees.LeafNode[Expression] { + /** + * Returns true iff the expression id is the same for both attributes. + */ + def sameRef(other: AttributeReference): Boolean = this.exprId == other.exprId + override def equals(other: Any): Boolean = other match { case ar: AttributeReference => name == ar.name && exprId == ar.exprId && dataType == ar.dataType case _ => false diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala index 9cb00cb2732ff..26c38c56c04f5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala @@ -70,16 +70,14 @@ trait PredicateHelper { expr.references.subsetOf(plan.outputSet) } -abstract class BinaryPredicate extends BinaryExpression with Predicate { - self: Product => - override def nullable: Boolean = left.nullable || right.nullable -} -case class Not(child: Expression) extends UnaryExpression with Predicate { +case class Not(child: Expression) extends UnaryExpression with Predicate with ExpectsInputTypes { override def foldable: Boolean = child.foldable override def nullable: Boolean = child.nullable override def toString: String = s"NOT $child" + override def expectedChildTypes: Seq[DataType] = Seq(BooleanType) + override def eval(input: Row): Any = { child.eval(input) match { case null => null @@ -120,7 +118,11 @@ case class InSet(value: Expression, hset: Set[Any]) } } -case class And(left: Expression, right: Expression) extends BinaryPredicate { +case class And(left: Expression, right: Expression) + extends BinaryExpression with Predicate with ExpectsInputTypes { + + override def expectedChildTypes: Seq[DataType] = Seq(BooleanType, BooleanType) + override def symbol: String = "&&" override def eval(input: Row): Any = { @@ -142,7 +144,11 @@ case class And(left: Expression, right: Expression) extends BinaryPredicate { } } -case class Or(left: Expression, right: Expression) extends BinaryPredicate { +case class Or(left: Expression, right: Expression) + extends BinaryExpression with Predicate with ExpectsInputTypes { + + override def expectedChildTypes: Seq[DataType] = Seq(BooleanType, BooleanType) + override def symbol: String = "||" override def eval(input: Row): Any = { @@ -164,7 +170,7 @@ case class Or(left: Expression, right: Expression) extends BinaryPredicate { } } -abstract class BinaryComparison extends BinaryPredicate { +abstract class BinaryComparison extends BinaryExpression with Predicate { self: Product => } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala new file mode 100644 index 0000000000000..66d7c8b07cce8 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.TaskContext +import org.apache.spark.sql.types.{DataType, DoubleType} +import org.apache.spark.util.Utils +import org.apache.spark.util.random.XORShiftRandom + +/** + * A Random distribution generating expression. + * TODO: This can be made generic to generate any type of random distribution, or any type of + * StructType. + * + * Since this expression is stateful, it cannot be a case object. + */ +abstract class RDG(seed: Long) extends LeafExpression with Serializable { + self: Product => + + /** + * Record ID within each partition. By being transient, the Random Number Generator is + * reset every time we serialize and deserialize it. + */ + @transient protected lazy val rng = new XORShiftRandom(seed + TaskContext.get().partitionId()) + + override type EvaluatedType = Double + + override def nullable: Boolean = false + + override def dataType: DataType = DoubleType +} + +/** Generate a random column with i.i.d. uniformly distributed values in [0, 1). */ +case class Rand(seed: Long = Utils.random.nextLong()) extends RDG(seed) { + override def eval(input: Row): Double = rng.nextDouble() +} + +/** Generate a random column with i.i.d. gaussian random distribution. */ +case class Randn(seed: Long = Utils.random.nextLong()) extends RDG(seed) { + override def eval(input: Row): Double = rng.nextGaussian() +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala index d597bf7ce756a..7683e0990ce80 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala @@ -22,7 +22,7 @@ import java.util.regex.Pattern import org.apache.spark.sql.catalyst.analysis.UnresolvedException import org.apache.spark.sql.types._ -trait StringRegexExpression { +trait StringRegexExpression extends ExpectsInputTypes { self: BinaryExpression => type EvaluatedType = Any @@ -32,6 +32,7 @@ trait StringRegexExpression { override def nullable: Boolean = left.nullable || right.nullable override def dataType: DataType = BooleanType + override def expectedChildTypes: Seq[DataType] = Seq(StringType, StringType) // try cache the pattern for Literal private lazy val cache: Pattern = right match { @@ -57,11 +58,11 @@ trait StringRegexExpression { if(r == null) { null } else { - val regex = pattern(r.asInstanceOf[UTF8String].toString) + val regex = pattern(r.asInstanceOf[UTF8String].toString()) if(regex == null) { null } else { - matches(regex, l.asInstanceOf[UTF8String].toString) + matches(regex, l.asInstanceOf[UTF8String].toString()) } } } @@ -110,7 +111,7 @@ case class RLike(left: Expression, right: Expression) override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0) } -trait CaseConversionExpression { +trait CaseConversionExpression extends ExpectsInputTypes { self: UnaryExpression => type EvaluatedType = Any @@ -118,8 +119,9 @@ trait CaseConversionExpression { def convert(v: UTF8String): UTF8String override def foldable: Boolean = child.foldable - def nullable: Boolean = child.nullable - def dataType: DataType = StringType + override def nullable: Boolean = child.nullable + override def dataType: DataType = StringType + override def expectedChildTypes: Seq[DataType] = Seq(StringType) override def eval(input: Row): Any = { val evaluated = child.eval(input) @@ -136,7 +138,7 @@ trait CaseConversionExpression { */ case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression { - override def convert(v: UTF8String): UTF8String = v.toUpperCase + override def convert(v: UTF8String): UTF8String = v.toUpperCase() override def toString: String = s"Upper($child)" } @@ -146,20 +148,22 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE */ case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression { - override def convert(v: UTF8String): UTF8String = v.toLowerCase + override def convert(v: UTF8String): UTF8String = v.toLowerCase() override def toString: String = s"Lower($child)" } /** A base trait for functions that compare two strings, returning a boolean. */ -trait StringComparison { - self: BinaryPredicate => +trait StringComparison extends ExpectsInputTypes { + self: BinaryExpression => + + def compare(l: UTF8String, r: UTF8String): Boolean override type EvaluatedType = Any override def nullable: Boolean = left.nullable || right.nullable - def compare(l: UTF8String, r: UTF8String): Boolean + override def expectedChildTypes: Seq[DataType] = Seq(StringType, StringType) override def eval(input: Row): Any = { val leftEval = left.eval(input) @@ -181,7 +185,7 @@ trait StringComparison { * A function that returns true if the string `left` contains the string `right`. */ case class Contains(left: Expression, right: Expression) - extends BinaryPredicate with StringComparison { + extends BinaryExpression with Predicate with StringComparison { override def compare(l: UTF8String, r: UTF8String): Boolean = l.contains(r) } @@ -189,7 +193,7 @@ case class Contains(left: Expression, right: Expression) * A function that returns true if the string `left` starts with the string `right`. */ case class StartsWith(left: Expression, right: Expression) - extends BinaryPredicate with StringComparison { + extends BinaryExpression with Predicate with StringComparison { override def compare(l: UTF8String, r: UTF8String): Boolean = l.startsWith(r) } @@ -197,7 +201,7 @@ case class StartsWith(left: Expression, right: Expression) * A function that returns true if the string `left` ends with the string `right`. */ case class EndsWith(left: Expression, right: Expression) - extends BinaryPredicate with StringComparison { + extends BinaryExpression with Predicate with StringComparison { override def compare(l: UTF8String, r: UTF8String): Boolean = l.endsWith(r) } @@ -205,7 +209,8 @@ case class EndsWith(left: Expression, right: Expression) * A function that takes a substring of its first argument starting at a given position. * Defined for String and Binary types. */ -case class Substring(str: Expression, pos: Expression, len: Expression) extends Expression { +case class Substring(str: Expression, pos: Expression, len: Expression) + extends Expression with ExpectsInputTypes { type EvaluatedType = Any @@ -219,6 +224,8 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends if (str.dataType == BinaryType) str.dataType else StringType } + override def expectedChildTypes: Seq[DataType] = Seq(StringType, IntegerType, IntegerType) + override def children: Seq[Expression] = str :: pos :: len :: Nil @inline @@ -258,7 +265,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends val (st, end) = slicePos(start, length, () => ba.length) ba.slice(st, end) case s: UTF8String => - val (st, end) = slicePos(start, length, () => s.length) + val (st, end) = slicePos(start, length, () => s.length()) s.slice(st, end) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala new file mode 100644 index 0000000000000..099d67ca7fee3 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala @@ -0,0 +1,340 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.sql.catalyst.analysis.UnresolvedException +import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.types.{NumericType, DataType} + +/** + * The trait of the Window Specification (specified in the OVER clause or WINDOW clause) for + * Window Functions. + */ +sealed trait WindowSpec + +/** + * The specification for a window function. + * @param partitionSpec It defines the way that input rows are partitioned. + * @param orderSpec It defines the ordering of rows in a partition. + * @param frameSpecification It defines the window frame in a partition. + */ +case class WindowSpecDefinition( + partitionSpec: Seq[Expression], + orderSpec: Seq[SortOrder], + frameSpecification: WindowFrame) extends Expression with WindowSpec { + + def validate: Option[String] = frameSpecification match { + case UnspecifiedFrame => + Some("Found a UnspecifiedFrame. It should be converted to a SpecifiedWindowFrame " + + "during analysis. Please file a bug report.") + case frame: SpecifiedWindowFrame => frame.validate.orElse { + def checkValueBasedBoundaryForRangeFrame(): Option[String] = { + if (orderSpec.length > 1) { + // It is not allowed to have a value-based PRECEDING and FOLLOWING + // as the boundary of a Range Window Frame. + Some("This Range Window Frame only accepts at most one ORDER BY expression.") + } else if (orderSpec.nonEmpty && !orderSpec.head.dataType.isInstanceOf[NumericType]) { + Some("The data type of the expression in the ORDER BY clause should be a numeric type.") + } else { + None + } + } + + (frame.frameType, frame.frameStart, frame.frameEnd) match { + case (RangeFrame, vp: ValuePreceding, _) => checkValueBasedBoundaryForRangeFrame() + case (RangeFrame, vf: ValueFollowing, _) => checkValueBasedBoundaryForRangeFrame() + case (RangeFrame, _, vp: ValuePreceding) => checkValueBasedBoundaryForRangeFrame() + case (RangeFrame, _, vf: ValueFollowing) => checkValueBasedBoundaryForRangeFrame() + case (_, _, _) => None + } + } + } + + type EvaluatedType = Any + + override def children: Seq[Expression] = partitionSpec ++ orderSpec + + override lazy val resolved: Boolean = + childrenResolved && frameSpecification.isInstanceOf[SpecifiedWindowFrame] + + + override def toString: String = simpleString + + override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException + override def nullable: Boolean = true + override def foldable: Boolean = false + override def dataType: DataType = throw new UnsupportedOperationException +} + +/** + * A Window specification reference that refers to the [[WindowSpecDefinition]] defined + * under the name `name`. + */ +case class WindowSpecReference(name: String) extends WindowSpec + +/** + * The trait used to represent the type of a Window Frame. + */ +sealed trait FrameType + +/** + * RowFrame treats rows in a partition individually. When a [[ValuePreceding]] + * or a [[ValueFollowing]] is used as its [[FrameBoundary]], the value is considered + * as a physical offset. + * For example, `ROW BETWEEN 1 PRECEDING AND 1 FOLLOWING` represents a 3-row frame, + * from the row precedes the current row to the row follows the current row. + */ +case object RowFrame extends FrameType + +/** + * RangeFrame treats rows in a partition as groups of peers. + * All rows having the same `ORDER BY` ordering are considered as peers. + * When a [[ValuePreceding]] or a [[ValueFollowing]] is used as its [[FrameBoundary]], + * the value is considered as a logical offset. + * For example, assuming the value of the current row's `ORDER BY` expression `expr` is `v`, + * `RANGE BETWEEN 1 PRECEDING AND 1 FOLLOWING` represents a frame containing rows whose values + * `expr` are in the range of [v-1, v+1]. + * + * If `ORDER BY` clause is not defined, all rows in the partition is considered as peers + * of the current row. + */ +case object RangeFrame extends FrameType + +/** + * The trait used to represent the type of a Window Frame Boundary. + */ +sealed trait FrameBoundary { + def notFollows(other: FrameBoundary): Boolean +} + +/** UNBOUNDED PRECEDING boundary. */ +case object UnboundedPreceding extends FrameBoundary { + def notFollows(other: FrameBoundary): Boolean = other match { + case UnboundedPreceding => true + case vp: ValuePreceding => true + case CurrentRow => true + case vf: ValueFollowing => true + case UnboundedFollowing => true + } + + override def toString: String = "UNBOUNDED PRECEDING" +} + +/** PRECEDING boundary. */ +case class ValuePreceding(value: Int) extends FrameBoundary { + def notFollows(other: FrameBoundary): Boolean = other match { + case UnboundedPreceding => false + case ValuePreceding(anotherValue) => value >= anotherValue + case CurrentRow => true + case vf: ValueFollowing => true + case UnboundedFollowing => true + } + + override def toString: String = s"$value PRECEDING" +} + +/** CURRENT ROW boundary. */ +case object CurrentRow extends FrameBoundary { + def notFollows(other: FrameBoundary): Boolean = other match { + case UnboundedPreceding => false + case vp: ValuePreceding => false + case CurrentRow => true + case vf: ValueFollowing => true + case UnboundedFollowing => true + } + + override def toString: String = "CURRENT ROW" +} + +/** FOLLOWING boundary. */ +case class ValueFollowing(value: Int) extends FrameBoundary { + def notFollows(other: FrameBoundary): Boolean = other match { + case UnboundedPreceding => false + case vp: ValuePreceding => false + case CurrentRow => false + case ValueFollowing(anotherValue) => value <= anotherValue + case UnboundedFollowing => true + } + + override def toString: String = s"$value FOLLOWING" +} + +/** UNBOUNDED FOLLOWING boundary. */ +case object UnboundedFollowing extends FrameBoundary { + def notFollows(other: FrameBoundary): Boolean = other match { + case UnboundedPreceding => false + case vp: ValuePreceding => false + case CurrentRow => false + case vf: ValueFollowing => false + case UnboundedFollowing => true + } + + override def toString: String = "UNBOUNDED FOLLOWING" +} + +/** + * The trait used to represent the a Window Frame. + */ +sealed trait WindowFrame + +/** Used as a place holder when a frame specification is not defined. */ +case object UnspecifiedFrame extends WindowFrame + +/** A specified Window Frame. */ +case class SpecifiedWindowFrame( + frameType: FrameType, + frameStart: FrameBoundary, + frameEnd: FrameBoundary) extends WindowFrame { + + /** If this WindowFrame is valid or not. */ + def validate: Option[String] = (frameType, frameStart, frameEnd) match { + case (_, UnboundedFollowing, _) => + Some(s"$UnboundedFollowing is not allowed as the start of a Window Frame.") + case (_, _, UnboundedPreceding) => + Some(s"$UnboundedPreceding is not allowed as the end of a Window Frame.") + // case (RowFrame, start, end) => ??? RowFrame specific rule + // case (RangeFrame, start, end) => ??? RangeFrame specific rule + case (_, start, end) => + if (start.notFollows(end)) { + None + } else { + val reason = + s"The end of this Window Frame $end is smaller than the start of " + + s"this Window Frame $start." + Some(reason) + } + } + + override def toString: String = frameType match { + case RowFrame => s"ROWS BETWEEN $frameStart AND $frameEnd" + case RangeFrame => s"RANGE BETWEEN $frameStart AND $frameEnd" + } +} + +object SpecifiedWindowFrame { + /** + * + * @param hasOrderSpecification If the window spec has order by expressions. + * @param acceptWindowFrame If the window function accepts user-specified frame. + * @return + */ + def defaultWindowFrame( + hasOrderSpecification: Boolean, + acceptWindowFrame: Boolean): SpecifiedWindowFrame = { + if (hasOrderSpecification && acceptWindowFrame) { + // If order spec is defined and the window function supports user specified window frames, + // the default frame is RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. + SpecifiedWindowFrame(RangeFrame, UnboundedPreceding, CurrentRow) + } else { + // Otherwise, the default frame is + // ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING. + SpecifiedWindowFrame(RowFrame, UnboundedPreceding, UnboundedFollowing) + } + } +} + +/** + * Every window function needs to maintain a output buffer for its output. + * It should expect that for a n-row window frame, it will be called n times + * to retrieve value corresponding with these n rows. + */ +trait WindowFunction extends Expression { + self: Product => + + def init(): Unit + + def reset(): Unit + + def prepareInputParameters(input: Row): AnyRef + + def update(input: AnyRef): Unit + + def batchUpdate(inputs: Array[AnyRef]): Unit + + def evaluate(): Unit + + def get(index: Int): Any + + def newInstance(): WindowFunction +} + +case class UnresolvedWindowFunction( + name: String, + children: Seq[Expression]) + extends Expression with WindowFunction { + + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def foldable: Boolean = throw new UnresolvedException(this, "foldable") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") + override lazy val resolved = false + + override def init(): Unit = + throw new UnresolvedException(this, "init") + override def reset(): Unit = + throw new UnresolvedException(this, "reset") + override def prepareInputParameters(input: Row): AnyRef = + throw new UnresolvedException(this, "prepareInputParameters") + override def update(input: AnyRef): Unit = + throw new UnresolvedException(this, "update") + override def batchUpdate(inputs: Array[AnyRef]): Unit = + throw new UnresolvedException(this, "batchUpdate") + override def evaluate(): Unit = + throw new UnresolvedException(this, "evaluate") + override def get(index: Int): Any = + throw new UnresolvedException(this, "get") + // Unresolved functions are transient at compile time and don't get evaluated during execution. + override def eval(input: Row = null): EvaluatedType = + throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") + + override def toString: String = s"'$name(${children.mkString(",")})" + + override def newInstance(): WindowFunction = + throw new UnresolvedException(this, "newInstance") +} + +case class UnresolvedWindowExpression( + child: UnresolvedWindowFunction, + windowSpec: WindowSpecReference) extends UnaryExpression { + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def foldable: Boolean = throw new UnresolvedException(this, "foldable") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") + override lazy val resolved = false + + // Unresolved functions are transient at compile time and don't get evaluated during execution. + override def eval(input: Row = null): EvaluatedType = + throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") +} + +case class WindowExpression( + windowFunction: WindowFunction, + windowSpec: WindowSpecDefinition) extends Expression { + override type EvaluatedType = Any + + override def children: Seq[Expression] = + windowFunction :: windowSpec :: Nil + + override def eval(input: Row): EvaluatedType = + throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") + + override def dataType: DataType = windowFunction.dataType + override def foldable: Boolean = windowFunction.foldable + override def nullable: Boolean = windowFunction.nullable + + override def toString: String = s"$windowFunction $windowSpec" +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 2d03fbfb0d311..e4a60f53d6c09 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -36,7 +36,13 @@ object DefaultOptimizer extends Optimizer { // SubQueries are only needed for analysis and can be removed before execution. Batch("Remove SubQueries", FixedPoint(100), EliminateSubQueries) :: - Batch("Combine Limits", FixedPoint(100), + Batch("Operator Reordering", FixedPoint(100), + UnionPushdown, + CombineFilters, + PushPredicateThroughProject, + PushPredicateThroughJoin, + PushPredicateThroughGenerate, + ColumnPruning, CombineLimits) :: Batch("ConstantFolding", FixedPoint(100), NullPropagation, @@ -49,13 +55,6 @@ object DefaultOptimizer extends Optimizer { OptimizeIn) :: Batch("Decimal Optimizations", FixedPoint(100), DecimalAggregates) :: - Batch("Filter Pushdown", FixedPoint(100), - UnionPushdown, - CombineFilters, - PushPredicateThroughProject, - PushPredicateThroughJoin, - PushPredicateThroughGenerate, - ColumnPruning) :: Batch("LocalRelation", FixedPoint(100), ConvertToLocalRelation) :: Nil } @@ -171,6 +170,9 @@ object ColumnPruning extends Rule[LogicalPlan] { Project(substitutedProjection, child) + case Project(projectList, Limit(exp, child)) => + Limit(exp, Project(projectList, child)) + // Eliminate no-op Projects case Project(projectList, child) if child.output == projectList => child } @@ -308,8 +310,8 @@ object OptimizeIn extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { case q: LogicalPlan => q transformExpressionsDown { case In(v, list) if !list.exists(!_.isInstanceOf[Literal]) => - val hSet = list.map(e => e.eval(null)) - InSet(v, HashSet() ++ hSet) + val hSet = list.map(e => e.eval(null)) + InSet(v, HashSet() ++ hSet) } } } @@ -569,7 +571,7 @@ object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper { split(joinCondition.map(splitConjunctivePredicates).getOrElse(Nil), left, right) joinType match { - case Inner => + case _ @ (Inner | LeftSemi) => // push down the single side only join filter for both sides sub queries val newLeft = leftJoinConditions. reduceLeftOption(And).map(Filter(_, left)).getOrElse(left) @@ -577,7 +579,7 @@ object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper { reduceLeftOption(And).map(Filter(_, right)).getOrElse(right) val newJoinCond = commonJoinCondition.reduceLeftOption(And) - Join(newLeft, newRight, Inner, newJoinCond) + Join(newLeft, newRight, joinType, newJoinCond) case RightOuter => // push down the left side only join filter for left side sub query val newLeft = leftJoinConditions. @@ -586,14 +588,14 @@ object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper { val newJoinCond = (rightJoinConditions ++ commonJoinCondition).reduceLeftOption(And) Join(newLeft, newRight, RightOuter, newJoinCond) - case _ @ (LeftOuter | LeftSemi) => + case LeftOuter => // push down the right side only join filter for right sub query val newLeft = left val newRight = rightJoinConditions. reduceLeftOption(And).map(Filter(_, right)).getOrElse(right) val newJoinCond = (leftJoinConditions ++ commonJoinCondition).reduceLeftOption(And) - Join(newLeft, newRight, joinType, newJoinCond) + Join(newLeft, newRight, LeftOuter, newJoinCond) case FullOuter => f } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 608e272da7784..ba0abb2df596c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -25,13 +25,14 @@ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extend override def output: Seq[Attribute] = projectList.map(_.toAttribute) override lazy val resolved: Boolean = { - val containsAggregatesOrGenerators = projectList.exists ( _.collect { + val hasSpecialExpressions = projectList.exists ( _.collect { case agg: AggregateExpression => agg case generator: Generator => generator + case window: WindowExpression => window }.nonEmpty ) - !expressions.exists(!_.resolved) && childrenResolved && !containsAggregatesOrGenerators + !expressions.exists(!_.resolved) && childrenResolved && !hasSpecialExpressions } } @@ -170,6 +171,12 @@ case class With(child: LogicalPlan, cteRelations: Map[String, Subquery]) extends override def output: Seq[Attribute] = child.output } +case class WithWindowDefinition( + windowDefinitions: Map[String, WindowSpecDefinition], + child: LogicalPlan) extends UnaryNode { + override def output: Seq[Attribute] = child.output +} + case class WriteToFile( path: String, child: LogicalPlan) extends UnaryNode { @@ -195,9 +202,28 @@ case class Aggregate( child: LogicalPlan) extends UnaryNode { + override lazy val resolved: Boolean = { + val hasWindowExpressions = aggregateExpressions.exists ( _.collect { + case window: WindowExpression => window + }.nonEmpty + ) + + !expressions.exists(!_.resolved) && childrenResolved && !hasWindowExpressions + } + override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute) } +case class Window( + projectList: Seq[Attribute], + windowExpressions: Seq[NamedExpression], + windowSpec: WindowSpecDefinition, + child: LogicalPlan) extends UnaryNode { + + override def output: Seq[Attribute] = + (projectList ++ windowExpressions).map(_.toAttribute) +} + /** * Apply the all of the GroupExpressions to every input row, hence we will get * multiple output rows for a input row. @@ -300,8 +326,22 @@ case class Subquery(alias: String, child: LogicalPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output.map(_.withQualifiers(alias :: Nil)) } -case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: LogicalPlan) - extends UnaryNode { +/** + * Sample the dataset. + * + * @param lowerBound Lower-bound of the sampling probability (usually 0.0) + * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled + * will be ub - lb. + * @param withReplacement Whether to sample with replacement. + * @param seed the random seed + * @param child the LogicalPlan + */ +case class Sample( + lowerBound: Double, + upperBound: Double, + withReplacement: Boolean, + seed: Long, + child: LogicalPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala index 97502ed3afe72..4b93f7d31b808 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala @@ -71,6 +71,15 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { this.eq(other) || this == other } + /** + * Find the first [[TreeNode]] that satisfies the condition specified by `f`. + * The condition is recursively applied to this node and all of its children (pre-order). + */ + def find(f: BaseType => Boolean): Option[BaseType] = f(this) match { + case true => Some(this) + case false => children.foldLeft(None: Option[BaseType]) { (l, r) => l.orElse(r.find(f)) } + } + /** * Runs the given function on this node and then recursively on [[children]]. * @param f the function to be applied to each node in the tree. @@ -151,6 +160,20 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { val remainingNewChildren = newChildren.toBuffer val remainingOldChildren = children.toBuffer val newArgs = productIterator.map { + // This rule is used to handle children is a input argument. + case s: Seq[_] => s.map { + case arg: TreeNode[_] if children contains arg => + val newChild = remainingNewChildren.remove(0) + val oldChild = remainingOldChildren.remove(0) + if (newChild fastEquals oldChild) { + oldChild + } else { + changed = true + newChild + } + case nonChild: AnyRef => nonChild + case null => null + } case arg: TreeNode[_] if children contains arg => val newChild = remainingNewChildren.remove(0) val oldChild = remainingOldChildren.remove(0) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala index c86214a2aa944..9d613a940ee86 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala @@ -17,12 +17,31 @@ package org.apache.spark.sql.catalyst -import java.io.{PrintWriter, ByteArrayOutputStream, FileInputStream, File} +import java.io._ import org.apache.spark.util.Utils package object util { + /** Silences output to stderr or stdout for the duration of f */ + def quietly[A](f: => A): A = { + val origErr = System.err + val origOut = System.out + try { + System.setErr(new PrintStream(new OutputStream { + def write(b: Int) = {} + })) + System.setOut(new PrintStream(new OutputStream { + def write(b: Int) = {} + })) + + f + } finally { + System.setErr(origErr) + System.setOut(origOut) + } + } + def fileToString(file: File, encoding: String = "UTF-8"): String = { val inStream = new FileInputStream(file) val outStream = new ByteArrayOutputStream @@ -42,10 +61,9 @@ package object util { new String(outStream.toByteArray, encoding) } - def resourceToString( - resource:String, - encoding: String = "UTF-8", - classLoader: ClassLoader = Utils.getSparkClassLoader): String = { + def resourceToBytes( + resource: String, + classLoader: ClassLoader = Utils.getSparkClassLoader): Array[Byte] = { val inStream = classLoader.getResourceAsStream(resource) val outStream = new ByteArrayOutputStream try { @@ -61,7 +79,14 @@ package object util { finally { inStream.close() } - new String(outStream.toByteArray, encoding) + outStream.toByteArray + } + + def resourceToString( + resource:String, + encoding: String = "UTF-8", + classLoader: ClassLoader = Utils.getSparkClassLoader): String = { + new String(resourceToBytes(resource, classLoader), encoding) } def stringToFile(file: File, str: String): File = { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala index d80ffca18ec9a..7e00a27dfe724 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala @@ -134,6 +134,10 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru throw new IllegalArgumentException(s"""Field "$name" does not exist.""")) } + private[sql] def getFieldIndex(name: String): Option[Int] = { + nameToIndex.get(name) + } + protected[sql] def toAttributes: Seq[AttributeReference] = map(f => AttributeReference(f.name, f.dataType, f.nullable, f.metadata)()) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index 5390ce43c6639..faaa55aa5e6f1 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -1217,11 +1217,11 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { unaryMathFunctionEvaluation(Tanh, math.tanh) } - test("toDeg") { + test("toDegrees") { unaryMathFunctionEvaluation(ToDegrees, math.toDegrees) } - test("toRad") { + test("toRadians") { unaryMathFunctionEvaluation(ToRadians, math.toRadians) } @@ -1253,18 +1253,6 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { unaryMathFunctionEvaluation[Double](Signum, math.signum) } - test("isignum") { - unaryMathFunctionEvaluation[Int](ISignum, math.signum, (-5 to 5)) - } - - test("fsignum") { - unaryMathFunctionEvaluation[Float](FSignum, math.signum, (-5 to 5).map(_.toFloat)) - } - - test("lsignum") { - unaryMathFunctionEvaluation[Long](LSignum, math.signum, (5 to 5).map(_.toLong)) - } - test("log") { unaryMathFunctionEvaluation(Log, math.log, (0 to 20).map(_ * 0.1)) unaryMathFunctionEvaluation(Log, math.log, (-5 to -1).map(_ * 0.1), true) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala new file mode 100644 index 0000000000000..7a19e511eb8b5 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import scala.collection.JavaConverters._ +import scala.util.Random + +import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, TaskMemoryManager, MemoryAllocator} +import org.scalatest.{BeforeAndAfterEach, FunSuite, Matchers} + +import org.apache.spark.sql.types._ + +class UnsafeFixedWidthAggregationMapSuite extends FunSuite with Matchers with BeforeAndAfterEach { + + import UnsafeFixedWidthAggregationMap._ + + private val groupKeySchema = StructType(StructField("product", StringType) :: Nil) + private val aggBufferSchema = StructType(StructField("salePrice", IntegerType) :: Nil) + private def emptyAggregationBuffer: Row = new GenericRow(Array[Any](0)) + + private var memoryManager: TaskMemoryManager = null + + override def beforeEach(): Unit = { + memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP)) + } + + override def afterEach(): Unit = { + if (memoryManager != null) { + memoryManager.cleanUpAllAllocatedMemory() + memoryManager = null + } + } + + test("supported schemas") { + assert(!supportsAggregationBufferSchema(StructType(StructField("x", StringType) :: Nil))) + assert(supportsGroupKeySchema(StructType(StructField("x", StringType) :: Nil))) + + assert( + !supportsAggregationBufferSchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil))) + assert( + !supportsGroupKeySchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil))) + } + + test("empty map") { + val map = new UnsafeFixedWidthAggregationMap( + emptyAggregationBuffer, + aggBufferSchema, + groupKeySchema, + memoryManager, + 1024, // initial capacity + false // disable perf metrics + ) + assert(!map.iterator().hasNext) + map.free() + } + + test("updating values for a single key") { + val map = new UnsafeFixedWidthAggregationMap( + emptyAggregationBuffer, + aggBufferSchema, + groupKeySchema, + memoryManager, + 1024, // initial capacity + false // disable perf metrics + ) + val groupKey = new GenericRow(Array[Any](UTF8String("cats"))) + + // Looking up a key stores a zero-entry in the map (like Python Counters or DefaultDicts) + map.getAggregationBuffer(groupKey) + val iter = map.iterator() + val entry = iter.next() + assert(!iter.hasNext) + entry.key.getString(0) should be ("cats") + entry.value.getInt(0) should be (0) + + // Modifications to rows retrieved from the map should update the values in the map + entry.value.setInt(0, 42) + map.getAggregationBuffer(groupKey).getInt(0) should be (42) + + map.free() + } + + test("inserting large random keys") { + val map = new UnsafeFixedWidthAggregationMap( + emptyAggregationBuffer, + aggBufferSchema, + groupKeySchema, + memoryManager, + 128, // initial capacity + false // disable perf metrics + ) + val rand = new Random(42) + val groupKeys: Set[String] = Seq.fill(512)(rand.nextString(1024)).toSet + groupKeys.foreach { keyString => + map.getAggregationBuffer(new GenericRow(Array[Any](UTF8String(keyString)))) + } + val seenKeys: Set[String] = map.iterator().asScala.map { entry => + entry.key.getString(0) + }.toSet + seenKeys.size should be (groupKeys.size) + seenKeys should be (groupKeys) + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala new file mode 100644 index 0000000000000..3a60c7fd32675 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala @@ -0,0 +1,153 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import java.util.Arrays + +import org.scalatest.{FunSuite, Matchers} + +import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.PlatformDependent +import org.apache.spark.unsafe.array.ByteArrayMethods + +class UnsafeRowConverterSuite extends FunSuite with Matchers { + + test("basic conversion with only primitive types") { + val fieldTypes: Array[DataType] = Array(LongType, LongType, IntegerType) + val converter = new UnsafeRowConverter(fieldTypes) + + val row = new SpecificMutableRow(fieldTypes) + row.setLong(0, 0) + row.setLong(1, 1) + row.setInt(2, 2) + + val sizeRequired: Int = converter.getSizeRequirement(row) + sizeRequired should be (8 + (3 * 8)) + val buffer: Array[Long] = new Array[Long](sizeRequired / 8) + val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET) + numBytesWritten should be (sizeRequired) + + val unsafeRow = new UnsafeRow() + unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null) + unsafeRow.getLong(0) should be (0) + unsafeRow.getLong(1) should be (1) + unsafeRow.getInt(2) should be (2) + } + + test("basic conversion with primitive and string types") { + val fieldTypes: Array[DataType] = Array(LongType, StringType, StringType) + val converter = new UnsafeRowConverter(fieldTypes) + + val row = new SpecificMutableRow(fieldTypes) + row.setLong(0, 0) + row.setString(1, "Hello") + row.setString(2, "World") + + val sizeRequired: Int = converter.getSizeRequirement(row) + sizeRequired should be (8 + (8 * 3) + + ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length + 8) + + ByteArrayMethods.roundNumberOfBytesToNearestWord("World".getBytes.length + 8)) + val buffer: Array[Long] = new Array[Long](sizeRequired / 8) + val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET) + numBytesWritten should be (sizeRequired) + + val unsafeRow = new UnsafeRow() + unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null) + unsafeRow.getLong(0) should be (0) + unsafeRow.getString(1) should be ("Hello") + unsafeRow.getString(2) should be ("World") + } + + test("null handling") { + val fieldTypes: Array[DataType] = Array( + NullType, + BooleanType, + ByteType, + ShortType, + IntegerType, + LongType, + FloatType, + DoubleType) + val converter = new UnsafeRowConverter(fieldTypes) + + val rowWithAllNullColumns: Row = { + val r = new SpecificMutableRow(fieldTypes) + for (i <- 0 to fieldTypes.length - 1) { + r.setNullAt(i) + } + r + } + + val sizeRequired: Int = converter.getSizeRequirement(rowWithAllNullColumns) + val createdFromNullBuffer: Array[Long] = new Array[Long](sizeRequired / 8) + val numBytesWritten = converter.writeRow( + rowWithAllNullColumns, createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET) + numBytesWritten should be (sizeRequired) + + val createdFromNull = new UnsafeRow() + createdFromNull.pointTo( + createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null) + for (i <- 0 to fieldTypes.length - 1) { + assert(createdFromNull.isNullAt(i)) + } + createdFromNull.getBoolean(1) should be (false) + createdFromNull.getByte(2) should be (0) + createdFromNull.getShort(3) should be (0) + createdFromNull.getInt(4) should be (0) + createdFromNull.getLong(5) should be (0) + assert(java.lang.Float.isNaN(createdFromNull.getFloat(6))) + assert(java.lang.Double.isNaN(createdFromNull.getFloat(7))) + + // If we have an UnsafeRow with columns that are initially non-null and we null out those + // columns, then the serialized row representation should be identical to what we would get by + // creating an entirely null row via the converter + val rowWithNoNullColumns: Row = { + val r = new SpecificMutableRow(fieldTypes) + r.setNullAt(0) + r.setBoolean(1, false) + r.setByte(2, 20) + r.setShort(3, 30) + r.setInt(4, 400) + r.setLong(5, 500) + r.setFloat(6, 600) + r.setDouble(7, 700) + r + } + val setToNullAfterCreationBuffer: Array[Long] = new Array[Long](sizeRequired / 8) + converter.writeRow( + rowWithNoNullColumns, setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET) + val setToNullAfterCreation = new UnsafeRow() + setToNullAfterCreation.pointTo( + setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null) + + setToNullAfterCreation.isNullAt(0) should be (rowWithNoNullColumns.isNullAt(0)) + setToNullAfterCreation.getBoolean(1) should be (rowWithNoNullColumns.getBoolean(1)) + setToNullAfterCreation.getByte(2) should be (rowWithNoNullColumns.getByte(2)) + setToNullAfterCreation.getShort(3) should be (rowWithNoNullColumns.getShort(3)) + setToNullAfterCreation.getInt(4) should be (rowWithNoNullColumns.getInt(4)) + setToNullAfterCreation.getLong(5) should be (rowWithNoNullColumns.getLong(5)) + setToNullAfterCreation.getFloat(6) should be (rowWithNoNullColumns.getFloat(6)) + setToNullAfterCreation.getDouble(7) should be (rowWithNoNullColumns.getDouble(7)) + + for (i <- 0 to fieldTypes.length - 1) { + setToNullAfterCreation.setNullAt(i) + } + assert(Arrays.equals(createdFromNullBuffer, setToNullAfterCreationBuffer)) + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala index 2d16d668fd522..a30052b38fc11 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala @@ -27,6 +27,8 @@ class CombiningLimitsSuite extends PlanTest { object Optimize extends RuleExecutor[LogicalPlan] { val batches = + Batch("Filter Pushdown", FixedPoint(100), + ColumnPruning) :: Batch("Combine Limit", FixedPoint(10), CombineLimits) :: Batch("Constant Folding", FixedPoint(10), @@ -69,4 +71,21 @@ class CombiningLimitsSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + + test("limits: combines two limits after ColumnPruning") { + val originalQuery = + testRelation + .select('a) + .limit(2) + .select('a) + .limit(5) + + val optimized = Optimize.execute(originalQuery.analyze) + val correctAnswer = + testRelation + .select('a) + .limit(2).analyze + + comparePlans(optimized, correctAnswer) + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala index 14b28e8402610..18f92150b0966 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala @@ -160,7 +160,7 @@ class ConstantFoldingSuite extends PlanTest { val originalQuery = testRelation .select( - Rand + Literal(1) as Symbol("c1"), + Rand(5L) + Literal(1) as Symbol("c1"), Sum('a) as Symbol("c2")) val optimized = Optimize.execute(originalQuery.analyze) @@ -168,7 +168,7 @@ class ConstantFoldingSuite extends PlanTest { val correctAnswer = testRelation .select( - Rand + Literal(1.0) as Symbol("c1"), + Rand(5L) + Literal(1.0) as Symbol("c1"), Sum('a) as Symbol("c2")) .analyze diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala index aa9708b164efa..58d415d9011e1 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala @@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.analysis import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries import org.apache.spark.sql.catalyst.expressions.{Count, Explode} import org.apache.spark.sql.catalyst.plans.logical._ -import org.apache.spark.sql.catalyst.plans.{PlanTest, LeftOuter, RightOuter} +import org.apache.spark.sql.catalyst.plans.{LeftSemi, PlanTest, LeftOuter, RightOuter} import org.apache.spark.sql.catalyst.rules._ import org.apache.spark.sql.catalyst.dsl.plans._ import org.apache.spark.sql.catalyst.dsl.expressions._ @@ -43,6 +43,8 @@ class FilterPushdownSuite extends PlanTest { val testRelation = LocalRelation('a.int, 'b.int, 'c.int) + val testRelation1 = LocalRelation('d.int) + // This test already passes. test("eliminate subqueries") { val originalQuery = @@ -90,7 +92,23 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + + test("column pruning for Project(ne, Limit)") { + val originalQuery = + testRelation + .select('a,'b) + .limit(2) + .select('a) + val optimized = Optimize.execute(originalQuery.analyze) + val correctAnswer = + testRelation + .select('a) + .limit(2).analyze + + comparePlans(optimized, correctAnswer) + } + // After this line is unimplemented. test("simple push down") { val originalQuery = @@ -197,6 +215,23 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + test("joins: push down left semi join") { + val x = testRelation.subquery('x) + val y = testRelation1.subquery('y) + + val originalQuery = { + x.join(y, LeftSemi, Option("x.a".attr === "y.d".attr && "x.b".attr >= 1 && "y.d".attr >= 2)) + } + + val optimized = Optimize.execute(originalQuery.analyze) + val left = testRelation.where('b >= 1) + val right = testRelation1.where('d >= 2) + val correctAnswer = + left.join(right, LeftSemi, Option("a".attr === "d".attr)).analyze + + comparePlans(optimized, correctAnswer) + } + test("joins: push down left outer join #1") { val x = testRelation.subquery('x) val y = testRelation.subquery('y) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala index 6b393327cc97a..786ddba403f2c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala @@ -22,7 +22,7 @@ import scala.collection.mutable.ArrayBuffer import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.types.{StringType, NullType} +import org.apache.spark.sql.types.{IntegerType, StringType, NullType} case class Dummy(optKey: Option[Expression]) extends Expression { def children: Seq[Expression] = optKey.toSeq @@ -129,5 +129,47 @@ class TreeNodeSuite extends FunSuite { assert(expected === actual) } + test("find") { + val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4)))) + // Find the top node. + var actual: Option[Expression] = expression.find { + case add: Add => true + case other => false + } + var expected: Option[Expression] = + Some(Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))) + assert(expected === actual) + + // Find the first children. + actual = expression.find { + case Literal(1, IntegerType) => true + case other => false + } + expected = Some(Literal(1)) + assert(expected === actual) + // Find an internal node (Subtract). + actual = expression.find { + case sub: Subtract => true + case other => false + } + expected = Some(Subtract(Literal(3), Literal(4))) + assert(expected === actual) + + // Find a leaf node. + actual = expression.find { + case Literal(3, IntegerType) => true + case other => false + } + expected = Some(Literal(3)) + assert(expected === actual) + + // Find nothing. + actual = expression.find { + case Literal(100, IntegerType) => true + case other => false + } + expected = None + assert(expected === actual) + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala index 33f9d0b37d006..8eb632d3d600b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala @@ -84,14 +84,14 @@ class Column(protected[sql] val expr: Expression) extends Logging { /** * Inversion of boolean expression, i.e. NOT. - * {{ + * {{{ * // Scala: select rows that are not active (isActive === false) * df.filter( !df("isActive") ) * * // Java: * import static org.apache.spark.sql.functions.*; * df.filter( not(df.col("isActive")) ); - * }} + * }}} * * @group expr_ops */ @@ -295,6 +295,15 @@ class Column(protected[sql] val expr: Expression) extends Logging { */ def eqNullSafe(other: Any): Column = this <=> other + /** + * True if the current column is between the lower bound and upper bound, inclusive. + * + * @group java_expr_ops + */ + def between(lowerBound: Any, upperBound: Any): Column = { + (this >= lowerBound) && (this <= upperBound) + } + /** * True if the current expression is null. * diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 2affba7d42cc8..79fbf50300d4d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql import java.io.CharArrayWriter import java.sql.DriverManager + import scala.collection.JavaConversions._ import scala.language.implicitConversions import scala.reflect.ClassTag @@ -28,6 +29,7 @@ import scala.util.control.NonFatal import com.fasterxml.jackson.core.JsonFactory +import org.apache.commons.lang3.StringUtils import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.api.java.JavaRDD import org.apache.spark.api.python.SerDeUtil @@ -40,7 +42,7 @@ import org.apache.spark.sql.catalyst.plans.{JoinType, Inner} import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD} import org.apache.spark.sql.jdbc.JDBCWriteDetails -import org.apache.spark.sql.json.JsonRDD +import org.apache.spark.sql.json.{JacksonGenerator, JsonRDD} import org.apache.spark.sql.types._ import org.apache.spark.sql.sources.{ResolvedDataSource, CreateTableUsingAsSelect} import org.apache.spark.util.Utils @@ -175,6 +177,7 @@ class DataFrame private[sql]( * @param numRows Number of rows to show */ private[sql] def showString(numRows: Int): String = { + val sb = new StringBuilder val data = take(numRows) val numCols = schema.fieldNames.length @@ -194,12 +197,25 @@ class DataFrame private[sql]( } } - // Pad the cells - rows.map { row => - row.zipWithIndex.map { case (cell, i) => - String.format(s"%-${colWidths(i)}s", cell) - }.mkString(" ") - }.mkString("\n") + // Create SeparateLine + val sep: String = colWidths.map("-" * _).addString(sb, "+", "+", "+\n").toString() + + // column names + rows.head.zipWithIndex.map { case (cell, i) => + StringUtils.leftPad(cell.toString, colWidths(i)) + }.addString(sb, "|", "|", "|\n") + + sb.append(sep) + + // data + rows.tail.map { + _.zipWithIndex.map { case (cell, i) => + StringUtils.leftPad(cell.toString, colWidths(i)) + }.addString(sb, "|", "|", "|\n") + } + + sb.append(sep) + sb.toString() } override def toString: String = { @@ -330,6 +346,17 @@ class DataFrame private[sql]( */ def na: DataFrameNaFunctions = new DataFrameNaFunctions(this) + /** + * Returns a [[DataFrameStatFunctions]] for working statistic functions support. + * {{{ + * // Finding frequent items in column with name 'a'. + * df.stat.freqItems(Seq("a")) + * }}} + * + * @group dfops + */ + def stat: DataFrameStatFunctions = new DataFrameStatFunctions(this) + /** * Cartesian join with another [[DataFrame]]. * @@ -389,9 +416,7 @@ class DataFrame private[sql]( * }}} * @group dfops */ - def join(right: DataFrame, joinExprs: Column): DataFrame = { - Join(logicalPlan, right.logicalPlan, joinType = Inner, Some(joinExprs.expr)) - } + def join(right: DataFrame, joinExprs: Column): DataFrame = join(right, joinExprs, "inner") /** * Join with another [[DataFrame]], using the given join expression. The following performs @@ -413,7 +438,39 @@ class DataFrame private[sql]( * @group dfops */ def join(right: DataFrame, joinExprs: Column, joinType: String): DataFrame = { - Join(logicalPlan, right.logicalPlan, JoinType(joinType), Some(joinExprs.expr)) + // Note that in this function, we introduce a hack in the case of self-join to automatically + // resolve ambiguous join conditions into ones that might make sense [SPARK-6231]. + // Consider this case: df.join(df, df("key") === df("key")) + // Since df("key") === df("key") is a trivially true condition, this actually becomes a + // cartesian join. However, most likely users expect to perform a self join using "key". + // With that assumption, this hack turns the trivially true condition into equality on join + // keys that are resolved to both sides. + + // Trigger analysis so in the case of self-join, the analyzer will clone the plan. + // After the cloning, left and right side will have distinct expression ids. + val plan = Join(logicalPlan, right.logicalPlan, JoinType(joinType), Some(joinExprs.expr)) + .queryExecution.analyzed.asInstanceOf[Join] + + // If auto self join alias is disabled, return the plan. + if (!sqlContext.conf.dataFrameSelfJoinAutoResolveAmbiguity) { + return plan + } + + // If left/right have no output set intersection, return the plan. + val lanalyzed = this.logicalPlan.queryExecution.analyzed + val ranalyzed = right.logicalPlan.queryExecution.analyzed + if (lanalyzed.outputSet.intersect(ranalyzed.outputSet).isEmpty) { + return plan + } + + // Otherwise, find the trivially true predicates and automatically resolves them to both sides. + // By the time we get here, since we have already run analysis, all attributes should've been + // resolved and become AttributeReference. + val cond = plan.condition.map { _.transform { + case EqualTo(a: AttributeReference, b: AttributeReference) if a.sameRef(b) => + EqualTo(plan.left.resolve(a.name), plan.right.resolve(b.name)) + }} + plan.copy(condition = cond) } /** @@ -623,16 +680,11 @@ class DataFrame private[sql]( } /** - * (Scala-specific) Compute aggregates by specifying a map from column name to - * aggregate methods. The resulting [[DataFrame]] will also contain the grouping columns. - * - * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`. + * (Scala-specific) Aggregates on the entire [[DataFrame]] without groups. * {{{ - * // Selects the age of the oldest employee and the aggregate expense for each department - * df.groupBy("department").agg( - * "age" -> "max", - * "expense" -> "sum" - * ) + * // df.agg(...) is a shorthand for df.groupBy().agg(...) + * df.agg("age" -> "max", "salary" -> "avg") + * df.groupBy().agg("age" -> "max", "salary" -> "avg") * }}} * @group dfops */ @@ -642,33 +694,33 @@ class DataFrame private[sql]( /** * (Scala-specific) Aggregates on the entire [[DataFrame]] without groups. - * {{ + * {{{ * // df.agg(...) is a shorthand for df.groupBy().agg(...) * df.agg(Map("age" -> "max", "salary" -> "avg")) * df.groupBy().agg(Map("age" -> "max", "salary" -> "avg")) - * }} + * }}} * @group dfops */ def agg(exprs: Map[String, String]): DataFrame = groupBy().agg(exprs) /** * (Java-specific) Aggregates on the entire [[DataFrame]] without groups. - * {{ + * {{{ * // df.agg(...) is a shorthand for df.groupBy().agg(...) * df.agg(Map("age" -> "max", "salary" -> "avg")) * df.groupBy().agg(Map("age" -> "max", "salary" -> "avg")) - * }} + * }}} * @group dfops */ def agg(exprs: java.util.Map[String, String]): DataFrame = groupBy().agg(exprs) /** * Aggregates on the entire [[DataFrame]] without groups. - * {{ + * {{{ * // df.agg(...) is a shorthand for df.groupBy().agg(...) * df.agg(max($"age"), avg($"salary")) * df.groupBy().agg(max($"age"), avg($"salary")) - * }} + * }}} * @group dfops */ @scala.annotation.varargs @@ -711,7 +763,7 @@ class DataFrame private[sql]( * @group dfops */ def sample(withReplacement: Boolean, fraction: Double, seed: Long): DataFrame = { - Sample(fraction, withReplacement, seed, logicalPlan) + Sample(0.0, fraction, withReplacement, seed, logicalPlan) } /** @@ -725,6 +777,42 @@ class DataFrame private[sql]( sample(withReplacement, fraction, Utils.random.nextLong) } + /** + * Randomly splits this [[DataFrame]] with the provided weights. + * + * @param weights weights for splits, will be normalized if they don't sum to 1. + * @param seed Seed for sampling. + * @group dfops + */ + def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame] = { + val sum = weights.sum + val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _) + normalizedCumWeights.sliding(2).map { x => + new DataFrame(sqlContext, Sample(x(0), x(1), false, seed, logicalPlan)) + }.toArray + } + + /** + * Randomly splits this [[DataFrame]] with the provided weights. + * + * @param weights weights for splits, will be normalized if they don't sum to 1. + * @group dfops + */ + def randomSplit(weights: Array[Double]): Array[DataFrame] = { + randomSplit(weights, Utils.random.nextLong) + } + + /** + * Randomly splits this [[DataFrame]] with the provided weights. Provided for the Python Api. + * + * @param weights weights for splits, will be normalized if they don't sum to 1. + * @param seed Seed for sampling. + * @group dfops + */ + private[spark] def randomSplit(weights: List[Double], seed: Long): Array[DataFrame] = { + randomSplit(weights.toArray, seed) + } + /** * (Scala-specific) Returns a new [[DataFrame]] where each row has been expanded to zero or more * rows by the provided function. This is similar to a `LATERAL VIEW` in HiveQL. The columns of @@ -809,15 +897,40 @@ class DataFrame private[sql]( /** * Returns a new [[DataFrame]] with a column renamed. + * This is a no-op if schema doesn't contain existingName. * @group dfops */ def withColumnRenamed(existingName: String, newName: String): DataFrame = { val resolver = sqlContext.analyzer.resolver - val colNames = schema.map { field => - val name = field.name - if (resolver(name, existingName)) Column(name).as(newName) else Column(name) + val shouldRename = schema.exists(f => resolver(f.name, existingName)) + if (shouldRename) { + val colNames = schema.map { field => + val name = field.name + if (resolver(name, existingName)) Column(name).as(newName) else Column(name) + } + select(colNames : _*) + } else { + this + } + } + + /** + * Returns a new [[DataFrame]] with a column dropped. + * This is a no-op if schema doesn't contain column name. + * @group dfops + */ + def drop(colName: String): DataFrame = { + val resolver = sqlContext.analyzer.resolver + val shouldDrop = schema.exists(f => resolver(f.name, colName)) + if (shouldDrop) { + val colsAfterDrop = schema.filter { field => + val name = field.name + !resolver(name, colName) + }.map(f => Column(f.name)) + select(colsAfterDrop : _*) + } else { + this } - select(colNames :_*) } /** @@ -1302,7 +1415,7 @@ class DataFrame private[sql]( new Iterator[String] { override def hasNext: Boolean = iter.hasNext override def next(): String = { - JsonRDD.rowToJSON(rowSchema, gen)(iter.next()) + JacksonGenerator(rowSchema, gen)(iter.next()) gen.flush() val json = writer.toString diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala new file mode 100644 index 0000000000000..a1e74470afc89 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala @@ -0,0 +1,136 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.execution.stat._ + +/** + * :: Experimental :: + * Statistic functions for [[DataFrame]]s. + */ +@Experimental +final class DataFrameStatFunctions private[sql](df: DataFrame) { + + /** + * Calculate the sample covariance of two numerical columns of a DataFrame. + * @param col1 the name of the first column + * @param col2 the name of the second column + * @return the covariance of the two columns. + */ + def cov(col1: String, col2: String): Double = { + StatFunctions.calculateCov(df, Seq(col1, col2)) + } + + /** + * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson + * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in + * MLlib's Statistics. + * + * @param col1 the name of the column + * @param col2 the name of the column to calculate the correlation against + * @return The Pearson Correlation Coefficient as a Double. + */ + def corr(col1: String, col2: String, method: String): Double = { + require(method == "pearson", "Currently only the calculation of the Pearson Correlation " + + "coefficient is supported.") + StatFunctions.pearsonCorrelation(df, Seq(col1, col2)) + } + + /** + * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame. + * + * @param col1 the name of the column + * @param col2 the name of the column to calculate the correlation against + * @return The Pearson Correlation Coefficient as a Double. + */ + def corr(col1: String, col2: String): Double = { + corr(col1, col2, "pearson") + } + + /** + * Computes a pair-wise frequency table of the given columns. Also known as a contingency table. + * The number of distinct values for each column should be less than 1e4. At most 1e6 non-zero + * pair frequencies will be returned. + * The first column of each row will be the distinct values of `col1` and the column names will + * be the distinct values of `col2`. The name of the first column will be `$col1_$col2`. Counts + * will be returned as `Long`s. Pairs that have no occurrences will have `null` as their counts. + * + * @param col1 The name of the first column. Distinct items will make the first item of + * each row. + * @param col2 The name of the second column. Distinct items will make the column names + * of the DataFrame. + * @return A DataFrame containing for the contingency table. + */ + def crosstab(col1: String, col2: String): DataFrame = { + StatFunctions.crossTabulate(df, col1, col2) + } + + /** + * Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in + * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. + * The `support` should be greater than 1e-4. + * + * @param cols the names of the columns to search frequent items in. + * @param support The minimum frequency for an item to be considered `frequent`. Should be greater + * than 1e-4. + * @return A Local DataFrame with the Array of frequent items for each column. + */ + def freqItems(cols: Array[String], support: Double): DataFrame = { + FrequentItems.singlePassFreqItems(df, cols, support) + } + + /** + * Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in + * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. + * Uses a `default` support of 1%. + * + * @param cols the names of the columns to search frequent items in. + * @return A Local DataFrame with the Array of frequent items for each column. + */ + def freqItems(cols: Array[String]): DataFrame = { + FrequentItems.singlePassFreqItems(df, cols, 0.01) + } + + /** + * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in + * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. + * + * @param cols the names of the columns to search frequent items in. + * @return A Local DataFrame with the Array of frequent items for each column. + */ + def freqItems(cols: Seq[String], support: Double): DataFrame = { + FrequentItems.singlePassFreqItems(df, cols, support) + } + + /** + * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in + * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. + * Uses a `default` support of 1%. + * + * @param cols the names of the columns to search frequent items in. + * @return A Local DataFrame with the Array of frequent items for each column. + */ + def freqItems(cols: Seq[String]): DataFrame = { + FrequentItems.singlePassFreqItems(df, cols, 0.01) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index 4fc5de7e824fe..bfaddd0f2ce1b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -30,6 +30,7 @@ private[spark] object SQLConf { val DEFAULT_SIZE_IN_BYTES = "spark.sql.defaultSizeInBytes" val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions" val CODEGEN_ENABLED = "spark.sql.codegen" + val UNSAFE_ENABLED = "spark.sql.unsafe.enabled" val DIALECT = "spark.sql.dialect" val PARQUET_BINARY_AS_STRING = "spark.sql.parquet.binaryAsString" @@ -51,6 +52,8 @@ private[spark] object SQLConf { // This is only used for the thriftserver val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool" + val THRIFTSERVER_UI_STATEMENT_LIMIT = "spark.sql.thriftserver.ui.retainedStatements" + val THRIFTSERVER_UI_SESSION_LIMIT = "spark.sql.thriftserver.ui.retainedSessions" // This is used to set the default data source val DEFAULT_DATA_SOURCE_NAME = "spark.sql.sources.default" @@ -64,8 +67,14 @@ private[spark] object SQLConf { // Set to false when debugging requires the ability to look at invalid query plans. val DATAFRAME_EAGER_ANALYSIS = "spark.sql.eagerAnalysis" + // Whether to automatically resolve ambiguity in join conditions for self-joins. + // See SPARK-6231. + val DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY = "spark.sql.selfJoinAutoResolveAmbiguity" + val USE_SQL_SERIALIZER2 = "spark.sql.useSerializer2" + val USE_JACKSON_STREAMING_API = "spark.sql.json.useJacksonStreamingAPI" + object Deprecated { val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks" } @@ -149,8 +158,22 @@ private[sql] class SQLConf extends Serializable { */ private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, "false").toBoolean + /** + * When set to true, Spark SQL will use managed memory for certain operations. This option only + * takes effect if codegen is enabled. + * + * Defaults to false as this feature is currently experimental. + */ + private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED, "false").toBoolean + private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2, "true").toBoolean + /** + * Selects between the new (true) and old (false) JSON handlers, to be removed in Spark 1.5.0 + */ + private[spark] def useJacksonStreamingAPI: Boolean = + getConf(USE_JACKSON_STREAMING_API, "true").toBoolean + /** * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to * a broadcast value during the physical executions of join operations. Setting this to -1 @@ -208,6 +231,9 @@ private[sql] class SQLConf extends Serializable { private[spark] def dataFrameEagerAnalysis: Boolean = getConf(DATAFRAME_EAGER_ANALYSIS, "true").toBoolean + private[spark] def dataFrameSelfJoinAutoResolveAmbiguity: Boolean = + getConf(DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY, "true").toBoolean + /** ********************** SQLConf functionality methods ************ */ /** Set Spark SQL configuration properties. */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index a279b0f07c38a..0563430a6fdc7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -24,6 +24,7 @@ import scala.collection.JavaConversions._ import scala.collection.immutable import scala.language.implicitConversions import scala.reflect.runtime.universe.TypeTag +import scala.util.control.NonFatal import com.google.common.reflect.TypeToken @@ -32,9 +33,11 @@ import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.errors.DialectException import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer} import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor +import org.apache.spark.sql.catalyst.Dialect import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, expressions} import org.apache.spark.sql.execution.{Filter, _} import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation} @@ -44,6 +47,42 @@ import org.apache.spark.sql.types._ import org.apache.spark.util.Utils import org.apache.spark.{Partition, SparkContext} +/** + * Currently we support the default dialect named "sql", associated with the class + * [[DefaultDialect]] + * + * And we can also provide custom SQL Dialect, for example in Spark SQL CLI: + * {{{ + *-- switch to "hiveql" dialect + * spark-sql>SET spark.sql.dialect=hiveql; + * spark-sql>SELECT * FROM src LIMIT 1; + * + *-- switch to "sql" dialect + * spark-sql>SET spark.sql.dialect=sql; + * spark-sql>SELECT * FROM src LIMIT 1; + * + *-- register the new SQL dialect + * spark-sql> SET spark.sql.dialect=com.xxx.xxx.SQL99Dialect; + * spark-sql> SELECT * FROM src LIMIT 1; + * + *-- register the non-exist SQL dialect + * spark-sql> SET spark.sql.dialect=NotExistedClass; + * spark-sql> SELECT * FROM src LIMIT 1; + * + *-- Exception will be thrown and switch to dialect + *-- "sql" (for SQLContext) or + *-- "hiveql" (for HiveContext) + * }}} + */ +private[spark] class DefaultDialect extends Dialect { + @transient + protected val sqlParser = new catalyst.SqlParser + + override def parse(sqlText: String): LogicalPlan = { + sqlParser.parse(sqlText) + } +} + /** * The entry point for working with structured data (rows and columns) in Spark. Allows the * creation of [[DataFrame]] objects as well as the execution of SQL queries. @@ -135,14 +174,27 @@ class SQLContext(@transient val sparkContext: SparkContext) protected[sql] val ddlParser = new DDLParser(sqlParser.parse(_)) @transient - protected[sql] val sqlParser = { - val fallback = new catalyst.SqlParser - new SparkSQLParser(fallback.parse(_)) + protected[sql] val sqlParser = new SparkSQLParser(getSQLDialect().parse(_)) + + protected[sql] def getSQLDialect(): Dialect = { + try { + val clazz = Utils.classForName(dialectClassName) + clazz.newInstance().asInstanceOf[Dialect] + } catch { + case NonFatal(e) => + // Since we didn't find the available SQL Dialect, it will fail even for SET command: + // SET spark.sql.dialect=sql; Let's reset as default dialect automatically. + val dialect = conf.dialect + // reset the sql dialect + conf.unsetConf(SQLConf.DIALECT) + // throw out the exception, and the default sql dialect will take effect for next query. + throw new DialectException( + s"""Instantiating dialect '$dialect' failed. + |Reverting to default dialect '${conf.dialect}'""".stripMargin, e) + } } - protected[sql] def parseSql(sql: String): LogicalPlan = { - ddlParser.parse(sql, false).getOrElse(sqlParser.parse(sql)) - } + protected[sql] def parseSql(sql: String): LogicalPlan = ddlParser.parse(sql, false) protected[sql] def executeSql(sql: String): this.QueryExecution = executePlan(parseSql(sql)) @@ -156,6 +208,12 @@ class SQLContext(@transient val sparkContext: SparkContext) @transient protected[sql] val defaultSession = createSession() + protected[sql] def dialectClassName = if (conf.dialect == "sql") { + classOf[DefaultDialect].getCanonicalName + } else { + conf.dialect + } + sparkContext.getConf.getAll.foreach { case (key, value) if key.startsWith("spark.sql") => setConf(key, value) case _ => @@ -428,20 +486,6 @@ class SQLContext(@transient val sparkContext: SparkContext) createDataFrame(rowRDD.rdd, schema) } - /** - * Creates a [[DataFrame]] from an [[JavaRDD]] containing [[Row]]s by applying - * a seq of names of columns to this RDD, the data type for each column will - * be inferred by the first row. - * - * @param rowRDD an JavaRDD of Row - * @param columns names for each column - * @return DataFrame - * @group dataframes - */ - def createDataFrame(rowRDD: JavaRDD[Row], columns: java.util.List[String]): DataFrame = { - createDataFrame(rowRDD.rdd, columns.toSeq) - } - /** * Applies a schema to an RDD of Java Beans. * @@ -615,13 +659,17 @@ class SQLContext(@transient val sparkContext: SparkContext) */ @Experimental def jsonRDD(json: RDD[String], schema: StructType): DataFrame = { - val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord - val appliedSchema = - Option(schema).getOrElse( - JsonRDD.nullTypeToStringType( - JsonRDD.inferSchema(json, 1.0, columnNameOfCorruptJsonRecord))) - val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) - createDataFrame(rowRDD, appliedSchema, needsConversion = false) + if (conf.useJacksonStreamingAPI) { + baseRelationToDataFrame(new JSONRelation(() => json, None, 1.0, Some(schema))(this)) + } else { + val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord + val appliedSchema = + Option(schema).getOrElse( + JsonRDD.nullTypeToStringType( + JsonRDD.inferSchema(json, 1.0, columnNameOfCorruptJsonRecord))) + val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) + createDataFrame(rowRDD, appliedSchema, needsConversion = false) + } } /** @@ -645,12 +693,16 @@ class SQLContext(@transient val sparkContext: SparkContext) */ @Experimental def jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame = { - val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord - val appliedSchema = - JsonRDD.nullTypeToStringType( - JsonRDD.inferSchema(json, samplingRatio, columnNameOfCorruptJsonRecord)) - val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) - createDataFrame(rowRDD, appliedSchema, needsConversion = false) + if (conf.useJacksonStreamingAPI) { + baseRelationToDataFrame(new JSONRelation(() => json, None, samplingRatio, None)(this)) + } else { + val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord + val appliedSchema = + JsonRDD.nullTypeToStringType( + JsonRDD.inferSchema(json, samplingRatio, columnNameOfCorruptJsonRecord)) + val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) + createDataFrame(rowRDD, appliedSchema, needsConversion = false) + } } /** @@ -945,11 +997,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * @group basic */ def sql(sqlText: String): DataFrame = { - if (conf.dialect == "sql") { - DataFrame(this, parseSql(sqlText)) - } else { - sys.error(s"Unsupported SQL dialect: ${conf.dialect}") - } + DataFrame(this, parseSql(sqlText)) } /** @@ -1011,6 +1059,8 @@ class SQLContext(@transient val sparkContext: SparkContext) def codegenEnabled: Boolean = self.conf.codegenEnabled + def unsafeEnabled: Boolean = self.conf.unsafeEnabled + def numPartitions: Int = self.conf.numShufflePartitions def strategies: Seq[Strategy] = diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala index 1fd387eec7e57..57effbf7ec501 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala @@ -84,7 +84,7 @@ object RDDConversions { } /** Logical plan node for scanning data from an RDD. */ -case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlContext: SQLContext) +private[sql] case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlContext: SQLContext) extends LogicalPlan with MultiInstanceRelation { override def children: Seq[LogicalPlan] = Nil @@ -105,11 +105,12 @@ case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlContext: SQLCont } /** Physical plan node for scanning data from an RDD. */ -case class PhysicalRDD(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode { +private[sql] case class PhysicalRDD(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode { override def execute(): RDD[Row] = rdd } /** Logical plan node for scanning data from a local collection. */ +private[sql] case class LogicalLocalTable(output: Seq[Attribute], rows: Seq[Row])(sqlContext: SQLContext) extends LogicalPlan with MultiInstanceRelation { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index b1ef6556de1e9..5d9f202681045 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.execution +import org.apache.spark.TaskContext import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.trees._ @@ -40,6 +41,7 @@ case class AggregateEvaluation( * ensure all values where `groupingExpressions` are equal are present. * @param groupingExpressions expressions that are evaluated to determine grouping. * @param aggregateExpressions expressions that are computed for each group. + * @param unsafeEnabled whether to allow Unsafe-based aggregation buffers to be used. * @param child the input data source. */ @DeveloperApi @@ -47,6 +49,7 @@ case class GeneratedAggregate( partial: Boolean, groupingExpressions: Seq[Expression], aggregateExpressions: Seq[NamedExpression], + unsafeEnabled: Boolean, child: SparkPlan) extends UnaryNode { @@ -225,6 +228,21 @@ case class GeneratedAggregate( case e: Expression if groupMap.contains(e) => groupMap(e) }) + val aggregationBufferSchema: StructType = StructType.fromAttributes(computationSchema) + + val groupKeySchema: StructType = { + val fields = groupingExpressions.zipWithIndex.map { case (expr, idx) => + // This is a dummy field name + StructField(idx.toString, expr.dataType, expr.nullable) + } + StructType(fields) + } + + val schemaSupportsUnsafe: Boolean = { + UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) && + UnsafeFixedWidthAggregationMap.supportsGroupKeySchema(groupKeySchema) + } + child.execute().mapPartitions { iter => // Builds a new custom class for holding the results of aggregation for a group. val initialValues = computeFunctions.flatMap(_.initialValues) @@ -265,7 +283,49 @@ case class GeneratedAggregate( val resultProjection = resultProjectionBuilder() Iterator(resultProjection(buffer)) + } else if (unsafeEnabled && schemaSupportsUnsafe) { + log.info("Using Unsafe-based aggregator") + val aggregationMap = new UnsafeFixedWidthAggregationMap( + newAggregationBuffer(EmptyRow), + aggregationBufferSchema, + groupKeySchema, + TaskContext.get.taskMemoryManager(), + 1024 * 16, // initial capacity + false // disable tracking of performance metrics + ) + + while (iter.hasNext) { + val currentRow: Row = iter.next() + val groupKey: Row = groupProjection(currentRow) + val aggregationBuffer = aggregationMap.getAggregationBuffer(groupKey) + updateProjection.target(aggregationBuffer)(joinedRow(aggregationBuffer, currentRow)) + } + + new Iterator[Row] { + private[this] val mapIterator = aggregationMap.iterator() + private[this] val resultProjection = resultProjectionBuilder() + + def hasNext: Boolean = mapIterator.hasNext + + def next(): Row = { + val entry = mapIterator.next() + val result = resultProjection(joinedRow(entry.key, entry.value)) + if (hasNext) { + result + } else { + // This is the last element in the iterator, so let's free the buffer. Before we do, + // though, we need to make a defensive copy of the result so that we don't return an + // object that might contain dangling pointers to the freed memory + val resultCopy = result.copy() + aggregationMap.free() + resultCopy + } + } + } } else { + if (unsafeEnabled) { + log.info("Not using Unsafe-based aggregator because it is not supported for this schema") + } val buffers = new java.util.HashMap[Row, MutableRow]() var currentRow: Row = null diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala index 8a8c3a404323a..ace9af5f384c9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala @@ -26,7 +26,7 @@ import org.apache.spark.sql.catalyst.expressions.Attribute /** * Physical plan node for scanning data from a local collection. */ -case class LocalTableScan(output: Seq[Attribute], rows: Seq[Row]) extends LeafNode { +private[sql] case class LocalTableScan(output: Seq[Attribute], rows: Seq[Row]) extends LeafNode { private lazy val rdd = sqlContext.sparkContext.parallelize(rows) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala index cec97de2cd8e4..35ad987eb1a63 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala @@ -50,10 +50,10 @@ private[sql] class Serializer2SerializationStream( extends SerializationStream with Logging { val rowOut = new DataOutputStream(out) - val writeKey = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut) - val writeValue = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut) + val writeKeyFunc = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut) + val writeValueFunc = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut) - def writeObject[T: ClassTag](t: T): SerializationStream = { + override def writeObject[T: ClassTag](t: T): SerializationStream = { val kv = t.asInstanceOf[Product2[Row, Row]] writeKey(kv._1) writeValue(kv._2) @@ -61,6 +61,16 @@ private[sql] class Serializer2SerializationStream( this } + override def writeKey[T: ClassTag](t: T): SerializationStream = { + writeKeyFunc(t.asInstanceOf[Row]) + this + } + + override def writeValue[T: ClassTag](t: T): SerializationStream = { + writeValueFunc(t.asInstanceOf[Row]) + this + } + def flush(): Unit = { rowOut.flush() } @@ -83,17 +93,27 @@ private[sql] class Serializer2DeserializationStream( val key = if (keySchema != null) new SpecificMutableRow(keySchema) else null val value = if (valueSchema != null) new SpecificMutableRow(valueSchema) else null - val readKey = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn, key) - val readValue = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn, value) + val readKeyFunc = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn, key) + val readValueFunc = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn, value) - def readObject[T: ClassTag](): T = { - readKey() - readValue() + override def readObject[T: ClassTag](): T = { + readKeyFunc() + readValueFunc() (key, value).asInstanceOf[T] } - def close(): Unit = { + override def readKey[T: ClassTag](): T = { + readKeyFunc() + key.asInstanceOf[T] + } + + override def readValue[T: ClassTag](): T = { + readValueFunc() + value.asInstanceOf[T] + } + + override def close(): Unit = { rowIn.close() } } @@ -134,6 +154,11 @@ private[sql] class SparkSqlSerializer2(keySchema: Array[DataType], valueSchema: with Serializable{ def newInstance(): SerializerInstance = new ShuffleSerializerInstance(keySchema, valueSchema) + + override def supportsRelocationOfSerializedObjects: Boolean = { + // SparkSqlSerializer2 is stateless and writes no stream headers + true + } } private[sql] object SparkSqlSerializer2 { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 3a0a6c86700a8..56a4689eb58f0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -136,10 +136,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { partial = false, namedGroupingAttributes, rewrittenAggregateExpressions, + unsafeEnabled, execution.GeneratedAggregate( partial = true, groupingExpressions, partialComputation, + unsafeEnabled, planLater(child))) :: Nil // Cases where some aggregate can not be codegened @@ -301,8 +303,10 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { execution.Expand(projections, output, planLater(child)) :: Nil case logical.Aggregate(group, agg, child) => execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil - case logical.Sample(fraction, withReplacement, seed, child) => - execution.Sample(fraction, withReplacement, seed, planLater(child)) :: Nil + case logical.Window(projectList, windowExpressions, spec, child) => + execution.Window(projectList, windowExpressions, spec, planLater(child)) :: Nil + case logical.Sample(lb, ub, withReplacement, seed, child) => + execution.Sample(lb, ub, withReplacement, seed, planLater(child)) :: Nil case logical.LocalRelation(output, data) => LocalTableScan(output, data) :: Nil case logical.Limit(IntegerLiteral(limit), child) => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala new file mode 100644 index 0000000000000..217b559def512 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala @@ -0,0 +1,480 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution + +import java.util + +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, Distribution, ClusteredDistribution, Partitioning} +import org.apache.spark.util.collection.CompactBuffer + +/** + * :: DeveloperApi :: + * For every row, evaluates `windowExpression` containing Window Functions and attaches + * the results with other regular expressions (presented by `projectList`). + * Evert operator handles a single Window Specification, `windowSpec`. + */ +case class Window( + projectList: Seq[Attribute], + windowExpression: Seq[NamedExpression], + windowSpec: WindowSpecDefinition, + child: SparkPlan) + extends UnaryNode { + + override def output: Seq[Attribute] = + (projectList ++ windowExpression).map(_.toAttribute) + + override def requiredChildDistribution: Seq[Distribution] = + if (windowSpec.partitionSpec.isEmpty) { + // This operator will be very expensive. + AllTuples :: Nil + } else { + ClusteredDistribution(windowSpec.partitionSpec) :: Nil + } + + // Since window functions are adding columns to the input rows, the child's outputPartitioning + // is preserved. + override def outputPartitioning: Partitioning = child.outputPartitioning + + override def requiredChildOrdering: Seq[Seq[SortOrder]] = { + // The required child ordering has two parts. + // The first part is the expressions in the partition specification. + // We add these expressions to the required ordering to make sure input rows are grouped + // based on the partition specification. So, we only need to process a single partition + // at a time. + // The second part is the expressions specified in the ORDER BY cluase. + // Basically, we first use sort to group rows based on partition specifications and then sort + // Rows in a group based on the order specification. + (windowSpec.partitionSpec.map(SortOrder(_, Ascending)) ++ windowSpec.orderSpec) :: Nil + } + + // Since window functions basically add columns to input rows, this operator + // will not change the ordering of input rows. + override def outputOrdering: Seq[SortOrder] = child.outputOrdering + + case class ComputedWindow( + unbound: WindowExpression, + windowFunction: WindowFunction, + resultAttribute: AttributeReference) + + // A list of window functions that need to be computed for each group. + private[this] val computedWindowExpressions = windowExpression.flatMap { window => + window.collect { + case w: WindowExpression => + ComputedWindow( + w, + BindReferences.bindReference(w.windowFunction, child.output), + AttributeReference(s"windowResult:$w", w.dataType, w.nullable)()) + } + }.toArray + + private[this] val windowFrame = + windowSpec.frameSpecification.asInstanceOf[SpecifiedWindowFrame] + + // Create window functions. + private[this] def windowFunctions(): Array[WindowFunction] = { + val functions = new Array[WindowFunction](computedWindowExpressions.length) + var i = 0 + while (i < computedWindowExpressions.length) { + functions(i) = computedWindowExpressions(i).windowFunction.newInstance() + functions(i).init() + i += 1 + } + functions + } + + // The schema of the result of all window function evaluations + private[this] val computedSchema = computedWindowExpressions.map(_.resultAttribute) + + private[this] val computedResultMap = + computedWindowExpressions.map { w => w.unbound -> w.resultAttribute }.toMap + + private[this] val windowExpressionResult = windowExpression.map { window => + window.transform { + case w: WindowExpression if computedResultMap.contains(w) => computedResultMap(w) + } + } + + def execute(): RDD[Row] = { + child.execute().mapPartitions { iter => + new Iterator[Row] { + + // Although input rows are grouped based on windowSpec.partitionSpec, we need to + // know when we have a new partition. + // This is to manually construct an ordering that can be used to compare rows. + // TODO: We may want to have a newOrdering that takes BoundReferences. + // So, we can take advantave of code gen. + private val partitionOrdering: Ordering[Row] = + RowOrdering.forSchema(windowSpec.partitionSpec.map(_.dataType)) + + // This is used to project expressions for the partition specification. + protected val partitionGenerator = + newMutableProjection(windowSpec.partitionSpec, child.output)() + + // This is ued to project expressions for the order specification. + protected val rowOrderGenerator = + newMutableProjection(windowSpec.orderSpec.map(_.child), child.output)() + + // The position of next output row in the inputRowBuffer. + var rowPosition: Int = 0 + // The number of buffered rows in the inputRowBuffer (the size of the current partition). + var partitionSize: Int = 0 + // The buffer used to buffer rows in a partition. + var inputRowBuffer: CompactBuffer[Row] = _ + // The partition key of the current partition. + var currentPartitionKey: Row = _ + // The partition key of next partition. + var nextPartitionKey: Row = _ + // The first row of next partition. + var firstRowInNextPartition: Row = _ + // Indicates if this partition is the last one in the iter. + var lastPartition: Boolean = false + + def createBoundaryEvaluator(): () => Unit = { + def findPhysicalBoundary( + boundary: FrameBoundary): () => Int = boundary match { + case UnboundedPreceding => () => 0 + case UnboundedFollowing => () => partitionSize - 1 + case CurrentRow => () => rowPosition + case ValuePreceding(value) => + () => + val newPosition = rowPosition - value + if (newPosition > 0) newPosition else 0 + case ValueFollowing(value) => + () => + val newPosition = rowPosition + value + if (newPosition < partitionSize) newPosition else partitionSize - 1 + } + + def findLogicalBoundary( + boundary: FrameBoundary, + searchDirection: Int, + evaluator: Expression, + joinedRow: JoinedRow): () => Int = boundary match { + case UnboundedPreceding => () => 0 + case UnboundedFollowing => () => partitionSize - 1 + case other => + () => { + // CurrentRow, ValuePreceding, or ValueFollowing. + var newPosition = rowPosition + searchDirection + var stopSearch = false + // rowOrderGenerator is a mutable projection. + // We need to make a copy of the returned by rowOrderGenerator since we will + // compare searched row with this currentOrderByValue. + val currentOrderByValue = rowOrderGenerator(inputRowBuffer(rowPosition)).copy() + while (newPosition >= 0 && newPosition < partitionSize && !stopSearch) { + val r = rowOrderGenerator(inputRowBuffer(newPosition)) + stopSearch = + !(evaluator.eval(joinedRow(currentOrderByValue, r)).asInstanceOf[Boolean]) + if (!stopSearch) { + newPosition += searchDirection + } + } + newPosition -= searchDirection + + if (newPosition < 0) { + 0 + } else if (newPosition >= partitionSize) { + partitionSize - 1 + } else { + newPosition + } + } + } + + windowFrame.frameType match { + case RowFrame => + val findStart = findPhysicalBoundary(windowFrame.frameStart) + val findEnd = findPhysicalBoundary(windowFrame.frameEnd) + () => { + frameStart = findStart() + frameEnd = findEnd() + } + case RangeFrame => + val joinedRowForBoundaryEvaluation: JoinedRow = new JoinedRow() + val orderByExpr = windowSpec.orderSpec.head + val currentRowExpr = + BoundReference(0, orderByExpr.dataType, orderByExpr.nullable) + val examedRowExpr = + BoundReference(1, orderByExpr.dataType, orderByExpr.nullable) + val differenceExpr = Abs(Subtract(currentRowExpr, examedRowExpr)) + + val frameStartEvaluator = windowFrame.frameStart match { + case CurrentRow => EqualTo(currentRowExpr, examedRowExpr) + case ValuePreceding(value) => + LessThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType)) + case ValueFollowing(value) => + GreaterThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType)) + case o => Literal(true) // This is just a dummy expression, we will not use it. + } + + val frameEndEvaluator = windowFrame.frameEnd match { + case CurrentRow => EqualTo(currentRowExpr, examedRowExpr) + case ValuePreceding(value) => + GreaterThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType)) + case ValueFollowing(value) => + LessThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType)) + case o => Literal(true) // This is just a dummy expression, we will not use it. + } + + val findStart = + findLogicalBoundary( + boundary = windowFrame.frameStart, + searchDirection = -1, + evaluator = frameStartEvaluator, + joinedRow = joinedRowForBoundaryEvaluation) + val findEnd = + findLogicalBoundary( + boundary = windowFrame.frameEnd, + searchDirection = 1, + evaluator = frameEndEvaluator, + joinedRow = joinedRowForBoundaryEvaluation) + () => { + frameStart = findStart() + frameEnd = findEnd() + } + } + } + + val boundaryEvaluator = createBoundaryEvaluator() + // Indicates if we the specified window frame requires us to maintain a sliding frame + // (e.g. RANGES BETWEEN 1 PRECEDING AND CURRENT ROW) or the window frame + // is the entire partition (e.g. ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING). + val requireUpdateFrame: Boolean = { + def requireUpdateBoundary(boundary: FrameBoundary): Boolean = boundary match { + case UnboundedPreceding => false + case UnboundedFollowing => false + case _ => true + } + + requireUpdateBoundary(windowFrame.frameStart) || + requireUpdateBoundary(windowFrame.frameEnd) + } + // The start position of the current frame in the partition. + var frameStart: Int = 0 + // The end position of the current frame in the partition. + var frameEnd: Int = -1 + // Window functions. + val functions: Array[WindowFunction] = windowFunctions() + // Buffers used to store input parameters for window functions. Because we may need to + // maintain a sliding frame, we use this buffer to avoid evaluate the parameters from + // the same row multiple times. + val windowFunctionParameterBuffers: Array[util.LinkedList[AnyRef]] = + functions.map(_ => new util.LinkedList[AnyRef]()) + + // The projection used to generate the final result rows of this operator. + private[this] val resultProjection = + newMutableProjection( + projectList ++ windowExpressionResult, + projectList ++ computedSchema)() + + // The row used to hold results of window functions. + private[this] val windowExpressionResultRow = + new GenericMutableRow(computedSchema.length) + + private[this] val joinedRow = new JoinedRow6 + + // Initialize this iterator. + initialize() + + private def initialize(): Unit = { + if (iter.hasNext) { + val currentRow = iter.next().copy() + // partitionGenerator is a mutable projection. Since we need to track nextPartitionKey, + // we are making a copy of the returned partitionKey at here. + nextPartitionKey = partitionGenerator(currentRow).copy() + firstRowInNextPartition = currentRow + fetchNextPartition() + } else { + // The iter is an empty one. So, we set all of the following variables + // to make sure hasNext will return false. + lastPartition = true + rowPosition = 0 + partitionSize = 0 + } + } + + // Indicates if we will have new output row. + override final def hasNext: Boolean = { + !lastPartition || (rowPosition < partitionSize) + } + + override final def next(): Row = { + if (hasNext) { + if (rowPosition == partitionSize) { + // All rows of this buffer have been consumed. + // We will move to next partition. + fetchNextPartition() + } + // Get the input row for the current output row. + val inputRow = inputRowBuffer(rowPosition) + // Get all results of the window functions for this output row. + var i = 0 + while (i < functions.length) { + windowExpressionResultRow.update(i, functions(i).get(rowPosition)) + i += 1 + } + + // Construct the output row. + val outputRow = resultProjection(joinedRow(inputRow, windowExpressionResultRow)) + // We will move to the next one. + rowPosition += 1 + if (requireUpdateFrame && rowPosition < partitionSize) { + // If we need to maintain a sliding frame and + // we will still work on this partition when next is called next time, do the update. + updateFrame() + } + + // Return the output row. + outputRow + } else { + // no more result + throw new NoSuchElementException + } + } + + // Fetch the next partition. + private def fetchNextPartition(): Unit = { + // Create a new buffer for input rows. + inputRowBuffer = new CompactBuffer[Row]() + // We already have the first row for this partition + // (recorded in firstRowInNextPartition). Add it back. + inputRowBuffer += firstRowInNextPartition + // Set the current partition key. + currentPartitionKey = nextPartitionKey + // Now, we will start to find all rows belonging to this partition. + // Create a variable to track if we see the next partition. + var findNextPartition = false + // The search will stop when we see the next partition or there is no + // input row left in the iter. + while (iter.hasNext && !findNextPartition) { + // Make a copy of the input row since we will put it in the buffer. + val currentRow = iter.next().copy() + // Get the partition key based on the partition specification. + // For the below compare method, we do not need to make a copy of partitionKey. + val partitionKey = partitionGenerator(currentRow) + // Check if the current row belongs the current input row. + val comparing = partitionOrdering.compare(currentPartitionKey, partitionKey) + if (comparing == 0) { + // This row is still in the current partition. + inputRowBuffer += currentRow + } else { + // The current input row is in a different partition. + findNextPartition = true + // partitionGenerator is a mutable projection. + // Since we need to track nextPartitionKey and we determine that it should be set + // as partitionKey, we are making a copy of the partitionKey at here. + nextPartitionKey = partitionKey.copy() + firstRowInNextPartition = currentRow + } + } + + // We have not seen a new partition. It means that there is no new row in the + // iter. The current partition is the last partition of the iter. + if (!findNextPartition) { + lastPartition = true + } + + // We have got all rows for the current partition. + // Set rowPosition to 0 (the next output row will be based on the first + // input row of this partition). + rowPosition = 0 + // The size of this partition. + partitionSize = inputRowBuffer.size + // Reset all parameter buffers of window functions. + var i = 0 + while (i < windowFunctionParameterBuffers.length) { + windowFunctionParameterBuffers(i).clear() + i += 1 + } + frameStart = 0 + frameEnd = -1 + // Create the first window frame for this partition. + // If we do not need to maintain a sliding frame, this frame will + // have the entire partition. + updateFrame() + } + + /** The function used to maintain the sliding frame. */ + private def updateFrame(): Unit = { + // Based on the difference between the new frame and old frame, + // updates the buffers holding input parameters of window functions. + // We will start to prepare input parameters starting from the row + // indicated by offset in the input row buffer. + def updateWindowFunctionParameterBuffers( + numToRemove: Int, + numToAdd: Int, + offset: Int): Unit = { + // First, remove unneeded entries from the head of every buffer. + var i = 0 + while (i < numToRemove) { + var j = 0 + while (j < windowFunctionParameterBuffers.length) { + windowFunctionParameterBuffers(j).remove() + j += 1 + } + i += 1 + } + // Then, add needed entries to the tail of every buffer. + i = 0 + while (i < numToAdd) { + var j = 0 + while (j < windowFunctionParameterBuffers.length) { + // Ask the function to prepare the input parameters. + val parameters = functions(j).prepareInputParameters(inputRowBuffer(i + offset)) + windowFunctionParameterBuffers(j).add(parameters) + j += 1 + } + i += 1 + } + } + + // Record the current frame start point and end point before + // we update them. + val previousFrameStart = frameStart + val previousFrameEnd = frameEnd + boundaryEvaluator() + updateWindowFunctionParameterBuffers( + frameStart - previousFrameStart, + frameEnd - previousFrameEnd, + previousFrameEnd + 1) + // Evaluate the current frame. + evaluateCurrentFrame() + } + + /** Evaluate the current window frame. */ + private def evaluateCurrentFrame(): Unit = { + var i = 0 + while (i < functions.length) { + // Reset the state of the window function. + functions(i).reset() + // Get all buffered input parameters based on rows of this window frame. + val inputParameters = windowFunctionParameterBuffers(i).toArray() + // Send these input parameters to the window function. + functions(i).batchUpdate(inputParameters) + // Ask the function to evaluate based on this window frame. + functions(i).evaluate() + i += 1 + } + } + } + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index 1afdb409417ce..5ca11e67a9434 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -63,16 +63,32 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { /** * :: DeveloperApi :: + * Sample the dataset. + * @param lowerBound Lower-bound of the sampling probability (usually 0.0) + * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled + * will be ub - lb. + * @param withReplacement Whether to sample with replacement. + * @param seed the random seed + * @param child the QueryPlan */ @DeveloperApi -case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: SparkPlan) +case class Sample( + lowerBound: Double, + upperBound: Double, + withReplacement: Boolean, + seed: Long, + child: SparkPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output // TODO: How to pick seed? override def execute(): RDD[Row] = { - child.execute().map(_.copy()).sample(withReplacement, fraction, seed) + if (withReplacement) { + child.execute().map(_.copy()).sample(withReplacement, upperBound - lowerBound, seed) + } else { + child.execute().map(_.copy()).randomSampleWithRange(lowerBound, upperBound, seed) + } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala index 99f24910fd61f..98df5bef34efa 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala @@ -42,7 +42,7 @@ trait RunnableCommand extends logical.Command { * A physical operator that executes the run method of a `RunnableCommand` and * saves the result to prevent multiple executions. */ -case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan { +private[sql] case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan { /** * A concrete command should override this lazy field to wrap up any side effects caused by the * command or any other computation that should be evaluated exactly once. The value of this field diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala index 56200f6b8c8a9..6aaf35fb429e2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala @@ -59,10 +59,7 @@ case class BroadcastNestedLoopJoin( } @transient private lazy val boundCondition = - InterpretedPredicate.create( - condition - .map(c => BindReferences.bindReference(c, left.output ++ right.output)) - .getOrElse(Literal(true))) + newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output) override def execute(): RDD[Row] = { val broadcastedRelation = diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala index e06f63f94b78b..b03af410dca08 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala @@ -45,10 +45,7 @@ case class LeftSemiJoinBNL( override def right: SparkPlan = broadcast @transient private lazy val boundCondition = - InterpretedPredicate.create( - condition - .map(c => BindReferences.bindReference(c, left.output ++ right.output)) - .getOrElse(Literal(true))) + newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output) override def execute(): RDD[Row] = { val broadcastedRelation = diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala new file mode 100644 index 0000000000000..5ae7e107544f8 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala @@ -0,0 +1,121 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.execution.stat + +import scala.collection.mutable.{Map => MutableMap} + +import org.apache.spark.Logging +import org.apache.spark.sql.{Column, DataFrame, Row} +import org.apache.spark.sql.catalyst.plans.logical.LocalRelation +import org.apache.spark.sql.types.{ArrayType, StructField, StructType} + +private[sql] object FrequentItems extends Logging { + + /** A helper class wrapping `MutableMap[Any, Long]` for simplicity. */ + private class FreqItemCounter(size: Int) extends Serializable { + val baseMap: MutableMap[Any, Long] = MutableMap.empty[Any, Long] + + /** + * Add a new example to the counts if it exists, otherwise deduct the count + * from existing items. + */ + def add(key: Any, count: Long): this.type = { + if (baseMap.contains(key)) { + baseMap(key) += count + } else { + if (baseMap.size < size) { + baseMap += key -> count + } else { + // TODO: Make this more efficient... A flatMap? + baseMap.retain((k, v) => v > count) + baseMap.transform((k, v) => v - count) + } + } + this + } + + /** + * Merge two maps of counts. + * @param other The map containing the counts for that partition + */ + def merge(other: FreqItemCounter): this.type = { + other.baseMap.foreach { case (k, v) => + add(k, v) + } + this + } + } + + /** + * Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in + * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. + * The `support` should be greater than 1e-4. + * For Internal use only. + * + * @param df The input DataFrame + * @param cols the names of the columns to search frequent items in + * @param support The minimum frequency for an item to be considered `frequent`. Should be greater + * than 1e-4. + * @return A Local DataFrame with the Array of frequent items for each column. + */ + private[sql] def singlePassFreqItems( + df: DataFrame, + cols: Seq[String], + support: Double): DataFrame = { + require(support >= 1e-4, s"support ($support) must be greater than 1e-4.") + val numCols = cols.length + // number of max items to keep counts for + val sizeOfMap = (1 / support).toInt + val countMaps = Seq.tabulate(numCols)(i => new FreqItemCounter(sizeOfMap)) + val originalSchema = df.schema + val colInfo = cols.map { name => + val index = originalSchema.fieldIndex(name) + (name, originalSchema.fields(index).dataType) + } + + val freqItems = df.select(cols.map(Column(_)):_*).rdd.aggregate(countMaps)( + seqOp = (counts, row) => { + var i = 0 + while (i < numCols) { + val thisMap = counts(i) + val key = row.get(i) + thisMap.add(key, 1L) + i += 1 + } + counts + }, + combOp = (baseCounts, counts) => { + var i = 0 + while (i < numCols) { + baseCounts(i).merge(counts(i)) + i += 1 + } + baseCounts + } + ) + val justItems = freqItems.map(m => m.baseMap.keys.toSeq) + val resultRow = Row(justItems:_*) + // append frequent Items to the column name for easy debugging + val outputCols = colInfo.map { v => + StructField(v._1 + "_freqItems", ArrayType(v._2, false)) + } + val schema = StructType(outputCols).toAttributes + new DataFrame(df.sqlContext, LocalRelation(schema, Seq(resultRow))) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala new file mode 100644 index 0000000000000..386ac969f1e7d --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala @@ -0,0 +1,129 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.stat + +import org.apache.spark.Logging +import org.apache.spark.sql.{Column, DataFrame} +import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, Cast} +import org.apache.spark.sql.catalyst.plans.logical.LocalRelation +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ + +private[sql] object StatFunctions extends Logging { + + /** Calculate the Pearson Correlation Coefficient for the given columns */ + private[sql] def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double = { + val counts = collectStatisticalData(df, cols) + counts.Ck / math.sqrt(counts.MkX * counts.MkY) + } + + /** Helper class to simplify tracking and merging counts. */ + private class CovarianceCounter extends Serializable { + var xAvg = 0.0 // the mean of all examples seen so far in col1 + var yAvg = 0.0 // the mean of all examples seen so far in col2 + var Ck = 0.0 // the co-moment after k examples + var MkX = 0.0 // sum of squares of differences from the (current) mean for col1 + var MkY = 0.0 // sum of squares of differences from the (current) mean for col1 + var count = 0L // count of observed examples + // add an example to the calculation + def add(x: Double, y: Double): this.type = { + val deltaX = x - xAvg + val deltaY = y - yAvg + count += 1 + xAvg += deltaX / count + yAvg += deltaY / count + Ck += deltaX * (y - yAvg) + MkX += deltaX * (x - xAvg) + MkY += deltaY * (y - yAvg) + this + } + // merge counters from other partitions. Formula can be found at: + // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance + def merge(other: CovarianceCounter): this.type = { + val totalCount = count + other.count + val deltaX = xAvg - other.xAvg + val deltaY = yAvg - other.yAvg + Ck += other.Ck + deltaX * deltaY * count / totalCount * other.count + xAvg = (xAvg * count + other.xAvg * other.count) / totalCount + yAvg = (yAvg * count + other.yAvg * other.count) / totalCount + MkX += other.MkX + deltaX * deltaX * count / totalCount * other.count + MkY += other.MkY + deltaY * deltaY * count / totalCount * other.count + count = totalCount + this + } + // return the sample covariance for the observed examples + def cov: Double = Ck / (count - 1) + } + + private def collectStatisticalData(df: DataFrame, cols: Seq[String]): CovarianceCounter = { + require(cols.length == 2, "Currently cov supports calculating the covariance " + + "between two columns.") + cols.map(name => (name, df.schema.fields.find(_.name == name))).foreach { case (name, data) => + require(data.nonEmpty, s"Couldn't find column with name $name") + require(data.get.dataType.isInstanceOf[NumericType], "Covariance calculation for columns " + + s"with dataType ${data.get.dataType} not supported.") + } + val columns = cols.map(n => Column(Cast(Column(n).expr, DoubleType))) + df.select(columns: _*).rdd.aggregate(new CovarianceCounter)( + seqOp = (counter, row) => { + counter.add(row.getDouble(0), row.getDouble(1)) + }, + combOp = (baseCounter, other) => { + baseCounter.merge(other) + }) + } + + /** + * Calculate the covariance of two numerical columns of a DataFrame. + * @param df The DataFrame + * @param cols the column names + * @return the covariance of the two columns. + */ + private[sql] def calculateCov(df: DataFrame, cols: Seq[String]): Double = { + val counts = collectStatisticalData(df, cols) + counts.cov + } + + /** Generate a table of frequencies for the elements of two columns. */ + private[sql] def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame = { + val tableName = s"${col1}_$col2" + val counts = df.groupBy(col1, col2).agg(col(col1), col(col2), count("*")).take(1e6.toInt) + if (counts.length == 1e6.toInt) { + logWarning("The maximum limit of 1e6 pairs have been collected, which may not be all of " + + "the pairs. Please try reducing the amount of distinct items in your columns.") + } + // get the distinct values of column 2, so that we can make them the column names + val distinctCol2 = counts.map(_.get(1)).distinct.zipWithIndex.toMap + val columnSize = distinctCol2.size + require(columnSize < 1e4, s"The number of distinct values for $col2, can't " + + s"exceed 1e4. Currently $columnSize") + val table = counts.groupBy(_.get(0)).map { case (col1Item, rows) => + val countsRow = new GenericMutableRow(columnSize + 1) + rows.foreach { row => + countsRow.setLong(distinctCol2.get(row.get(1)).get + 1, row.getLong(2)) + } + // the value of col1 is the first value, the rest are the counts + countsRow.setString(0, col1Item.toString) + countsRow + }.toSeq + val headerNames = distinctCol2.map(r => StructField(r._1.toString, LongType)).toSeq + val schema = StructType(StructField(tableName, StringType) +: headerNames) + + new DataFrame(df.sqlContext, LocalRelation(schema.toAttributes, table)) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index aa31d04a0cbe4..830b5017717b5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -24,8 +24,9 @@ import org.apache.spark.annotation.Experimental import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, Star} import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.mathfuncs._ import org.apache.spark.sql.types._ - +import org.apache.spark.util.Utils /** * :: Experimental :: @@ -35,6 +36,7 @@ import org.apache.spark.sql.types._ * @groupname agg_funcs Aggregate functions * @groupname sort_funcs Sorting functions * @groupname normal_funcs Non-aggregate functions + * @groupname math_funcs Math functions * @groupname Ungrouped Support functions for DataFrames. */ @Experimental @@ -84,10 +86,10 @@ object functions { /** * Returns a sort expression based on ascending order of the column. - * {{ + * {{{ * // Sort by dept in ascending order, and then age in descending order. * df.sort(asc("dept"), desc("age")) - * }} + * }}} * * @group sort_funcs */ @@ -95,10 +97,10 @@ object functions { /** * Returns a sort expression based on the descending order of the column. - * {{ + * {{{ * // Sort by dept in ascending order, and then age in descending order. * df.sort(asc("dept"), desc("age")) - * }} + * }}} * * @group sort_funcs */ @@ -283,6 +285,23 @@ object functions { */ def abs(e: Column): Column = Abs(e.expr) + /** + * Creates a new array column. The input columns must all have the same data type. + * + * @group normal_funcs + */ + @scala.annotation.varargs + def array(cols: Column*): Column = CreateArray(cols.map(_.expr)) + + /** + * Creates a new array column. The input columns must all have the same data type. + * + * @group normal_funcs + */ + def array(colName: String, colNames: String*): Column = { + array((colName +: colNames).map(col) : _*) + } + /** * Returns the first column that is not null. * {{{ @@ -334,18 +353,46 @@ object functions { /** * Inversion of boolean expression, i.e. NOT. - * {{ + * {{{ * // Scala: select rows that are not active (isActive === false) * df.filter( !df("isActive") ) * * // Java: * df.filter( not(df.col("isActive")) ); - * }} + * }}} * * @group normal_funcs */ def not(e: Column): Column = !e + /** + * Generate a random column with i.i.d. samples from U[0.0, 1.0]. + * + * @group normal_funcs + */ + def rand(seed: Long): Column = Rand(seed) + + /** + * Generate a random column with i.i.d. samples from U[0.0, 1.0]. + * + * @group normal_funcs + */ + def rand(): Column = rand(Utils.random.nextLong) + + /** + * Generate a column with i.i.d. samples from the standard normal distribution. + * + * @group normal_funcs + */ + def randn(seed: Long): Column = Randn(seed) + + /** + * Generate a column with i.i.d. samples from the standard normal distribution. + * + * @group normal_funcs + */ + def randn(): Column = randn(Utils.random.nextLong) + /** * Partition ID of the Spark task. * @@ -362,6 +409,28 @@ object functions { */ def sqrt(e: Column): Column = Sqrt(e.expr) + /** + * Creates a new struct column. The input column must be a column in a [[DataFrame]], or + * a derived column expression that is named (i.e. aliased). + * + * @group normal_funcs + */ + @scala.annotation.varargs + def struct(cols: Column*): Column = { + require(cols.forall(_.expr.isInstanceOf[NamedExpression]), + s"struct input columns must all be named or aliased ($cols)") + CreateStruct(cols.map(_.expr.asInstanceOf[NamedExpression])) + } + + /** + * Creates a new struct column that composes multiple input columns. + * + * @group normal_funcs + */ + def struct(colName: String, colNames: String*): Column = { + struct((colName +: colNames).map(col) : _*) + } + /** * Converts a string expression to upper case. * @@ -369,6 +438,489 @@ object functions { */ def upper(e: Column): Column = Upper(e.expr) + ////////////////////////////////////////////////////////////////////////////////////////////// + // Math Functions + ////////////////////////////////////////////////////////////////////////////////////////////// + + /** + * Computes the cosine inverse of the given value; the returned angle is in the range + * 0.0 through pi. + * + * @group math_funcs + */ + def acos(e: Column): Column = Acos(e.expr) + + /** + * Computes the cosine inverse of the given column; the returned angle is in the range + * 0.0 through pi. + * + * @group math_funcs + */ + def acos(columnName: String): Column = acos(Column(columnName)) + + /** + * Computes the sine inverse of the given value; the returned angle is in the range + * -pi/2 through pi/2. + * + * @group math_funcs + */ + def asin(e: Column): Column = Asin(e.expr) + + /** + * Computes the sine inverse of the given column; the returned angle is in the range + * -pi/2 through pi/2. + * + * @group math_funcs + */ + def asin(columnName: String): Column = asin(Column(columnName)) + + /** + * Computes the tangent inverse of the given value. + * + * @group math_funcs + */ + def atan(e: Column): Column = Atan(e.expr) + + /** + * Computes the tangent inverse of the given column. + * + * @group math_funcs + */ + def atan(columnName: String): Column = atan(Column(columnName)) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group math_funcs + */ + def atan2(l: Column, r: Column): Column = Atan2(l.expr, r.expr) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group math_funcs + */ + def atan2(l: Column, rightName: String): Column = atan2(l, Column(rightName)) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group math_funcs + */ + def atan2(leftName: String, r: Column): Column = atan2(Column(leftName), r) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group math_funcs + */ + def atan2(leftName: String, rightName: String): Column = + atan2(Column(leftName), Column(rightName)) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group math_funcs + */ + def atan2(l: Column, r: Double): Column = atan2(l, lit(r).expr) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group math_funcs + */ + def atan2(leftName: String, r: Double): Column = atan2(Column(leftName), r) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group math_funcs + */ + def atan2(l: Double, r: Column): Column = atan2(lit(l).expr, r) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group math_funcs + */ + def atan2(l: Double, rightName: String): Column = atan2(l, Column(rightName)) + + /** + * Computes the cube-root of the given value. + * + * @group math_funcs + */ + def cbrt(e: Column): Column = Cbrt(e.expr) + + /** + * Computes the cube-root of the given column. + * + * @group math_funcs + */ + def cbrt(columnName: String): Column = cbrt(Column(columnName)) + + /** + * Computes the ceiling of the given value. + * + * @group math_funcs + */ + def ceil(e: Column): Column = Ceil(e.expr) + + /** + * Computes the ceiling of the given column. + * + * @group math_funcs + */ + def ceil(columnName: String): Column = ceil(Column(columnName)) + + /** + * Computes the cosine of the given value. + * + * @group math_funcs + */ + def cos(e: Column): Column = Cos(e.expr) + + /** + * Computes the cosine of the given column. + * + * @group math_funcs + */ + def cos(columnName: String): Column = cos(Column(columnName)) + + /** + * Computes the hyperbolic cosine of the given value. + * + * @group math_funcs + */ + def cosh(e: Column): Column = Cosh(e.expr) + + /** + * Computes the hyperbolic cosine of the given column. + * + * @group math_funcs + */ + def cosh(columnName: String): Column = cosh(Column(columnName)) + + /** + * Computes the exponential of the given value. + * + * @group math_funcs + */ + def exp(e: Column): Column = Exp(e.expr) + + /** + * Computes the exponential of the given column. + * + * @group math_funcs + */ + def exp(columnName: String): Column = exp(Column(columnName)) + + /** + * Computes the exponential of the given value minus one. + * + * @group math_funcs + */ + def expm1(e: Column): Column = Expm1(e.expr) + + /** + * Computes the exponential of the given column. + * + * @group math_funcs + */ + def expm1(columnName: String): Column = expm1(Column(columnName)) + + /** + * Computes the floor of the given value. + * + * @group math_funcs + */ + def floor(e: Column): Column = Floor(e.expr) + + /** + * Computes the floor of the given column. + * + * @group math_funcs + */ + def floor(columnName: String): Column = floor(Column(columnName)) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group math_funcs + */ + def hypot(l: Column, r: Column): Column = Hypot(l.expr, r.expr) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group math_funcs + */ + def hypot(l: Column, rightName: String): Column = hypot(l, Column(rightName)) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group math_funcs + */ + def hypot(leftName: String, r: Column): Column = hypot(Column(leftName), r) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group math_funcs + */ + def hypot(leftName: String, rightName: String): Column = + hypot(Column(leftName), Column(rightName)) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group math_funcs + */ + def hypot(l: Column, r: Double): Column = hypot(l, lit(r).expr) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group math_funcs + */ + def hypot(leftName: String, r: Double): Column = hypot(Column(leftName), r) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group math_funcs + */ + def hypot(l: Double, r: Column): Column = hypot(lit(l).expr, r) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group math_funcs + */ + def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName)) + + /** + * Computes the natural logarithm of the given value. + * + * @group math_funcs + */ + def log(e: Column): Column = Log(e.expr) + + /** + * Computes the natural logarithm of the given column. + * + * @group math_funcs + */ + def log(columnName: String): Column = log(Column(columnName)) + + /** + * Computes the logarithm of the given value in Base 10. + * + * @group math_funcs + */ + def log10(e: Column): Column = Log10(e.expr) + + /** + * Computes the logarithm of the given value in Base 10. + * + * @group math_funcs + */ + def log10(columnName: String): Column = log10(Column(columnName)) + + /** + * Computes the natural logarithm of the given value plus one. + * + * @group math_funcs + */ + def log1p(e: Column): Column = Log1p(e.expr) + + /** + * Computes the natural logarithm of the given column plus one. + * + * @group math_funcs + */ + def log1p(columnName: String): Column = log1p(Column(columnName)) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group math_funcs + */ + def pow(l: Column, r: Column): Column = Pow(l.expr, r.expr) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group math_funcs + */ + def pow(l: Column, rightName: String): Column = pow(l, Column(rightName)) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group math_funcs + */ + def pow(leftName: String, r: Column): Column = pow(Column(leftName), r) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group math_funcs + */ + def pow(leftName: String, rightName: String): Column = pow(Column(leftName), Column(rightName)) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group math_funcs + */ + def pow(l: Column, r: Double): Column = pow(l, lit(r).expr) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group math_funcs + */ + def pow(leftName: String, r: Double): Column = pow(Column(leftName), r) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group math_funcs + */ + def pow(l: Double, r: Column): Column = pow(lit(l).expr, r) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group math_funcs + */ + def pow(l: Double, rightName: String): Column = pow(l, Column(rightName)) + + /** + * Returns the double value that is closest in value to the argument and + * is equal to a mathematical integer. + * + * @group math_funcs + */ + def rint(e: Column): Column = Rint(e.expr) + + /** + * Returns the double value that is closest in value to the argument and + * is equal to a mathematical integer. + * + * @group math_funcs + */ + def rint(columnName: String): Column = rint(Column(columnName)) + + /** + * Computes the signum of the given value. + * + * @group math_funcs + */ + def signum(e: Column): Column = Signum(e.expr) + + /** + * Computes the signum of the given column. + * + * @group math_funcs + */ + def signum(columnName: String): Column = signum(Column(columnName)) + + /** + * Computes the sine of the given value. + * + * @group math_funcs + */ + def sin(e: Column): Column = Sin(e.expr) + + /** + * Computes the sine of the given column. + * + * @group math_funcs + */ + def sin(columnName: String): Column = sin(Column(columnName)) + + /** + * Computes the hyperbolic sine of the given value. + * + * @group math_funcs + */ + def sinh(e: Column): Column = Sinh(e.expr) + + /** + * Computes the hyperbolic sine of the given column. + * + * @group math_funcs + */ + def sinh(columnName: String): Column = sinh(Column(columnName)) + + /** + * Computes the tangent of the given value. + * + * @group math_funcs + */ + def tan(e: Column): Column = Tan(e.expr) + + /** + * Computes the tangent of the given column. + * + * @group math_funcs + */ + def tan(columnName: String): Column = tan(Column(columnName)) + + /** + * Computes the hyperbolic tangent of the given value. + * + * @group math_funcs + */ + def tanh(e: Column): Column = Tanh(e.expr) + + /** + * Computes the hyperbolic tangent of the given column. + * + * @group math_funcs + */ + def tanh(columnName: String): Column = tanh(Column(columnName)) + + /** + * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. + * + * @group math_funcs + */ + def toDegrees(e: Column): Column = ToDegrees(e.expr) + + /** + * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. + * + * @group math_funcs + */ + def toDegrees(columnName: String): Column = toDegrees(Column(columnName)) + + /** + * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. + * + * @group math_funcs + */ + def toRadians(e: Column): Column = ToRadians(e.expr) + + /** + * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. + * + * @group math_funcs + */ + def toRadians(columnName: String): Column = toRadians(Column(columnName)) + + ////////////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index f3b5455574d1a..325a326e2b5b2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -37,7 +37,7 @@ private[sql] object JDBCRDD extends Logging { * @param sqlType - A field of java.sql.Types * @return The Catalyst type corresponding to sqlType. */ - private def getCatalystType(sqlType: Int): DataType = { + private def getCatalystType(sqlType: Int, precision: Int, scale: Int): DataType = { val answer = sqlType match { case java.sql.Types.ARRAY => null case java.sql.Types.BIGINT => LongType @@ -49,6 +49,8 @@ private[sql] object JDBCRDD extends Logging { case java.sql.Types.CLOB => StringType case java.sql.Types.DATALINK => null case java.sql.Types.DATE => DateType + case java.sql.Types.DECIMAL + if precision != 0 || scale != 0 => DecimalType(precision, scale) case java.sql.Types.DECIMAL => DecimalType.Unlimited case java.sql.Types.DISTINCT => null case java.sql.Types.DOUBLE => DoubleType @@ -61,6 +63,8 @@ private[sql] object JDBCRDD extends Logging { case java.sql.Types.NCHAR => StringType case java.sql.Types.NCLOB => StringType case java.sql.Types.NULL => null + case java.sql.Types.NUMERIC + if precision != 0 || scale != 0 => DecimalType(precision, scale) case java.sql.Types.NUMERIC => DecimalType.Unlimited case java.sql.Types.NVARCHAR => StringType case java.sql.Types.OTHER => null @@ -109,10 +113,11 @@ private[sql] object JDBCRDD extends Logging { val dataType = rsmd.getColumnType(i + 1) val typeName = rsmd.getColumnTypeName(i + 1) val fieldSize = rsmd.getPrecision(i + 1) + val fieldScale = rsmd.getScale(i + 1) val nullable = rsmd.isNullable(i + 1) != ResultSetMetaData.columnNoNulls val metadata = new MetadataBuilder().putString("name", columnName) var columnType = quirks.getCatalystType(dataType, typeName, fieldSize, metadata) - if (columnType == null) columnType = getCatalystType(dataType) + if (columnType == null) columnType = getCatalystType(dataType, fieldSize, fieldScale) fields(i) = StructField(columnName, columnType, nullable, metadata.build()) i = i + 1 } @@ -154,7 +159,7 @@ private[sql] object JDBCRDD extends Logging { def getConnector(driver: String, url: String, properties: Properties): () => Connection = { () => { try { - if (driver != null) Utils.getContextOrSparkClassLoader.loadClass(driver) + if (driver != null) DriverRegistry.register(driver) } catch { case e: ClassNotFoundException => { logWarning(s"Couldn't find class $driver", e); @@ -307,6 +312,7 @@ private[sql] class JDBCRDD( case BooleanType => BooleanConversion case DateType => DateConversion case DecimalType.Unlimited => DecimalConversion + case DecimalType.Fixed(d) => DecimalConversion case DoubleType => DoubleConversion case FloatType => FloatConversion case IntegerType => IntegerConversion @@ -357,7 +363,13 @@ private[sql] class JDBCRDD( case BooleanConversion => mutableRow.setBoolean(i, rs.getBoolean(pos)) case DateConversion => mutableRow.update(i, DateUtils.fromJavaDate(rs.getDate(pos))) - case DecimalConversion => mutableRow.update(i, rs.getBigDecimal(pos)) + case DecimalConversion => + val decimalVal = rs.getBigDecimal(pos) + if (decimalVal == null) { + mutableRow.update(i, null) + } else { + mutableRow.update(i, Decimal(decimalVal)) + } case DoubleConversion => mutableRow.setDouble(i, rs.getDouble(pos)) case FloatConversion => mutableRow.setFloat(i, rs.getFloat(pos)) case IntegerConversion => mutableRow.setInt(i, rs.getInt(pos)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala index 5f480083d5a49..d6b3fb3291a2e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala @@ -100,7 +100,7 @@ private[sql] class DefaultSource extends RelationProvider { val upperBound = parameters.getOrElse("upperBound", null) val numPartitions = parameters.getOrElse("numPartitions", null) - if (driver != null) Utils.getContextOrSparkClassLoader.loadClass(driver) + if (driver != null) DriverRegistry.register(driver) if (partitionColumn != null && (lowerBound == null || upperBound == null || numPartitions == null)) { @@ -136,7 +136,7 @@ private[sql] case class JDBCRelation( override val schema: StructType = JDBCRDD.resolveTable(url, table, properties) override def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = { - val driver: String = DriverManager.getDriver(url).getClass.getCanonicalName + val driver: String = DriverRegistry.getDriverClassName(url) JDBCRDD.scanTable( sqlContext.sparkContext, schema, diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala index d4e0abc040bc6..3a6c2c1e9101f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala @@ -17,10 +17,14 @@ package org.apache.spark.sql -import java.sql.{Connection, DriverManager, PreparedStatement} +import java.sql.{Connection, Driver, DriverManager, DriverPropertyInfo, PreparedStatement, SQLFeatureNotSupportedException} +import java.util.Properties + +import scala.collection.mutable import org.apache.spark.Logging import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils package object jdbc { private[sql] object JDBCWriteDetails extends Logging { @@ -179,4 +183,60 @@ package object jdbc { } } + + private [sql] class DriverWrapper(val wrapped: Driver) extends Driver { + override def acceptsURL(url: String): Boolean = wrapped.acceptsURL(url) + + override def jdbcCompliant(): Boolean = wrapped.jdbcCompliant() + + override def getPropertyInfo(url: String, info: Properties): Array[DriverPropertyInfo] = { + wrapped.getPropertyInfo(url, info) + } + + override def getMinorVersion: Int = wrapped.getMinorVersion + + def getParentLogger: java.util.logging.Logger = + throw new SQLFeatureNotSupportedException( + s"${this.getClass().getName}.getParentLogger is not yet implemented.") + + override def connect(url: String, info: Properties): Connection = wrapped.connect(url, info) + + override def getMajorVersion: Int = wrapped.getMajorVersion + } + + /** + * java.sql.DriverManager is always loaded by bootstrap classloader, + * so it can't load JDBC drivers accessible by Spark ClassLoader. + * + * To solve the problem, drivers from user-supplied jars are wrapped + * into thin wrapper. + */ + private [sql] object DriverRegistry extends Logging { + + private val wrapperMap: mutable.Map[String, DriverWrapper] = mutable.Map.empty + + def register(className: String): Unit = { + val cls = Utils.getContextOrSparkClassLoader.loadClass(className) + if (cls.getClassLoader == null) { + logTrace(s"$className has been loaded with bootstrap ClassLoader, wrapper is not required") + } else if (wrapperMap.get(className).isDefined) { + logTrace(s"Wrapper for $className already exists") + } else { + synchronized { + if (wrapperMap.get(className).isEmpty) { + val wrapper = new DriverWrapper(cls.newInstance().asInstanceOf[Driver]) + DriverManager.registerDriver(wrapper) + wrapperMap(className) = wrapper + logTrace(s"Wrapper for $className registered") + } + } + } + } + + def getDriverClassName(url: String): String = DriverManager.getDriver(url) match { + case wrapper: DriverWrapper => wrapper.wrapped.getClass.getCanonicalName + case driver => driver.getClass.getCanonicalName + } + } + } // package object jdbc diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala new file mode 100644 index 0000000000000..9c58b8e4bb16a --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala @@ -0,0 +1,171 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.json + +import com.fasterxml.jackson.core._ + +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion +import org.apache.spark.sql.json.JacksonUtils.nextUntil +import org.apache.spark.sql.types._ + +private[sql] object InferSchema { + /** + * Infer the type of a collection of json records in three stages: + * 1. Infer the type of each record + * 2. Merge types by choosing the lowest type necessary to cover equal keys + * 3. Replace any remaining null fields with string, the top type + */ + def apply( + json: RDD[String], + samplingRatio: Double = 1.0, + columnNameOfCorruptRecords: String): StructType = { + require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0") + val schemaData = if (samplingRatio > 0.99) { + json + } else { + json.sample(withReplacement = false, samplingRatio, 1) + } + + // perform schema inference on each row and merge afterwards + schemaData.mapPartitions { iter => + val factory = new JsonFactory() + iter.map { row => + try { + val parser = factory.createParser(row) + parser.nextToken() + inferField(parser) + } catch { + case _: JsonParseException => + StructType(Seq(StructField(columnNameOfCorruptRecords, StringType))) + } + } + }.treeAggregate[DataType](StructType(Seq()))(compatibleRootType, compatibleRootType) match { + case st: StructType => nullTypeToStringType(st) + } + } + + /** + * Infer the type of a json document from the parser's token stream + */ + private def inferField(parser: JsonParser): DataType = { + import com.fasterxml.jackson.core.JsonToken._ + parser.getCurrentToken match { + case null | VALUE_NULL => NullType + + case FIELD_NAME => + parser.nextToken() + inferField(parser) + + case VALUE_STRING if parser.getTextLength < 1 => + // Zero length strings and nulls have special handling to deal + // with JSON generators that do not distinguish between the two. + // To accurately infer types for empty strings that are really + // meant to represent nulls we assume that the two are isomorphic + // but will defer treating null fields as strings until all the + // record fields' types have been combined. + NullType + + case VALUE_STRING => StringType + case START_OBJECT => + val builder = Seq.newBuilder[StructField] + while (nextUntil(parser, END_OBJECT)) { + builder += StructField(parser.getCurrentName, inferField(parser), nullable = true) + } + + StructType(builder.result().sortBy(_.name)) + + case START_ARRAY => + // If this JSON array is empty, we use NullType as a placeholder. + // If this array is not empty in other JSON objects, we can resolve + // the type as we pass through all JSON objects. + var elementType: DataType = NullType + while (nextUntil(parser, END_ARRAY)) { + elementType = compatibleType(elementType, inferField(parser)) + } + + ArrayType(elementType) + + case VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT => + import JsonParser.NumberType._ + parser.getNumberType match { + // For Integer values, use LongType by default. + case INT | LONG => LongType + // Since we do not have a data type backed by BigInteger, + // when we see a Java BigInteger, we use DecimalType. + case BIG_INTEGER | BIG_DECIMAL => DecimalType.Unlimited + case FLOAT | DOUBLE => DoubleType + } + + case VALUE_TRUE | VALUE_FALSE => BooleanType + } + } + + private def nullTypeToStringType(struct: StructType): StructType = { + val fields = struct.fields.map { + case StructField(fieldName, dataType, nullable, _) => + val newType = dataType match { + case NullType => StringType + case ArrayType(NullType, containsNull) => ArrayType(StringType, containsNull) + case ArrayType(struct: StructType, containsNull) => + ArrayType(nullTypeToStringType(struct), containsNull) + case struct: StructType =>nullTypeToStringType(struct) + case other: DataType => other + } + + StructField(fieldName, newType, nullable) + } + + StructType(fields) + } + + /** + * Remove top-level ArrayType wrappers and merge the remaining schemas + */ + private def compatibleRootType: (DataType, DataType) => DataType = { + case (ArrayType(ty1, _), ty2) => compatibleRootType(ty1, ty2) + case (ty1, ArrayType(ty2, _)) => compatibleRootType(ty1, ty2) + case (ty1, ty2) => compatibleType(ty1, ty2) + } + + /** + * Returns the most general data type for two given data types. + */ + private[json] def compatibleType(t1: DataType, t2: DataType): DataType = { + HiveTypeCoercion.findTightestCommonType(t1, t2).getOrElse { + // t1 or t2 is a StructType, ArrayType, or an unexpected type. + (t1, t2) match { + case (other: DataType, NullType) => other + case (NullType, other: DataType) => other + case (StructType(fields1), StructType(fields2)) => + val newFields = (fields1 ++ fields2).groupBy(field => field.name).map { + case (name, fieldTypes) => + val dataType = fieldTypes.view.map(_.dataType).reduce(compatibleType) + StructField(name, dataType, nullable = true) + } + StructType(newFields.toSeq.sortBy(_.name)) + + case (ArrayType(elementType1, containsNull1), ArrayType(elementType2, containsNull2)) => + ArrayType(compatibleType(elementType1, elementType2), containsNull1 || containsNull2) + + // strings and every string is a Json object. + case (_, _) => StringType + } + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala index e3352d02787fd..c772cd1f53e53 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala @@ -22,14 +22,16 @@ import java.io.IOException import org.apache.hadoop.fs.Path import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.sql.catalyst.expressions.{Expression, Attribute, Row} import org.apache.spark.sql.sources._ -import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.types.{StructField, StructType} import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode} private[sql] class DefaultSource - extends RelationProvider with SchemaRelationProvider with CreatableRelationProvider { + extends RelationProvider + with SchemaRelationProvider + with CreatableRelationProvider { private def checkPath(parameters: Map[String, String]): String = { parameters.getOrElse("path", sys.error("'path' must be specified for json data.")) @@ -42,7 +44,7 @@ private[sql] class DefaultSource val path = checkPath(parameters) val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0) - JSONRelation(path, samplingRatio, None)(sqlContext) + new JSONRelation(path, samplingRatio, None, sqlContext) } /** Returns a new base relation with the given schema and parameters. */ @@ -53,7 +55,7 @@ private[sql] class DefaultSource val path = checkPath(parameters) val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0) - JSONRelation(path, samplingRatio, Some(schema))(sqlContext) + new JSONRelation(path, samplingRatio, Some(schema), sqlContext) } override def createRelation( @@ -101,32 +103,87 @@ private[sql] class DefaultSource } } -private[sql] case class JSONRelation( - path: String, - samplingRatio: Double, +private[sql] class JSONRelation( + // baseRDD is not immutable with respect to INSERT OVERWRITE + // and so it must be recreated at least as often as the + // underlying inputs are modified. To be safe, a function is + // used instead of a regular RDD value to ensure a fresh RDD is + // recreated for each and every operation. + baseRDD: () => RDD[String], + val path: Option[String], + val samplingRatio: Double, userSpecifiedSchema: Option[StructType])( @transient val sqlContext: SQLContext) extends BaseRelation with TableScan - with InsertableRelation { - - // TODO: Support partitioned JSON relation. - private def baseRDD = sqlContext.sparkContext.textFile(path) + with InsertableRelation + with CatalystScan { + + def this( + path: String, + samplingRatio: Double, + userSpecifiedSchema: Option[StructType], + sqlContext: SQLContext) = + this( + () => sqlContext.sparkContext.textFile(path), + Some(path), + samplingRatio, + userSpecifiedSchema)(sqlContext) + + private val useJacksonStreamingAPI: Boolean = sqlContext.conf.useJacksonStreamingAPI override val needConversion: Boolean = false - override val schema = userSpecifiedSchema.getOrElse( - JsonRDD.nullTypeToStringType( - JsonRDD.inferSchema( - baseRDD, + override lazy val schema = userSpecifiedSchema.getOrElse { + if (useJacksonStreamingAPI) { + InferSchema( + baseRDD(), samplingRatio, - sqlContext.conf.columnNameOfCorruptRecord))) + sqlContext.conf.columnNameOfCorruptRecord) + } else { + JsonRDD.nullTypeToStringType( + JsonRDD.inferSchema( + baseRDD(), + samplingRatio, + sqlContext.conf.columnNameOfCorruptRecord)) + } + } - override def buildScan(): RDD[Row] = - JsonRDD.jsonStringToRow(baseRDD, schema, sqlContext.conf.columnNameOfCorruptRecord) + override def buildScan(): RDD[Row] = { + if (useJacksonStreamingAPI) { + JacksonParser( + baseRDD(), + schema, + sqlContext.conf.columnNameOfCorruptRecord) + } else { + JsonRDD.jsonStringToRow( + baseRDD(), + schema, + sqlContext.conf.columnNameOfCorruptRecord) + } + } + + override def buildScan(requiredColumns: Seq[Attribute], filters: Seq[Expression]): RDD[Row] = { + if (useJacksonStreamingAPI) { + JacksonParser( + baseRDD(), + StructType.fromAttributes(requiredColumns), + sqlContext.conf.columnNameOfCorruptRecord) + } else { + JsonRDD.jsonStringToRow( + baseRDD(), + StructType.fromAttributes(requiredColumns), + sqlContext.conf.columnNameOfCorruptRecord) + } + } override def insert(data: DataFrame, overwrite: Boolean): Unit = { - val filesystemPath = new Path(path) + val filesystemPath = path match { + case Some(p) => new Path(p) + case None => + throw new IOException(s"Cannot INSERT into table with no path defined") + } + val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration) if (overwrite) { @@ -147,7 +204,7 @@ private[sql] case class JSONRelation( } } // Write the data. - data.toJSON.saveAsTextFile(path) + data.toJSON.saveAsTextFile(filesystemPath.toString) // Right now, we assume that the schema is not changed. We will not update the schema. // schema = data.schema } else { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala new file mode 100644 index 0000000000000..80bf74aa02602 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala @@ -0,0 +1,77 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.json + +import scala.collection.Map + +import com.fasterxml.jackson.core._ + +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.types._ + +private[sql] object JacksonGenerator { + /** Transforms a single Row to JSON using Jackson + * + * @param rowSchema the schema object used for conversion + * @param gen a JsonGenerator object + * @param row The row to convert + */ + def apply(rowSchema: StructType, gen: JsonGenerator)(row: Row): Unit = { + def valWriter: (DataType, Any) => Unit = { + case (_, null) | (NullType, _) => gen.writeNull() + case (StringType, v: String) => gen.writeString(v) + case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString) + case (IntegerType, v: Int) => gen.writeNumber(v) + case (ShortType, v: Short) => gen.writeNumber(v) + case (FloatType, v: Float) => gen.writeNumber(v) + case (DoubleType, v: Double) => gen.writeNumber(v) + case (LongType, v: Long) => gen.writeNumber(v) + case (DecimalType(), v: java.math.BigDecimal) => gen.writeNumber(v) + case (ByteType, v: Byte) => gen.writeNumber(v.toInt) + case (BinaryType, v: Array[Byte]) => gen.writeBinary(v) + case (BooleanType, v: Boolean) => gen.writeBoolean(v) + case (DateType, v) => gen.writeString(v.toString) + case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v)) + + case (ArrayType(ty, _), v: Seq[_] ) => + gen.writeStartArray() + v.foreach(valWriter(ty,_)) + gen.writeEndArray() + + case (MapType(kv,vv, _), v: Map[_,_]) => + gen.writeStartObject() + v.foreach { p => + gen.writeFieldName(p._1.toString) + valWriter(vv,p._2) + } + gen.writeEndObject() + + case (StructType(ty), v: Row) => + gen.writeStartObject() + ty.zip(v.toSeq).foreach { + case (_, null) => + case (field, v) => + gen.writeFieldName(field.name) + valWriter(field.dataType, v) + } + gen.writeEndObject() + } + + valWriter(rowSchema, row) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala new file mode 100644 index 0000000000000..a8e69ae61174f --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala @@ -0,0 +1,215 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.json + +import java.io.ByteArrayOutputStream +import java.sql.Timestamp + +import scala.collection.Map + +import com.fasterxml.jackson.core._ + +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.json.JacksonUtils.nextUntil +import org.apache.spark.sql.types._ + +private[sql] object JacksonParser { + def apply( + json: RDD[String], + schema: StructType, + columnNameOfCorruptRecords: String): RDD[Row] = { + parseJson(json, schema, columnNameOfCorruptRecords) + } + + /** + * Parse the current token (and related children) according to a desired schema + */ + private[sql] def convertField( + factory: JsonFactory, + parser: JsonParser, + schema: DataType): Any = { + import com.fasterxml.jackson.core.JsonToken._ + (parser.getCurrentToken, schema) match { + case (null | VALUE_NULL, _) => + null + + case (FIELD_NAME, _) => + parser.nextToken() + convertField(factory, parser, schema) + + case (VALUE_STRING, StringType) => + UTF8String(parser.getText) + + case (VALUE_STRING, _) if parser.getTextLength < 1 => + // guard the non string type + null + + case (VALUE_STRING, DateType) => + DateUtils.millisToDays(DateUtils.stringToTime(parser.getText).getTime) + + case (VALUE_STRING, TimestampType) => + new Timestamp(DateUtils.stringToTime(parser.getText).getTime) + + case (VALUE_NUMBER_INT, TimestampType) => + new Timestamp(parser.getLongValue) + + case (_, StringType) => + val writer = new ByteArrayOutputStream() + val generator = factory.createGenerator(writer, JsonEncoding.UTF8) + generator.copyCurrentStructure(parser) + generator.close() + UTF8String(writer.toByteArray) + + case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, FloatType) => + parser.getFloatValue + + case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DoubleType) => + parser.getDoubleValue + + case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DecimalType()) => + // TODO: add fixed precision and scale handling + Decimal(parser.getDecimalValue) + + case (VALUE_NUMBER_INT, ByteType) => + parser.getByteValue + + case (VALUE_NUMBER_INT, ShortType) => + parser.getShortValue + + case (VALUE_NUMBER_INT, IntegerType) => + parser.getIntValue + + case (VALUE_NUMBER_INT, LongType) => + parser.getLongValue + + case (VALUE_TRUE, BooleanType) => + true + + case (VALUE_FALSE, BooleanType) => + false + + case (START_OBJECT, st: StructType) => + convertObject(factory, parser, st) + + case (START_ARRAY, ArrayType(st, _)) => + convertList(factory, parser, st) + + case (START_OBJECT, ArrayType(st, _)) => + // the business end of SPARK-3308: + // when an object is found but an array is requested just wrap it in a list + convertField(factory, parser, st) :: Nil + + case (START_OBJECT, MapType(StringType, kt, _)) => + convertMap(factory, parser, kt) + + case (_, udt: UserDefinedType[_]) => + udt.deserialize(convertField(factory, parser, udt.sqlType)) + } + } + + /** + * Parse an object from the token stream into a new Row representing the schema. + * + * Fields in the json that are not defined in the requested schema will be dropped. + */ + private def convertObject(factory: JsonFactory, parser: JsonParser, schema: StructType): Row = { + val row = new GenericMutableRow(schema.length) + while (nextUntil(parser, JsonToken.END_OBJECT)) { + schema.getFieldIndex(parser.getCurrentName) match { + case Some(index) => + row.update(index, convertField(factory, parser, schema(index).dataType)) + + case None => + parser.skipChildren() + } + } + + row + } + + /** + * Parse an object as a Map, preserving all fields + */ + private def convertMap( + factory: JsonFactory, + parser: JsonParser, + valueType: DataType): Map[String, Any] = { + val builder = Map.newBuilder[String, Any] + while (nextUntil(parser, JsonToken.END_OBJECT)) { + builder += parser.getCurrentName -> convertField(factory, parser, valueType) + } + + builder.result() + } + + private def convertList( + factory: JsonFactory, + parser: JsonParser, + schema: DataType): Seq[Any] = { + val builder = Seq.newBuilder[Any] + while (nextUntil(parser, JsonToken.END_ARRAY)) { + builder += convertField(factory, parser, schema) + } + + builder.result() + } + + private def parseJson( + json: RDD[String], + schema: StructType, + columnNameOfCorruptRecords: String): RDD[Row] = { + + def failedRecord(record: String): Seq[Row] = { + // create a row even if no corrupt record column is present + val row = new GenericMutableRow(schema.length) + for (corruptIndex <- schema.getFieldIndex(columnNameOfCorruptRecords)) { + require(schema(corruptIndex).dataType == StringType) + row.update(corruptIndex, record) + } + + Seq(row) + } + + json.mapPartitions { iter => + val factory = new JsonFactory() + + iter.flatMap { record => + try { + val parser = factory.createParser(record) + parser.nextToken() + + // to support both object and arrays (see SPARK-3308) we'll start + // by converting the StructType schema to an ArrayType and let + // convertField wrap an object into a single value array when necessary. + convertField(factory, parser, ArrayType(schema)) match { + case null => failedRecord(record) + case list: Seq[Row @unchecked] => list + case _ => + sys.error( + s"Failed to parse record $record. Please make sure that each line of the file " + + "(or each string in the RDD) is a valid JSON object or an array of JSON objects.") + } + } catch { + case _: JsonProcessingException => + failedRecord(record) + } + } + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala new file mode 100644 index 0000000000000..fde96852ce68e --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala @@ -0,0 +1,32 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.json + +import com.fasterxml.jackson.core.{JsonParser, JsonToken} + +private object JacksonUtils { + /** + * Advance the parser until a null or a specific token is found + */ + def nextUntil(parser: JsonParser, stopOn: JsonToken): Boolean = { + parser.nextToken() match { + case null => false + case x => x != stopOn + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala index 6e94e7056eb0b..f62973d5fcfab 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala @@ -440,54 +440,4 @@ private[sql] object JsonRDD extends Logging { row } - - /** Transforms a single Row to JSON using Jackson - * - * @param rowSchema the schema object used for conversion - * @param gen a JsonGenerator object - * @param row The row to convert - */ - private[sql] def rowToJSON(rowSchema: StructType, gen: JsonGenerator)(row: Row) = { - def valWriter: (DataType, Any) => Unit = { - case (_, null) | (NullType, _) => gen.writeNull() - case (StringType, v: String) => gen.writeString(v) - case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString) - case (IntegerType, v: Int) => gen.writeNumber(v) - case (ShortType, v: Short) => gen.writeNumber(v) - case (FloatType, v: Float) => gen.writeNumber(v) - case (DoubleType, v: Double) => gen.writeNumber(v) - case (LongType, v: Long) => gen.writeNumber(v) - case (DecimalType(), v: java.math.BigDecimal) => gen.writeNumber(v) - case (ByteType, v: Byte) => gen.writeNumber(v.toInt) - case (BinaryType, v: Array[Byte]) => gen.writeBinary(v) - case (BooleanType, v: Boolean) => gen.writeBoolean(v) - case (DateType, v) => gen.writeString(v.toString) - case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, v) - - case (ArrayType(ty, _), v: Seq[_] ) => - gen.writeStartArray() - v.foreach(valWriter(ty,_)) - gen.writeEndArray() - - case (MapType(kv,vv, _), v: Map[_,_]) => - gen.writeStartObject() - v.foreach { p => - gen.writeFieldName(p._1.toString) - valWriter(vv,p._2) - } - gen.writeEndObject() - - case (StructType(ty), v: Row) => - gen.writeStartObject() - ty.zip(v.toSeq).foreach { - case (_, null) => - case (field, v) => - gen.writeFieldName(field.name) - valWriter(field.dataType, v) - } - gen.writeEndObject() - } - - valWriter(rowSchema, row) - } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala deleted file mode 100644 index 84f62bf47f955..0000000000000 --- a/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala +++ /dev/null @@ -1,562 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql - -import scala.language.implicitConversions - -import org.apache.spark.annotation.Experimental -import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.expressions.mathfuncs._ -import org.apache.spark.sql.functions.lit - -/** - * :: Experimental :: - * Mathematical Functions available for [[DataFrame]]. - * - * @groupname double_funcs Functions that require DoubleType as an input - * @groupname int_funcs Functions that require IntegerType as an input - * @groupname float_funcs Functions that require FloatType as an input - * @groupname long_funcs Functions that require LongType as an input - */ -@Experimental -// scalastyle:off -object mathfunctions { -// scalastyle:on - - private[this] implicit def toColumn(expr: Expression): Column = Column(expr) - - /** - * Computes the sine of the given value. - * - * @group double_funcs - */ - def sin(e: Column): Column = Sin(e.expr) - - /** - * Computes the sine of the given column. - * - * @group double_funcs - */ - def sin(columnName: String): Column = sin(Column(columnName)) - - /** - * Computes the sine inverse of the given value; the returned angle is in the range - * -pi/2 through pi/2. - * - * @group double_funcs - */ - def asin(e: Column): Column = Asin(e.expr) - - /** - * Computes the sine inverse of the given column; the returned angle is in the range - * -pi/2 through pi/2. - * - * @group double_funcs - */ - def asin(columnName: String): Column = asin(Column(columnName)) - - /** - * Computes the hyperbolic sine of the given value. - * - * @group double_funcs - */ - def sinh(e: Column): Column = Sinh(e.expr) - - /** - * Computes the hyperbolic sine of the given column. - * - * @group double_funcs - */ - def sinh(columnName: String): Column = sinh(Column(columnName)) - - /** - * Computes the cosine of the given value. - * - * @group double_funcs - */ - def cos(e: Column): Column = Cos(e.expr) - - /** - * Computes the cosine of the given column. - * - * @group double_funcs - */ - def cos(columnName: String): Column = cos(Column(columnName)) - - /** - * Computes the cosine inverse of the given value; the returned angle is in the range - * 0.0 through pi. - * - * @group double_funcs - */ - def acos(e: Column): Column = Acos(e.expr) - - /** - * Computes the cosine inverse of the given column; the returned angle is in the range - * 0.0 through pi. - * - * @group double_funcs - */ - def acos(columnName: String): Column = acos(Column(columnName)) - - /** - * Computes the hyperbolic cosine of the given value. - * - * @group double_funcs - */ - def cosh(e: Column): Column = Cosh(e.expr) - - /** - * Computes the hyperbolic cosine of the given column. - * - * @group double_funcs - */ - def cosh(columnName: String): Column = cosh(Column(columnName)) - - /** - * Computes the tangent of the given value. - * - * @group double_funcs - */ - def tan(e: Column): Column = Tan(e.expr) - - /** - * Computes the tangent of the given column. - * - * @group double_funcs - */ - def tan(columnName: String): Column = tan(Column(columnName)) - - /** - * Computes the tangent inverse of the given value. - * - * @group double_funcs - */ - def atan(e: Column): Column = Atan(e.expr) - - /** - * Computes the tangent inverse of the given column. - * - * @group double_funcs - */ - def atan(columnName: String): Column = atan(Column(columnName)) - - /** - * Computes the hyperbolic tangent of the given value. - * - * @group double_funcs - */ - def tanh(e: Column): Column = Tanh(e.expr) - - /** - * Computes the hyperbolic tangent of the given column. - * - * @group double_funcs - */ - def tanh(columnName: String): Column = tanh(Column(columnName)) - - /** - * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. - * - * @group double_funcs - */ - def toDeg(e: Column): Column = ToDegrees(e.expr) - - /** - * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. - * - * @group double_funcs - */ - def toDeg(columnName: String): Column = toDeg(Column(columnName)) - - /** - * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. - * - * @group double_funcs - */ - def toRad(e: Column): Column = ToRadians(e.expr) - - /** - * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. - * - * @group double_funcs - */ - def toRad(columnName: String): Column = toRad(Column(columnName)) - - /** - * Computes the ceiling of the given value. - * - * @group double_funcs - */ - def ceil(e: Column): Column = Ceil(e.expr) - - /** - * Computes the ceiling of the given column. - * - * @group double_funcs - */ - def ceil(columnName: String): Column = ceil(Column(columnName)) - - /** - * Computes the floor of the given value. - * - * @group double_funcs - */ - def floor(e: Column): Column = Floor(e.expr) - - /** - * Computes the floor of the given column. - * - * @group double_funcs - */ - def floor(columnName: String): Column = floor(Column(columnName)) - - /** - * Returns the double value that is closest in value to the argument and - * is equal to a mathematical integer. - * - * @group double_funcs - */ - def rint(e: Column): Column = Rint(e.expr) - - /** - * Returns the double value that is closest in value to the argument and - * is equal to a mathematical integer. - * - * @group double_funcs - */ - def rint(columnName: String): Column = rint(Column(columnName)) - - /** - * Computes the cube-root of the given value. - * - * @group double_funcs - */ - def cbrt(e: Column): Column = Cbrt(e.expr) - - /** - * Computes the cube-root of the given column. - * - * @group double_funcs - */ - def cbrt(columnName: String): Column = cbrt(Column(columnName)) - - /** - * Computes the signum of the given value. - * - * @group double_funcs - */ - def signum(e: Column): Column = Signum(e.expr) - - /** - * Computes the signum of the given column. - * - * @group double_funcs - */ - def signum(columnName: String): Column = signum(Column(columnName)) - - /** - * Computes the signum of the given value. For IntegerType. - * - * @group int_funcs - */ - def isignum(e: Column): Column = ISignum(e.expr) - - /** - * Computes the signum of the given column. For IntegerType. - * - * @group int_funcs - */ - def isignum(columnName: String): Column = isignum(Column(columnName)) - - /** - * Computes the signum of the given value. For FloatType. - * - * @group float_funcs - */ - def fsignum(e: Column): Column = FSignum(e.expr) - - /** - * Computes the signum of the given column. For FloatType. - * - * @group float_funcs - */ - def fsignum(columnName: String): Column = fsignum(Column(columnName)) - - /** - * Computes the signum of the given value. For LongType. - * - * @group long_funcs - */ - def lsignum(e: Column): Column = LSignum(e.expr) - - /** - * Computes the signum of the given column. For FloatType. - * - * @group long_funcs - */ - def lsignum(columnName: String): Column = lsignum(Column(columnName)) - - /** - * Computes the natural logarithm of the given value. - * - * @group double_funcs - */ - def log(e: Column): Column = Log(e.expr) - - /** - * Computes the natural logarithm of the given column. - * - * @group double_funcs - */ - def log(columnName: String): Column = log(Column(columnName)) - - /** - * Computes the logarithm of the given value in Base 10. - * - * @group double_funcs - */ - def log10(e: Column): Column = Log10(e.expr) - - /** - * Computes the logarithm of the given value in Base 10. - * - * @group double_funcs - */ - def log10(columnName: String): Column = log10(Column(columnName)) - - /** - * Computes the natural logarithm of the given value plus one. - * - * @group double_funcs - */ - def log1p(e: Column): Column = Log1p(e.expr) - - /** - * Computes the natural logarithm of the given column plus one. - * - * @group double_funcs - */ - def log1p(columnName: String): Column = log1p(Column(columnName)) - - /** - * Computes the exponential of the given value. - * - * @group double_funcs - */ - def exp(e: Column): Column = Exp(e.expr) - - /** - * Computes the exponential of the given column. - * - * @group double_funcs - */ - def exp(columnName: String): Column = exp(Column(columnName)) - - /** - * Computes the exponential of the given value minus one. - * - * @group double_funcs - */ - def expm1(e: Column): Column = Expm1(e.expr) - - /** - * Computes the exponential of the given column. - * - * @group double_funcs - */ - def expm1(columnName: String): Column = expm1(Column(columnName)) - - /** - * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs - */ - def pow(l: Column, r: Column): Column = Pow(l.expr, r.expr) - - /** - * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs - */ - def pow(l: Column, rightName: String): Column = pow(l, Column(rightName)) - - /** - * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs - */ - def pow(leftName: String, r: Column): Column = pow(Column(leftName), r) - - /** - * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs - */ - def pow(leftName: String, rightName: String): Column = pow(Column(leftName), Column(rightName)) - - /** - * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs - */ - def pow(l: Column, r: Double): Column = pow(l, lit(r).expr) - - /** - * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs - */ - def pow(leftName: String, r: Double): Column = pow(Column(leftName), r) - - /** - * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs - */ - def pow(l: Double, r: Column): Column = pow(lit(l).expr, r) - - /** - * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs - */ - def pow(l: Double, rightName: String): Column = pow(l, Column(rightName)) - - /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs - */ - def hypot(l: Column, r: Column): Column = Hypot(l.expr, r.expr) - - /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs - */ - def hypot(l: Column, rightName: String): Column = hypot(l, Column(rightName)) - - /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs - */ - def hypot(leftName: String, r: Column): Column = hypot(Column(leftName), r) - - /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs - */ - def hypot(leftName: String, rightName: String): Column = - hypot(Column(leftName), Column(rightName)) - - /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs - */ - def hypot(l: Column, r: Double): Column = hypot(l, lit(r).expr) - - /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs - */ - def hypot(leftName: String, r: Double): Column = hypot(Column(leftName), r) - - /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs - */ - def hypot(l: Double, r: Column): Column = hypot(lit(l).expr, r) - - /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs - */ - def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName)) - - /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs - */ - def atan2(l: Column, r: Column): Column = Atan2(l.expr, r.expr) - - /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs - */ - def atan2(l: Column, rightName: String): Column = atan2(l, Column(rightName)) - - /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs - */ - def atan2(leftName: String, r: Column): Column = atan2(Column(leftName), r) - - /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs - */ - def atan2(leftName: String, rightName: String): Column = - atan2(Column(leftName), Column(rightName)) - - /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs - */ - def atan2(l: Column, r: Double): Column = atan2(l, lit(r).expr) - - /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs - */ - def atan2(leftName: String, r: Double): Column = atan2(Column(leftName), r) - - /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs - */ - def atan2(l: Double, r: Column): Column = atan2(lit(l).expr, r) - - /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs - */ - def atan2(l: Double, rightName: String): Column = atan2(l, Column(rightName)) -} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index e7a0685e013d8..1abf3aa51cb25 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -38,12 +38,12 @@ private[sql] class DDLParser( parseQuery: String => LogicalPlan) extends AbstractSparkSQLParser with DataTypeParser with Logging { - def parse(input: String, exceptionOnError: Boolean): Option[LogicalPlan] = { + def parse(input: String, exceptionOnError: Boolean): LogicalPlan = { try { - Some(parse(input)) + parse(input) } catch { case ddlException: DDLException => throw ddlException - case _ if !exceptionOnError => None + case _ if !exceptionOnError => parseQuery(input) case x: Throwable => throw x } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index e5c9504d21042..72c42f4fe376b 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -22,10 +22,7 @@ import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.sql.DataFrame; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SQLContext; -import org.apache.spark.sql.TestData$; +import org.apache.spark.sql.*; import org.apache.spark.sql.test.TestSQLContext; import org.apache.spark.sql.test.TestSQLContext$; import org.apache.spark.sql.types.*; @@ -37,11 +34,11 @@ import java.io.Serializable; import java.util.Arrays; +import java.util.Comparator; import java.util.List; import java.util.Map; import static org.apache.spark.sql.functions.*; -import static org.apache.spark.sql.mathfunctions.*; public class JavaDataFrameSuite { private transient JavaSparkContext jsc; @@ -107,6 +104,9 @@ public void testVarargMethods() { df2.select(pow("a", "a"), pow("b", 2.0)); df2.select(pow(col("a"), col("b")), exp("b")); df2.select(sin("a"), acos("b")); + + df2.select(rand(), acos("b")); + df2.select(col("*"), randn(5L)); } @Ignore @@ -179,4 +179,52 @@ public void testCreateDataFrameFromJavaBeans() { } } + private static Comparator CrosstabRowComparator = new Comparator() { + public int compare(Row row1, Row row2) { + String item1 = row1.getString(0); + String item2 = row2.getString(0); + return item1.compareTo(item2); + } + }; + + @Test + public void testCrosstab() { + DataFrame df = context.table("testData2"); + DataFrame crosstab = df.stat().crosstab("a", "b"); + String[] columnNames = crosstab.schema().fieldNames(); + Assert.assertEquals(columnNames[0], "a_b"); + Assert.assertEquals(columnNames[1], "1"); + Assert.assertEquals(columnNames[2], "2"); + Row[] rows = crosstab.collect(); + Arrays.sort(rows, CrosstabRowComparator); + Integer count = 1; + for (Row row : rows) { + Assert.assertEquals(row.get(0).toString(), count.toString()); + Assert.assertEquals(row.getLong(1), 1L); + Assert.assertEquals(row.getLong(2), 1L); + count++; + } + } + + @Test + public void testFrequentItems() { + DataFrame df = context.table("testData2"); + String[] cols = new String[]{"a"}; + DataFrame results = df.stat().freqItems(cols, 0.2); + Assert.assertTrue(results.collect()[0].getSeq(0).contains(1)); + } + + @Test + public void testCorrelation() { + DataFrame df = context.table("testData2"); + Double pearsonCorr = df.stat().corr("a", "b", "pearson"); + Assert.assertTrue(Math.abs(pearsonCorr) < 1e-6); + } + + @Test + public void testCovariance() { + DataFrame df = context.table("testData2"); + Double result = df.stat().cov("a", "b"); + Assert.assertTrue(Math.abs(result) < 1e-6); + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala index 2ba5fc21ff57c..3c1ad656fc855 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql +import org.scalatest.Matchers._ + import org.apache.spark.sql.functions._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.implicits._ @@ -206,6 +208,20 @@ class ColumnExpressionSuite extends QueryTest { testData2.collect().toSeq.filter(r => r.getInt(0) <= r.getInt(1))) } + test("between") { + val testData = TestSQLContext.sparkContext.parallelize( + (0, 1, 2) :: + (1, 2, 3) :: + (2, 1, 0) :: + (2, 2, 4) :: + (3, 1, 6) :: + (3, 2, 0) :: Nil).toDF("a", "b", "c") + val expectAnswer = testData.collect().toSeq. + filter(r => r.getInt(0) >= r.getInt(1) && r.getInt(0) <= r.getInt(2)) + + checkAnswer(testData.filter($"a".between($"b", $"c")), expectAnswer) + } + val booleanData = TestSQLContext.createDataFrame(TestSQLContext.sparkContext.parallelize( Row(false, false) :: Row(false, true) :: @@ -349,4 +365,24 @@ class ColumnExpressionSuite extends QueryTest { assert(schema("value").metadata === Metadata.empty) assert(schema("abc").metadata === metadata) } + + test("rand") { + val randCol = testData.select('key, rand(5L).as("rand")) + randCol.columns.length should be (2) + val rows = randCol.collect() + rows.foreach { row => + assert(row.getDouble(1) <= 1.0) + assert(row.getDouble(1) >= 0.0) + } + } + + test("randn") { + val randCol = testData.select('key, randn(5L).as("rand")) + randCol.columns.length should be (2) + val rows = randCol.collect() + rows.foreach { row => + assert(row.getDouble(1) <= 4.0) + assert(row.getDouble(1) >= -4.0) + } + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala new file mode 100644 index 0000000000000..ca03713ef4658 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala @@ -0,0 +1,84 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.types._ + +/** + * Test suite for functions in [[org.apache.spark.sql.functions]]. + */ +class DataFrameFunctionsSuite extends QueryTest { + + test("array with column name") { + val df = Seq((0, 1)).toDF("a", "b") + val row = df.select(array("a", "b")).first() + + val expectedType = ArrayType(IntegerType, containsNull = false) + assert(row.schema(0).dataType === expectedType) + assert(row.getAs[Seq[Int]](0) === Seq(0, 1)) + } + + test("array with column expression") { + val df = Seq((0, 1)).toDF("a", "b") + val row = df.select(array(col("a"), col("b") + col("b"))).first() + + val expectedType = ArrayType(IntegerType, containsNull = false) + assert(row.schema(0).dataType === expectedType) + assert(row.getAs[Seq[Int]](0) === Seq(0, 2)) + } + + // Turn this on once we add a rule to the analyzer to throw a friendly exception + ignore("array: throw exception if putting columns of different types into an array") { + val df = Seq((0, "str")).toDF("a", "b") + intercept[AnalysisException] { + df.select(array("a", "b")) + } + } + + test("struct with column name") { + val df = Seq((1, "str")).toDF("a", "b") + val row = df.select(struct("a", "b")).first() + + val expectedType = StructType(Seq( + StructField("a", IntegerType, nullable = false), + StructField("b", StringType) + )) + assert(row.schema(0).dataType === expectedType) + assert(row.getAs[Row](0) === Row(1, "str")) + } + + test("struct with column expression") { + val df = Seq((1, "str")).toDF("a", "b") + val row = df.select(struct((col("a") * 2).as("c"), col("b"))).first() + + val expectedType = StructType(Seq( + StructField("c", IntegerType, nullable = false), + StructField("b", StringType) + )) + assert(row.schema(0).dataType === expectedType) + assert(row.getAs[Row](0) === Row(2, "str")) + } + + test("struct: must use named column expression") { + intercept[IllegalArgumentException] { + struct(col("a") * 2) + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala new file mode 100644 index 0000000000000..f005f55b6432e --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala @@ -0,0 +1,86 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.apache.spark.sql.TestData._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.test.TestSQLContext._ +import org.apache.spark.sql.test.TestSQLContext.implicits._ + + +class DataFrameJoinSuite extends QueryTest { + + test("join - join using") { + val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") + val df2 = Seq(1, 2, 3).map(i => (i, (i + 1).toString)).toDF("int", "str") + + checkAnswer( + df.join(df2, "int"), + Row(1, "1", "2") :: Row(2, "2", "3") :: Row(3, "3", "4") :: Nil) + } + + test("join - join using self join") { + val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") + + // self join + checkAnswer( + df.join(df, "int"), + Row(1, "1", "1") :: Row(2, "2", "2") :: Row(3, "3", "3") :: Nil) + } + + test("join - self join") { + val df1 = testData.select(testData("key")).as('df1) + val df2 = testData.select(testData("key")).as('df2) + + checkAnswer( + df1.join(df2, $"df1.key" === $"df2.key"), + sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key").collect().toSeq) + } + + test("join - using aliases after self join") { + val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") + checkAnswer( + df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count(), + Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) + + checkAnswer( + df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("y.str").count(), + Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) + } + + test("[SPARK-6231] join - self join auto resolve ambiguity") { + val df = Seq((1, "1"), (2, "2")).toDF("key", "value") + checkAnswer( + df.join(df, df("key") === df("key")), + Row(1, "1", 1, "1") :: Row(2, "2", 2, "2") :: Nil) + + checkAnswer( + df.join(df.filter($"value" === "2"), df("key") === df("key")), + Row(2, "2", 2, "2") :: Nil) + + checkAnswer( + df.join(df, df("key") === df("key") && df("value") === 1), + Row(1, "1", 1, "1") :: Nil) + + val left = df.groupBy("key").agg($"key", count("*")) + val right = df.groupBy("key").agg($"key", sum("key")) + checkAnswer( + left.join(right, left("key") === right("key")), + Row(1, 1, 1, 1) :: Row(2, 1, 2, 2) :: Nil) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala new file mode 100644 index 0000000000000..46b1845a9180c --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala @@ -0,0 +1,101 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.scalatest.FunSuite +import org.scalatest.Matchers._ + +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.test.TestSQLContext.implicits._ + +class DataFrameStatSuite extends FunSuite { + + val sqlCtx = TestSQLContext + def toLetter(i: Int): String = (i + 97).toChar.toString + + test("pearson correlation") { + val df = Seq.tabulate(10)(i => (i, 2 * i, i * -1.0)).toDF("a", "b", "c") + val corr1 = df.stat.corr("a", "b", "pearson") + assert(math.abs(corr1 - 1.0) < 1e-12) + val corr2 = df.stat.corr("a", "c", "pearson") + assert(math.abs(corr2 + 1.0) < 1e-12) + // non-trivial example. To reproduce in python, use: + // >>> from scipy.stats import pearsonr + // >>> import numpy as np + // >>> a = np.array(range(20)) + // >>> b = np.array([x * x - 2 * x + 3.5 for x in range(20)]) + // >>> pearsonr(a, b) + // (0.95723391394758572, 3.8902121417802199e-11) + // In R, use: + // > a <- 0:19 + // > b <- mapply(function(x) x * x - 2 * x + 3.5, a) + // > cor(a, b) + // [1] 0.957233913947585835 + val df2 = Seq.tabulate(20)(x => (x, x * x - 2 * x + 3.5)).toDF("a", "b") + val corr3 = df2.stat.corr("a", "b", "pearson") + assert(math.abs(corr3 - 0.95723391394758572) < 1e-12) + } + + test("covariance") { + val df = Seq.tabulate(10)(i => (i, 2.0 * i, toLetter(i))).toDF("singles", "doubles", "letters") + + val results = df.stat.cov("singles", "doubles") + assert(math.abs(results - 55.0 / 3) < 1e-12) + intercept[IllegalArgumentException] { + df.stat.cov("singles", "letters") // doesn't accept non-numerical dataTypes + } + val decimalData = Seq.tabulate(6)(i => (BigDecimal(i % 3), BigDecimal(i % 2))).toDF("a", "b") + val decimalRes = decimalData.stat.cov("a", "b") + assert(math.abs(decimalRes) < 1e-12) + } + + test("crosstab") { + val df = Seq((0, 0), (2, 1), (1, 0), (2, 0), (0, 0), (2, 0)).toDF("a", "b") + val crosstab = df.stat.crosstab("a", "b") + val columnNames = crosstab.schema.fieldNames + assert(columnNames(0) === "a_b") + assert(columnNames(1) === "0") + assert(columnNames(2) === "1") + val rows: Array[Row] = crosstab.collect().sortBy(_.getString(0)) + assert(rows(0).get(0).toString === "0") + assert(rows(0).getLong(1) === 2L) + assert(rows(0).get(2) === null) + assert(rows(1).get(0).toString === "1") + assert(rows(1).getLong(1) === 1L) + assert(rows(1).get(2) === null) + assert(rows(2).get(0).toString === "2") + assert(rows(2).getLong(1) === 2L) + assert(rows(2).getLong(2) === 1L) + } + + test("Frequent Items") { + val rows = Seq.tabulate(1000) { i => + if (i % 3 == 0) (1, toLetter(1), -1.0) else (i, toLetter(i), i * -1.0) + } + val df = rows.toDF("numbers", "letters", "negDoubles") + + val results = df.stat.freqItems(Array("numbers", "letters"), 0.1) + val items = results.collect().head + items.getSeq[Int](0) should contain (1) + items.getSeq[String](1) should contain (toLetter(1)) + + val singleColResults = df.stat.freqItems(Array("negDoubles"), 0.1) + val items2 = singleColResults.collect().head + items2.getSeq[Double](0) should contain (-1.0) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 5ec06d448e50f..1515e9b843771 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -24,7 +24,6 @@ import org.apache.spark.sql.types._ import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, TestSQLContext} import org.apache.spark.sql.test.TestSQLContext.logicalPlanToSparkQuery import org.apache.spark.sql.test.TestSQLContext.implicits._ -import org.apache.spark.sql.test.TestSQLContext.sql class DataFrameSuite extends QueryTest { @@ -118,44 +117,6 @@ class DataFrameSuite extends QueryTest { ) } - test("join - join using") { - val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") - val df2 = Seq(1, 2, 3).map(i => (i, (i + 1).toString)).toDF("int", "str") - - checkAnswer( - df.join(df2, "int"), - Row(1, "1", "2") :: Row(2, "2", "3") :: Row(3, "3", "4") :: Nil) - } - - test("join - join using self join") { - val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") - - // self join - checkAnswer( - df.join(df, "int"), - Row(1, "1", "1") :: Row(2, "2", "2") :: Row(3, "3", "3") :: Nil) - } - - test("join - self join") { - val df1 = testData.select(testData("key")).as('df1) - val df2 = testData.select(testData("key")).as('df2) - - checkAnswer( - df1.join(df2, $"df1.key" === $"df2.key"), - sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key").collect().toSeq) - } - - test("join - using aliases after self join") { - val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") - checkAnswer( - df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count(), - Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) - - checkAnswer( - df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("y.str").count(), - Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) - } - test("explode") { val df = Seq((1, "a b c"), (2, "a b"), (3, "a")).toDF("number", "letters") val df2 = @@ -499,6 +460,22 @@ class DataFrameSuite extends QueryTest { Row(2) :: Row(3) :: Row(4) :: Nil) } + test("drop column using drop") { + val df = testData.drop("key") + checkAnswer( + df, + testData.collect().map(x => Row(x.getString(1))).toSeq) + assert(df.schema.map(_.name) === Seq("value")) + } + + test("drop unknown column (no-op)") { + val df = testData.drop("random") + checkAnswer( + df, + testData.collect().toSeq) + assert(df.schema.map(_.name) === Seq("key","value")) + } + test("withColumnRenamed") { val df = testData.toDF().withColumn("newCol", col("key") + 1) .withColumnRenamed("value", "valueRenamed") @@ -510,6 +487,23 @@ class DataFrameSuite extends QueryTest { assert(df.schema.map(_.name).toSeq === Seq("key", "valueRenamed", "newCol")) } + test("randomSplit") { + val n = 600 + val data = TestSQLContext.sparkContext.parallelize(1 to n, 2).toDF("id") + for (seed <- 1 to 5) { + val splits = data.randomSplit(Array[Double](1, 2, 3), seed) + assert(splits.length == 3, "wrong number of splits") + + assert(splits.reduce((a, b) => a.unionAll(b)).sort("id").collect().toList == + data.collect().toList, "incomplete or wrong split") + + val s = splits.map(_.count()) + assert(math.abs(s(0) - 100) < 50) // std = 9.13 + assert(math.abs(s(1) - 200) < 50) // std = 11.55 + assert(math.abs(s(2) - 300) < 50) // std = 12.25 + } + } + test("describe") { val describeTestData = Seq( ("Bob", 16, 176), @@ -565,6 +559,25 @@ class DataFrameSuite extends QueryTest { testData.select($"*").show(1000) } + test("SPARK-7319 showString") { + val expectedAnswer = """+---+-----+ + ||key|value| + |+---+-----+ + || 1| 1| + |+---+-----+ + |""".stripMargin + assert(testData.select($"*").showString(1) === expectedAnswer) + } + + test("SPARK-7327 show with empty dataFrame") { + val expectedAnswer = """+---+-----+ + ||key|value| + |+---+-----+ + |+---+-----+ + |""".stripMargin + assert(testData.select($"*").filter($"key" < 0).showString(1) === expectedAnswer) + } + test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") { val rowRDD = TestSQLContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0)))) val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false))) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala index 561553cc925cb..c4281c4b55c02 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala @@ -19,8 +19,7 @@ package org.apache.spark.sql import java.lang.{Double => JavaDouble} -import org.apache.spark.sql.functions.lit -import org.apache.spark.sql.mathfunctions._ +import org.apache.spark.sql.functions._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.implicits._ @@ -159,11 +158,11 @@ class MathExpressionsSuite extends QueryTest { } test("toDeg") { - testOneToOneMathFunction(toDeg, math.toDegrees) + testOneToOneMathFunction(toDegrees, math.toDegrees) } test("toRad") { - testOneToOneMathFunction(toRad, math.toRadians) + testOneToOneMathFunction(toRadians, math.toRadians) } test("cbrt") { @@ -194,18 +193,6 @@ class MathExpressionsSuite extends QueryTest { testOneToOneMathFunction[Double](signum, math.signum) } - test("isignum") { - testOneToOneMathFunction[Int](isignum, math.signum) - } - - test("fsignum") { - testOneToOneMathFunction[Float](fsignum, math.signum) - } - - test("lsignum") { - testOneToOneMathFunction[Long](lsignum, math.signum) - } - test("pow") { testTwoToOneMathFunction(pow, pow, math.pow) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala index 59f9508444f25..bbf9ab113ca43 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala @@ -132,11 +132,7 @@ object QueryTest { val errorMessage = s""" |Results do not match for query: - |${df.logicalPlan} - |== Analyzed Plan == - |${df.queryExecution.analyzed} - |== Physical Plan == - |${df.queryExecution.executedPlan} + |${df.queryExecution} |== Results == |${sideBySide( s"== Correct Answer - ${expectedAnswer.size} ==" +: diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 9e02e69fda3f2..208cec6a32d4d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -19,13 +19,18 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfterAll +import org.apache.spark.sql.catalyst.errors.DialectException import org.apache.spark.sql.execution.GeneratedAggregate import org.apache.spark.sql.functions._ import org.apache.spark.sql.TestData._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.{udf => _, _} + import org.apache.spark.sql.types._ +/** A SQL Dialect for testing purpose, and it can not be nested type */ +class MyDialect extends DefaultDialect + class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { // Make sure the tables are loaded. TestData @@ -46,6 +51,16 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) } + test("support table.star") { + checkAnswer( + sql( + """ + |SELECT r.* + |FROM testData l join testData2 r on (l.key = r.a) + """.stripMargin), + Row(1, 1) :: Row(1, 2) :: Row(2, 1) :: Row(2, 2) :: Row(3, 1) :: Row(3, 2) :: Nil) + } + test("self join with alias in agg") { Seq(1,2,3) .map(i => (i, i.toString)) @@ -64,6 +79,23 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) } + test("SQL Dialect Switching to a new SQL parser") { + val newContext = new SQLContext(TestSQLContext.sparkContext) + newContext.setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName()) + assert(newContext.getSQLDialect().getClass === classOf[MyDialect]) + assert(newContext.sql("SELECT 1").collect() === Array(Row(1))) + } + + test("SQL Dialect Switch to an invalid parser with alias") { + val newContext = new SQLContext(TestSQLContext.sparkContext) + newContext.sql("SET spark.sql.dialect=MyTestClass") + intercept[DialectException] { + newContext.sql("SELECT 1") + } + // test if the dialect set back to DefaultSQLDialect + assert(newContext.getSQLDialect().getClass === classOf[DefaultDialect]) + } + test("SPARK-4625 support SORT BY in SimpleSQLParser & DSL") { checkAnswer( sql("SELECT a FROM testData2 SORT BY a"), @@ -88,6 +120,15 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row(1, 1) :: Nil) } + test("SPARK-6201 IN type conversion") { + jsonRDD(sparkContext.parallelize(Seq("{\"a\": \"1\"}}", "{\"a\": \"2\"}}", "{\"a\": \"3\"}}"))) + .registerTempTable("d") + + checkAnswer( + sql("select * from d where d.a in (1,2)"), + Seq(Row("1"), Row("2"))) + } + test("SPARK-3176 Added Parser of SQL ABS()") { checkAnswer( sql("SELECT ABS(-1.3)"), diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index db096af4535a9..3ec17d37c025b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -22,6 +22,7 @@ import java.sql.DriverManager import java.util.{Calendar, GregorianCalendar, Properties} import org.apache.spark.sql.test._ +import org.apache.spark.sql.types._ import org.h2.jdbc.JdbcSQLException import org.scalatest.{FunSuite, BeforeAndAfter} import TestSQLContext._ @@ -256,12 +257,25 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(cachedRows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) } + test("test DATE types in cache") { + val rows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.TIMETYPES").collect() + TestSQLContext + .jdbc(urlWithUserAndPass, "TEST.TIMETYPES").cache().registerTempTable("mycached_date") + val cachedRows = sql("select * from mycached_date").collect() + assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) + assert(cachedRows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) + } + test("H2 floating-point types") { val rows = sql("SELECT * FROM flttypes").collect() assert(rows(0).getDouble(0) === 1.00000000000000022) // Yes, I meant ==. assert(rows(0).getDouble(1) === 1.00000011920928955) // Yes, I meant ==. assert(rows(0).getAs[BigDecimal](2) - .equals(new BigDecimal("123456789012345.54321543215432100000"))) + .equals(new BigDecimal("123456789012345.54321543215432100000"))) + assert(rows(0).schema.fields(2).dataType === DecimalType(40, 20)) + val compareDecimal = sql("SELECT C FROM flttypes where C > C - 1").collect() + assert(compareDecimal(0).getAs[BigDecimal](0) + .equals(new BigDecimal("123456789012345.54321543215432100000"))) } test("SQL query as table name") { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala index fd0e2746dc045..263fafba930ce 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala @@ -17,13 +17,15 @@ package org.apache.spark.sql.json +import java.io.StringWriter import java.sql.{Date, Timestamp} +import com.fasterxml.jackson.core.JsonFactory import org.scalactic.Tolerance._ import org.apache.spark.sql.TestData._ import org.apache.spark.sql.functions._ -import org.apache.spark.sql.json.JsonRDD.{compatibleType, enforceCorrectType} +import org.apache.spark.sql.json.InferSchema.compatibleType import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext._ @@ -46,6 +48,18 @@ class JsonSuite extends QueryTest { s"${expected}(${expected.getClass}).") } + val factory = new JsonFactory() + def enforceCorrectType(value: Any, dataType: DataType): Any = { + val writer = new StringWriter() + val generator = factory.createGenerator(writer) + generator.writeObject(value) + generator.flush() + + val parser = factory.createParser(writer.toString) + parser.nextToken() + JacksonParser.convertField(factory, parser, dataType) + } + val intNumber: Int = 2147483647 checkTypePromotion(intNumber, enforceCorrectType(intNumber, IntegerType)) checkTypePromotion(intNumber.toLong, enforceCorrectType(intNumber, LongType)) @@ -439,7 +453,7 @@ class JsonSuite extends QueryTest { val jsonDF = jsonRDD(primitiveFieldValueTypeConflict) jsonDF.registerTempTable("jsonTable") - // Right now, the analyzer does not promote strings in a boolean expreesion. + // Right now, the analyzer does not promote strings in a boolean expression. // Number and Boolean conflict: resolve the type as boolean in this query. checkAnswer( sql("select num_bool from jsonTable where NOT num_bool"), @@ -508,7 +522,7 @@ class JsonSuite extends QueryTest { Row(Seq(), "11", "[1,2,3]", Row(null), "[]") :: Row(null, """{"field":false}""", null, null, "{}") :: Row(Seq(4, 5, 6), null, "str", Row(null), "[7,8,9]") :: - Row(Seq(7), "{}","[str1,str2,33]", Row("str"), """{"field":true}""") :: Nil + Row(Seq(7), "{}","""["str1","str2",33]""", Row("str"), """{"field":true}""") :: Nil ) } @@ -566,19 +580,19 @@ class JsonSuite extends QueryTest { val analyzed = jsonDF.queryExecution.analyzed assert( analyzed.isInstanceOf[LogicalRelation], - "The DataFrame returned by jsonFile should be based on JSONRelation.") + "The DataFrame returned by jsonFile should be based on LogicalRelation.") val relation = analyzed.asInstanceOf[LogicalRelation].relation assert( relation.isInstanceOf[JSONRelation], "The DataFrame returned by jsonFile should be based on JSONRelation.") - assert(relation.asInstanceOf[JSONRelation].path === path) + assert(relation.asInstanceOf[JSONRelation].path === Some(path)) assert(relation.asInstanceOf[JSONRelation].samplingRatio === (0.49 +- 0.001)) val schema = StructType(StructField("a", LongType, true) :: Nil) val logicalRelation = jsonFile(path, schema).queryExecution.analyzed.asInstanceOf[LogicalRelation] val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation] - assert(relationWithSchema.path === path) + assert(relationWithSchema.path === Some(path)) assert(relationWithSchema.schema === schema) assert(relationWithSchema.samplingRatio > 0.99) } @@ -1020,15 +1034,24 @@ class JsonSuite extends QueryTest { } test("JSONRelation equality test") { - val relation1 = - JSONRelation("path", 1.0, Some(StructType(StructField("a", IntegerType, true) :: Nil)))(null) + val context = org.apache.spark.sql.test.TestSQLContext + val relation1 = new JSONRelation( + "path", + 1.0, + Some(StructType(StructField("a", IntegerType, true) :: Nil)), + context) val logicalRelation1 = LogicalRelation(relation1) - val relation2 = - JSONRelation("path", 0.5, Some(StructType(StructField("a", IntegerType, true) :: Nil)))( - org.apache.spark.sql.test.TestSQLContext) + val relation2 = new JSONRelation( + "path", + 0.5, + Some(StructType(StructField("a", IntegerType, true) :: Nil)), + context) val logicalRelation2 = LogicalRelation(relation2) - val relation3 = - JSONRelation("path", 1.0, Some(StructType(StructField("b", StringType, true) :: Nil)))(null) + val relation3 = new JSONRelation( + "path", + 1.0, + Some(StructType(StructField("b", StringType, true) :: Nil)), + context) val logicalRelation3 = LogicalRelation(relation3) assert(relation1 === relation2) @@ -1046,7 +1069,7 @@ class JsonSuite extends QueryTest { test("SPARK-6245 JsonRDD.inferSchema on empty RDD") { // This is really a test that it doesn't throw an exception - val emptySchema = JsonRDD.inferSchema(empty, 1.0, "") + val emptySchema = InferSchema(empty, 1.0, "") assert(StructType(Seq()) === emptySchema) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala index 80efe9728fbc2..50629ea4dc066 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala @@ -21,7 +21,7 @@ import java.io.File import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.{AnalysisException, Row} +import org.apache.spark.sql.{SaveMode, AnalysisException, Row} import org.apache.spark.util.Utils class InsertSuite extends DataSourceTest with BeforeAndAfterAll { @@ -100,23 +100,48 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { test("INSERT OVERWRITE a JSONRelation multiple times") { sql( s""" - |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt - """.stripMargin) + |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt + """.stripMargin) + checkAnswer( + sql("SELECT a, b FROM jsonTable"), + (1 to 10).map(i => Row(i, s"str$i")) + ) + // Writing the table to less part files. + val rdd1 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 5) + jsonRDD(rdd1).registerTempTable("jt1") sql( s""" - |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt - """.stripMargin) + |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt1 + """.stripMargin) + checkAnswer( + sql("SELECT a, b FROM jsonTable"), + (1 to 10).map(i => Row(i, s"str$i")) + ) + // Writing the table to more part files. + val rdd2 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 10) + jsonRDD(rdd2).registerTempTable("jt2") sql( s""" - |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt - """.stripMargin) - + |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt2 + """.stripMargin) checkAnswer( sql("SELECT a, b FROM jsonTable"), (1 to 10).map(i => Row(i, s"str$i")) ) + + sql( + s""" + |INSERT OVERWRITE TABLE jsonTable SELECT a * 10, b FROM jt1 + """.stripMargin) + checkAnswer( + sql("SELECT a, b FROM jsonTable"), + (1 to 10).map(i => Row(i * 10, s"str$i")) + ) + + dropTempTable("jt1") + dropTempTable("jt2") } test("INSERT INTO not supported for JSONRelation for now") { @@ -128,6 +153,20 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll { } } + test("save directly to the path of a JSON table") { + table("jt").selectExpr("a * 5 as a", "b").save(path.toString, "json", SaveMode.Overwrite) + checkAnswer( + sql("SELECT a, b FROM jsonTable"), + (1 to 10).map(i => Row(i * 5, s"str$i")) + ) + + table("jt").save(path.toString, "json", SaveMode.Overwrite) + checkAnswer( + sql("SELECT a, b FROM jsonTable"), + (1 to 10).map(i => Row(i, s"str$i")) + ) + } + test("it is not allowed to write to a table while querying it.") { val message = intercept[AnalysisException] { sql( diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml index f38c796241df1..437f697d25bf3 100644 --- a/sql/hive-thriftserver/pom.xml +++ b/sql/hive-thriftserver/pom.xml @@ -57,6 +57,18 @@ ${hive.group} hive-beeline + + + org.seleniumhq.selenium + selenium-java + test + + + io.netty + netty + + + target/scala-${scala.binary.version}/classes diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala index 832596fc8bee5..0be5a92c2546c 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala @@ -22,20 +22,27 @@ import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.hive.service.cli.thrift.{ThriftBinaryCLIService, ThriftHttpCLIService} import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor} +import org.apache.spark.sql.SQLConf -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.hive.HiveContext import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ -import org.apache.spark.scheduler.{SparkListenerApplicationEnd, SparkListener} +import org.apache.spark.scheduler.{SparkListenerJobStart, SparkListenerApplicationEnd, SparkListener} +import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab import org.apache.spark.util.Utils +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer + /** * The main entry point for the Spark SQL port of HiveServer2. Starts up a `SparkSQLContext` and a * `HiveThriftServer2` thrift server. */ object HiveThriftServer2 extends Logging { var LOG = LogFactory.getLog(classOf[HiveServer2]) + var uiTab: Option[ThriftServerTab] = _ + var listener: HiveThriftServer2Listener = _ /** * :: DeveloperApi :: @@ -46,7 +53,13 @@ object HiveThriftServer2 extends Logging { val server = new HiveThriftServer2(sqlContext) server.init(sqlContext.hiveconf) server.start() - sqlContext.sparkContext.addSparkListener(new HiveThriftServer2Listener(server)) + listener = new HiveThriftServer2Listener(server, sqlContext.conf) + sqlContext.sparkContext.addSparkListener(listener) + uiTab = if (sqlContext.sparkContext.getConf.getBoolean("spark.ui.enabled", true)) { + Some(new ThriftServerTab(sqlContext.sparkContext)) + } else { + None + } } def main(args: Array[String]) { @@ -58,14 +71,23 @@ object HiveThriftServer2 extends Logging { logInfo("Starting SparkContext") SparkSQLEnv.init() - Utils.addShutdownHook { () => SparkSQLEnv.stop() } + Utils.addShutdownHook { () => + SparkSQLEnv.stop() + uiTab.foreach(_.detach()) + } try { val server = new HiveThriftServer2(SparkSQLEnv.hiveContext) server.init(SparkSQLEnv.hiveContext.hiveconf) server.start() logInfo("HiveThriftServer2 started") - SparkSQLEnv.sparkContext.addSparkListener(new HiveThriftServer2Listener(server)) + listener = new HiveThriftServer2Listener(server, SparkSQLEnv.hiveContext.conf) + SparkSQLEnv.sparkContext.addSparkListener(listener) + uiTab = if (SparkSQLEnv.sparkContext.getConf.getBoolean("spark.ui.enabled", true)) { + Some(new ThriftServerTab(SparkSQLEnv.sparkContext)) + } else { + None + } } catch { case e: Exception => logError("Error starting HiveThriftServer2", e) @@ -73,15 +95,140 @@ object HiveThriftServer2 extends Logging { } } + private[thriftserver] class SessionInfo( + val sessionId: String, + val startTimestamp: Long, + val ip: String, + val userName: String) { + var finishTimestamp: Long = 0L + var totalExecution: Int = 0 + def totalTime: Long = { + if (finishTimestamp == 0L) { + System.currentTimeMillis - startTimestamp + } else { + finishTimestamp - startTimestamp + } + } + } + + private[thriftserver] object ExecutionState extends Enumeration { + val STARTED, COMPILED, FAILED, FINISHED = Value + type ExecutionState = Value + } + + private[thriftserver] class ExecutionInfo( + val statement: String, + val sessionId: String, + val startTimestamp: Long, + val userName: String) { + var finishTimestamp: Long = 0L + var executePlan: String = "" + var detail: String = "" + var state: ExecutionState.Value = ExecutionState.STARTED + val jobId: ArrayBuffer[String] = ArrayBuffer[String]() + var groupId: String = "" + def totalTime: Long = { + if (finishTimestamp == 0L) { + System.currentTimeMillis - startTimestamp + } else { + finishTimestamp - startTimestamp + } + } + } + + /** * A inner sparkListener called in sc.stop to clean up the HiveThriftServer2 */ - class HiveThriftServer2Listener(val server: HiveServer2) extends SparkListener { + private[thriftserver] class HiveThriftServer2Listener( + val server: HiveServer2, + val conf: SQLConf) extends SparkListener { + override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = { server.stop() } - } + val sessionList = new mutable.LinkedHashMap[String, SessionInfo] + val executionList = new mutable.LinkedHashMap[String, ExecutionInfo] + val retainedStatements = + conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT, "200").toInt + val retainedSessions = + conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT, "200").toInt + var totalRunning = 0 + + override def onJobStart(jobStart: SparkListenerJobStart): Unit = { + for { + props <- Option(jobStart.properties) + groupId <- Option(props.getProperty(SparkContext.SPARK_JOB_GROUP_ID)) + (_, info) <- executionList if info.groupId == groupId + } { + info.jobId += jobStart.jobId.toString + info.groupId = groupId + } + } + + def onSessionCreated(ip: String, sessionId: String, userName: String = "UNKNOWN"): Unit = { + val info = new SessionInfo(sessionId, System.currentTimeMillis, ip, userName) + sessionList.put(sessionId, info) + trimSessionIfNecessary() + } + + def onSessionClosed(sessionId: String): Unit = { + sessionList(sessionId).finishTimestamp = System.currentTimeMillis + } + + def onStatementStart( + id: String, + sessionId: String, + statement: String, + groupId: String, + userName: String = "UNKNOWN"): Unit = { + val info = new ExecutionInfo(statement, sessionId, System.currentTimeMillis, userName) + info.state = ExecutionState.STARTED + executionList.put(id, info) + trimExecutionIfNecessary() + sessionList(sessionId).totalExecution += 1 + executionList(id).groupId = groupId + totalRunning += 1 + } + + def onStatementParsed(id: String, executionPlan: String): Unit = { + executionList(id).executePlan = executionPlan + executionList(id).state = ExecutionState.COMPILED + } + + def onStatementError(id: String, errorMessage: String, errorTrace: String): Unit = { + executionList(id).finishTimestamp = System.currentTimeMillis + executionList(id).detail = errorMessage + executionList(id).state = ExecutionState.FAILED + totalRunning -= 1 + } + + def onStatementFinish(id: String): Unit = { + executionList(id).finishTimestamp = System.currentTimeMillis + executionList(id).state = ExecutionState.FINISHED + totalRunning -= 1 + } + + private def trimExecutionIfNecessary() = synchronized { + if (executionList.size > retainedStatements) { + val toRemove = math.max(retainedStatements / 10, 1) + executionList.take(toRemove).foreach { s => + executionList.remove(s._1) + } + } + } + + private def trimSessionIfNecessary() = synchronized { + if (sessionList.size > retainedSessions) { + val toRemove = math.max(retainedSessions / 10, 1) + sessionList.take(toRemove).foreach { s => + sessionList.remove(s._1) + } + } + + } + } } private[hive] class HiveThriftServer2(hiveContext: HiveContext) diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala new file mode 100644 index 0000000000000..71b16b6bebffb --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala @@ -0,0 +1,190 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver.ui + +import java.util.Calendar +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import org.apache.commons.lang3.StringEscapeUtils +import org.apache.spark.Logging +import org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.{SessionInfo, ExecutionState, ExecutionInfo} +import org.apache.spark.ui.UIUtils._ +import org.apache.spark.ui._ + + +/** Page for Spark Web UI that shows statistics of a streaming job */ +private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage("") with Logging { + + private val listener = parent.listener + private val startTime = Calendar.getInstance().getTime() + private val emptyCell = "-" + + /** Render the page */ + def render(request: HttpServletRequest): Seq[Node] = { + val content = + generateBasicStats() ++ +
++ +

+ {listener.sessionList.size} session(s) are online, + running {listener.totalRunning} SQL statement(s) +

++ + generateSessionStatsTable() ++ + generateSQLStatsTable() + UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000)) + } + + /** Generate basic stats of the streaming program */ + private def generateBasicStats(): Seq[Node] = { + val timeSinceStart = System.currentTimeMillis() - startTime.getTime +
    +
  • + Started at: {startTime.toString} +
    • +
  • + Time since start: {formatDurationVerbose(timeSinceStart)} +
    • +
+ } + + /** Generate stats of batch statements of the thrift server program */ + private def generateSQLStatsTable(): Seq[Node] = { + val numStatement = listener.executionList.size + val table = if (numStatement > 0) { + val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration", + "Statement", "State", "Detail") + val dataRows = listener.executionList.values + + def generateDataRow(info: ExecutionInfo): Seq[Node] = { + val jobLink = info.jobId.map { id: String => + + [{id}] + + } + val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan + + {info.userName} + + {jobLink} + + {info.groupId} + {formatDate(info.startTimestamp)} + {if(info.finishTimestamp > 0) formatDate(info.finishTimestamp)} + {formatDurationOption(Some(info.totalTime))} + {info.statement} + {info.state} + {errorMessageCell(detail)} + + } + + Some(UIUtils.listingTable(headerRow, generateDataRow, + dataRows, false, None, Seq(null), false)) + } else { + None + } + + val content = +
SQL Statistics
++ +
+
    + {table.getOrElse("No statistics have been generated yet.")} +
+
+ + content + } + + private def errorMessageCell(errorMessage: String): Seq[Node] = { + val isMultiline = errorMessage.indexOf('\n') >= 0 + val errorSummary = StringEscapeUtils.escapeHtml4( + if (isMultiline) { + errorMessage.substring(0, errorMessage.indexOf('\n')) + } else { + errorMessage + }) + val details = if (isMultiline) { + // scalastyle:off + + + details + ++ + + // scalastyle:on + } else { + "" + } + {errorSummary}{details} + } + + /** Generate stats of batch sessions of the thrift server program */ + private def generateSessionStatsTable(): Seq[Node] = { + val numBatches = listener.sessionList.size + val table = if (numBatches > 0) { + val dataRows = + listener.sessionList.values + val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration", + "Total Execute") + def generateDataRow(session: SessionInfo): Seq[Node] = { + val sessionLink = "%s/ThriftServer/session?id=%s" + .format(UIUtils.prependBaseUri(parent.basePath), session.sessionId) + + {session.userName} + {session.ip} + {session.sessionId} , + {formatDate(session.startTimestamp)} + {if(session.finishTimestamp > 0) formatDate(session.finishTimestamp)} + {formatDurationOption(Some(session.totalTime))} + {session.totalExecution.toString} + + } + Some(UIUtils.listingTable(headerRow, generateDataRow, dataRows, true, None, Seq(null), false)) + } else { + None + } + + val content = +
Session Statistics
++ +
+
    + {table.getOrElse("No statistics have been generated yet.")} +
+
+ + content + } + + + /** + * Returns a human-readable string representing a duration such as "5 second 35 ms" + */ + private def formatDurationOption(msOption: Option[Long]): String = { + msOption.map(formatDurationVerbose).getOrElse(emptyCell) + } + + /** Generate HTML table from string data */ + private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = { + def generateDataRow(data: Seq[String]): Seq[Node] = { + {data.map(d => {d})} + } + UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true) + } +} + diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala new file mode 100644 index 0000000000000..33ba038ecce73 --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala @@ -0,0 +1,197 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver.ui + +import java.util.Calendar +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import org.apache.commons.lang3.StringEscapeUtils +import org.apache.spark.Logging +import org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.{ExecutionInfo, ExecutionState} +import org.apache.spark.ui.UIUtils._ +import org.apache.spark.ui._ + +/** Page for Spark Web UI that shows statistics of a streaming job */ +private[ui] class ThriftServerSessionPage(parent: ThriftServerTab) + extends WebUIPage("session") with Logging { + + private val listener = parent.listener + private val startTime = Calendar.getInstance().getTime() + private val emptyCell = "-" + + /** Render the page */ + def render(request: HttpServletRequest): Seq[Node] = { + val parameterId = request.getParameter("id") + require(parameterId != null && parameterId.nonEmpty, "Missing id parameter") + val sessionStat = listener.sessionList.find(stat => { + stat._1 == parameterId + }).getOrElse(null) + require(sessionStat != null, "Invalid sessionID[" + parameterId + "]") + + val content = + generateBasicStats() ++ +
++ +

+ User {sessionStat._2.userName}, + IP {sessionStat._2.ip}, + Session created at {formatDate(sessionStat._2.startTimestamp)}, + Total run {sessionStat._2.totalExecution} SQL +

++ + generateSQLStatsTable(sessionStat._2.sessionId) + UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000)) + } + + /** Generate basic stats of the streaming program */ + private def generateBasicStats(): Seq[Node] = { + val timeSinceStart = System.currentTimeMillis() - startTime.getTime +
    +
  • + Started at: {startTime.toString} +
    • +
  • + Time since start: {formatDurationVerbose(timeSinceStart)} +
    • +
+ } + + /** Generate stats of batch statements of the thrift server program */ + private def generateSQLStatsTable(sessionID: String): Seq[Node] = { + val executionList = listener.executionList + .filter(_._2.sessionId == sessionID) + val numStatement = executionList.size + val table = if (numStatement > 0) { + val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration", + "Statement", "State", "Detail") + val dataRows = executionList.values.toSeq.sortBy(_.startTimestamp).reverse + + def generateDataRow(info: ExecutionInfo): Seq[Node] = { + val jobLink = info.jobId.map { id: String => + + [{id}] + + } + val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan + + {info.userName} + + {jobLink} + + {info.groupId} + {formatDate(info.startTimestamp)} + {formatDate(info.finishTimestamp)} + {formatDurationOption(Some(info.totalTime))} + {info.statement} + {info.state} + {errorMessageCell(detail)} + + } + + Some(UIUtils.listingTable(headerRow, generateDataRow, + dataRows, false, None, Seq(null), false)) + } else { + None + } + + val content = +
SQL Statistics
++ +
+
    + {table.getOrElse("No statistics have been generated yet.")} +
+
+ + content + } + + private def errorMessageCell(errorMessage: String): Seq[Node] = { + val isMultiline = errorMessage.indexOf('\n') >= 0 + val errorSummary = StringEscapeUtils.escapeHtml4( + if (isMultiline) { + errorMessage.substring(0, errorMessage.indexOf('\n')) + } else { + errorMessage + }) + val details = if (isMultiline) { + // scalastyle:off + + + details + ++ + + // scalastyle:on + } else { + "" + } + {errorSummary}{details} + } + + /** Generate stats of batch sessions of the thrift server program */ + private def generateSessionStatsTable(): Seq[Node] = { + val numBatches = listener.sessionList.size + val table = if (numBatches > 0) { + val dataRows = + listener.sessionList.values.toSeq.sortBy(_.startTimestamp).reverse.map ( session => + Seq( + session.userName, + session.ip, + session.sessionId, + formatDate(session.startTimestamp), + formatDate(session.finishTimestamp), + formatDurationOption(Some(session.totalTime)), + session.totalExecution.toString + ) + ).toSeq + val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration", + "Total Execute") + Some(listingTable(headerRow, dataRows)) + } else { + None + } + + val content = +
Session Statistics
++ +
+
    + {table.getOrElse("No statistics have been generated yet.")} +
+
+ + content + } + + + /** + * Returns a human-readable string representing a duration such as "5 second 35 ms" + */ + private def formatDurationOption(msOption: Option[Long]): String = { + msOption.map(formatDurationVerbose).getOrElse(emptyCell) + } + + /** Generate HTML table from string data */ + private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = { + def generateDataRow(data: Seq[String]): Seq[Node] = { + {data.map(d => {d})} + } + UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true) + } +} + diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala new file mode 100644 index 0000000000000..343031f10c75c --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala @@ -0,0 +1,50 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver.ui + +import org.apache.spark.sql.hive.thriftserver.{HiveThriftServer2, SparkSQLEnv} +import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab._ +import org.apache.spark.ui.{SparkUI, SparkUITab} +import org.apache.spark.{SparkContext, Logging, SparkException} + +/** + * Spark Web UI tab that shows statistics of a streaming job. + * This assumes the given SparkContext has enabled its SparkUI. + */ +private[thriftserver] class ThriftServerTab(sparkContext: SparkContext) + extends SparkUITab(getSparkUI(sparkContext), "ThriftServer") with Logging { + + val parent = getSparkUI(sparkContext) + val listener = HiveThriftServer2.listener + + attachPage(new ThriftServerPage(this)) + attachPage(new ThriftServerSessionPage(this)) + parent.attachTab(this) + + def detach() { + getSparkUI(sparkContext).detachTab(this) + } +} + +private[thriftserver] object ThriftServerTab { + def getSparkUI(sparkContext: SparkContext): SparkUI = { + sparkContext.ui.getOrElse { + throw new SparkException("Parent SparkUI to attach this tab to not found!") + } + } +} diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala index 4cf95e7bdfb2b..1fadea97fd07f 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala @@ -409,24 +409,24 @@ abstract class HiveThriftServer2Test extends FunSuite with BeforeAndAfterAll wit private val CLASS_NAME = HiveThriftServer2.getClass.getCanonicalName.stripSuffix("$") private val LOG_FILE_MARK = s"starting $CLASS_NAME, logging to " - private val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator) - private val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator) + protected val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator) + protected val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator) private var listeningPort: Int = _ protected def serverPort: Int = listeningPort protected def user = System.getProperty("user.name") - private var warehousePath: File = _ - private var metastorePath: File = _ - private def metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true" + protected var warehousePath: File = _ + protected var metastorePath: File = _ + protected def metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true" private val pidDir: File = Utils.createTempDir("thriftserver-pid") private var logPath: File = _ private var logTailingProcess: Process = _ private var diagnosisBuffer: ArrayBuffer[String] = ArrayBuffer.empty[String] - private def serverStartCommand(port: Int) = { + protected def serverStartCommand(port: Int) = { val portConf = if (mode == ServerMode.binary) { ConfVars.HIVE_SERVER2_THRIFT_PORT } else { diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala new file mode 100644 index 0000000000000..47541015a3611 --- /dev/null +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala @@ -0,0 +1,105 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver + + + +import scala.util.Random + +import org.openqa.selenium.WebDriver +import org.openqa.selenium.htmlunit.HtmlUnitDriver +import org.scalatest.{Matchers, BeforeAndAfterAll} +import org.scalatest.concurrent.Eventually._ +import org.scalatest.selenium.WebBrowser +import org.scalatest.time.SpanSugar._ + +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.spark.sql.hive.HiveContext + + +class UISeleniumSuite + extends HiveThriftJdbcTest + with WebBrowser with Matchers with BeforeAndAfterAll { + + implicit var webDriver: WebDriver = _ + var server: HiveThriftServer2 = _ + var hc: HiveContext = _ + val uiPort = 20000 + Random.nextInt(10000) + override def mode: ServerMode.Value = ServerMode.binary + + override def beforeAll(): Unit = { + webDriver = new HtmlUnitDriver + super.beforeAll() + } + + override def afterAll(): Unit = { + if (webDriver != null) { + webDriver.quit() + } + super.afterAll() + } + + override protected def serverStartCommand(port: Int) = { + val portConf = if (mode == ServerMode.binary) { + ConfVars.HIVE_SERVER2_THRIFT_PORT + } else { + ConfVars.HIVE_SERVER2_THRIFT_HTTP_PORT + } + + s"""$startScript + | --master local + | --hiveconf hive.root.logger=INFO,console + | --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$metastoreJdbcUri + | --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath + | --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=localhost + | --hiveconf ${ConfVars.HIVE_SERVER2_TRANSPORT_MODE}=$mode + | --hiveconf $portConf=$port + | --driver-class-path ${sys.props("java.class.path")} + | --conf spark.ui.enabled=true + | --conf spark.ui.port=$uiPort + """.stripMargin.split("\\s+").toSeq + } + + test("thrift server ui test") { + withJdbcStatement(statement =>{ + val baseURL = s"http://localhost:${uiPort}" + + val queries = Seq( + "CREATE TABLE test_map(key INT, value STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_map") + + queries.foreach(statement.execute) + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (baseURL) + find(cssSelector("""ul li a[href*="ThriftServer"]""")) should not be(None) + } + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (baseURL + "/ThriftServer") + find(id("sessionstat")) should not be(None) + find(id("sqlstat")) should not be(None) + + // check whether statements exists + queries.foreach { line => + findAll(cssSelector("""ul table tbody tr td""")).map(_.text).toList should contain (line) + } + } + }) + } +} diff --git a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala index 95a6e86d0546d..b3a79ba1c7d6b 100644 --- a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala +++ b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver import java.sql.{Date, Timestamp} import java.util.concurrent.Executors -import java.util.{ArrayList => JArrayList, Map => JMap} +import java.util.{ArrayList => JArrayList, Map => JMap, UUID} import org.apache.commons.logging.Log import org.apache.hadoop.hive.conf.HiveConf @@ -190,9 +190,12 @@ private[hive] class SparkExecuteStatementOperation( } def run(): Unit = { + val statementId = UUID.randomUUID().toString logInfo(s"Running query '$statement'") setState(OperationState.RUNNING) - hiveContext.sparkContext.setJobDescription(statement) + HiveThriftServer2.listener.onStatementStart( + statementId, parentSession.getSessionHandle.getSessionId.toString, statement, statementId) + hiveContext.sparkContext.setJobGroup(statementId, statement) sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) } @@ -205,6 +208,7 @@ private[hive] class SparkExecuteStatementOperation( logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") case _ => } + HiveThriftServer2.listener.onStatementParsed(statementId, result.queryExecution.toString()) iter = { val useIncrementalCollect = hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean @@ -221,10 +225,13 @@ private[hive] class SparkExecuteStatementOperation( // HiveServer will silently swallow them. case e: Throwable => setState(OperationState.ERROR) + HiveThriftServer2.listener.onStatementError( + statementId, e.getMessage, e.getStackTraceString) logError("Error executing query:",e) throw new HiveSQLException(e.toString) } setState(OperationState.FINISHED) + HiveThriftServer2.listener.onStatementFinish(statementId) } } @@ -255,11 +262,14 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) withImpersonation: Boolean, delegationToken: String): SessionHandle = { hiveContext.openSession() - - super.openSession(username, passwd, sessionConf, withImpersonation, delegationToken) + val sessionHandle = super.openSession( + username, passwd, sessionConf, withImpersonation, delegationToken) + HiveThriftServer2.listener.onSessionCreated("UNKNOWN", sessionHandle.getSessionId.toString) + sessionHandle } override def closeSession(sessionHandle: SessionHandle) { + HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString) super.closeSession(sessionHandle) sparkSqlOperationManager.sessionToActivePool -= sessionHandle diff --git a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala index 178eb1af7cdcd..b9d4f1c58c982 100644 --- a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala +++ b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver import java.sql.{Date, Timestamp} import java.util.concurrent.Executors -import java.util.{ArrayList => JArrayList, List => JList, Map => JMap} +import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, UUID} import org.apache.commons.logging.Log import org.apache.hadoop.hive.conf.HiveConf @@ -36,7 +36,7 @@ import org.apache.hive.service.cli._ import org.apache.hive.service.cli.operation.ExecuteStatementOperation import org.apache.hive.service.cli.session.{SessionManager, HiveSession} -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, Logging} import org.apache.spark.sql.{DataFrame, Row => SparkRow, SQLConf} import org.apache.spark.sql.execution.SetCommand import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ @@ -161,9 +161,16 @@ private[hive] class SparkExecuteStatementOperation( } def run(): Unit = { + val statementId = UUID.randomUUID().toString logInfo(s"Running query '$statement'") setState(OperationState.RUNNING) - hiveContext.sparkContext.setJobDescription(statement) + HiveThriftServer2.listener.onStatementStart( + statementId, + parentSession.getSessionHandle.getSessionId.toString, + statement, + statementId, + parentSession.getUsername) + hiveContext.sparkContext.setJobGroup(statementId, statement) sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) } @@ -176,6 +183,7 @@ private[hive] class SparkExecuteStatementOperation( logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") case _ => } + HiveThriftServer2.listener.onStatementParsed(statementId, result.queryExecution.toString()) iter = { val useIncrementalCollect = hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean @@ -192,10 +200,13 @@ private[hive] class SparkExecuteStatementOperation( // HiveServer will silently swallow them. case e: Throwable => setState(OperationState.ERROR) + HiveThriftServer2.listener.onStatementError( + statementId, e.getMessage, e.getStackTraceString) logError("Error executing query:", e) throw new HiveSQLException(e.toString) } setState(OperationState.FINISHED) + HiveThriftServer2.listener.onStatementFinish(statementId) } } @@ -227,11 +238,16 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) withImpersonation: Boolean, delegationToken: String): SessionHandle = { hiveContext.openSession() - - super.openSession(protocol, username, passwd, sessionConf, withImpersonation, delegationToken) + val sessionHandle = super.openSession( + protocol, username, passwd, sessionConf, withImpersonation, delegationToken) + val session = super.getSession(sessionHandle) + HiveThriftServer2.listener.onSessionCreated( + session.getIpAddress, sessionHandle.getSessionId.toString, session.getUsername) + sessionHandle } override def closeSession(sessionHandle: SessionHandle) { + HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString) super.closeSession(sessionHandle) sparkSqlOperationManager.sessionToActivePool -= sessionHandle diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala index 81ee48ef4152f..5e411c2fdba9d 100644 --- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala @@ -185,7 +185,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { // Hive does not support buckets. ".*bucket.*", - // No window support yet + // We have our own tests based on these query files. ".*window.*", // Fails in hive with authorization errors. diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala new file mode 100644 index 0000000000000..934452fe579a1 --- /dev/null +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala @@ -0,0 +1,845 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.execution + +import java.io.File +import java.util.{Locale, TimeZone} + +import org.scalatest.BeforeAndAfter + +import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.util.Utils + +/** + * The test suite for window functions. To actually compare results with Hive, + * every test should be created by `createQueryTest`. Because we are reusing tables + * for different tests and there are a few properties needed to let Hive generate golden + * files, every `createQueryTest` calls should explicitly set `reset` to `false`. + */ +abstract class HiveWindowFunctionQueryBaseSuite extends HiveComparisonTest with BeforeAndAfter { + private val originalTimeZone = TimeZone.getDefault + private val originalLocale = Locale.getDefault + private val testTempDir = Utils.createTempDir() + + override def beforeAll() { + TestHive.cacheTables = true + // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*) + TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles")) + // Add Locale setting + Locale.setDefault(Locale.US) + + // Create the table used in windowing.q + sql("DROP TABLE IF EXISTS part") + sql( + """ + |CREATE TABLE part( + | p_partkey INT, + | p_name STRING, + | p_mfgr STRING, + | p_brand STRING, + | p_type STRING, + | p_size INT, + | p_container STRING, + | p_retailprice DOUBLE, + | p_comment STRING) + """.stripMargin) + val testData1 = TestHive.getHiveFile("data/files/part_tiny.txt").getCanonicalPath + sql( + s""" + |LOAD DATA LOCAL INPATH '$testData1' overwrite into table part + """.stripMargin) + + sql("DROP TABLE IF EXISTS over1k") + sql( + """ + |create table over1k( + | t tinyint, + | si smallint, + | i int, + | b bigint, + | f float, + | d double, + | bo boolean, + | s string, + | ts timestamp, + | dec decimal(4,2), + | bin binary) + |row format delimited + |fields terminated by '|' + """.stripMargin) + val testData2 = TestHive.getHiveFile("data/files/over1k").getCanonicalPath + sql( + s""" + |LOAD DATA LOCAL INPATH '$testData2' overwrite into table over1k + """.stripMargin) + + // The following settings are used for generating golden files with Hive. + // We have to use kryo to correctly let Hive serialize plans with window functions. + // This is used to generate golden files. + sql("set hive.plan.serialization.format=kryo") + // Explicitly set fs to local fs. + sql(s"set fs.default.name=file://$testTempDir/") + // Ask Hive to run jobs in-process as a single map and reduce task. + sql("set mapred.job.tracker=local") + } + + override def afterAll() { + TestHive.cacheTables = false + TimeZone.setDefault(originalTimeZone) + Locale.setDefault(originalLocale) + TestHive.reset() + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests based on windowing_multipartitioning.q + // Results of the original query file are not deterministic. + ///////////////////////////////////////////////////////////////////////////// + createQueryTest("windowing_multipartitioning.q (deterministic) 1", + s""" + |select s, + |rank() over (partition by s order by si) r, + |sum(b) over (partition by s order by si) sum + |from over1k + |order by s, r, sum; + """.stripMargin, reset = false) + + /* timestamp comparison issue with Hive? + createQueryTest("windowing_multipartitioning.q (deterministic) 2", + s""" + |select s, + |rank() over (partition by s order by dec desc) r, + |sum(b) over (partition by s order by ts desc) as sum + |from over1k + |where s = 'tom allen' or s = 'bob steinbeck' + |order by s, r, sum; + """.stripMargin, reset = false) + */ + + createQueryTest("windowing_multipartitioning.q (deterministic) 3", + s""" + |select s, sum(i) over (partition by s), sum(f) over (partition by si) + |from over1k where s = 'tom allen' or s = 'bob steinbeck'; + """.stripMargin, reset = false) + + createQueryTest("windowing_multipartitioning.q (deterministic) 4", + s""" + |select s, rank() over (partition by s order by bo), + |rank() over (partition by si order by bin desc) from over1k + |where s = 'tom allen' or s = 'bob steinbeck'; + """.stripMargin, reset = false) + + createQueryTest("windowing_multipartitioning.q (deterministic) 5", + s""" + |select s, sum(f) over (partition by i), row_number() over (order by f) + |from over1k where s = 'tom allen' or s = 'bob steinbeck'; + """.stripMargin, reset = false) + + createQueryTest("windowing_multipartitioning.q (deterministic) 6", + s""" + |select s, rank() over w1, + |rank() over w2 + |from over1k + |where s = 'tom allen' or s = 'bob steinbeck' + |window + |w1 as (partition by s order by dec), + |w2 as (partition by si order by f) ; + """.stripMargin, reset = false) + + ///////////////////////////////////////////////////////////////////////////// + // Tests based on windowing_navfn.q + // Results of the original query file are not deterministic. + // Also, the original query of + // select i, lead(s) over (partition by bin order by d,i desc) from over1k ; + ///////////////////////////////////////////////////////////////////////////// + createQueryTest("windowing_navfn.q (deterministic)", + s""" + |select s, row_number() over (partition by d order by dec) rn from over1k + |order by s, rn desc; + |select i, lead(s) over (partition by cast(bin as string) order by d,i desc) as l + |from over1k + |order by i desc, l; + |select i, lag(dec) over (partition by i order by s,i,dec) l from over1k + |order by i, l; + |select s, last_value(t) over (partition by d order by f) l from over1k + |order by s, l; + |select s, first_value(s) over (partition by bo order by s) f from over1k + |order by s, f; + |select t, s, i, last_value(i) over (partition by t order by s) + |from over1k where (s = 'oscar allen' or s = 'oscar carson') and t = 10; + """.stripMargin, reset = false) + + ///////////////////////////////////////////////////////////////////////////// + // Tests based on windowing_ntile.q + // Results of the original query file are not deterministic. + ///////////////////////////////////////////////////////////////////////////// + createQueryTest("windowing_ntile.q (deterministic)", + s""" + |select i, ntile(10) over (partition by s order by i) n from over1k + |order by i, n; + |select s, ntile(100) over (partition by i order by s) n from over1k + |order by s, n; + |select f, ntile(4) over (partition by d order by f) n from over1k + |order by f, n; + |select d, ntile(1000) over (partition by dec order by d) n from over1k + |order by d, n; + """.stripMargin, reset = false) + + ///////////////////////////////////////////////////////////////////////////// + // Tests based on windowing_udaf.q + // Results of the original query file are not deterministic. + ///////////////////////////////////////////////////////////////////////////// + createQueryTest("windowing_udaf.q (deterministic)", + s""" + |select s, min(i) over (partition by s) m from over1k + |order by s, m; + |select s, avg(f) over (partition by si order by s) a from over1k + |order by s, a; + |select s, avg(i) over (partition by t, b order by s) a from over1k + |order by s, a; + |select max(i) over w m from over1k + |order by m window w as (partition by f) ; + |select s, avg(d) over (partition by t order by f) a from over1k + |order by s, a; + """.stripMargin, reset = false) + + ///////////////////////////////////////////////////////////////////////////// + // Tests based on windowing_windowspec.q + // Results of the original query file are not deterministic. + ///////////////////////////////////////////////////////////////////////////// + createQueryTest("windowing_windowspec.q (deterministic)", + s""" + |select s, sum(b) over (partition by i order by s,b rows unbounded preceding) as sum + |from over1k order by s, sum; + |select s, sum(f) over (partition by d order by s,f rows unbounded preceding) as sum + |from over1k order by s, sum; + |select s, sum(f) over + |(partition by ts order by f range between current row and unbounded following) as sum + |from over1k order by s, sum; + |select s, avg(f) + |over (partition by ts order by s,f rows between current row and 5 following) avg + |from over1k order by s, avg; + |select s, avg(d) over + |(partition by t order by s,d desc rows between 5 preceding and 5 following) avg + |from over1k order by s, avg; + |select s, sum(i) over(partition by ts order by s) sum from over1k + |order by s, sum; + |select f, sum(f) over + |(partition by ts order by f range between unbounded preceding and current row) sum + |from over1k order by f, sum; + |select s, i, round(avg(d) over (partition by s order by i) / 10.0 , 2) avg + |from over1k order by s, i, avg; + |select s, i, round((avg(d) over w1 + 10.0) - (avg(d) over w1 - 10.0),2) avg + |from over1k + |order by s, i, avg window w1 as (partition by s order by i); + """.stripMargin, reset = false) + + ///////////////////////////////////////////////////////////////////////////// + // Tests based on windowing_rank.q + // Results of the original query file are not deterministic. + ///////////////////////////////////////////////////////////////////////////// + createQueryTest("windowing_rank.q (deterministic) 1", + s""" + |select s, rank() over (partition by f order by t) r from over1k order by s, r; + |select s, dense_rank() over (partition by ts order by i,s desc) as r from over1k + |order by s desc, r desc; + |select s, cume_dist() over (partition by bo order by b,s) cd from over1k + |order by s, cd; + |select s, percent_rank() over (partition by dec order by f) r from over1k + |order by s desc, r desc; + """.stripMargin, reset = false) + + createQueryTest("windowing_rank.q (deterministic) 2", + s""" + |select ts, dec, rnk + |from + | (select ts, dec, + | rank() over (partition by ts order by dec) as rnk + | from + | (select other.ts, other.dec + | from over1k other + | join over1k on (other.b = over1k.b) + | ) joined + | ) ranked + |where rnk = 1 + |order by ts, dec, rnk; + """.stripMargin, reset = false) + + createQueryTest("windowing_rank.q (deterministic) 3", + s""" + |select ts, dec, rnk + |from + | (select ts, dec, + | rank() over (partition by ts order by dec) as rnk + | from + | (select other.ts, other.dec + | from over1k other + | join over1k on (other.b = over1k.b) + | ) joined + | ) ranked + |where dec = 89.5 + |order by ts, dec, rnk; + """.stripMargin, reset = false) + + createQueryTest("windowing_rank.q (deterministic) 4", + s""" + |select ts, dec, rnk + |from + | (select ts, dec, + | rank() over (partition by ts order by dec) as rnk + | from + | (select other.ts, other.dec + | from over1k other + | join over1k on (other.b = over1k.b) + | where other.t < 10 + | ) joined + | ) ranked + |where rnk = 1 + |order by ts, dec, rnk; + """.stripMargin, reset = false) + + ///////////////////////////////////////////////////////////////////////////// + // Tests from windowing.q + // We port tests in windowing.q to here because this query file contains too + // many tests and the syntax of test "-- 7. testJoinWithWindowingAndPTF" + // is not supported right now. + ///////////////////////////////////////////////////////////////////////////// + createQueryTest("windowing.q -- 1. testWindowing", + s""" + |select p_mfgr, p_name, p_size, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |sum(p_retailprice) over + |(distribute by p_mfgr sort by p_name rows between unbounded preceding and current row) as s1 + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 2. testGroupByWithPartitioning", + s""" + |select p_mfgr, p_name, p_size, + |min(p_retailprice), + |rank() over(distribute by p_mfgr sort by p_name)as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz + |from part + |group by p_mfgr, p_name, p_size + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 3. testGroupByHavingWithSWQ", + s""" + |select p_mfgr, p_name, p_size, min(p_retailprice), + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz + |from part + |group by p_mfgr, p_name, p_size + |having p_size > 0 + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 4. testCount", + s""" + |select p_mfgr, p_name, + |count(p_size) over(distribute by p_mfgr sort by p_name) as cd + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 5. testCountWithWindowingUDAF", + s""" + |select p_mfgr, p_name, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |count(p_size) over(distribute by p_mfgr sort by p_name) as cd, + |p_retailprice, sum(p_retailprice) over (distribute by p_mfgr sort by p_name + | rows between unbounded preceding and current row) as s1, + |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 6. testCountInSubQ", + s""" + |select sub1.r, sub1.dr, sub1.cd, sub1.s1, sub1.deltaSz + |from (select p_mfgr, p_name, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |count(p_size) over(distribute by p_mfgr sort by p_name) as cd, + |p_retailprice, sum(p_retailprice) over (distribute by p_mfgr sort by p_name + | rows between unbounded preceding and current row) as s1, + |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz + |from part + |) sub1 + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 8. testMixedCaseAlias", + s""" + |select p_mfgr, p_name, p_size, + |rank() over(distribute by p_mfgr sort by p_name, p_size desc) as R + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 9. testHavingWithWindowingNoGBY", + s""" + |select p_mfgr, p_name, p_size, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |sum(p_retailprice) over (distribute by p_mfgr sort by p_name + | rows between unbounded preceding and current row) as s1 + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 10. testHavingWithWindowingCondRankNoGBY", + s""" + |select p_mfgr, p_name, p_size, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |sum(p_retailprice) over (distribute by p_mfgr sort by p_name + | rows between unbounded preceding and current row) as s1 + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 11. testFirstLast", + s""" + |select p_mfgr,p_name, p_size, + |sum(p_size) over (distribute by p_mfgr sort by p_name + |rows between current row and current row) as s2, + |first_value(p_size) over w1 as f, + |last_value(p_size, false) over w1 as l + |from part + |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 12. testFirstLastWithWhere", + s""" + |select p_mfgr,p_name, p_size, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |sum(p_size) over (distribute by p_mfgr sort by p_name + |rows between current row and current row) as s2, + |first_value(p_size) over w1 as f, + |last_value(p_size, false) over w1 as l + |from part + |where p_mfgr = 'Manufacturer#3' + |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 13. testSumWindow", + s""" + |select p_mfgr,p_name, p_size, + |sum(p_size) over w1 as s1, + |sum(p_size) over (distribute by p_mfgr sort by p_name + |rows between current row and current row) as s2 + |from part + |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 14. testNoSortClause", + s""" + |select p_mfgr,p_name, p_size, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr + |from part + |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 15. testExpressions", + s""" + |select p_mfgr,p_name, p_size, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |cume_dist() over(distribute by p_mfgr sort by p_name) as cud, + |percent_rank() over(distribute by p_mfgr sort by p_name) as pr, + |ntile(3) over(distribute by p_mfgr sort by p_name) as nt, + |count(p_size) over(distribute by p_mfgr sort by p_name) as ca, + |avg(p_size) over(distribute by p_mfgr sort by p_name) as avg, + |stddev(p_size) over(distribute by p_mfgr sort by p_name) as st, + |first_value(p_size % 5) over(distribute by p_mfgr sort by p_name) as fv, + |last_value(p_size) over(distribute by p_mfgr sort by p_name) as lv, + |first_value(p_size) over w1 as fvW1 + |from part + |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name + | rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 16. testMultipleWindows", + s""" + |select p_mfgr,p_name, p_size, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |cume_dist() over(distribute by p_mfgr sort by p_name) as cud, + |sum(p_size) over (distribute by p_mfgr sort by p_name + |range between unbounded preceding and current row) as s1, + |sum(p_size) over (distribute by p_mfgr sort by p_size + |range between 5 preceding and current row) as s2, + |first_value(p_size) over w1 as fv1 + |from part + |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name + | rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + + createQueryTest("windowing.q -- 17. testCountStar", + s""" + |select p_mfgr,p_name, p_size, + |count(*) over(distribute by p_mfgr sort by p_name ) as c, + |count(p_size) over(distribute by p_mfgr sort by p_name) as ca, + |first_value(p_size) over w1 as fvW1 + |from part + |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name + | rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 18. testUDAFs", + s""" + |select p_mfgr,p_name, p_size, + |sum(p_retailprice) over w1 as s, + |min(p_retailprice) over w1 as mi, + |max(p_retailprice) over w1 as ma, + |avg(p_retailprice) over w1 as ag + |from part + |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name + | rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 19. testUDAFsWithGBY", + """ + |select p_mfgr,p_name, p_size, p_retailprice, + |sum(p_retailprice) over w1 as s, + |min(p_retailprice) as mi , + |max(p_retailprice) as ma , + |avg(p_retailprice) over w1 as ag + |from part + |group by p_mfgr,p_name, p_size, p_retailprice + |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name + | rows between 2 preceding and 2 following); + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 20. testSTATs", + """ + |select p_mfgr,p_name, p_size, + |stddev(p_retailprice) over w1 as sdev, + |stddev_pop(p_retailprice) over w1 as sdev_pop, + |collect_set(p_size) over w1 as uniq_size, + |variance(p_retailprice) over w1 as var, + |corr(p_size, p_retailprice) over w1 as cor, + |covar_pop(p_size, p_retailprice) over w1 as covarp + |from part + |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name + | rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 21. testDISTs", + """ + |select p_mfgr,p_name, p_size, + |histogram_numeric(p_retailprice, 5) over w1 as hist, + |percentile(p_partkey, 0.5) over w1 as per, + |row_number() over(distribute by p_mfgr sort by p_name) as rn + |from part + |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name + | rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 24. testLateralViews", + """ + |select p_mfgr, p_name, + |lv_col, p_size, sum(p_size) over w1 as s + |from (select p_mfgr, p_name, p_size, array(1,2,3) arr from part) p + |lateral view explode(arr) part_lv as lv_col + |window w1 as (distribute by p_mfgr sort by p_size, lv_col + | rows between 2 preceding and current row) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 26. testGroupByHavingWithSWQAndAlias", + """ + |select p_mfgr, p_name, p_size, min(p_retailprice) as mi, + |rank() over(distribute by p_mfgr sort by p_name) as r, + |dense_rank() over(distribute by p_mfgr sort by p_name) as dr, + |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz + |from part + |group by p_mfgr, p_name, p_size + |having p_size > 0 + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 27. testMultipleRangeWindows", + """ + |select p_mfgr,p_name, p_size, + |sum(p_size) over (distribute by p_mfgr sort by p_size + |range between 10 preceding and current row) as s2, + |sum(p_size) over (distribute by p_mfgr sort by p_size + |range between current row and 10 following ) as s1 + |from part + |window w1 as (rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 28. testPartOrderInUDAFInvoke", + """ + |select p_mfgr, p_name, p_size, + |sum(p_size) over (partition by p_mfgr order by p_name + |rows between 2 preceding and 2 following) as s + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 29. testPartOrderInWdwDef", + """ + |select p_mfgr, p_name, p_size, + |sum(p_size) over w1 as s + |from part + |window w1 as (partition by p_mfgr order by p_name + | rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 30. testDefaultPartitioningSpecRules", + """ + |select p_mfgr, p_name, p_size, + |sum(p_size) over w1 as s, + |sum(p_size) over w2 as s2 + |from part + |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following), + | w2 as (partition by p_mfgr order by p_name) + """.stripMargin, reset = false) + + /* p_name is not a numeric column. What is Hive's semantic? + createQueryTest("windowing.q -- 31. testWindowCrossReference", + """ + |select p_mfgr, p_name, p_size, + |sum(p_size) over w1 as s1, + |sum(p_size) over w2 as s2 + |from part + |window w1 as (partition by p_mfgr order by p_name + | range between 2 preceding and 2 following), + | w2 as w1 + """.stripMargin, reset = false) + */ + /* + createQueryTest("windowing.q -- 32. testWindowInheritance", + """ + |select p_mfgr, p_name, p_size, + |sum(p_size) over w1 as s1, + |sum(p_size) over w2 as s2 + |from part + |window w1 as (partition by p_mfgr order by p_name + | range between 2 preceding and 2 following), + | w2 as (w1 rows between unbounded preceding and current row) + """.stripMargin, reset = false) + */ + + /* p_name is not a numeric column. What is Hive's semantic? + createQueryTest("windowing.q -- 33. testWindowForwardReference", + """ + |select p_mfgr, p_name, p_size, + |sum(p_size) over w1 as s1, + |sum(p_size) over w2 as s2, + |sum(p_size) over w3 as s3 + |from part + |window w1 as (distribute by p_mfgr sort by p_name + | range between 2 preceding and 2 following), + | w2 as w3, + | w3 as (distribute by p_mfgr sort by p_name + | range between unbounded preceding and current row) + """.stripMargin, reset = false) + */ + /* + createQueryTest("windowing.q -- 34. testWindowDefinitionPropagation", + """ + |select p_mfgr, p_name, p_size, + |sum(p_size) over w1 as s1, + |sum(p_size) over w2 as s2, + |sum(p_size) over (w3 rows between 2 preceding and 2 following) as s3 + |from part + |window w1 as (distribute by p_mfgr sort by p_name + | range between 2 preceding and 2 following), + | w2 as w3, + | w3 as (distribute by p_mfgr sort by p_name + | range between unbounded preceding and current row) + """.stripMargin, reset = false) + */ + + /* Seems Hive evaluate SELECT DISTINCT before window functions? + createQueryTest("windowing.q -- 35. testDistinctWithWindowing", + """ + |select DISTINCT p_mfgr, p_name, p_size, + |sum(p_size) over w1 as s + |from part + |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following) + """.stripMargin, reset = false) + */ + + createQueryTest("windowing.q -- 36. testRankWithPartitioning", + """ + |select p_mfgr, p_name, p_size, + |rank() over (partition by p_mfgr order by p_name ) as r + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 37. testPartitioningVariousForms", + """ + |select p_mfgr, + |round(sum(p_retailprice) over (partition by p_mfgr order by p_mfgr),2) as s1, + |min(p_retailprice) over (partition by p_mfgr) as s2, + |max(p_retailprice) over (distribute by p_mfgr sort by p_mfgr) as s3, + |round(avg(p_retailprice) over (distribute by p_mfgr),2) as s4, + |count(p_retailprice) over (cluster by p_mfgr ) as s5 + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 38. testPartitioningVariousForms2", + """ + |select p_mfgr, p_name, p_size, + |sum(p_retailprice) over (partition by p_mfgr, p_name order by p_mfgr, p_name + |rows between unbounded preceding and current row) as s1, + |min(p_retailprice) over (distribute by p_mfgr, p_name sort by p_mfgr, p_name + |rows between unbounded preceding and current row) as s2, + |max(p_retailprice) over (partition by p_mfgr, p_name order by p_name) as s3 + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 39. testUDFOnOrderCols", + """ + |select p_mfgr, p_type, substr(p_type, 2) as short_ptype, + |rank() over (partition by p_mfgr order by substr(p_type, 2)) as r + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 40. testNoBetweenForRows", + """ + |select p_mfgr, p_name, p_size, + |sum(p_retailprice) over (distribute by p_mfgr sort by p_name rows unbounded preceding) as s1 + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 41. testNoBetweenForRange", + """ + |select p_mfgr, p_name, p_size, + |sum(p_retailprice) over (distribute by p_mfgr sort by p_size range unbounded preceding) as s1 + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 42. testUnboundedFollowingForRows", + """ + |select p_mfgr, p_name, p_size, + |sum(p_retailprice) over (distribute by p_mfgr sort by p_name + |rows between current row and unbounded following) as s1 + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 43. testUnboundedFollowingForRange", + """ + |select p_mfgr, p_name, p_size, + |sum(p_retailprice) over (distribute by p_mfgr sort by p_size + |range between current row and unbounded following) as s1 + |from part + """.stripMargin, reset = false) + + createQueryTest("windowing.q -- 44. testOverNoPartitionSingleAggregate", + """ + |select p_name, p_retailprice, + |round(avg(p_retailprice) over(),2) + |from part + |order by p_name + """.stripMargin, reset = false) +} + +class HiveWindowFunctionQueryWithoutCodeGenSuite extends HiveWindowFunctionQueryBaseSuite { + var originalCodegenEnabled: Boolean = _ + override def beforeAll(): Unit = { + super.beforeAll() + originalCodegenEnabled = conf.codegenEnabled + sql("set spark.sql.codegen=false") + } + + override def afterAll(): Unit = { + sql(s"set spark.sql.codegen=$originalCodegenEnabled") + super.afterAll() + } +} + +abstract class HiveWindowFunctionQueryFileBaseSuite + extends HiveCompatibilitySuite with BeforeAndAfter { + private val originalTimeZone = TimeZone.getDefault + private val originalLocale = Locale.getDefault + private val testTempDir = Utils.createTempDir() + + override def beforeAll() { + TestHive.cacheTables = true + // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*) + TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles")) + // Add Locale setting + Locale.setDefault(Locale.US) + + // The following settings are used for generating golden files with Hive. + // We have to use kryo to correctly let Hive serialize plans with window functions. + // This is used to generate golden files. + sql("set hive.plan.serialization.format=kryo") + // Explicitly set fs to local fs. + sql(s"set fs.default.name=file://$testTempDir/") + // Ask Hive to run jobs in-process as a single map and reduce task. + sql("set mapred.job.tracker=local") + } + + override def afterAll() { + TestHive.cacheTables = false + TimeZone.setDefault(originalTimeZone) + Locale.setDefault(originalLocale) + TestHive.reset() + } + + override def blackList: Seq[String] = Seq( + // Partitioned table functions are not supported. + "ptf*", + // tests of windowing.q are in HiveWindowFunctionQueryBaseSuite + "windowing.q", + + // This one failed on the expression of + // sum(lag(p_retailprice,1,0.0)) over w1 + // lag(p_retailprice,1,0.0) is a GenericUDF and the argument inspector of + // p_retailprice created by HiveInspectors is + // PrimitiveObjectInspectorFactory.javaDoubleObjectInspector. + // However, seems Hive assumes it is + // PrimitiveObjectInspectorFactory.writableDoubleObjectInspector, which introduces an error. + "windowing_expressions", + + // Hive's results are not deterministic + "windowing_multipartitioning", + "windowing_navfn", + "windowing_ntile", + "windowing_udaf", + "windowing_windowspec", + "windowing_rank" + ) + + override def whiteList: Seq[String] = Seq( + "windowing_udaf2", + "windowing_columnPruning", + "windowing_adjust_rowcontainer_sz" + ) + + override def testCases: Seq[(String, File)] = super.testCases.filter { + case (name, _) => realWhiteList.contains(name) + } +} + +class HiveWindowFunctionQueryFileWithoutCodeGenSuite extends HiveWindowFunctionQueryFileBaseSuite { + var originalCodegenEnabled: Boolean = _ + override def beforeAll(): Unit = { + super.beforeAll() + originalCodegenEnabled = conf.codegenEnabled + sql("set spark.sql.codegen=false") + } + + override def afterAll(): Unit = { + sql(s"set spark.sql.codegen=$originalCodegenEnabled") + super.afterAll() + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index dd06b2620c5ee..f25723e53f07c 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -20,6 +20,9 @@ package org.apache.spark.sql.hive import java.io.{BufferedReader, InputStreamReader, PrintStream} import java.sql.Timestamp +import org.apache.hadoop.hive.ql.parse.VariableSubstitution +import org.apache.spark.sql.catalyst.Dialect + import scala.collection.JavaConversions._ import scala.language.implicitConversions @@ -42,6 +45,15 @@ import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNative import org.apache.spark.sql.sources.{DDLParser, DataSourceStrategy} import org.apache.spark.sql.types._ +/** + * This is the HiveQL Dialect, this dialect is strongly bind with HiveContext + */ +private[hive] class HiveQLDialect extends Dialect { + override def parse(sqlText: String): LogicalPlan = { + HiveQl.parseSql(sqlText) + } +} + /** * An instance of the Spark SQL execution engine that integrates with data stored in Hive. * Configuration for Hive is read from hive-site.xml on the classpath. @@ -81,25 +93,16 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { protected[sql] def convertCTAS: Boolean = getConf("spark.sql.hive.convertCTAS", "false").toBoolean - override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution = - new this.QueryExecution(plan) - @transient - protected[sql] val ddlParserWithHiveQL = new DDLParser(HiveQl.parseSql(_)) - - override def sql(sqlText: String): DataFrame = { - val substituted = new VariableSubstitution().substitute(hiveconf, sqlText) - // TODO: Create a framework for registering parsers instead of just hardcoding if statements. - if (conf.dialect == "sql") { - super.sql(substituted) - } else if (conf.dialect == "hiveql") { - val ddlPlan = ddlParserWithHiveQL.parse(sqlText, exceptionOnError = false) - DataFrame(this, ddlPlan.getOrElse(HiveQl.parseSql(substituted))) - } else { - sys.error(s"Unsupported SQL dialect: ${conf.dialect}. Try 'sql' or 'hiveql'") - } + protected[sql] lazy val substitutor = new VariableSubstitution() + + protected[sql] override def parseSql(sql: String): LogicalPlan = { + super.parseSql(substitutor.substitute(hiveconf, sql)) } + override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution = + new this.QueryExecution(plan) + /** * Invalidate and refresh all the cached the metadata of the given table. For performance reasons, * Spark SQL or the external data source library it uses might cache certain metadata about a @@ -249,6 +252,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { catalog.CreateTables :: catalog.PreInsertionCasts :: ExtractPythonUdfs :: + ResolveHiveWindowFunction :: sources.PreInsertCastAndRename :: Nil } @@ -356,6 +360,12 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { } } + override protected[sql] def dialectClassName = if (conf.dialect == "hiveql") { + classOf[HiveQLDialect].getCanonicalName + } else { + super.dialectClassName + } + @transient private val hivePlanner = new SparkPlanner with HiveStrategies { val hiveContext = self diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 2dc6463abafa7..8a0686a2d81c9 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -19,13 +19,11 @@ package org.apache.spark.sql.hive import java.sql.Date - -import org.apache.hadoop.hive.ql.exec.{FunctionRegistry, FunctionInfo} - import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.ql.Context +import org.apache.hadoop.hive.ql.exec.{FunctionRegistry, FunctionInfo} import org.apache.hadoop.hive.ql.lib.Node import org.apache.hadoop.hive.ql.metadata.Table import org.apache.hadoop.hive.ql.parse._ @@ -423,16 +421,16 @@ private[hive] object HiveQl { } /** - * SELECT MAX(value) FROM src GROUP BY k1, k2, k3 GROUPING SETS((k1, k2), (k2)) - * is equivalent to + * SELECT MAX(value) FROM src GROUP BY k1, k2, k3 GROUPING SETS((k1, k2), (k2)) + * is equivalent to * SELECT MAX(value) FROM src GROUP BY k1, k2 UNION SELECT MAX(value) FROM src GROUP BY k2 * Check the following link for details. - * + * https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup * * The bitmask denotes the grouping expressions validity for a grouping set, * the bitmask also be called as grouping id (`GROUPING__ID`, the virtual column in Hive) - * e.g. In superset (k1, k2, k3), (bit 0: k1, bit 1: k2, and bit 2: k3), the grouping id of + * e.g. In superset (k1, k2, k3), (bit 0: k1, bit 1: k2, and bit 2: k3), the grouping id of * GROUPING SETS (k1, k2) and (k2) should be 3 and 2 respectively. */ protected def extractGroupingSet(children: Seq[ASTNode]): (Seq[Expression], Seq[Int]) = { @@ -446,7 +444,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val bitmasks: Seq[Int] = setASTs.map(set => set match { case Token("TOK_GROUPING_SETS_EXPRESSION", null) => 0 - case Token("TOK_GROUPING_SETS_EXPRESSION", children) => + case Token("TOK_GROUPING_SETS_EXPRESSION", children) => children.foldLeft(0)((bitmap, col) => { val colString = col.asInstanceOf[ASTNode].toStringTree() require(keyMap.contains(colString), s"$colString doens't show up in the GROUP BY list") @@ -615,7 +613,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C clusterByClause :: distributeByClause :: limitClause :: - lateralViewClause :: Nil) = { + lateralViewClause :: + windowClause :: Nil) = { getClauses( Seq( "TOK_INSERT_INTO", @@ -633,15 +632,16 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C "TOK_CLUSTERBY", "TOK_DISTRIBUTEBY", "TOK_LIMIT", - "TOK_LATERAL_VIEW"), + "TOK_LATERAL_VIEW", + "WINDOW"), singleInsert) } - + val relations = fromClause match { case Some(f) => nodeToRelation(f) case None => OneRowRelation } - + val withWhere = whereClause.map { whereNode => val Seq(whereExpr) = whereNode.getChildren.toSeq Filter(nodeToExpr(whereExpr), relations) @@ -693,7 +693,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val serdeProps = propsClause.map { case Token("TOK_TABLEPROPERTY", Token(name, Nil) :: Token(value, Nil) :: Nil) => (name, value) - } + } (Nil, serdeClass, serdeProps) case Nil => (Nil, "", Nil) @@ -738,7 +738,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C // The projection of the query can either be a normal projection, an aggregation // (if there is a group by) or a script transformation. val withProject: LogicalPlan = transformation.getOrElse { - val selectExpressions = + val selectExpressions = nameExpressions(select.getChildren.flatMap(selExprNodeToExpr).toSeq) Seq( groupByClause.map(e => e match { @@ -766,31 +766,34 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C Some(Project(selectExpressions, withLateralView))).flatten.head } - val withDistinct = - if (selectDistinctClause.isDefined) Distinct(withProject) else withProject - + // Handle HAVING clause. val withHaving = havingClause.map { h => val havingExpr = h.getChildren.toSeq match { case Seq(hexpr) => nodeToExpr(hexpr) } // Note that we added a cast to boolean. If the expression itself is already boolean, // the optimizer will get rid of the unnecessary cast. - Filter(Cast(havingExpr, BooleanType), withDistinct) - }.getOrElse(withDistinct) + Filter(Cast(havingExpr, BooleanType), withProject) + }.getOrElse(withProject) + + // Handle SELECT DISTINCT + val withDistinct = + if (selectDistinctClause.isDefined) Distinct(withHaving) else withHaving + // Handle ORDER BY, SORT BY, DISTRIBETU BY, and CLUSTER BY clause. val withSort = (orderByClause, sortByClause, distributeByClause, clusterByClause) match { case (Some(totalOrdering), None, None, None) => - Sort(totalOrdering.getChildren.map(nodeToSortOrder), true, withHaving) + Sort(totalOrdering.getChildren.map(nodeToSortOrder), true, withDistinct) case (None, Some(perPartitionOrdering), None, None) => - Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, withHaving) + Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, withDistinct) case (None, None, Some(partitionExprs), None) => - RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withHaving) + RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct) case (None, Some(perPartitionOrdering), Some(partitionExprs), None) => Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, - RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withHaving)) + RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct)) case (None, None, None, Some(clusterExprs)) => Sort(clusterExprs.getChildren.map(nodeToExpr).map(SortOrder(_, Ascending)), false, - RepartitionByExpression(clusterExprs.getChildren.map(nodeToExpr), withHaving)) - case (None, None, None, None) => withHaving + RepartitionByExpression(clusterExprs.getChildren.map(nodeToExpr), withDistinct)) + case (None, None, None, None) => withDistinct case _ => sys.error("Unsupported set of ordering / distribution clauses.") } @@ -799,6 +802,27 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C .map(Limit(_, withSort)) .getOrElse(withSort) + // Collect all window specifications defined in the WINDOW clause. + val windowDefinitions = windowClause.map(_.getChildren.toSeq.collect { + case Token("TOK_WINDOWDEF", + Token(windowName, Nil) :: Token("TOK_WINDOWSPEC", spec) :: Nil) => + windowName -> nodesToWindowSpecification(spec) + }.toMap) + // Handle cases like + // window w1 as (partition by p_mfgr order by p_name + // range between 2 preceding and 2 following), + // w2 as w1 + val resolvedCrossReference = windowDefinitions.map { + windowDefMap => windowDefMap.map { + case (windowName, WindowSpecReference(other)) => + (windowName, windowDefMap(other).asInstanceOf[WindowSpecDefinition]) + case o => o.asInstanceOf[(String, WindowSpecDefinition)] + } + } + + val withWindowDefinitions = + resolvedCrossReference.map(WithWindowDefinition(_, withLimit)).getOrElse(withLimit) + // TOK_INSERT_INTO means to add files to the table. // TOK_DESTINATION means to overwrite the table. val resultDestination = @@ -806,7 +830,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val overwrite = intoClause.isEmpty nodeToDest( resultDestination, - withLimit, + withWindowDefinitions, overwrite) } @@ -887,13 +911,13 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C fraction.toDouble >= (0.0 - RandomSampler.roundingEpsilon) && fraction.toDouble <= (100.0 + RandomSampler.roundingEpsilon), s"Sampling fraction ($fraction) must be on interval [0, 100]") - Sample(fraction.toDouble / 100, withReplacement = false, (math.random * 1000).toInt, + Sample(0.0, fraction.toDouble / 100, withReplacement = false, (math.random * 1000).toInt, relation) case Token("TOK_TABLEBUCKETSAMPLE", Token(numerator, Nil) :: Token(denominator, Nil) :: Nil) => val fraction = numerator.toDouble / denominator.toDouble - Sample(fraction, withReplacement = false, (math.random * 1000).toInt, relation) + Sample(0.0, fraction, withReplacement = false, (math.random * 1000).toInt, relation) case a: ASTNode => throw new NotImplementedError( s"""No parse rules for sampling clause: ${a.getType}, text: ${a.getText} : @@ -1055,7 +1079,6 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ") } - protected val escapedIdentifier = "`([^`]+)`".r /** Strips backticks from ident if present */ protected def cleanIdentifier(ident: String): String = ident match { @@ -1244,13 +1267,33 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C /* Other functions */ case Token("TOK_FUNCTION", Token(ARRAY(), Nil) :: children) => CreateArray(children.map(nodeToExpr)) - case Token("TOK_FUNCTION", Token(RAND(), Nil) :: Nil) => Rand + case Token("TOK_FUNCTION", Token(RAND(), Nil) :: Nil) => Rand() + case Token("TOK_FUNCTION", Token(RAND(), Nil) :: seed :: Nil) => Rand(seed.toString.toLong) case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: Nil) => Substring(nodeToExpr(string), nodeToExpr(pos), Literal.create(Integer.MAX_VALUE, IntegerType)) case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: length :: Nil) => Substring(nodeToExpr(string), nodeToExpr(pos), nodeToExpr(length)) case Token("TOK_FUNCTION", Token(COALESCE(), Nil) :: list) => Coalesce(list.map(nodeToExpr)) + /* Window Functions */ + case Token("TOK_FUNCTION", Token(name, Nil) +: args :+ Token("TOK_WINDOWSPEC", spec)) => + val function = UnresolvedWindowFunction(name, args.map(nodeToExpr)) + nodesToWindowSpecification(spec) match { + case reference: WindowSpecReference => + UnresolvedWindowExpression(function, reference) + case definition: WindowSpecDefinition => + WindowExpression(function, definition) + } + case Token("TOK_FUNCTIONSTAR", Token(name, Nil) :: Token("TOK_WINDOWSPEC", spec) :: Nil) => + // Safe to use Literal(1)? + val function = UnresolvedWindowFunction(name, Literal(1) :: Nil) + nodesToWindowSpecification(spec) match { + case reference: WindowSpecReference => + UnresolvedWindowExpression(function, reference) + case definition: WindowSpecDefinition => + WindowExpression(function, definition) + } + /* UDFs - Must be last otherwise will preempt built in functions */ case Token("TOK_FUNCTION", Token(name, Nil) :: args) => UnresolvedFunction(name, args.map(nodeToExpr)) @@ -1313,6 +1356,89 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C """.stripMargin) } + def nodesToWindowSpecification(nodes: Seq[ASTNode]): WindowSpec = nodes match { + case Token(windowName, Nil) :: Nil => + // Refer to a window spec defined in the window clause. + WindowSpecReference(windowName) + case Nil => + // OVER() + WindowSpecDefinition( + partitionSpec = Nil, + orderSpec = Nil, + frameSpecification = UnspecifiedFrame) + case spec => + val (partitionClause :: rowFrame :: rangeFrame :: Nil) = + getClauses( + Seq( + "TOK_PARTITIONINGSPEC", + "TOK_WINDOWRANGE", + "TOK_WINDOWVALUES"), + spec) + + // Handle Partition By and Order By. + val (partitionSpec, orderSpec) = partitionClause.map { partitionAndOrdering => + val (partitionByClause :: orderByClause :: sortByClause :: clusterByClause :: Nil) = + getClauses( + Seq("TOK_DISTRIBUTEBY", "TOK_ORDERBY", "TOK_SORTBY", "TOK_CLUSTERBY"), + partitionAndOrdering.getChildren.toSeq.asInstanceOf[Seq[ASTNode]]) + + (partitionByClause, orderByClause.orElse(sortByClause), clusterByClause) match { + case (Some(partitionByExpr), Some(orderByExpr), None) => + (partitionByExpr.getChildren.map(nodeToExpr), + orderByExpr.getChildren.map(nodeToSortOrder)) + case (Some(partitionByExpr), None, None) => + (partitionByExpr.getChildren.map(nodeToExpr), Nil) + case (None, Some(orderByExpr), None) => + (Nil, orderByExpr.getChildren.map(nodeToSortOrder)) + case (None, None, Some(clusterByExpr)) => + val expressions = clusterByExpr.getChildren.map(nodeToExpr) + (expressions, expressions.map(SortOrder(_, Ascending))) + case _ => + throw new NotImplementedError( + s"""No parse rules for Node ${partitionAndOrdering.getName} + """.stripMargin) + } + }.getOrElse { + (Nil, Nil) + } + + // Handle Window Frame + val windowFrame = + if (rowFrame.isEmpty && rangeFrame.isEmpty) { + UnspecifiedFrame + } else { + val frameType = rowFrame.map(_ => RowFrame).getOrElse(RangeFrame) + def nodeToBoundary(node: Node): FrameBoundary = node match { + case Token("preceding", Token(count, Nil) :: Nil) => + if (count == "unbounded") UnboundedPreceding else ValuePreceding(count.toInt) + case Token("following", Token(count, Nil) :: Nil) => + if (count == "unbounded") UnboundedFollowing else ValueFollowing(count.toInt) + case Token("current", Nil) => CurrentRow + case _ => + throw new NotImplementedError( + s"""No parse rules for the Window Frame Boundary based on Node ${node.getName} + """.stripMargin) + } + + rowFrame.orElse(rangeFrame).map { frame => + frame.getChildren.toList match { + case precedingNode :: followingNode :: Nil => + SpecifiedWindowFrame( + frameType, + nodeToBoundary(precedingNode), + nodeToBoundary(followingNode)) + case precedingNode :: Nil => + SpecifiedWindowFrame(frameType, nodeToBoundary(precedingNode), CurrentRow) + case _ => + throw new NotImplementedError( + s"""No parse rules for the Window Frame based on Node ${frame.getName} + """.stripMargin) + } + }.getOrElse(sys.error(s"If you see this, please file a bug report with your query.")) + } + + WindowSpecDefinition(partitionSpec, orderSpec, windowFrame) + } val explode = "(?i)explode".r def nodesToGenerator(nodes: Seq[Node]): (Generator, Seq[String]) = { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala new file mode 100644 index 0000000000000..a863aa77cb7e0 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala @@ -0,0 +1,149 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import org.apache.spark.sql.catalyst.analysis.{NoSuchDatabaseException, NoSuchTableException} + +case class HiveDatabase( + name: String, + location: String) + +abstract class TableType { val name: String } +case object ExternalTable extends TableType { override val name = "EXTERNAL_TABLE" } +case object IndexTable extends TableType { override val name = "INDEX_TABLE" } +case object ManagedTable extends TableType { override val name = "MANAGED_TABLE" } +case object VirtualView extends TableType { override val name = "VIRTUAL_VIEW" } + +case class HiveStorageDescriptor( + location: String, + inputFormat: String, + outputFormat: String, + serde: String) + +case class HivePartition( + values: Seq[String], + storage: HiveStorageDescriptor) + +case class HiveColumn(name: String, hiveType: String, comment: String) +case class HiveTable( + specifiedDatabase: Option[String], + name: String, + schema: Seq[HiveColumn], + partitionColumns: Seq[HiveColumn], + properties: Map[String, String], + serdeProperties: Map[String, String], + tableType: TableType, + location: Option[String] = None, + inputFormat: Option[String] = None, + outputFormat: Option[String] = None, + serde: Option[String] = None) { + + @transient + private[client] var client: ClientInterface = _ + + private[client] def withClient(ci: ClientInterface): this.type = { + client = ci + this + } + + def database: String = specifiedDatabase.getOrElse(sys.error("database not resolved")) + + def isPartitioned: Boolean = partitionColumns.nonEmpty + + def getAllPartitions: Seq[HivePartition] = client.getAllPartitions(this) + + // Hive does not support backticks when passing names to the client. + def qualifiedName: String = s"$database.$name" +} + +/** + * An externally visible interface to the Hive client. This interface is shared across both the + * internal and external classloaders for a given version of Hive and thus must expose only + * shared classes. + */ +trait ClientInterface { + /** + * Runs a HiveQL command using Hive, returning the results as a list of strings. Each row will + * result in one string. + */ + def runSqlHive(sql: String): Seq[String] + + /** Returns the names of all tables in the given database. */ + def listTables(dbName: String): Seq[String] + + /** Returns the name of the active database. */ + def currentDatabase: String + + /** Returns the metadata for specified database, throwing an exception if it doesn't exist */ + def getDatabase(name: String): HiveDatabase = { + getDatabaseOption(name).getOrElse(throw new NoSuchDatabaseException) + } + + /** Returns the metadata for a given database, or None if it doesn't exist. */ + def getDatabaseOption(name: String): Option[HiveDatabase] + + /** Returns the specified table, or throws [[NoSuchTableException]]. */ + def getTable(dbName: String, tableName: String): HiveTable = { + getTableOption(dbName, tableName).getOrElse(throw new NoSuchTableException) + } + + /** Returns the metadata for the specified table or None if it doens't exist. */ + def getTableOption(dbName: String, tableName: String): Option[HiveTable] + + /** Creates a table with the given metadata. */ + def createTable(table: HiveTable): Unit + + /** Updates the given table with new metadata. */ + def alterTable(table: HiveTable): Unit + + /** Creates a new database with the given name. */ + def createDatabase(database: HiveDatabase): Unit + + /** Returns all partitions for the given table. */ + def getAllPartitions(hTable: HiveTable): Seq[HivePartition] + + /** Loads a static partition into an existing table. */ + def loadPartition( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], // Hive relies on LinkedHashMap ordering + replace: Boolean, + holdDDLTime: Boolean, + inheritTableSpecs: Boolean, + isSkewedStoreAsSubdir: Boolean): Unit + + /** Loads data into an existing table. */ + def loadTable( + loadPath: String, // TODO URI + tableName: String, + replace: Boolean, + holdDDLTime: Boolean): Unit + + /** Loads new dynamic partitions into an existing table. */ + def loadDynamicPartitions( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], // Hive relies on LinkedHashMap ordering + replace: Boolean, + numDP: Int, + holdDDLTime: Boolean, + listBucketingEnabled: Boolean): Unit + + /** Used for testing only. Removes all metadata from this instance of Hive. */ + def reset(): Unit +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala new file mode 100644 index 0000000000000..ea52fea037f1f --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala @@ -0,0 +1,395 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import java.io.{BufferedReader, InputStreamReader, File, PrintStream} +import java.net.URI +import java.util.{ArrayList => JArrayList} + +import scala.collection.JavaConversions._ +import scala.language.reflectiveCalls + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.hive.metastore.api.Database +import org.apache.hadoop.hive.conf.HiveConf +import org.apache.hadoop.hive.metastore.api +import org.apache.hadoop.hive.metastore.api.FieldSchema +import org.apache.hadoop.hive.ql.metadata +import org.apache.hadoop.hive.ql.metadata.Hive +import org.apache.hadoop.hive.ql.session.SessionState +import org.apache.hadoop.hive.ql.processors._ +import org.apache.hadoop.hive.ql.Driver + +import org.apache.spark.Logging +import org.apache.spark.sql.execution.QueryExecutionException + + +/** + * A class that wraps the HiveClient and converts its responses to externally visible classes. + * Note that this class is typically loaded with an internal classloader for each instantiation, + * allowing it to interact directly with a specific isolated version of Hive. Loading this class + * with the isolated classloader however will result in it only being visible as a ClientInterface, + * not a ClientWrapper. + * + * This class needs to interact with multiple versions of Hive, but will always be compiled with + * the 'native', execution version of Hive. Therefore, any places where hive breaks compatibility + * must use reflection after matching on `version`. + * + * @param version the version of hive used when pick function calls that are not compatible. + * @param config a collection of configuration options that will be added to the hive conf before + * opening the hive client. + */ +class ClientWrapper( + version: HiveVersion, + config: Map[String, String]) + extends ClientInterface + with Logging + with ReflectionMagic { + + private val conf = new HiveConf(classOf[SessionState]) + config.foreach { case (k, v) => + logDebug(s"Hive Config: $k=$v") + conf.set(k, v) + } + + // Circular buffer to hold what hive prints to STDOUT and ERR. Only printed when failures occur. + private val outputBuffer = new java.io.OutputStream { + var pos: Int = 0 + var buffer = new Array[Int](10240) + def write(i: Int): Unit = { + buffer(pos) = i + pos = (pos + 1) % buffer.size + } + + override def toString: String = { + val (end, start) = buffer.splitAt(pos) + val input = new java.io.InputStream { + val iterator = (start ++ end).iterator + + def read(): Int = if (iterator.hasNext) iterator.next() else -1 + } + val reader = new BufferedReader(new InputStreamReader(input)) + val stringBuilder = new StringBuilder + var line = reader.readLine() + while(line != null) { + stringBuilder.append(line) + stringBuilder.append("\n") + line = reader.readLine() + } + stringBuilder.toString() + } + } + + val state = { + val original = Thread.currentThread().getContextClassLoader + Thread.currentThread().setContextClassLoader(getClass.getClassLoader) + val ret = try { + val newState = new SessionState(conf) + SessionState.start(newState) + newState.out = new PrintStream(outputBuffer, true, "UTF-8") + newState.err = new PrintStream(outputBuffer, true, "UTF-8") + newState + } finally { + Thread.currentThread().setContextClassLoader(original) + } + ret + } + + private val client = Hive.get(conf) + + /** + * Runs `f` with ThreadLocal session state and classloaders configured for this version of hive. + */ + private def withHiveState[A](f: => A): A = synchronized { + val original = Thread.currentThread().getContextClassLoader + Thread.currentThread().setContextClassLoader(getClass.getClassLoader) + Hive.set(client) + version match { + case hive.v12 => + classOf[SessionState] + .callStatic[SessionState, SessionState]("start", state) + case hive.v13 => + classOf[SessionState] + .callStatic[SessionState, SessionState]("setCurrentSessionState", state) + } + val ret = try f finally { + Thread.currentThread().setContextClassLoader(original) + } + ret + } + + override def currentDatabase: String = withHiveState { + state.getCurrentDatabase + } + + override def createDatabase(database: HiveDatabase): Unit = withHiveState { + client.createDatabase( + new Database( + database.name, + "", + new File(database.location).toURI.toString, + new java.util.HashMap), + true) + } + + override def getDatabaseOption(name: String): Option[HiveDatabase] = withHiveState { + Option(client.getDatabase(name)).map { d => + HiveDatabase( + name = d.getName, + location = d.getLocationUri) + } + } + + override def getTableOption( + dbName: String, + tableName: String): Option[HiveTable] = withHiveState { + + logDebug(s"Looking up $dbName.$tableName") + + val hiveTable = Option(client.getTable(dbName, tableName, false)) + val converted = hiveTable.map { h => + + HiveTable( + name = h.getTableName, + specifiedDatabase = Option(h.getDbName), + schema = h.getCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)), + partitionColumns = h.getPartCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)), + properties = h.getParameters.toMap, + serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.toMap, + tableType = ManagedTable, // TODO + location = version match { + case hive.v12 => Option(h.call[URI]("getDataLocation")).map(_.toString) + case hive.v13 => Option(h.call[Path]("getDataLocation")).map(_.toString) + }, + inputFormat = Option(h.getInputFormatClass).map(_.getName), + outputFormat = Option(h.getOutputFormatClass).map(_.getName), + serde = Option(h.getSerializationLib)).withClient(this) + } + converted + } + + private def toInputFormat(name: String) = + Class.forName(name).asInstanceOf[Class[_ <: org.apache.hadoop.mapred.InputFormat[_, _]]] + + private def toOutputFormat(name: String) = + Class.forName(name) + .asInstanceOf[Class[_ <: org.apache.hadoop.hive.ql.io.HiveOutputFormat[_, _]]] + + private def toQlTable(table: HiveTable): metadata.Table = { + val qlTable = new metadata.Table(table.database, table.name) + + qlTable.setFields(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment))) + qlTable.setPartCols( + table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment))) + table.properties.foreach { case (k, v) => qlTable.setProperty(k, v) } + table.serdeProperties.foreach { case (k, v) => qlTable.setSerdeParam(k, v) } + version match { + case hive.v12 => + table.location.map(new URI(_)).foreach(u => qlTable.call[URI, Unit]("setDataLocation", u)) + case hive.v13 => + table.location + .map(new org.apache.hadoop.fs.Path(_)) + .foreach(qlTable.call[Path, Unit]("setDataLocation", _)) + } + table.inputFormat.map(toInputFormat).foreach(qlTable.setInputFormatClass) + table.outputFormat.map(toOutputFormat).foreach(qlTable.setOutputFormatClass) + table.serde.foreach(qlTable.setSerializationLib) + + qlTable + } + + override def createTable(table: HiveTable): Unit = withHiveState { + val qlTable = toQlTable(table) + client.createTable(qlTable) + } + + override def alterTable(table: HiveTable): Unit = withHiveState { + val qlTable = toQlTable(table) + client.alterTable(table.qualifiedName, qlTable) + } + + override def getAllPartitions(hTable: HiveTable): Seq[HivePartition] = withHiveState { + val qlTable = toQlTable(hTable) + val qlPartitions = version match { + case hive.v12 => + client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsForPruner", qlTable) + case hive.v13 => + client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsOf", qlTable) + } + qlPartitions.map(_.getTPartition).map { p => + HivePartition( + values = Option(p.getValues).map(_.toSeq).getOrElse(Seq.empty), + storage = HiveStorageDescriptor( + location = p.getSd.getLocation, + inputFormat = p.getSd.getInputFormat, + outputFormat = p.getSd.getOutputFormat, + serde = p.getSd.getSerdeInfo.getSerializationLib)) + }.toSeq + } + + override def listTables(dbName: String): Seq[String] = withHiveState { + client.getAllTables + } + + /** + * Runs the specified SQL query using Hive. + */ + override def runSqlHive(sql: String): Seq[String] = { + val maxResults = 100000 + val results = runHive(sql, maxResults) + // It is very confusing when you only get back some of the results... + if (results.size == maxResults) sys.error("RESULTS POSSIBLY TRUNCATED") + results + } + + /** + * Execute the command using Hive and return the results as a sequence. Each element + * in the sequence is one row. + */ + protected def runHive(cmd: String, maxRows: Int = 1000): Seq[String] = withHiveState { + logDebug(s"Running hiveql '$cmd'") + if (cmd.toLowerCase.startsWith("set")) { logDebug(s"Changing config: $cmd") } + try { + val cmd_trimmed: String = cmd.trim() + val tokens: Array[String] = cmd_trimmed.split("\\s+") + val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim() + val proc: CommandProcessor = version match { + case hive.v12 => + classOf[CommandProcessorFactory] + .callStatic[String, HiveConf, CommandProcessor]("get", cmd_1, conf) + case hive.v13 => + classOf[CommandProcessorFactory] + .callStatic[Array[String], HiveConf, CommandProcessor]("get", Array(tokens(0)), conf) + } + + proc match { + case driver: Driver => + val response: CommandProcessorResponse = driver.run(cmd) + // Throw an exception if there is an error in query processing. + if (response.getResponseCode != 0) { + driver.close() + throw new QueryExecutionException(response.getErrorMessage) + } + driver.setMaxRows(maxRows) + + val results = version match { + case hive.v12 => + val res = new JArrayList[String] + driver.call[JArrayList[String], Boolean]("getResults", res) + res.toSeq + case hive.v13 => + val res = new JArrayList[Object] + driver.call[JArrayList[Object], Boolean]("getResults", res) + res.map { r => + r match { + case s: String => s + case a: Array[Object] => a(0).asInstanceOf[String] + } + } + } + driver.close() + results + + case _ => + if (state.out != null) { + state.out.println(tokens(0) + " " + cmd_1) + } + Seq(proc.run(cmd_1).getResponseCode.toString) + } + } catch { + case e: Exception => + logError( + s""" + |====================== + |HIVE FAILURE OUTPUT + |====================== + |${outputBuffer.toString} + |====================== + |END HIVE FAILURE OUTPUT + |====================== + """.stripMargin) + throw e + } + } + + def loadPartition( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], + replace: Boolean, + holdDDLTime: Boolean, + inheritTableSpecs: Boolean, + isSkewedStoreAsSubdir: Boolean): Unit = withHiveState { + + client.loadPartition( + new Path(loadPath), // TODO: Use URI + tableName, + partSpec, + replace, + holdDDLTime, + inheritTableSpecs, + isSkewedStoreAsSubdir) + } + + def loadTable( + loadPath: String, // TODO URI + tableName: String, + replace: Boolean, + holdDDLTime: Boolean): Unit = withHiveState { + client.loadTable( + new Path(loadPath), + tableName, + replace, + holdDDLTime) + } + + def loadDynamicPartitions( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], + replace: Boolean, + numDP: Int, + holdDDLTime: Boolean, + listBucketingEnabled: Boolean): Unit = withHiveState { + client.loadDynamicPartitions( + new Path(loadPath), + tableName, + partSpec, + replace, + numDP, + holdDDLTime, + listBucketingEnabled) + } + + def reset(): Unit = withHiveState { + client.getAllTables("default").foreach { t => + logDebug(s"Deleting table $t") + val table = client.getTable("default", t) + client.getIndexes("default", t, 255).foreach { index => + client.dropIndex("default", t, index.getIndexName, true) + } + if (!table.isIndexTable) { + client.dropTable("default", t) + } + } + client.getAllDatabases.filterNot(_ == "default").foreach { db => + logDebug(s"Dropping Database: $db") + client.dropDatabase(db, true, false, true) + } + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala new file mode 100644 index 0000000000000..710dbca6e3c66 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala @@ -0,0 +1,172 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import java.io.File +import java.net.URLClassLoader +import java.util + +import scala.language.reflectiveCalls +import scala.util.Try + +import org.apache.commons.io.{FileUtils, IOUtils} + +import org.apache.spark.Logging +import org.apache.spark.deploy.SparkSubmitUtils + +import org.apache.spark.sql.catalyst.util.quietly + +/** Factory for `IsolatedClientLoader` with specific versions of hive. */ +object IsolatedClientLoader { + /** + * Creates isolated Hive client loaders by downloading the requested version from maven. + */ + def forVersion( + version: String, + config: Map[String, String] = Map.empty): IsolatedClientLoader = synchronized { + val resolvedVersion = hiveVersion(version) + val files = resolvedVersions.getOrElseUpdate(resolvedVersion, downloadVersion(resolvedVersion)) + new IsolatedClientLoader(hiveVersion(version), files, config) + } + + def hiveVersion(version: String): HiveVersion = version match { + case "12" | "0.12" | "0.12.0" => hive.v12 + case "13" | "0.13" | "0.13.0" | "0.13.1" => hive.v13 + } + + private def downloadVersion(version: HiveVersion): Seq[File] = { + val hiveArtifacts = + (Seq("hive-metastore", "hive-exec", "hive-common", "hive-serde") ++ + (if (version.hasBuiltinsJar) "hive-builtins" :: Nil else Nil)) + .map(a => s"org.apache.hive:$a:${version.fullVersion}") :+ + "com.google.guava:guava:14.0.1" :+ + "org.apache.hadoop:hadoop-client:2.4.0" :+ + "mysql:mysql-connector-java:5.1.12" + + val classpath = quietly { + SparkSubmitUtils.resolveMavenCoordinates( + hiveArtifacts.mkString(","), + Some("http://www.datanucleus.org/downloads/maven2"), + None) + } + val allFiles = classpath.split(",").map(new File(_)).toSet + + // TODO: Remove copy logic. + val tempDir = File.createTempFile("hive", "v" + version.toString) + tempDir.delete() + tempDir.mkdir() + + allFiles.foreach(f => FileUtils.copyFileToDirectory(f, tempDir)) + tempDir.listFiles() + } + + private def resolvedVersions = new scala.collection.mutable.HashMap[HiveVersion, Seq[File]] +} + +/** + * Creates a Hive `ClientInterface` using a classloader that works according to the following rules: + * - Shared classes: Java, Scala, logging, and Spark classes are delegated to `baseClassLoader` + * allowing the results of calls to the `ClientInterface` to be visible externally. + * - Hive classes: new instances are loaded from `execJars`. These classes are not + * accessible externally due to their custom loading. + * - ClientWrapper: a new copy is created for each instance of `IsolatedClassLoader`. + * This new instance is able to see a specific version of hive without using reflection. Since + * this is a unique instance, it is not visible externally other than as a generic + * `ClientInterface`, unless `isolationOn` is set to `false`. + * + * @param version The version of hive on the classpath. used to pick specific function signatures + * that are not compatibile accross versions. + * @param execJars A collection of jar files that must include hive and hadoop. + * @param config A set of options that will be added to the HiveConf of the constructed client. + * @param isolationOn When true, custom versions of barrier classes will be constructed. Must be + * true unless loading the version of hive that is on Sparks classloader. + * @param rootClassLoader The system root classloader. Must not know about hive classes. + * @param baseClassLoader The spark classloader that is used to load shared classes. + * + */ +class IsolatedClientLoader( + val version: HiveVersion, + val execJars: Seq[File] = Seq.empty, + val config: Map[String, String] = Map.empty, + val isolationOn: Boolean = true, + val rootClassLoader: ClassLoader = ClassLoader.getSystemClassLoader.getParent.getParent, + val baseClassLoader: ClassLoader = Thread.currentThread().getContextClassLoader) + extends Logging { + + // Check to make sure that the root classloader does not know about Hive. + assert(Try(baseClassLoader.loadClass("org.apache.hive.HiveConf")).isFailure) + + /** All jars used by the hive specific classloader. */ + protected def allJars = execJars.map(_.toURI.toURL).toArray + + protected def isSharedClass(name: String): Boolean = + name.contains("slf4j") || + name.contains("log4j") || + name.startsWith("org.apache.spark.") || + name.startsWith("scala.") || + name.startsWith("com.google") || + name.startsWith("java.lang.") || + name.startsWith("java.net") + + /** True if `name` refers to a spark class that must see specific version of Hive. */ + protected def isBarrierClass(name: String): Boolean = + name.startsWith("org.apache.spark.sql.hive.execution.PairSerDe") || + name.startsWith(classOf[ClientWrapper].getName) || + name.startsWith(classOf[ReflectionMagic].getName) + + protected def classToPath(name: String): String = + name.replaceAll("\\.", "/") + ".class" + + /** The classloader that is used to load an isolated version of Hive. */ + protected val classLoader: ClassLoader = new URLClassLoader(allJars, rootClassLoader) { + override def loadClass(name: String, resolve: Boolean): Class[_] = { + val loaded = findLoadedClass(name) + if (loaded == null) doLoadClass(name, resolve) else loaded + } + + def doLoadClass(name: String, resolve: Boolean): Class[_] = { + val classFileName = name.replaceAll("\\.", "/") + ".class" + if (isBarrierClass(name) && isolationOn) { + val bytes = IOUtils.toByteArray(baseClassLoader.getResourceAsStream(classFileName)) + logDebug(s"custom defining: $name - ${util.Arrays.hashCode(bytes)}") + defineClass(name, bytes, 0, bytes.length) + } else if (!isSharedClass(name)) { + logDebug(s"hive class: $name - ${getResource(classToPath(name))}") + super.loadClass(name, resolve) + } else { + logDebug(s"shared class: $name") + baseClassLoader.loadClass(name) + } + } + } + + // Pre-reflective instantiation setup. + logDebug("Initializing the logger to avoid disaster...") + Thread.currentThread.setContextClassLoader(classLoader) + + /** The isolated client interface to Hive. */ + val client: ClientInterface = try { + classLoader + .loadClass(classOf[ClientWrapper].getName) + .getConstructors.head + .newInstance(version, config) + .asInstanceOf[ClientInterface] + } finally { + Thread.currentThread.setContextClassLoader(baseClassLoader) + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala new file mode 100644 index 0000000000000..90d03049356b5 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala @@ -0,0 +1,200 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import scala.reflect._ + +/** + * Provides implicit functions on any object for calling methods reflectively. + */ +protected trait ReflectionMagic { + /** code for InstanceMagic + println( + (1 to 22).map { n => + def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ") + val types = repeat(n => s"A$n <: AnyRef : ClassTag") + val inArgs = repeat(n => s"a$n: A$n") + val erasure = repeat(n => s"classTag[A$n].erasure") + val outArgs = repeat(n => s"a$n") + s"""|def call[$types, R](name: String, $inArgs): R = { + | clazz.getMethod(name, $erasure).invoke(a, $outArgs).asInstanceOf[R] + |}""".stripMargin + }.mkString("\n") + ) + */ + + // scalastyle:off + protected implicit class InstanceMagic(a: Any) { + private val clazz = a.getClass + + def call[R](name: String): R = { + clazz.getMethod(name).invoke(a).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = { + clazz.getMethod(name, classTag[A1].erasure).invoke(a, a1).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(a, a1, a2).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(a, a1, a2, a3).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(a, a1, a2, a3, a4).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(a, a1, a2, a3, a4, a5).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(a, a1, a2, a3, a4, a5, a6).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R] + } + } + + /** code for StaticMagic + println( + (1 to 22).map { n => + def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ") + val types = repeat(n => s"A$n <: AnyRef : ClassTag") + val inArgs = repeat(n => s"a$n: A$n") + val erasure = repeat(n => s"classTag[A$n].erasure") + val outArgs = repeat(n => s"a$n") + s"""|def callStatic[$types, R](name: String, $inArgs): R = { + | c.getDeclaredMethod(name, $erasure).invoke(c, $outArgs).asInstanceOf[R] + |}""".stripMargin + }.mkString("\n") + ) + */ + + protected implicit class StaticMagic(c: Class[_]) { + def callStatic[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = { + c.getDeclaredMethod(name, classTag[A1].erasure).invoke(c, a1).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(c, a1, a2).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(c, a1, a2, a3).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(c, a1, a2, a3, a4).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(c, a1, a2, a3, a4, a5).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(c, a1, a2, a3, a4, a5, a6).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R] + } + } + // scalastyle:on +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala new file mode 100644 index 0000000000000..7db9200d47440 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +/** Support for interacting with different versions of the HiveMetastoreClient */ +package object client { + private[client] abstract class HiveVersion(val fullVersion: String, val hasBuiltinsJar: Boolean) + + // scalastyle:off + private[client] object hive { + case object v10 extends HiveVersion("0.10.0", true) + case object v11 extends HiveVersion("0.11.0", false) + case object v12 extends HiveVersion("0.12.0", false) + case object v13 extends HiveVersion("0.13.1", false) + } + // scalastyle:on + +} \ No newline at end of file diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala index 4b6f0ad75f54f..fd0b6f058595d 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala @@ -17,27 +17,27 @@ package org.apache.spark.sql.hive +import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator.AggregationBuffer import org.apache.hadoop.hive.ql.udf.generic.GenericUDFUtils.ConversionHelper +import org.apache.spark.sql.AnalysisException import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ConstantObjectInspector} import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory.ObjectInspectorOptions import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory -import org.apache.hadoop.hive.ql.exec.{UDF, UDAF} -import org.apache.hadoop.hive.ql.exec.{FunctionInfo, FunctionRegistry} +import org.apache.hadoop.hive.ql.exec._ import org.apache.hadoop.hive.ql.udf.{UDFType => HiveUDFType} import org.apache.hadoop.hive.ql.udf.generic._ import org.apache.hadoop.hive.ql.udf.generic.GenericUDF._ import org.apache.spark.Logging import org.apache.spark.sql.catalyst.analysis +import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical.{Generate, Project, LogicalPlan} +import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules.Rule import org.apache.spark.sql.types._ -import org.apache.spark.sql.catalyst.analysis.MultiAlias -import org.apache.spark.sql.catalyst.errors.TreeNodeException /* Implicit conversions */ import scala.collection.JavaConversions._ @@ -189,6 +189,219 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr } } +/** + * Resolves [[UnresolvedWindowFunction]] to [[HiveWindowFunction]]. + */ +private[spark] object ResolveHiveWindowFunction extends Rule[LogicalPlan] { + def apply(plan: LogicalPlan): LogicalPlan = plan transformUp { + case p: LogicalPlan if !p.childrenResolved => p + + // We are resolving WindowExpressions at here. When we get here, we have already + // replaced those WindowSpecReferences. + case p: LogicalPlan => + p transformExpressions { + case WindowExpression( + UnresolvedWindowFunction(name, children), + windowSpec: WindowSpecDefinition) => + // First, let's find the window function info. + val windowFunctionInfo: WindowFunctionInfo = + Option(FunctionRegistry.getWindowFunctionInfo(name.toLowerCase)).getOrElse( + throw new AnalysisException(s"Couldn't find window function $name")) + + // Get the class of this function. + // In Hive 0.12, there is no windowFunctionInfo.getFunctionClass. So, we use + // windowFunctionInfo.getfInfo().getFunctionClass for both Hive 0.13 and Hive 0.13.1. + val functionClass = windowFunctionInfo.getfInfo().getFunctionClass + val newChildren = + // Rank(), DENSE_RANK(), CUME_DIST(), and PERCENT_RANK() do not take explicit + // input parameters and requires implicit parameters, which + // are expressions in Order By clause. + if (classOf[GenericUDAFRank].isAssignableFrom(functionClass)) { + if (children.nonEmpty) { + throw new AnalysisException(s"$name does not take input parameters.") + } + windowSpec.orderSpec.map(_.child) + } else { + children + } + + // If the class is UDAF, we need to use UDAFBridge. + val isUDAFBridgeRequired = + if (classOf[UDAF].isAssignableFrom(functionClass)) { + true + } else { + false + } + + // Create the HiveWindowFunction. For the meaning of isPivotResult, see the doc of + // HiveWindowFunction. + val windowFunction = + HiveWindowFunction( + new HiveFunctionWrapper(functionClass.getName), + windowFunctionInfo.isPivotResult, + isUDAFBridgeRequired, + newChildren) + + // Second, check if the specified window function can accept window definition. + windowSpec.frameSpecification match { + case frame: SpecifiedWindowFrame if !windowFunctionInfo.isSupportsWindow => + // This Hive window function does not support user-speficied window frame. + throw new AnalysisException( + s"Window function $name does not take a frame specification.") + case frame: SpecifiedWindowFrame if windowFunctionInfo.isSupportsWindow && + windowFunctionInfo.isPivotResult => + // These two should not be true at the same time when a window frame is defined. + // If so, throw an exception. + throw new AnalysisException(s"Could not handle Hive window function $name because " + + s"it supports both a user specified window frame and pivot result.") + case _ => // OK + } + // Resolve those UnspecifiedWindowFrame because the physical Window operator still needs + // a window frame specification to work. + val newWindowSpec = windowSpec.frameSpecification match { + case UnspecifiedFrame => + val newWindowFrame = + SpecifiedWindowFrame.defaultWindowFrame( + windowSpec.orderSpec.nonEmpty, + windowFunctionInfo.isSupportsWindow) + WindowSpecDefinition(windowSpec.partitionSpec, windowSpec.orderSpec, newWindowFrame) + case _ => windowSpec + } + + // Finally, we create a WindowExpression with the resolved window function and + // specified window spec. + WindowExpression(windowFunction, newWindowSpec) + } + } +} + +/** + * A [[WindowFunction]] implementation wrapping Hive's window function. + * @param funcWrapper The wrapper for the Hive Window Function. + * @param pivotResult If it is true, the Hive function will return a list of values representing + * the values of the added columns. Otherwise, a single value is returned for + * current row. + * @param isUDAFBridgeRequired If it is true, the function returned by functionWrapper's + * createFunction is UDAF, we need to use GenericUDAFBridge to wrap + * it as a GenericUDAFResolver2. + * @param children Input parameters. + */ +private[hive] case class HiveWindowFunction( + funcWrapper: HiveFunctionWrapper, + pivotResult: Boolean, + isUDAFBridgeRequired: Boolean, + children: Seq[Expression]) extends WindowFunction + with HiveInspectors { + + // Hive window functions are based on GenericUDAFResolver2. + type UDFType = GenericUDAFResolver2 + + @transient + protected lazy val resolver: GenericUDAFResolver2 = + if (isUDAFBridgeRequired) { + new GenericUDAFBridge(funcWrapper.createFunction[UDAF]()) + } else { + funcWrapper.createFunction[GenericUDAFResolver2]() + } + + @transient + protected lazy val inputInspectors = children.map(toInspector).toArray + + // The GenericUDAFEvaluator used to evaluate the window function. + @transient + protected lazy val evaluator: GenericUDAFEvaluator = { + val parameterInfo = new SimpleGenericUDAFParameterInfo(inputInspectors, false, false) + resolver.getEvaluator(parameterInfo) + } + + // The object inspector of values returned from the Hive window function. + @transient + protected lazy val returnInspector = { + evaluator.init(GenericUDAFEvaluator.Mode.COMPLETE, inputInspectors) + } + + def dataType: DataType = + if (!pivotResult) { + inspectorToDataType(returnInspector) + } else { + // If pivotResult is true, we should take the element type out as the data type of this + // function. + inspectorToDataType(returnInspector) match { + case ArrayType(dt, _) => dt + case _ => + sys.error( + s"error resolve the data type of window function ${funcWrapper.functionClassName}") + } + } + + def nullable: Boolean = true + + override type EvaluatedType = Any + + override def eval(input: Row): Any = + throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") + + @transient + lazy val inputProjection = new InterpretedProjection(children) + + @transient + private var hiveEvaluatorBuffer: AggregationBuffer = _ + // Output buffer. + private var outputBuffer: Any = _ + + override def init(): Unit = { + evaluator.init(GenericUDAFEvaluator.Mode.COMPLETE, inputInspectors) + } + + // Reset the hiveEvaluatorBuffer and outputPosition + override def reset(): Unit = { + // We create a new aggregation buffer to workaround the bug in GenericUDAFRowNumber. + // Basically, GenericUDAFRowNumberEvaluator.reset calls RowNumberBuffer.init. + // However, RowNumberBuffer.init does not really reset this buffer. + hiveEvaluatorBuffer = evaluator.getNewAggregationBuffer + evaluator.reset(hiveEvaluatorBuffer) + } + + override def prepareInputParameters(input: Row): AnyRef = { + wrap(inputProjection(input), inputInspectors, new Array[AnyRef](children.length)) + } + // Add input parameters for a single row. + override def update(input: AnyRef): Unit = { + evaluator.iterate(hiveEvaluatorBuffer, input.asInstanceOf[Array[AnyRef]]) + } + + override def batchUpdate(inputs: Array[AnyRef]): Unit = { + var i = 0 + while (i < inputs.length) { + evaluator.iterate(hiveEvaluatorBuffer, inputs(i).asInstanceOf[Array[AnyRef]]) + i += 1 + } + } + + override def evaluate(): Unit = { + outputBuffer = unwrap(evaluator.evaluate(hiveEvaluatorBuffer), returnInspector) + } + + override def get(index: Int): Any = { + if (!pivotResult) { + // if pivotResult is false, we will get a single value for all rows in the frame. + outputBuffer + } else { + // if pivotResult is true, we will get a Seq having the same size with the size + // of the window frame. At here, we will return the result at the position of + // index in the output buffer. + outputBuffer.asInstanceOf[Seq[Any]].get(index) + } + } + + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } + + override def newInstance: WindowFunction = + new HiveWindowFunction(funcWrapper, pivotResult, isUDAFBridgeRequired, children) +} + private[hive] case class HiveGenericUdaf( funcWrapper: HiveFunctionWrapper, children: Seq[Expression]) extends AggregateExpression diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index 9f17bca083d13..edeab5158df62 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -107,7 +107,10 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { /** Fewer partitions to speed up testing. */ protected[sql] override lazy val conf: SQLConf = new SQLConf { override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt - override def dialect: String = getConf(SQLConf.DIALECT, "hiveql") + + // TODO as in unit test, conf.clear() probably be called, all of the value will be cleared. + // The super.getConf(SQLConf.DIALECT) is "sql" by default, we need to set it as "hiveql" + override def dialect: String = super.getConf(SQLConf.DIALECT, "hiveql") } } diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522 b/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522 new file mode 100644 index 0000000000000..850c41c8115d6 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1 1 1173.15 +Manufacturer#1 almond antique burnished rose metallic 2 1 1 2346.3 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 4100.06 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 5702.650000000001 +Manufacturer#1 almond aquamarine burnished black steel 28 5 4 7117.070000000001 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 8749.730000000001 +Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 1690.68 +Manufacturer#2 almond antique violet turquoise frosted 40 2 2 3491.38 +Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 5523.360000000001 +Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 7222.02 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 8923.62 +Manufacturer#3 almond antique chartreuse khaki white 17 1 1 1671.68 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 2861.95 +Manufacturer#3 almond antique metallic orange dim 19 3 3 4272.34 +Manufacturer#3 almond antique misty red olive 1 4 4 6195.32 +Manufacturer#3 almond antique olive coral navajo 45 5 5 7532.61 +Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 1620.67 +Manufacturer#4 almond antique violet mint lemon 39 2 2 2996.09 +Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 4202.35 +Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 6047.27 +Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 7337.620000000001 +Manufacturer#5 almond antique blue firebrick mint 31 1 1 1789.69 +Manufacturer#5 almond antique medium spring khaki 6 2 2 3401.3500000000004 +Manufacturer#5 almond antique sky peru orange 2 3 3 5190.08 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 6208.18 +Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 7672.66 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd b/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd new file mode 100644 index 0000000000000..850c41c8115d6 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1 1 1173.15 +Manufacturer#1 almond antique burnished rose metallic 2 1 1 2346.3 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 4100.06 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 5702.650000000001 +Manufacturer#1 almond aquamarine burnished black steel 28 5 4 7117.070000000001 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 8749.730000000001 +Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 1690.68 +Manufacturer#2 almond antique violet turquoise frosted 40 2 2 3491.38 +Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 5523.360000000001 +Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 7222.02 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 8923.62 +Manufacturer#3 almond antique chartreuse khaki white 17 1 1 1671.68 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 2861.95 +Manufacturer#3 almond antique metallic orange dim 19 3 3 4272.34 +Manufacturer#3 almond antique misty red olive 1 4 4 6195.32 +Manufacturer#3 almond antique olive coral navajo 45 5 5 7532.61 +Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 1620.67 +Manufacturer#4 almond antique violet mint lemon 39 2 2 2996.09 +Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 4202.35 +Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 6047.27 +Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 7337.620000000001 +Manufacturer#5 almond antique blue firebrick mint 31 1 1 1789.69 +Manufacturer#5 almond antique medium spring khaki 6 2 2 3401.3500000000004 +Manufacturer#5 almond antique sky peru orange 2 3 3 5190.08 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 6208.18 +Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 7672.66 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6 b/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6 new file mode 100644 index 0000000000000..921679cdcf569 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 2 2 34 +Manufacturer#1 almond antique burnished rose metallic 2 2 2 6 +Manufacturer#1 almond antique chartreuse lavender yellow 34 34 2 28 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 6 2 42 +Manufacturer#1 almond aquamarine burnished black steel 28 28 34 42 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 42 6 42 +Manufacturer#2 almond antique violet chocolate turquoise 14 14 14 2 +Manufacturer#2 almond antique violet turquoise frosted 40 40 14 25 +Manufacturer#2 almond aquamarine midnight light salmon 2 2 14 18 +Manufacturer#2 almond aquamarine rose maroon antique 25 25 40 18 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 18 2 18 +Manufacturer#3 almond antique chartreuse khaki white 17 17 17 19 +Manufacturer#3 almond antique forest lavender goldenrod 14 14 17 1 +Manufacturer#3 almond antique metallic orange dim 19 19 17 45 +Manufacturer#3 almond antique misty red olive 1 1 14 45 +Manufacturer#3 almond antique olive coral navajo 45 45 19 45 +Manufacturer#4 almond antique gainsboro frosted violet 10 10 10 27 +Manufacturer#4 almond antique violet mint lemon 39 39 10 7 +Manufacturer#4 almond aquamarine floral ivory bisque 27 27 10 12 +Manufacturer#4 almond aquamarine yellow dodger mint 7 7 39 12 +Manufacturer#4 almond azure aquamarine papaya violet 12 12 27 12 +Manufacturer#5 almond antique blue firebrick mint 31 31 31 2 +Manufacturer#5 almond antique medium spring khaki 6 6 31 46 +Manufacturer#5 almond antique sky peru orange 2 2 31 23 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 46 6 23 +Manufacturer#5 almond azure blanched chiffon midnight 23 23 2 23 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5 b/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5 new file mode 100644 index 0000000000000..09e30c7c57349 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5 @@ -0,0 +1,5 @@ +Manufacturer#3 almond antique chartreuse khaki white 17 1 17 17 19 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 14 17 1 +Manufacturer#3 almond antique metallic orange dim 19 3 19 17 45 +Manufacturer#3 almond antique misty red olive 1 4 1 14 45 +Manufacturer#3 almond antique olive coral navajo 45 5 45 19 45 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f b/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f new file mode 100644 index 0000000000000..01ee88ff23302 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 38 2 +Manufacturer#1 almond antique burnished rose metallic 2 44 2 +Manufacturer#1 almond antique chartreuse lavender yellow 34 72 34 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 112 6 +Manufacturer#1 almond aquamarine burnished black steel 28 110 28 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 76 42 +Manufacturer#2 almond antique violet chocolate turquoise 14 56 14 +Manufacturer#2 almond antique violet turquoise frosted 40 81 40 +Manufacturer#2 almond aquamarine midnight light salmon 2 99 2 +Manufacturer#2 almond aquamarine rose maroon antique 25 85 25 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 45 18 +Manufacturer#3 almond antique chartreuse khaki white 17 50 17 +Manufacturer#3 almond antique forest lavender goldenrod 14 51 14 +Manufacturer#3 almond antique metallic orange dim 19 96 19 +Manufacturer#3 almond antique misty red olive 1 79 1 +Manufacturer#3 almond antique olive coral navajo 45 65 45 +Manufacturer#4 almond antique gainsboro frosted violet 10 76 10 +Manufacturer#4 almond antique violet mint lemon 39 83 39 +Manufacturer#4 almond aquamarine floral ivory bisque 27 95 27 +Manufacturer#4 almond aquamarine yellow dodger mint 7 85 7 +Manufacturer#4 almond azure aquamarine papaya violet 12 46 12 +Manufacturer#5 almond antique blue firebrick mint 31 39 31 +Manufacturer#5 almond antique medium spring khaki 6 85 6 +Manufacturer#5 almond antique sky peru orange 2 108 2 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 77 46 +Manufacturer#5 almond azure blanched chiffon midnight 23 71 23 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1 b/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1 new file mode 100644 index 0000000000000..c78eb640c9c27 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1 1 +Manufacturer#1 almond antique burnished rose metallic 2 1 1 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 +Manufacturer#1 almond aquamarine burnished black steel 28 5 4 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 +Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 +Manufacturer#2 almond antique violet turquoise frosted 40 2 2 +Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 +Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 +Manufacturer#3 almond antique chartreuse khaki white 17 1 1 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 +Manufacturer#3 almond antique metallic orange dim 19 3 3 +Manufacturer#3 almond antique misty red olive 1 4 4 +Manufacturer#3 almond antique olive coral navajo 45 5 5 +Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 +Manufacturer#4 almond antique violet mint lemon 39 2 2 +Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 +Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 +Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 +Manufacturer#5 almond antique blue firebrick mint 31 1 1 +Manufacturer#5 almond antique medium spring khaki 6 2 2 +Manufacturer#5 almond antique sky peru orange 2 3 3 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 +Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e b/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e new file mode 100644 index 0000000000000..050138ccf04ce --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1 1 0.3333333333333333 0.0 1 2 2.0 0.0 2 2 2 +Manufacturer#1 almond antique burnished rose metallic 2 1 1 0.3333333333333333 0.0 1 2 2.0 0.0 2 2 2 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 0.5 0.4 2 3 12.666666666666666 15.084944665313014 2 34 2 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 0.6666666666666666 0.6 2 4 11.0 13.379088160259652 2 6 2 +Manufacturer#1 almond aquamarine burnished black steel 28 5 4 0.8333333333333334 0.8 3 5 14.4 13.763720427268202 2 28 34 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 1.0 1.0 3 6 19.0 16.237815945091466 2 42 6 +Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 0.2 0.0 1 1 14.0 0.0 4 14 14 +Manufacturer#2 almond antique violet turquoise frosted 40 2 2 0.4 0.25 1 2 27.0 13.0 4 40 14 +Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 0.6 0.5 2 3 18.666666666666668 15.86050300449376 4 2 14 +Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 0.8 0.75 2 4 20.25 14.00669482783144 4 25 40 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 1.0 1.0 3 5 19.8 12.560254774486067 4 18 2 +Manufacturer#3 almond antique chartreuse khaki white 17 1 1 0.2 0.0 1 1 17.0 0.0 2 17 17 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 0.4 0.25 1 2 15.5 1.5 2 14 17 +Manufacturer#3 almond antique metallic orange dim 19 3 3 0.6 0.5 2 3 16.666666666666668 2.0548046676563256 2 19 17 +Manufacturer#3 almond antique misty red olive 1 4 4 0.8 0.75 2 4 12.75 7.013380069552769 2 1 14 +Manufacturer#3 almond antique olive coral navajo 45 5 5 1.0 1.0 3 5 19.2 14.344336861632886 2 45 19 +Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 0.2 0.0 1 1 10.0 0.0 0 10 10 +Manufacturer#4 almond antique violet mint lemon 39 2 2 0.4 0.25 1 2 24.5 14.5 0 39 10 +Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 0.6 0.5 2 3 25.333333333333332 11.897712198383164 0 27 10 +Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 0.8 0.75 2 4 20.75 13.007209539328564 0 7 39 +Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 1.0 1.0 3 5 19.0 12.149074038789951 0 12 27 +Manufacturer#5 almond antique blue firebrick mint 31 1 1 0.2 0.0 1 1 31.0 0.0 1 31 31 +Manufacturer#5 almond antique medium spring khaki 6 2 2 0.4 0.25 1 2 18.5 12.5 1 6 31 +Manufacturer#5 almond antique sky peru orange 2 3 3 0.6 0.5 2 3 13.0 12.832251036613439 1 2 31 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 0.8 0.75 2 4 21.25 18.102140757380052 1 46 6 +Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 1.0 1.0 3 5 21.6 16.206171663906314 1 23 2 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a b/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a new file mode 100644 index 0000000000000..c10888852b504 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1 1 0.3333333333333333 4 4 2 +Manufacturer#1 almond antique burnished rose metallic 2 1 1 0.3333333333333333 4 4 2 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 0.5 38 34 2 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 0.6666666666666666 44 10 2 +Manufacturer#1 almond aquamarine burnished black steel 28 5 4 0.8333333333333334 72 28 34 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 1.0 114 42 6 +Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 0.2 14 14 14 +Manufacturer#2 almond antique violet turquoise frosted 40 2 2 0.4 54 40 14 +Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 0.6 56 2 14 +Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 0.8 81 25 40 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 1.0 99 32 2 +Manufacturer#3 almond antique chartreuse khaki white 17 1 1 0.2 17 31 17 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 0.4 31 14 17 +Manufacturer#3 almond antique metallic orange dim 19 3 3 0.6 50 50 17 +Manufacturer#3 almond antique misty red olive 1 4 4 0.8 51 1 14 +Manufacturer#3 almond antique olive coral navajo 45 5 5 1.0 96 45 19 +Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 0.2 10 17 10 +Manufacturer#4 almond antique violet mint lemon 39 2 2 0.4 49 39 10 +Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 0.6 76 27 10 +Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 0.8 83 7 39 +Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 1.0 95 29 27 +Manufacturer#5 almond antique blue firebrick mint 31 1 1 0.2 31 31 31 +Manufacturer#5 almond antique medium spring khaki 6 2 2 0.4 37 8 31 +Manufacturer#5 almond antique sky peru orange 2 3 3 0.6 39 2 31 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 0.8 85 46 6 +Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 1.0 108 23 2 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d b/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d new file mode 100644 index 0000000000000..b1309a497d68e --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 2 2 2 +Manufacturer#1 almond antique burnished rose metallic 2 2 2 2 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3 3 2 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 4 2 +Manufacturer#1 almond aquamarine burnished black steel 28 5 5 34 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 6 6 +Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 14 +Manufacturer#2 almond antique violet turquoise frosted 40 2 2 14 +Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 14 +Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 40 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 2 +Manufacturer#3 almond antique chartreuse khaki white 17 1 1 17 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 17 +Manufacturer#3 almond antique metallic orange dim 19 3 3 17 +Manufacturer#3 almond antique misty red olive 1 4 4 14 +Manufacturer#3 almond antique olive coral navajo 45 5 5 19 +Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 10 +Manufacturer#4 almond antique violet mint lemon 39 2 2 10 +Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 10 +Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 39 +Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 27 +Manufacturer#5 almond antique blue firebrick mint 31 1 1 31 +Manufacturer#5 almond antique medium spring khaki 6 2 2 31 +Manufacturer#5 almond antique sky peru orange 2 3 3 31 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 6 +Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 2 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a b/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a new file mode 100644 index 0000000000000..52d2ee8d0cd3f --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 4100.06 1173.15 1753.76 1366.6866666666667 +Manufacturer#1 almond antique burnished rose metallic 2 5702.650000000001 1173.15 1753.76 1425.6625000000001 +Manufacturer#1 almond antique chartreuse lavender yellow 34 7117.070000000001 1173.15 1753.76 1423.4140000000002 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 7576.58 1173.15 1753.76 1515.316 +Manufacturer#1 almond aquamarine burnished black steel 28 6403.43 1414.42 1753.76 1600.8575 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 4649.67 1414.42 1632.66 1549.89 +Manufacturer#2 almond antique violet chocolate turquoise 14 5523.360000000001 1690.68 2031.98 1841.1200000000001 +Manufacturer#2 almond antique violet turquoise frosted 40 7222.02 1690.68 2031.98 1805.505 +Manufacturer#2 almond aquamarine midnight light salmon 2 8923.62 1690.68 2031.98 1784.7240000000002 +Manufacturer#2 almond aquamarine rose maroon antique 25 7232.9400000000005 1698.66 2031.98 1808.2350000000001 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5432.24 1698.66 2031.98 1810.7466666666667 +Manufacturer#3 almond antique chartreuse khaki white 17 4272.34 1190.27 1671.68 1424.1133333333335 +Manufacturer#3 almond antique forest lavender goldenrod 14 6195.32 1190.27 1922.98 1548.83 +Manufacturer#3 almond antique metallic orange dim 19 7532.61 1190.27 1922.98 1506.522 +Manufacturer#3 almond antique misty red olive 1 5860.929999999999 1190.27 1922.98 1465.2324999999998 +Manufacturer#3 almond antique olive coral navajo 45 4670.66 1337.29 1922.98 1556.8866666666665 +Manufacturer#4 almond antique gainsboro frosted violet 10 4202.35 1206.26 1620.67 1400.7833333333335 +Manufacturer#4 almond antique violet mint lemon 39 6047.27 1206.26 1844.92 1511.8175 +Manufacturer#4 almond aquamarine floral ivory bisque 27 7337.620000000001 1206.26 1844.92 1467.5240000000001 +Manufacturer#4 almond aquamarine yellow dodger mint 7 5716.950000000001 1206.26 1844.92 1429.2375000000002 +Manufacturer#4 almond azure aquamarine papaya violet 12 4341.530000000001 1206.26 1844.92 1447.176666666667 +Manufacturer#5 almond antique blue firebrick mint 31 5190.08 1611.66 1789.69 1730.0266666666666 +Manufacturer#5 almond antique medium spring khaki 6 6208.18 1018.1 1789.69 1552.045 +Manufacturer#5 almond antique sky peru orange 2 7672.66 1018.1 1789.69 1534.532 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 5882.970000000001 1018.1 1788.73 1470.7425000000003 +Manufacturer#5 almond azure blanched chiffon midnight 23 4271.3099999999995 1018.1 1788.73 1423.7699999999998 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35 b/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35 new file mode 100644 index 0000000000000..6461642d34a21 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35 @@ -0,0 +1,25 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1173.15 4529.5 1173.15 1173.15 1509.8333333333333 +Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 5943.92 1753.76 1753.76 1485.98 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 7576.58 1602.59 1602.59 1515.316 +Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 6403.43 1414.42 1414.42 1600.8575 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 4649.67 1632.66 1632.66 1549.89 +Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 5523.360000000001 1690.68 1690.68 1841.1200000000001 +Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 7222.02 1800.7 1800.7 1805.505 +Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 8923.62 2031.98 2031.98 1784.7240000000002 +Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 7232.9400000000005 1698.66 1698.66 1808.2350000000001 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 5432.24 1701.6 1701.6 1810.7466666666667 +Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 4272.34 1671.68 1671.68 1424.1133333333335 +Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 6195.32 1190.27 1190.27 1548.83 +Manufacturer#3 almond antique metallic orange dim 19 1410.39 7532.61 1410.39 1410.39 1506.522 +Manufacturer#3 almond antique misty red olive 1 1922.98 5860.929999999999 1922.98 1922.98 1465.2324999999998 +Manufacturer#3 almond antique olive coral navajo 45 1337.29 4670.66 1337.29 1337.29 1556.8866666666665 +Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 4202.35 1620.67 1620.67 1400.7833333333335 +Manufacturer#4 almond antique violet mint lemon 39 1375.42 6047.27 1375.42 1375.42 1511.8175 +Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 7337.620000000001 1206.26 1206.26 1467.5240000000001 +Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 5716.950000000001 1844.92 1844.92 1429.2375000000002 +Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 4341.530000000001 1290.35 1290.35 1447.176666666667 +Manufacturer#5 almond antique blue firebrick mint 31 1789.69 5190.08 1789.69 1789.69 1730.0266666666666 +Manufacturer#5 almond antique medium spring khaki 6 1611.66 6208.18 1611.66 1611.66 1552.045 +Manufacturer#5 almond antique sky peru orange 2 1788.73 7672.66 1788.73 1788.73 1534.532 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 5882.970000000001 1018.1 1018.1 1470.7425000000003 +Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 4271.3099999999995 1464.48 1464.48 1423.7699999999998 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251 b/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251 new file mode 100644 index 0000000000000..2c30e652aa26d --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251 @@ -0,0 +1,25 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1173.15 1 1 2 0 +Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 2 2 34 32 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 3 3 6 -28 +Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 4 4 28 22 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 5 5 42 14 +Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 1 1 14 0 +Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 2 2 40 26 +Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 3 3 2 -38 +Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 4 4 25 23 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 5 5 18 -7 +Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 1 1 17 0 +Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 2 2 14 -3 +Manufacturer#3 almond antique metallic orange dim 19 1410.39 3 3 19 5 +Manufacturer#3 almond antique misty red olive 1 1922.98 4 4 1 -18 +Manufacturer#3 almond antique olive coral navajo 45 1337.29 5 5 45 44 +Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 1 1 10 0 +Manufacturer#4 almond antique violet mint lemon 39 1375.42 2 2 39 29 +Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 3 3 27 -12 +Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 4 4 7 -20 +Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 5 5 12 5 +Manufacturer#5 almond antique blue firebrick mint 31 1789.69 1 1 31 0 +Manufacturer#5 almond antique medium spring khaki 6 1611.66 2 2 6 -25 +Manufacturer#5 almond antique sky peru orange 2 1788.73 3 3 2 -4 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 4 4 46 44 +Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 5 5 23 -23 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838 b/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838 new file mode 100644 index 0000000000000..1f7e8a5d67036 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 273.70217881648074 273.70217881648074 [34,2] 74912.8826888888 1.0 4128.782222222221 +Manufacturer#1 almond antique burnished rose metallic 2 258.10677784349235 258.10677784349235 [34,2,6] 66619.10876874991 0.811328754177887 2801.7074999999995 +Manufacturer#1 almond antique chartreuse lavender yellow 34 230.90151585470358 230.90151585470358 [34,2,6,28] 53315.51002399992 0.695639377397664 2210.7864 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 202.73109328368946 202.73109328368946 [34,2,6,42,28] 41099.896184 0.630785977101214 2009.9536000000007 +Manufacturer#1 almond aquamarine burnished black steel 28 121.6064517973862 121.6064517973862 [34,6,42,28] 14788.129118750014 0.2036684720435979 331.1337500000004 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 96.5751586416853 96.5751586416853 [6,42,28] 9326.761266666683 -1.4442181184933883E-4 -0.20666666666708502 +Manufacturer#2 almond antique violet chocolate turquoise 14 142.2363169751898 142.2363169751898 [2,40,14] 20231.169866666663 -0.49369526554523185 -1113.7466666666658 +Manufacturer#2 almond antique violet turquoise frosted 40 137.76306498840682 137.76306498840682 [2,25,40,14] 18978.662075 -0.5205630897335946 -1004.4812499999995 +Manufacturer#2 almond aquamarine midnight light salmon 2 130.03972279269132 130.03972279269132 [2,18,25,40,14] 16910.329504000005 -0.46908967495720255 -766.1791999999995 +Manufacturer#2 almond aquamarine rose maroon antique 25 135.55100986344584 135.55100986344584 [2,18,25,40] 18374.07627499999 -0.6091405874714462 -1128.1787499999987 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 156.44019460768044 156.44019460768044 [2,18,25] 24473.534488888927 -0.9571686373491608 -1441.4466666666676 +Manufacturer#3 almond antique chartreuse khaki white 17 196.7742266885805 196.7742266885805 [17,19,14] 38720.09628888887 0.5557168646224995 224.6944444444446 +Manufacturer#3 almond antique forest lavender goldenrod 14 275.14144189852607 275.14144189852607 [17,1,19,14] 75702.81305 -0.6720833036576083 -1296.9000000000003 +Manufacturer#3 almond antique metallic orange dim 19 260.23473614412046 260.23473614412046 [17,1,19,14,45] 67722.117896 -0.5703526513979519 -2129.0664 +Manufacturer#3 almond antique misty red olive 1 275.9139962356932 275.9139962356932 [1,19,14,45] 76128.53331875012 -0.577476899644802 -2547.7868749999993 +Manufacturer#3 almond antique olive coral navajo 45 260.5815918713796 260.5815918713796 [1,19,45] 67902.76602222225 -0.8710736366736884 -4099.731111111111 +Manufacturer#4 almond antique gainsboro frosted violet 10 170.13011889596618 170.13011889596618 [39,27,10] 28944.25735555559 -0.6656975320098423 -1347.4777777777779 +Manufacturer#4 almond antique violet mint lemon 39 242.26834609323197 242.26834609323197 [39,7,27,10] 58693.95151875002 -0.8051852719193339 -2537.328125 +Manufacturer#4 almond aquamarine floral ivory bisque 27 234.10001662537326 234.10001662537326 [39,7,27,10,12] 54802.817784000035 -0.6046935574240581 -1719.8079999999995 +Manufacturer#4 almond aquamarine yellow dodger mint 7 247.3342714197732 247.3342714197732 [39,7,27,12] 61174.24181875003 -0.5508665654707869 -1719.0368749999975 +Manufacturer#4 almond azure aquamarine papaya violet 12 283.3344330566893 283.3344330566893 [7,27,12] 80278.40095555557 -0.7755740084632333 -1867.4888888888881 +Manufacturer#5 almond antique blue firebrick mint 31 83.69879024746363 83.69879024746363 [2,6,31] 7005.487488888913 0.39004303087285047 418.9233333333353 +Manufacturer#5 almond antique medium spring khaki 6 316.68049612345885 316.68049612345885 [2,6,46,31] 100286.53662500004 -0.713612911776183 -4090.853749999999 +Manufacturer#5 almond antique sky peru orange 2 285.40506298242155 285.40506298242155 [2,23,6,46,31] 81456.04997600002 -0.712858514567818 -3297.2011999999986 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 285.43749038756283 285.43749038756283 [2,23,6,46] 81474.56091875004 -0.984128787153391 -4871.028125000002 +Manufacturer#5 almond azure blanched chiffon midnight 23 315.9225931564038 315.9225931564038 [2,23,46] 99807.08486666664 -0.9978877469246936 -5664.856666666666 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad b/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad new file mode 100644 index 0000000000000..e7c39f454fb37 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 [{"x":1173.15,"y":2.0},{"x":1753.76,"y":1.0}] 121152.0 1 +Manufacturer#1 almond antique burnished rose metallic 2 [{"x":1173.15,"y":2.0},{"x":1602.59,"y":1.0},{"x":1753.76,"y":1.0}] 115872.0 2 +Manufacturer#1 almond antique chartreuse lavender yellow 34 [{"x":1173.15,"y":2.0},{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1753.76,"y":1.0}] 110592.0 3 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 [{"x":1173.15,"y":1.0},{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1632.66,"y":1.0},{"x":1753.76,"y":1.0}] 86428.0 4 +Manufacturer#1 almond aquamarine burnished black steel 28 [{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1632.66,"y":1.0},{"x":1753.76,"y":1.0}] 86098.0 5 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 [{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1632.66,"y":1.0}] 86428.0 6 +Manufacturer#2 almond antique violet chocolate turquoise 14 [{"x":1690.68,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}] 146985.0 1 +Manufacturer#2 almond antique violet turquoise frosted 40 [{"x":1690.68,"y":1.0},{"x":1698.66,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}] 139825.5 2 +Manufacturer#2 almond aquamarine midnight light salmon 2 [{"x":1690.68,"y":1.0},{"x":1698.66,"y":1.0},{"x":1701.6,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}] 146985.0 3 +Manufacturer#2 almond aquamarine rose maroon antique 25 [{"x":1698.66,"y":1.0},{"x":1701.6,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}] 169347.0 4 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 [{"x":1698.66,"y":1.0},{"x":1701.6,"y":1.0},{"x":2031.98,"y":1.0}] 146985.0 5 +Manufacturer#3 almond antique chartreuse khaki white 17 [{"x":1190.27,"y":1.0},{"x":1410.39,"y":1.0},{"x":1671.68,"y":1.0}] 90681.0 1 +Manufacturer#3 almond antique forest lavender goldenrod 14 [{"x":1190.27,"y":1.0},{"x":1410.39,"y":1.0},{"x":1671.68,"y":1.0},{"x":1922.98,"y":1.0}] 65831.5 2 +Manufacturer#3 almond antique metallic orange dim 19 [{"x":1190.27,"y":1.0},{"x":1337.29,"y":1.0},{"x":1410.39,"y":1.0},{"x":1671.68,"y":1.0},{"x":1922.98,"y":1.0}] 90681.0 3 +Manufacturer#3 almond antique misty red olive 1 [{"x":1190.27,"y":1.0},{"x":1337.29,"y":1.0},{"x":1410.39,"y":1.0},{"x":1922.98,"y":1.0}] 76690.0 4 +Manufacturer#3 almond antique olive coral navajo 45 [{"x":1337.29,"y":1.0},{"x":1410.39,"y":1.0},{"x":1922.98,"y":1.0}] 112398.0 5 +Manufacturer#4 almond antique gainsboro frosted violet 10 [{"x":1206.26,"y":1.0},{"x":1375.42,"y":1.0},{"x":1620.67,"y":1.0}] 48427.0 1 +Manufacturer#4 almond antique violet mint lemon 39 [{"x":1206.26,"y":1.0},{"x":1375.42,"y":1.0},{"x":1620.67,"y":1.0},{"x":1844.92,"y":1.0}] 46844.0 2 +Manufacturer#4 almond aquamarine floral ivory bisque 27 [{"x":1206.26,"y":1.0},{"x":1290.35,"y":1.0},{"x":1375.42,"y":1.0},{"x":1620.67,"y":1.0},{"x":1844.92,"y":1.0}] 45261.0 3 +Manufacturer#4 almond aquamarine yellow dodger mint 7 [{"x":1206.26,"y":1.0},{"x":1290.35,"y":1.0},{"x":1375.42,"y":1.0},{"x":1844.92,"y":1.0}] 39309.0 4 +Manufacturer#4 almond azure aquamarine papaya violet 12 [{"x":1206.26,"y":1.0},{"x":1290.35,"y":1.0},{"x":1844.92,"y":1.0}] 33357.0 5 +Manufacturer#5 almond antique blue firebrick mint 31 [{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0},{"x":1789.69,"y":1.0}] 155733.0 1 +Manufacturer#5 almond antique medium spring khaki 6 [{"x":1018.1,"y":1.0},{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0},{"x":1789.69,"y":1.0}] 99201.0 2 +Manufacturer#5 almond antique sky peru orange 2 [{"x":1018.1,"y":1.0},{"x":1464.48,"y":1.0},{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0},{"x":1789.69,"y":1.0}] 78486.0 3 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 [{"x":1018.1,"y":1.0},{"x":1464.48,"y":1.0},{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0}] 60577.5 4 +Manufacturer#5 almond azure blanched chiffon midnight 23 [{"x":1018.1,"y":1.0},{"x":1464.48,"y":1.0},{"x":1788.73,"y":1.0}] 78486.0 5 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a b/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a new file mode 100644 index 0000000000000..dc83c9fffe932 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a @@ -0,0 +1,78 @@ +Manufacturer#1 almond antique burnished rose metallic 1 2 2 +Manufacturer#1 almond antique burnished rose metallic 1 2 4 +Manufacturer#1 almond antique burnished rose metallic 2 2 6 +Manufacturer#1 almond antique burnished rose metallic 2 2 6 +Manufacturer#1 almond antique burnished rose metallic 3 2 6 +Manufacturer#1 almond antique burnished rose metallic 3 2 6 +Manufacturer#1 almond antique salmon chartreuse burlywood 1 6 10 +Manufacturer#1 almond antique salmon chartreuse burlywood 2 6 14 +Manufacturer#1 almond antique salmon chartreuse burlywood 3 6 18 +Manufacturer#1 almond aquamarine burnished black steel 1 28 40 +Manufacturer#1 almond aquamarine burnished black steel 2 28 62 +Manufacturer#1 almond aquamarine burnished black steel 3 28 84 +Manufacturer#1 almond antique chartreuse lavender yellow 1 34 90 +Manufacturer#1 almond antique chartreuse lavender yellow 2 34 96 +Manufacturer#1 almond antique chartreuse lavender yellow 3 34 102 +Manufacturer#1 almond aquamarine pink moccasin thistle 1 42 110 +Manufacturer#1 almond aquamarine pink moccasin thistle 2 42 118 +Manufacturer#1 almond aquamarine pink moccasin thistle 3 42 126 +Manufacturer#2 almond aquamarine midnight light salmon 1 2 2 +Manufacturer#2 almond aquamarine midnight light salmon 2 2 4 +Manufacturer#2 almond aquamarine midnight light salmon 3 2 6 +Manufacturer#2 almond antique violet chocolate turquoise 1 14 18 +Manufacturer#2 almond antique violet chocolate turquoise 2 14 30 +Manufacturer#2 almond antique violet chocolate turquoise 3 14 42 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 1 18 46 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 2 18 50 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 3 18 54 +Manufacturer#2 almond aquamarine rose maroon antique 1 25 61 +Manufacturer#2 almond aquamarine rose maroon antique 2 25 68 +Manufacturer#2 almond aquamarine rose maroon antique 3 25 75 +Manufacturer#2 almond antique violet turquoise frosted 1 40 90 +Manufacturer#2 almond antique violet turquoise frosted 2 40 105 +Manufacturer#2 almond antique violet turquoise frosted 3 40 120 +Manufacturer#3 almond antique misty red olive 1 1 1 +Manufacturer#3 almond antique misty red olive 2 1 2 +Manufacturer#3 almond antique misty red olive 3 1 3 +Manufacturer#3 almond antique forest lavender goldenrod 1 14 16 +Manufacturer#3 almond antique forest lavender goldenrod 2 14 29 +Manufacturer#3 almond antique forest lavender goldenrod 3 14 42 +Manufacturer#3 almond antique chartreuse khaki white 1 17 45 +Manufacturer#3 almond antique chartreuse khaki white 2 17 48 +Manufacturer#3 almond antique chartreuse khaki white 3 17 51 +Manufacturer#3 almond antique metallic orange dim 1 19 53 +Manufacturer#3 almond antique metallic orange dim 2 19 55 +Manufacturer#3 almond antique metallic orange dim 3 19 57 +Manufacturer#3 almond antique olive coral navajo 1 45 83 +Manufacturer#3 almond antique olive coral navajo 2 45 109 +Manufacturer#3 almond antique olive coral navajo 3 45 135 +Manufacturer#4 almond aquamarine yellow dodger mint 1 7 7 +Manufacturer#4 almond aquamarine yellow dodger mint 2 7 14 +Manufacturer#4 almond aquamarine yellow dodger mint 3 7 21 +Manufacturer#4 almond antique gainsboro frosted violet 1 10 24 +Manufacturer#4 almond antique gainsboro frosted violet 2 10 27 +Manufacturer#4 almond antique gainsboro frosted violet 3 10 30 +Manufacturer#4 almond azure aquamarine papaya violet 1 12 32 +Manufacturer#4 almond azure aquamarine papaya violet 2 12 34 +Manufacturer#4 almond azure aquamarine papaya violet 3 12 36 +Manufacturer#4 almond aquamarine floral ivory bisque 1 27 51 +Manufacturer#4 almond aquamarine floral ivory bisque 2 27 66 +Manufacturer#4 almond aquamarine floral ivory bisque 3 27 81 +Manufacturer#4 almond antique violet mint lemon 1 39 93 +Manufacturer#4 almond antique violet mint lemon 2 39 105 +Manufacturer#4 almond antique violet mint lemon 3 39 117 +Manufacturer#5 almond antique sky peru orange 1 2 2 +Manufacturer#5 almond antique sky peru orange 2 2 4 +Manufacturer#5 almond antique sky peru orange 3 2 6 +Manufacturer#5 almond antique medium spring khaki 1 6 10 +Manufacturer#5 almond antique medium spring khaki 2 6 14 +Manufacturer#5 almond antique medium spring khaki 3 6 18 +Manufacturer#5 almond azure blanched chiffon midnight 1 23 35 +Manufacturer#5 almond azure blanched chiffon midnight 2 23 52 +Manufacturer#5 almond azure blanched chiffon midnight 3 23 69 +Manufacturer#5 almond antique blue firebrick mint 1 31 77 +Manufacturer#5 almond antique blue firebrick mint 2 31 85 +Manufacturer#5 almond antique blue firebrick mint 3 31 93 +Manufacturer#5 almond aquamarine dodger light gainsboro 1 46 108 +Manufacturer#5 almond aquamarine dodger light gainsboro 2 46 123 +Manufacturer#5 almond aquamarine dodger light gainsboro 3 46 138 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc b/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc new file mode 100644 index 0000000000000..2c30e652aa26d --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc @@ -0,0 +1,25 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1173.15 1 1 2 0 +Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 2 2 34 32 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 3 3 6 -28 +Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 4 4 28 22 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 5 5 42 14 +Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 1 1 14 0 +Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 2 2 40 26 +Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 3 3 2 -38 +Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 4 4 25 23 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 5 5 18 -7 +Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 1 1 17 0 +Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 2 2 14 -3 +Manufacturer#3 almond antique metallic orange dim 19 1410.39 3 3 19 5 +Manufacturer#3 almond antique misty red olive 1 1922.98 4 4 1 -18 +Manufacturer#3 almond antique olive coral navajo 45 1337.29 5 5 45 44 +Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 1 1 10 0 +Manufacturer#4 almond antique violet mint lemon 39 1375.42 2 2 39 29 +Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 3 3 27 -12 +Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 4 4 7 -20 +Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 5 5 12 5 +Manufacturer#5 almond antique blue firebrick mint 31 1789.69 1 1 31 0 +Manufacturer#5 almond antique medium spring khaki 6 1611.66 2 2 6 -25 +Manufacturer#5 almond antique sky peru orange 2 1788.73 3 3 2 -4 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 4 4 46 44 +Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 5 5 23 -23 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4 b/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4 new file mode 100644 index 0000000000000..b2a91ba727a72 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 4 10 +Manufacturer#1 almond antique burnished rose metallic 2 4 10 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 10 6 +Manufacturer#1 almond aquamarine burnished black steel 28 28 62 +Manufacturer#1 almond antique chartreuse lavender yellow 34 62 76 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 76 42 +Manufacturer#2 almond aquamarine midnight light salmon 2 2 2 +Manufacturer#2 almond antique violet chocolate turquoise 14 14 32 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 32 43 +Manufacturer#2 almond aquamarine rose maroon antique 25 43 25 +Manufacturer#2 almond antique violet turquoise frosted 40 40 40 +Manufacturer#3 almond antique misty red olive 1 1 1 +Manufacturer#3 almond antique forest lavender goldenrod 14 14 50 +Manufacturer#3 almond antique chartreuse khaki white 17 31 36 +Manufacturer#3 almond antique metallic orange dim 19 50 19 +Manufacturer#3 almond antique olive coral navajo 45 45 45 +Manufacturer#4 almond aquamarine yellow dodger mint 7 7 29 +Manufacturer#4 almond antique gainsboro frosted violet 10 17 22 +Manufacturer#4 almond azure aquamarine papaya violet 12 29 12 +Manufacturer#4 almond aquamarine floral ivory bisque 27 27 27 +Manufacturer#4 almond antique violet mint lemon 39 39 39 +Manufacturer#5 almond antique sky peru orange 2 2 8 +Manufacturer#5 almond antique medium spring khaki 6 8 6 +Manufacturer#5 almond azure blanched chiffon midnight 23 23 54 +Manufacturer#5 almond antique blue firebrick mint 31 54 31 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 46 46 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db b/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db new file mode 100644 index 0000000000000..5bcb0fa941d65 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 38 +Manufacturer#1 almond antique burnished rose metallic 2 44 +Manufacturer#1 almond antique chartreuse lavender yellow 34 72 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 112 +Manufacturer#1 almond aquamarine burnished black steel 28 110 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 76 +Manufacturer#2 almond antique violet chocolate turquoise 14 56 +Manufacturer#2 almond antique violet turquoise frosted 40 81 +Manufacturer#2 almond aquamarine midnight light salmon 2 99 +Manufacturer#2 almond aquamarine rose maroon antique 25 85 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 45 +Manufacturer#3 almond antique chartreuse khaki white 17 50 +Manufacturer#3 almond antique forest lavender goldenrod 14 51 +Manufacturer#3 almond antique metallic orange dim 19 96 +Manufacturer#3 almond antique misty red olive 1 79 +Manufacturer#3 almond antique olive coral navajo 45 65 +Manufacturer#4 almond antique gainsboro frosted violet 10 76 +Manufacturer#4 almond antique violet mint lemon 39 83 +Manufacturer#4 almond aquamarine floral ivory bisque 27 95 +Manufacturer#4 almond aquamarine yellow dodger mint 7 85 +Manufacturer#4 almond azure aquamarine papaya violet 12 46 +Manufacturer#5 almond antique blue firebrick mint 31 39 +Manufacturer#5 almond antique medium spring khaki 6 85 +Manufacturer#5 almond antique sky peru orange 2 108 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 77 +Manufacturer#5 almond azure blanched chiffon midnight 23 71 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a b/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a new file mode 100644 index 0000000000000..5bcb0fa941d65 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 38 +Manufacturer#1 almond antique burnished rose metallic 2 44 +Manufacturer#1 almond antique chartreuse lavender yellow 34 72 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 112 +Manufacturer#1 almond aquamarine burnished black steel 28 110 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 76 +Manufacturer#2 almond antique violet chocolate turquoise 14 56 +Manufacturer#2 almond antique violet turquoise frosted 40 81 +Manufacturer#2 almond aquamarine midnight light salmon 2 99 +Manufacturer#2 almond aquamarine rose maroon antique 25 85 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 45 +Manufacturer#3 almond antique chartreuse khaki white 17 50 +Manufacturer#3 almond antique forest lavender goldenrod 14 51 +Manufacturer#3 almond antique metallic orange dim 19 96 +Manufacturer#3 almond antique misty red olive 1 79 +Manufacturer#3 almond antique olive coral navajo 45 65 +Manufacturer#4 almond antique gainsboro frosted violet 10 76 +Manufacturer#4 almond antique violet mint lemon 39 83 +Manufacturer#4 almond aquamarine floral ivory bisque 27 95 +Manufacturer#4 almond aquamarine yellow dodger mint 7 85 +Manufacturer#4 almond azure aquamarine papaya violet 12 46 +Manufacturer#5 almond antique blue firebrick mint 31 39 +Manufacturer#5 almond antique medium spring khaki 6 85 +Manufacturer#5 almond antique sky peru orange 2 108 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 77 +Manufacturer#5 almond azure blanched chiffon midnight 23 71 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100 b/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100 new file mode 100644 index 0000000000000..2c30e652aa26d --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100 @@ -0,0 +1,25 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1173.15 1 1 2 0 +Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 2 2 34 32 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 3 3 6 -28 +Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 4 4 28 22 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 5 5 42 14 +Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 1 1 14 0 +Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 2 2 40 26 +Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 3 3 2 -38 +Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 4 4 25 23 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 5 5 18 -7 +Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 1 1 17 0 +Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 2 2 14 -3 +Manufacturer#3 almond antique metallic orange dim 19 1410.39 3 3 19 5 +Manufacturer#3 almond antique misty red olive 1 1922.98 4 4 1 -18 +Manufacturer#3 almond antique olive coral navajo 45 1337.29 5 5 45 44 +Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 1 1 10 0 +Manufacturer#4 almond antique violet mint lemon 39 1375.42 2 2 39 29 +Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 3 3 27 -12 +Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 4 4 7 -20 +Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 5 5 12 5 +Manufacturer#5 almond antique blue firebrick mint 31 1789.69 1 1 31 0 +Manufacturer#5 almond antique medium spring khaki 6 1611.66 2 2 6 -25 +Manufacturer#5 almond antique sky peru orange 2 1788.73 3 3 2 -4 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 4 4 46 44 +Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 5 5 23 -23 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada b/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada new file mode 100644 index 0000000000000..698a44349d2a6 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 38 4 +Manufacturer#1 almond antique burnished rose metallic 2 44 4 +Manufacturer#1 almond antique chartreuse lavender yellow 34 72 38 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 112 44 +Manufacturer#1 almond aquamarine burnished black steel 28 110 72 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 76 114 +Manufacturer#2 almond antique violet chocolate turquoise 14 56 14 +Manufacturer#2 almond antique violet turquoise frosted 40 81 54 +Manufacturer#2 almond aquamarine midnight light salmon 2 99 56 +Manufacturer#2 almond aquamarine rose maroon antique 25 85 81 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 45 99 +Manufacturer#3 almond antique chartreuse khaki white 17 50 17 +Manufacturer#3 almond antique forest lavender goldenrod 14 51 31 +Manufacturer#3 almond antique metallic orange dim 19 96 50 +Manufacturer#3 almond antique misty red olive 1 79 51 +Manufacturer#3 almond antique olive coral navajo 45 65 96 +Manufacturer#4 almond antique gainsboro frosted violet 10 76 10 +Manufacturer#4 almond antique violet mint lemon 39 83 49 +Manufacturer#4 almond aquamarine floral ivory bisque 27 95 76 +Manufacturer#4 almond aquamarine yellow dodger mint 7 85 83 +Manufacturer#4 almond azure aquamarine papaya violet 12 46 95 +Manufacturer#5 almond antique blue firebrick mint 31 39 31 +Manufacturer#5 almond antique medium spring khaki 6 85 37 +Manufacturer#5 almond antique sky peru orange 2 108 39 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 77 85 +Manufacturer#5 almond azure blanched chiffon midnight 23 71 108 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768 b/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768 new file mode 100644 index 0000000000000..e35257d98382e --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1 +Manufacturer#1 almond antique burnished rose metallic 2 1 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 +Manufacturer#1 almond aquamarine burnished black steel 28 5 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 +Manufacturer#2 almond antique violet chocolate turquoise 14 1 +Manufacturer#2 almond antique violet turquoise frosted 40 2 +Manufacturer#2 almond aquamarine midnight light salmon 2 3 +Manufacturer#2 almond aquamarine rose maroon antique 25 4 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 +Manufacturer#3 almond antique chartreuse khaki white 17 1 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 +Manufacturer#3 almond antique metallic orange dim 19 3 +Manufacturer#3 almond antique misty red olive 1 4 +Manufacturer#3 almond antique olive coral navajo 45 5 +Manufacturer#4 almond antique gainsboro frosted violet 10 1 +Manufacturer#4 almond antique violet mint lemon 39 2 +Manufacturer#4 almond aquamarine floral ivory bisque 27 3 +Manufacturer#4 almond aquamarine yellow dodger mint 7 4 +Manufacturer#4 almond azure aquamarine papaya violet 12 5 +Manufacturer#5 almond antique blue firebrick mint 31 1 +Manufacturer#5 almond antique medium spring khaki 6 2 +Manufacturer#5 almond antique sky peru orange 2 3 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 +Manufacturer#5 almond azure blanched chiffon midnight 23 5 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a b/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a new file mode 100644 index 0000000000000..9c0ca6c7a00ba --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a @@ -0,0 +1,26 @@ +Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6 +Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6 +Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6 +Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6 +Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6 +Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6 +Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5 +Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5 +Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5 +Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5 +Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5 +Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5 +Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5 +Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5 +Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5 +Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5 +Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5 +Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5 +Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5 +Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5 +Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5 +Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5 +Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5 +Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5 +Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5 +Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e b/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e new file mode 100644 index 0000000000000..fc27df2f2b648 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1173.15 1173.15 1173.15 +Manufacturer#1 almond antique burnished rose metallic 2 2346.3 1173.15 1173.15 +Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 1753.76 1753.76 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 1602.59 1602.59 +Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 1414.42 1414.42 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 1632.66 1632.66 +Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 1690.68 1690.68 +Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 1800.7 1800.7 +Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 2031.98 2031.98 +Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 1698.66 1698.66 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 1701.6 1701.6 +Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 1671.68 1671.68 +Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 1190.27 1190.27 +Manufacturer#3 almond antique metallic orange dim 19 1410.39 1410.39 1410.39 +Manufacturer#3 almond antique misty red olive 1 1922.98 1922.98 1922.98 +Manufacturer#3 almond antique olive coral navajo 45 1337.29 1337.29 1337.29 +Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 1620.67 1620.67 +Manufacturer#4 almond antique violet mint lemon 39 1375.42 1375.42 1375.42 +Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 1206.26 1206.26 +Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 1844.92 1844.92 +Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 1290.35 1290.35 +Manufacturer#5 almond antique blue firebrick mint 31 1789.69 1789.69 1789.69 +Manufacturer#5 almond antique medium spring khaki 6 1611.66 1611.66 1611.66 +Manufacturer#5 almond antique sky peru orange 2 1788.73 1788.73 1788.73 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 1018.1 1018.1 +Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 1464.48 1464.48 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423 b/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423 new file mode 100644 index 0000000000000..e5a541f56f6f3 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423 @@ -0,0 +1,26 @@ +Manufacturer#1 LARGE BRUSHED STEEL ARGE BRUSHED STEEL 1 +Manufacturer#1 LARGE BURNISHED STEEL ARGE BURNISHED STEEL 2 +Manufacturer#1 PROMO BURNISHED NICKEL ROMO BURNISHED NICKEL 3 +Manufacturer#1 PROMO PLATED TIN ROMO PLATED TIN 4 +Manufacturer#1 PROMO PLATED TIN ROMO PLATED TIN 4 +Manufacturer#1 STANDARD ANODIZED STEEL TANDARD ANODIZED STEEL 6 +Manufacturer#2 ECONOMY POLISHED STEEL CONOMY POLISHED STEEL 1 +Manufacturer#2 MEDIUM ANODIZED COPPER EDIUM ANODIZED COPPER 2 +Manufacturer#2 MEDIUM BURNISHED COPPER EDIUM BURNISHED COPPER 3 +Manufacturer#2 SMALL POLISHED NICKEL MALL POLISHED NICKEL 4 +Manufacturer#2 STANDARD PLATED TIN TANDARD PLATED TIN 5 +Manufacturer#3 ECONOMY PLATED COPPER CONOMY PLATED COPPER 1 +Manufacturer#3 MEDIUM BURNISHED BRASS EDIUM BURNISHED BRASS 2 +Manufacturer#3 MEDIUM BURNISHED TIN EDIUM BURNISHED TIN 3 +Manufacturer#3 PROMO ANODIZED TIN ROMO ANODIZED TIN 4 +Manufacturer#3 STANDARD POLISHED STEEL TANDARD POLISHED STEEL 5 +Manufacturer#4 ECONOMY BRUSHED COPPER CONOMY BRUSHED COPPER 1 +Manufacturer#4 SMALL BRUSHED BRASS MALL BRUSHED BRASS 2 +Manufacturer#4 SMALL PLATED STEEL MALL PLATED STEEL 3 +Manufacturer#4 PROMO POLISHED STEEL ROMO POLISHED STEEL 4 +Manufacturer#4 STANDARD ANODIZED TIN TANDARD ANODIZED TIN 5 +Manufacturer#5 LARGE BRUSHED BRASS ARGE BRUSHED BRASS 1 +Manufacturer#5 ECONOMY BURNISHED STEEL CONOMY BURNISHED STEEL 2 +Manufacturer#5 MEDIUM BURNISHED TIN EDIUM BURNISHED TIN 3 +Manufacturer#5 SMALL PLATED BRASS MALL PLATED BRASS 4 +Manufacturer#5 STANDARD BURNISHED TIN TANDARD BURNISHED TIN 5 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c b/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c new file mode 100644 index 0000000000000..bf8e620a304af --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 +Manufacturer#1 almond antique burnished rose metallic 2 +Manufacturer#1 almond antique chartreuse lavender yellow 3 +Manufacturer#1 almond antique salmon chartreuse burlywood 4 +Manufacturer#1 almond aquamarine burnished black steel 5 +Manufacturer#1 almond aquamarine pink moccasin thistle 6 +Manufacturer#2 almond antique violet chocolate turquoise 1 +Manufacturer#2 almond antique violet turquoise frosted 2 +Manufacturer#2 almond aquamarine midnight light salmon 3 +Manufacturer#2 almond aquamarine rose maroon antique 4 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 5 +Manufacturer#3 almond antique chartreuse khaki white 1 +Manufacturer#3 almond antique forest lavender goldenrod 2 +Manufacturer#3 almond antique metallic orange dim 3 +Manufacturer#3 almond antique misty red olive 4 +Manufacturer#3 almond antique olive coral navajo 5 +Manufacturer#4 almond antique gainsboro frosted violet 1 +Manufacturer#4 almond antique violet mint lemon 2 +Manufacturer#4 almond aquamarine floral ivory bisque 3 +Manufacturer#4 almond aquamarine yellow dodger mint 4 +Manufacturer#4 almond azure aquamarine papaya violet 5 +Manufacturer#5 almond antique blue firebrick mint 1 +Manufacturer#5 almond antique medium spring khaki 2 +Manufacturer#5 almond antique sky peru orange 3 +Manufacturer#5 almond aquamarine dodger light gainsboro 4 +Manufacturer#5 almond azure blanched chiffon midnight 5 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1 b/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1 new file mode 100644 index 0000000000000..1e29df62901d0 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1173.15 +Manufacturer#1 almond antique burnished rose metallic 2 2346.3 +Manufacturer#1 almond antique chartreuse lavender yellow 34 4100.06 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 5702.650000000001 +Manufacturer#1 almond aquamarine burnished black steel 28 7117.070000000001 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 8749.730000000001 +Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 +Manufacturer#2 almond antique violet turquoise frosted 40 3491.38 +Manufacturer#2 almond aquamarine midnight light salmon 2 5523.360000000001 +Manufacturer#2 almond aquamarine rose maroon antique 25 7222.02 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 8923.62 +Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 +Manufacturer#3 almond antique forest lavender goldenrod 14 2861.95 +Manufacturer#3 almond antique metallic orange dim 19 4272.34 +Manufacturer#3 almond antique misty red olive 1 6195.32 +Manufacturer#3 almond antique olive coral navajo 45 7532.61 +Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 +Manufacturer#4 almond antique violet mint lemon 39 2996.09 +Manufacturer#4 almond aquamarine floral ivory bisque 27 4202.35 +Manufacturer#4 almond aquamarine yellow dodger mint 7 6047.27 +Manufacturer#4 almond azure aquamarine papaya violet 12 7337.620000000001 +Manufacturer#5 almond antique blue firebrick mint 31 1789.69 +Manufacturer#5 almond antique medium spring khaki 6 3401.3500000000004 +Manufacturer#5 almond antique sky peru orange 2 5190.08 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 6208.18 +Manufacturer#5 almond azure blanched chiffon midnight 23 7672.66 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283 b/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283 new file mode 100644 index 0000000000000..a620479fe4064 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 2346.3 +Manufacturer#1 almond antique burnished rose metallic 2 2346.3 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 3948.8900000000003 +Manufacturer#1 almond aquamarine burnished black steel 28 5363.31 +Manufacturer#1 almond antique chartreuse lavender yellow 34 7117.070000000001 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 8749.730000000001 +Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 +Manufacturer#2 almond antique violet chocolate turquoise 14 3722.66 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5424.26 +Manufacturer#2 almond aquamarine rose maroon antique 25 7122.92 +Manufacturer#2 almond antique violet turquoise frosted 40 8923.62 +Manufacturer#3 almond antique misty red olive 1 1922.98 +Manufacturer#3 almond antique forest lavender goldenrod 14 3113.25 +Manufacturer#3 almond antique chartreuse khaki white 17 4784.93 +Manufacturer#3 almond antique metallic orange dim 19 6195.320000000001 +Manufacturer#3 almond antique olive coral navajo 45 7532.610000000001 +Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 +Manufacturer#4 almond antique gainsboro frosted violet 10 3465.59 +Manufacturer#4 almond azure aquamarine papaya violet 12 4755.9400000000005 +Manufacturer#4 almond aquamarine floral ivory bisque 27 5962.200000000001 +Manufacturer#4 almond antique violet mint lemon 39 7337.620000000001 +Manufacturer#5 almond antique sky peru orange 2 1788.73 +Manufacturer#5 almond antique medium spring khaki 6 3400.3900000000003 +Manufacturer#5 almond azure blanched chiffon midnight 23 4864.870000000001 +Manufacturer#5 almond antique blue firebrick mint 31 6654.560000000001 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 7672.660000000002 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0 b/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0 new file mode 100644 index 0000000000000..74147d2571a15 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 8749.730000000001 +Manufacturer#1 almond antique burnished rose metallic 2 7576.58 +Manufacturer#1 almond antique chartreuse lavender yellow 34 6403.43 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4649.67 +Manufacturer#1 almond aquamarine burnished black steel 28 3047.08 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 +Manufacturer#2 almond antique violet chocolate turquoise 14 8923.62 +Manufacturer#2 almond antique violet turquoise frosted 40 7232.9400000000005 +Manufacturer#2 almond aquamarine midnight light salmon 2 5432.24 +Manufacturer#2 almond aquamarine rose maroon antique 25 3400.26 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 +Manufacturer#3 almond antique chartreuse khaki white 17 7532.61 +Manufacturer#3 almond antique forest lavender goldenrod 14 5860.929999999999 +Manufacturer#3 almond antique metallic orange dim 19 4670.66 +Manufacturer#3 almond antique misty red olive 1 3260.27 +Manufacturer#3 almond antique olive coral navajo 45 1337.29 +Manufacturer#4 almond antique gainsboro frosted violet 10 7337.620000000001 +Manufacturer#4 almond antique violet mint lemon 39 5716.950000000001 +Manufacturer#4 almond aquamarine floral ivory bisque 27 4341.530000000001 +Manufacturer#4 almond aquamarine yellow dodger mint 7 3135.27 +Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 +Manufacturer#5 almond antique blue firebrick mint 31 7672.66 +Manufacturer#5 almond antique medium spring khaki 6 5882.970000000001 +Manufacturer#5 almond antique sky peru orange 2 4271.3099999999995 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 2482.58 +Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39 b/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39 new file mode 100644 index 0000000000000..49d003b5de139 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 8749.730000000001 +Manufacturer#1 almond antique burnished rose metallic 2 8749.730000000001 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 6403.43 +Manufacturer#1 almond aquamarine burnished black steel 28 4800.84 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3386.42 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 +Manufacturer#2 almond aquamarine midnight light salmon 2 8923.62 +Manufacturer#2 almond antique violet chocolate turquoise 14 6891.639999999999 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5200.96 +Manufacturer#2 almond aquamarine rose maroon antique 25 3499.36 +Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 +Manufacturer#3 almond antique misty red olive 1 7532.610000000001 +Manufacturer#3 almond antique forest lavender goldenrod 14 5609.63 +Manufacturer#3 almond antique chartreuse khaki white 17 4419.360000000001 +Manufacturer#3 almond antique metallic orange dim 19 2747.6800000000003 +Manufacturer#3 almond antique olive coral navajo 45 1337.29 +Manufacturer#4 almond aquamarine yellow dodger mint 7 7337.620000000001 +Manufacturer#4 almond antique gainsboro frosted violet 10 5492.7 +Manufacturer#4 almond azure aquamarine papaya violet 12 3872.0299999999997 +Manufacturer#4 almond aquamarine floral ivory bisque 27 2581.6800000000003 +Manufacturer#4 almond antique violet mint lemon 39 1375.42 +Manufacturer#5 almond antique sky peru orange 2 7672.660000000002 +Manufacturer#5 almond antique medium spring khaki 6 5883.93 +Manufacturer#5 almond azure blanched chiffon midnight 23 4272.27 +Manufacturer#5 almond antique blue firebrick mint 31 2807.79 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae b/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae new file mode 100644 index 0000000000000..5982c9ee2a4d4 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae @@ -0,0 +1,26 @@ +almond antique blue firebrick mint 1789.69 1546.78 +almond antique burnished rose metallic 1173.15 1546.78 +almond antique burnished rose metallic 1173.15 1546.78 +almond antique chartreuse khaki white 1671.68 1546.78 +almond antique chartreuse lavender yellow 1753.76 1546.78 +almond antique forest lavender goldenrod 1190.27 1546.78 +almond antique gainsboro frosted violet 1620.67 1546.78 +almond antique medium spring khaki 1611.66 1546.78 +almond antique metallic orange dim 1410.39 1546.78 +almond antique misty red olive 1922.98 1546.78 +almond antique olive coral navajo 1337.29 1546.78 +almond antique salmon chartreuse burlywood 1602.59 1546.78 +almond antique sky peru orange 1788.73 1546.78 +almond antique violet chocolate turquoise 1690.68 1546.78 +almond antique violet mint lemon 1375.42 1546.78 +almond antique violet turquoise frosted 1800.7 1546.78 +almond aquamarine burnished black steel 1414.42 1546.78 +almond aquamarine dodger light gainsboro 1018.1 1546.78 +almond aquamarine floral ivory bisque 1206.26 1546.78 +almond aquamarine midnight light salmon 2031.98 1546.78 +almond aquamarine pink moccasin thistle 1632.66 1546.78 +almond aquamarine rose maroon antique 1698.66 1546.78 +almond aquamarine sandy cyan gainsboro 1701.6 1546.78 +almond aquamarine yellow dodger mint 1844.92 1546.78 +almond azure aquamarine papaya violet 1290.35 1546.78 +almond azure blanched chiffon midnight 1464.48 1546.78 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f b/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f new file mode 100644 index 0000000000000..00d41fc0bcd9c --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 1 1 2 1173.15 1173.15 2 0 +Manufacturer#1 almond antique burnished rose metallic 1 1 2 1173.15 2346.3 2 0 +Manufacturer#1 almond antique chartreuse lavender yellow 3 2 3 1753.76 4100.06 34 32 +Manufacturer#1 almond antique salmon chartreuse burlywood 4 3 4 1602.59 5702.650000000001 6 -28 +Manufacturer#1 almond aquamarine burnished black steel 5 4 5 1414.42 7117.070000000001 28 22 +Manufacturer#1 almond aquamarine pink moccasin thistle 6 5 6 1632.66 8749.730000000001 42 14 +Manufacturer#2 almond antique violet chocolate turquoise 1 1 1 1690.68 1690.68 14 0 +Manufacturer#2 almond antique violet turquoise frosted 2 2 2 1800.7 3491.38 40 26 +Manufacturer#2 almond aquamarine midnight light salmon 3 3 3 2031.98 5523.360000000001 2 -38 +Manufacturer#2 almond aquamarine rose maroon antique 4 4 4 1698.66 7222.02 25 23 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 5 5 5 1701.6 8923.62 18 -7 +Manufacturer#3 almond antique chartreuse khaki white 1 1 1 1671.68 1671.68 17 0 +Manufacturer#3 almond antique forest lavender goldenrod 2 2 2 1190.27 2861.95 14 -3 +Manufacturer#3 almond antique metallic orange dim 3 3 3 1410.39 4272.34 19 5 +Manufacturer#3 almond antique misty red olive 4 4 4 1922.98 6195.32 1 -18 +Manufacturer#3 almond antique olive coral navajo 5 5 5 1337.29 7532.61 45 44 +Manufacturer#4 almond antique gainsboro frosted violet 1 1 1 1620.67 1620.67 10 0 +Manufacturer#4 almond antique violet mint lemon 2 2 2 1375.42 2996.09 39 29 +Manufacturer#4 almond aquamarine floral ivory bisque 3 3 3 1206.26 4202.35 27 -12 +Manufacturer#4 almond aquamarine yellow dodger mint 4 4 4 1844.92 6047.27 7 -20 +Manufacturer#4 almond azure aquamarine papaya violet 5 5 5 1290.35 7337.620000000001 12 5 +Manufacturer#5 almond antique blue firebrick mint 1 1 1 1789.69 1789.69 31 0 +Manufacturer#5 almond antique medium spring khaki 2 2 2 1611.66 3401.3500000000004 6 -25 +Manufacturer#5 almond antique sky peru orange 3 3 3 1788.73 5190.08 2 -4 +Manufacturer#5 almond aquamarine dodger light gainsboro 4 4 4 1018.1 6208.18 46 44 +Manufacturer#5 almond azure blanched chiffon midnight 5 5 5 1464.48 7672.66 23 -23 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce b/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce new file mode 100644 index 0000000000000..98c09e4fe15c3 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce @@ -0,0 +1,26 @@ +1 1 2 1173.15 0 +1 1 2 2346.3 0 +3 2 3 4100.06 32 +4 3 4 5702.650000000001 -28 +5 4 5 7117.070000000001 22 +6 5 6 8749.730000000001 14 +1 1 1 1690.68 0 +2 2 2 3491.38 26 +3 3 3 5523.360000000001 -38 +4 4 4 7222.02 23 +5 5 5 8923.62 -7 +1 1 1 1671.68 0 +2 2 2 2861.95 -3 +3 3 3 4272.34 5 +4 4 4 6195.32 -18 +5 5 5 7532.61 44 +1 1 1 1620.67 0 +2 2 2 2996.09 29 +3 3 3 4202.35 -12 +4 4 4 6047.27 -20 +5 5 5 7337.620000000001 5 +1 1 1 1789.69 0 +2 2 2 3401.3500000000004 -25 +3 3 3 5190.08 -4 +4 4 4 6208.18 44 +5 5 5 7672.66 -23 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722 b/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722 new file mode 100644 index 0000000000000..e35257d98382e --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722 @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1 +Manufacturer#1 almond antique burnished rose metallic 2 1 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 +Manufacturer#1 almond aquamarine burnished black steel 28 5 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 +Manufacturer#2 almond antique violet chocolate turquoise 14 1 +Manufacturer#2 almond antique violet turquoise frosted 40 2 +Manufacturer#2 almond aquamarine midnight light salmon 2 3 +Manufacturer#2 almond aquamarine rose maroon antique 25 4 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 +Manufacturer#3 almond antique chartreuse khaki white 17 1 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 +Manufacturer#3 almond antique metallic orange dim 19 3 +Manufacturer#3 almond antique misty red olive 1 4 +Manufacturer#3 almond antique olive coral navajo 45 5 +Manufacturer#4 almond antique gainsboro frosted violet 10 1 +Manufacturer#4 almond antique violet mint lemon 39 2 +Manufacturer#4 almond aquamarine floral ivory bisque 27 3 +Manufacturer#4 almond aquamarine yellow dodger mint 7 4 +Manufacturer#4 almond azure aquamarine papaya violet 12 5 +Manufacturer#5 almond antique blue firebrick mint 31 1 +Manufacturer#5 almond antique medium spring khaki 6 2 +Manufacturer#5 almond antique sky peru orange 2 3 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 +Manufacturer#5 almond azure blanched chiffon midnight 23 5 diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 9. testHavingWithWindowingNoGBY-0-70cdc0555a61ef08534a9ebebb95ebbf b/sql/hive/src/test/resources/golden/windowing.q -- 9. testHavingWithWindowingNoGBY-0-70cdc0555a61ef08534a9ebebb95ebbf new file mode 100644 index 0000000000000..850c41c8115d6 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing.q -- 9. testHavingWithWindowingNoGBY-0-70cdc0555a61ef08534a9ebebb95ebbf @@ -0,0 +1,26 @@ +Manufacturer#1 almond antique burnished rose metallic 2 1 1 1173.15 +Manufacturer#1 almond antique burnished rose metallic 2 1 1 2346.3 +Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 4100.06 +Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 5702.650000000001 +Manufacturer#1 almond aquamarine burnished black steel 28 5 4 7117.070000000001 +Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 8749.730000000001 +Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 1690.68 +Manufacturer#2 almond antique violet turquoise frosted 40 2 2 3491.38 +Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 5523.360000000001 +Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 7222.02 +Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 8923.62 +Manufacturer#3 almond antique chartreuse khaki white 17 1 1 1671.68 +Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 2861.95 +Manufacturer#3 almond antique metallic orange dim 19 3 3 4272.34 +Manufacturer#3 almond antique misty red olive 1 4 4 6195.32 +Manufacturer#3 almond antique olive coral navajo 45 5 5 7532.61 +Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 1620.67 +Manufacturer#4 almond antique violet mint lemon 39 2 2 2996.09 +Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 4202.35 +Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 6047.27 +Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 7337.620000000001 +Manufacturer#5 almond antique blue firebrick mint 31 1 1 1789.69 +Manufacturer#5 almond antique medium spring khaki 6 2 2 3401.3500000000004 +Manufacturer#5 almond antique sky peru orange 2 3 3 5190.08 +Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 6208.18 +Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 7672.66 diff --git a/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-0-d3f50875bd5dff172cf813fdb7d738eb b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-0-d3f50875bd5dff172cf813fdb7d738eb new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-1-dda16565b98926fc3587de937b9401c7 b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-1-dda16565b98926fc3587de937b9401c7 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-2-374e39786feb745cd70f25be58bfa24 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b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 3-0-455e41d9949a2d22bab634fd8e42f2b1 new file mode 100644 index 0000000000000..f47923618a1aa --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 3-0-455e41d9949a2d22bab634fd8e42f2b1 @@ -0,0 +1 @@ +bob steinbeck 65637 9.699999809265137 diff --git a/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 4-0-cfad06ae8eba6b047d32a6a61dd59392 b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 4-0-cfad06ae8eba6b047d32a6a61dd59392 new file mode 100644 index 0000000000000..f41eaa259cec0 --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 4-0-cfad06ae8eba6b047d32a6a61dd59392 @@ -0,0 +1 @@ +bob steinbeck 1 1 diff --git a/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 5-0-d7ca7a61377cef3a9f721a28afdae012 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a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4 b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4 new file mode 100644 index 0000000000000..275772e1f643a --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4 @@ -0,0 +1,1049 @@ +65791 calvin nixon +65791 katie garcia +65790 fred nixon +65790 victor polk +65790 yuri ellison +65789 NULL +65789 alice laertes +65789 gabriella king +65789 katie zipper +65789 oscar zipper +65789 quinn davidson +65789 wendy brown +65788 irene brown +65788 oscar zipper +65788 rachel king +65788 xavier thompson +65787 alice laertes +65787 david laertes +65787 katie ichabod +65787 ulysses king +65786 bob carson +65786 quinn king +65786 victor xylophone +65786 xavier allen +65786 xavier davidson +65785 sarah carson +65785 sarah johnson +65784 holly young +65784 jessica ellison +65784 jessica ovid +65784 jessica zipper +65784 quinn van buren +65783 david allen +65783 fred ellison +65783 irene nixon +65783 mike ichabod +65783 wendy miller +65783 zach garcia +65782 tom miller +65782 ulysses underhill +65782 victor nixon +65782 yuri white +65782 zach ovid +65781 ethan carson +65781 luke laertes +65781 quinn brown +65780 holly hernandez +65780 holly zipper +65780 wendy falkner +65779 gabriella ichabod +65779 irene brown +65779 irene underhill +65779 ulysses xylophone +65779 wendy hernandez +65779 yuri nixon +65779 zach ellison +65778 jessica davidson +65778 katie allen +65778 oscar van buren +65778 oscar white +65778 rachel johnson +65778 victor quirinius +65778 yuri polk +65778 yuri quirinius +65777 ethan garcia +65777 irene garcia +65777 katie polk +65777 rachel thompson +65776 NULL +65776 luke garcia +65776 luke quirinius +65776 priscilla ovid +65776 sarah king +65775 bob ellison +65775 calvin steinbeck +65775 ethan laertes +65775 luke robinson +65775 oscar polk +65774 calvin young +65774 irene quirinius +65774 katie brown +65774 oscar hernandez +65774 sarah ichabod +65774 zach king +65773 NULL +65773 calvin garcia +65773 irene polk +65773 jessica davidson +65773 nick ichabod +65773 nick zipper +65773 quinn ovid +65773 zach falkner +65772 oscar garcia +65771 ethan underhill +65771 yuri hernandez +65771 zach brown +65770 alice nixon +65770 gabriella ellison +65769 mike king +65769 nick steinbeck +65769 oscar nixon +65769 rachel zipper +65769 xavier quirinius +65769 zach ichabod +65768 jessica davidson +65767 tom robinson +65767 victor davidson +65767 xavier king +65766 fred davidson +65766 luke underhill +65766 ulysses carson +65766 wendy nixon +65766 xavier brown +65765 bob polk +65765 gabriella brown +65765 wendy allen +65764 alice hernandez +65764 alice robinson +65764 luke ellison +65763 calvin van buren +65763 ethan brown +65763 priscilla johnson +65763 tom ichabod +65763 tom miller +65762 david xylophone +65762 priscilla xylophone +65762 quinn allen +65762 sarah johnson +65762 tom carson +65761 david ovid +65760 NULL +65760 ethan king +65760 priscilla johnson +65760 wendy van buren +65759 alice xylophone +65759 ethan quirinius +65759 fred steinbeck +65759 sarah robinson +65759 xavier johnson +65758 alice van buren +65758 ethan white +65758 irene johnson +65758 jessica underhill +65758 rachel laertes +65757 bob quirinius +65757 nick quirinius +65757 tom van buren +65757 xavier carson +65757 zach brown +65756 gabriella falkner +65756 mike polk +65756 nick allen +65756 yuri xylophone +65755 alice johnson +65755 david ellison +65755 fred polk +65755 gabriella hernandez +65755 irene van buren +65755 jessica nixon +65755 mike ellison +65755 nick van buren +65755 zach miller +65754 luke johnson +65754 xavier quirinius +65753 rachel robinson +65753 yuri garcia +65752 bob miller +65752 oscar zipper +65751 calvin quirinius +65751 katie king +65751 mike allen +65751 mike quirinius +65751 mike white +65751 zach zipper +65750 mike white +65750 nick thompson +65750 oscar quirinius +65750 priscilla polk +65750 rachel brown +65749 david allen +65749 gabriella polk +65749 mike ellison +65749 sarah carson +65749 wendy thompson +65748 david davidson +65748 oscar laertes +65747 calvin falkner +65747 fred steinbeck +65747 priscilla zipper +65747 quinn underhill +65747 rachel falkner +65747 wendy falkner +65747 yuri falkner +65746 +65746 katie robinson +65746 luke garcia +65746 tom brown +65746 zach steinbeck +65745 oscar steinbeck +65745 oscar xylophone +65745 priscilla nixon +65745 victor laertes +65744 alice allen +65744 priscilla ichabod +65743 jessica carson +65743 oscar ichabod +65743 sarah falkner +65743 tom brown +65742 jessica brown +65742 jessica ellison +65742 wendy polk +65741 calvin thompson +65740 irene van buren +65740 mike zipper +65740 rachel quirinius +65739 gabriella van buren +65739 victor robinson +65739 wendy nixon +65738 sarah ichabod +65738 wendy robinson +65738 wendy young +65738 xavier king +65737 holly brown +65737 irene falkner +65737 jessica white +65737 quinn garcia +65737 wendy laertes +65736 fred young +65736 quinn laertes +65736 rachel allen +65736 victor ellison +65735 oscar laertes +65735 tom falkner +65735 ulysses thompson +65734 calvin falkner +65734 mike garcia +65733 gabriella van buren +65733 holly nixon +65733 luke underhill +65733 oscar allen +65733 xavier polk +65733 yuri ellison +65732 mike steinbeck +65732 tom carson +65732 tom ellison +65731 alice robinson +65731 priscilla carson +65731 tom johnson +65731 ulysses ovid +65730 bob king +65730 calvin polk +65730 gabriella ichabod +65730 rachel brown +65729 fred quirinius +65728 priscilla king +65728 victor brown +65727 ethan brown +65727 fred polk +65727 jessica white +65727 priscilla underhill +65727 sarah white +65726 mike garcia +65726 ulysses hernandez +65726 wendy quirinius +65725 zach thompson +65724 NULL +65724 alice king +65724 alice steinbeck +65724 mike ellison +65724 nick ovid +65724 priscilla zipper +65723 nick allen +65723 rachel white +65722 fred falkner +65722 jessica underhill +65722 luke ichabod +65721 ethan falkner +65721 jessica zipper +65721 luke laertes +65721 luke underhill +65721 mike carson +65721 oscar laertes +65721 ulysses ichabod +65720 calvin nixon +65720 calvin thompson +65720 gabriella young +65720 holly polk +65720 mike king +65719 bob brown +65719 holly ichabod +65719 ulysses ellison +65719 ulysses young +65718 jessica ichabod +65718 oscar johnson +65718 victor hernandez +65718 zach ovid +65717 holly hernandez +65717 mike nixon +65717 quinn garcia +65717 ulysses davidson +65717 ulysses polk +65716 ethan zipper +65716 holly xylophone +65716 jessica garcia +65716 nick falkner +65716 sarah king +65716 victor xylophone +65715 alice hernandez +65715 david young +65715 ethan polk +65715 oscar falkner +65715 priscilla brown +65714 NULL +65714 ethan laertes +65714 luke allen +65713 bob ellison +65713 nick nixon +65713 ulysses carson +65713 ulysses ichabod +65713 zach garcia +65712 NULL +65712 katie king +65712 luke davidson +65712 quinn garcia +65712 zach carson +65711 bob zipper +65711 fred miller +65711 holly nixon +65711 katie ellison +65711 wendy ovid +65711 zach zipper +65710 luke polk +65710 mike young +65709 bob laertes +65709 calvin laertes +65709 oscar laertes +65708 gabriella laertes +65708 priscilla van buren +65707 bob garcia +65707 mike steinbeck +65706 bob ellison +65706 bob xylophone +65706 luke allen +65706 ulysses xylophone +65706 wendy ichabod +65705 david ellison +65705 mike white +65705 priscilla johnson +65705 tom ellison +65704 ethan brown +65704 holly king +65704 jessica young +65704 tom steinbeck +65704 victor steinbeck +65704 zach falkner +65703 holly underhill +65703 mike ellison +65703 victor davidson +65703 xavier underhill +65702 NULL +65702 bob brown +65702 bob zipper +65702 ulysses quirinius +65701 alice allen +65701 mike steinbeck +65701 oscar thompson +65700 luke allen +65700 quinn laertes +65700 tom quirinius +65699 ethan brown +65699 ethan van buren +65699 irene laertes +65699 mike young +65699 nick garcia +65699 sarah white +65698 gabriella thompson +65698 nick polk +65697 NULL +65697 NULL +65697 holly miller +65697 oscar quirinius +65697 oscar thompson +65697 xavier davidson +65696 alice xylophone +65696 jessica davidson +65696 luke allen +65696 oscar xylophone +65695 oscar king +65695 rachel young +65695 wendy ellison +65695 yuri quirinius +65694 david brown +65694 holly underhill +65694 victor quirinius +65694 zach brown +65693 bob hernandez +65693 bob young +65693 david brown +65693 holly hernandez +65693 tom polk +65693 ulysses polk +65693 victor brown +65692 holly johnson +65692 tom robinson +65691 calvin ovid +65691 ethan nixon +65691 ethan robinson +65691 fred underhill +65691 holly white +65691 irene polk +65691 oscar white +65691 rachel brown +65690 fred van buren +65690 jessica quirinius +65689 oscar ovid +65689 wendy thompson +65688 bob steinbeck +65688 victor steinbeck +65687 gabriella ichabod +65687 jessica underhill +65687 mike zipper +65687 quinn thompson +65686 bob king +65686 bob zipper +65686 david quirinius +65686 luke ichabod +65685 ethan robinson +65685 gabriella hernandez +65685 katie garcia +65685 sarah ellison +65685 victor hernandez +65685 victor nixon +65684 priscilla brown +65684 victor laertes +65684 wendy van buren +65683 NULL +65683 mike king +65683 tom laertes +65682 calvin quirinius +65682 ethan brown +65682 katie ellison +65681 gabriella allen +65681 luke laertes +65681 oscar quirinius +65681 ulysses laertes +65681 wendy ellison +65681 xavier polk +65680 NULL +65680 alice nixon +65680 gabriella ovid +65680 jessica carson +65680 ulysses nixon +65680 zach zipper +65679 bob garcia +65679 wendy underhill +65678 bob falkner +65678 victor xylophone +65678 wendy king +65677 alice allen +65677 fred van buren +65677 mike brown +65677 nick xylophone +65677 ulysses underhill +65677 zach robinson +65676 bob davidson +65676 bob laertes +65676 tom ovid +65676 xavier johnson +65675 david hernandez +65675 david nixon +65675 holly falkner +65675 quinn steinbeck +65675 rachel robinson +65675 sarah zipper +65675 tom polk +65675 victor allen +65674 gabriella falkner +65673 nick johnson +65673 quinn brown +65673 quinn underhill +65673 rachel ovid +65673 wendy brown +65672 nick laertes +65672 nick underhill +65672 rachel zipper +65672 tom white +65672 victor king +65671 fred ellison +65671 fred falkner +65671 zach white +65670 david robinson +65670 jessica zipper +65670 luke van buren +65670 oscar ovid +65670 quinn steinbeck +65669 NULL +65669 alice king +65669 calvin hernandez +65669 katie polk +65669 nick miller +65669 oscar van buren +65668 luke ellison +65667 bob brown +65667 irene nixon +65667 oscar brown +65667 tom falkner +65666 +65666 david underhill +65666 fred van buren +65665 rachel brown +65664 NULL +65664 bob davidson +65664 david ichabod +65664 ethan laertes +65664 irene robinson +65664 mike carson +65664 priscilla young +65664 victor king +65663 calvin underhill +65663 jessica johnson +65663 priscilla carson +65663 zach ichabod +65662 ethan allen +65662 katie ovid +65662 oscar johnson +65662 ulysses carson +65662 ulysses polk +65662 victor ovid +65661 david van buren +65661 luke xylophone +65661 mike falkner +65661 priscilla van buren +65661 victor johnson +65660 holly ichabod +65660 priscilla johnson +65660 victor thompson +65659 david robinson +65659 gabriella king +65659 luke davidson +65659 mike king +65659 mike zipper +65659 nick brown +65659 nick zipper +65659 yuri underhill +65658 NULL +65658 alice zipper +65658 calvin allen +65658 calvin johnson +65658 jessica garcia +65658 quinn davidson +65658 sarah ovid +65658 ulysses brown +65658 ulysses miller +65658 yuri king +65657 ethan falkner +65657 holly zipper +65657 irene ovid +65657 luke ovid +65657 priscilla white +65656 david davidson +65656 irene ovid +65656 jessica xylophone +65656 luke laertes +65656 oscar ichabod +65656 xavier ellison +65655 calvin falkner +65655 yuri laertes +65654 alice carson +65654 alice quirinius +65654 gabriella falkner +65654 nick young +65654 oscar robinson +65654 quinn robinson +65654 rachel falkner +65654 tom laertes +65654 yuri johnson +65653 calvin ellison +65653 holly underhill +65653 ulysses polk +65653 wendy xylophone +65652 NULL +65652 tom ellison +65652 victor johnson +65651 NULL +65651 ethan laertes +65651 ethan laertes +65651 irene garcia +65651 mike young +65650 irene ellison +65650 oscar white +65650 sarah steinbeck +65650 ulysses underhill +65650 ulysses xylophone +65650 victor xylophone +65649 irene underhill +65649 priscilla quirinius +65649 quinn ellison +65649 tom quirinius +65648 alice nixon +65648 calvin brown +65648 sarah carson +65648 xavier ellison +65647 irene allen +65647 mike ellison +65646 bob ovid +65646 xavier brown +65646 xavier ovid +65645 jessica quirinius +65645 katie miller +65645 ulysses hernandez +65644 alice king +65644 calvin ovid +65644 jessica white +65644 katie van buren +65644 sarah young +65644 ulysses hernandez +65644 yuri carson +65643 david davidson +65643 ethan polk +65643 ethan zipper +65643 gabriella ichabod +65643 mike davidson +65643 mike hernandez +65643 oscar robinson +65643 priscilla underhill +65643 zach king +65642 gabriella thompson +65641 fred laertes +65641 sarah garcia +65641 tom miller +65641 xavier hernandez +65640 david underhill +65639 wendy garcia +65638 fred nixon +65638 luke polk +65638 rachel carson +65637 alice underhill +65637 david davidson +65637 fred davidson +65637 gabriella davidson +65637 oscar carson +65637 rachel laertes +65637 sarah garcia +65637 wendy garcia +65636 +65636 irene polk +65636 wendy allen +65635 alice steinbeck +65635 alice zipper +65635 ulysses white +65634 NULL +65634 calvin white +65634 holly underhill +65634 sarah falkner +65633 NULL +65633 holly polk +65633 jessica nixon +65633 oscar quirinius +65632 alice falkner +65632 zach quirinius +65631 fred ellison +65630 rachel brown +65630 xavier hernandez +65629 jessica quirinius +65629 priscilla carson +65629 victor young +65629 xavier carson +65628 bob ovid +65628 ethan ovid +65628 irene ichabod +65628 oscar hernandez +65628 oscar robinson +65628 xavier quirinius +65627 alice miller +65627 holly johnson +65627 luke falkner +65627 yuri polk +65626 ethan polk +65626 holly robinson +65626 tom young +65626 yuri johnson +65625 david xylophone +65625 fred ichabod +65625 katie white +65625 ulysses garcia +65624 calvin steinbeck +65624 calvin xylophone +65624 rachel carson +65624 tom van buren +65624 yuri brown +65623 alice quirinius +65623 jessica miller +65623 oscar ichabod +65623 quinn zipper +65623 tom van buren +65623 victor brown +65623 wendy young +65622 nick davidson +65622 rachel king +65622 wendy robinson +65622 xavier ichabod +65622 zach xylophone +65622 zach young +65621 quinn underhill +65621 ulysses young +65620 nick garcia +65620 oscar thompson +65620 quinn quirinius +65620 victor white +65620 victor xylophone +65620 wendy quirinius +65619 calvin brown +65619 gabriella polk +65619 oscar king +65619 ulysses miller +65619 ulysses robinson +65619 ulysses steinbeck +65618 gabriella ovid +65618 irene laertes +65618 katie king +65618 oscar ovid +65618 quinn thompson +65617 fred van buren +65617 gabriella carson +65617 sarah johnson +65617 ulysses underhill +65616 calvin steinbeck +65616 xavier ichabod +65615 alice ovid +65615 david quirinius +65615 irene quirinius +65615 katie nixon +65614 wendy king +65614 xavier quirinius +65614 xavier white +65613 xavier zipper +65612 irene miller +65612 victor hernandez +65612 wendy white +65612 yuri polk +65611 ethan johnson +65611 fred zipper +65611 irene carson +65611 nick quirinius +65610 tom king +65610 victor steinbeck +65610 wendy garcia +65610 yuri carson +65610 zach ovid +65609 sarah robinson +65608 katie van buren +65608 mike van buren +65608 quinn ichabod +65608 zach underhill +65607 katie miller +65607 luke falkner +65607 mike polk +65607 priscilla xylophone +65607 yuri allen +65607 yuri allen +65606 bob white +65606 gabriella white +65606 oscar carson +65606 victor white +65606 xavier allen +65606 zach allen +65605 holly king +65604 katie zipper +65604 oscar davidson +65604 wendy laertes +65604 zach brown +65603 alice davidson +65603 ethan miller +65603 katie davidson +65603 katie young +65603 mike garcia +65602 NULL +65602 calvin laertes +65602 ethan laertes +65602 fred steinbeck +65602 jessica young +65602 xavier brown +65601 priscilla ovid +65601 sarah xylophone +65601 tom robinson +65600 gabriella thompson +65600 jessica polk +65600 nick robinson +65600 rachel allen +65599 fred quirinius +65599 luke johnson +65599 nick garcia +65599 oscar xylophone +65599 ulysses underhill +65598 ulysses van buren +65598 victor zipper +65597 ethan ellison +65597 nick ellison +65597 quinn davidson +65596 NULL +65596 calvin zipper +65596 david ellison +65596 irene ichabod +65596 wendy laertes +65595 bob white +65595 holly hernandez +65595 luke brown +65595 oscar ellison +65595 oscar ichabod +65595 quinn ellison +65594 gabriella ellison +65594 oscar robinson +65594 ulysses underhill +65594 victor robinson +65593 oscar white +65593 zach xylophone +65592 calvin xylophone +65591 alice zipper +65591 nick ichabod +65591 priscilla ichabod +65591 rachel underhill +65590 NULL +65590 katie falkner +65590 oscar van buren +65590 xavier garcia +65590 yuri underhill +65589 ethan white +65589 gabriella zipper +65589 irene ovid +65589 oscar king +65589 wendy xylophone +65588 bob van buren +65588 david ichabod +65588 mike miller +65588 tom hernandez +65588 victor van buren +65587 bob garcia +65587 luke johnson +65587 mike king +65587 victor allen +65587 xavier white +65586 david young +65586 irene brown +65586 priscilla brown +65586 wendy allen +65586 xavier laertes +65585 alice garcia +65585 bob garcia +65585 ethan ellison +65585 nick ellison +65585 priscilla thompson +65584 jessica carson +65584 jessica van buren +65584 jessica white +65583 bob xylophone +65583 nick ichabod +65583 yuri brown +65583 yuri steinbeck +65582 holly johnson +65582 mike carson +65582 victor van buren +65582 zach miller +65581 gabriella steinbeck +65581 irene quirinius +65581 luke allen +65581 nick robinson +65581 nick young +65581 wendy robinson +65580 alice steinbeck +65580 alice xylophone +65580 irene xylophone +65579 irene polk +65579 luke ovid +65579 quinn nixon +65579 sarah garcia +65579 wendy ovid +65578 calvin robinson +65578 fred king +65578 holly thompson +65578 katie ichabod +65578 quinn king +65578 rachel davidson +65578 victor hernandez +65577 holly white +65576 calvin falkner +65576 calvin ovid +65576 fred polk +65576 luke robinson +65575 calvin falkner +65575 irene steinbeck +65575 luke zipper +65575 zach king +65574 gabriella steinbeck +65574 priscilla nixon +65574 rachel thompson +65573 victor ellison +65573 victor hernandez +65573 yuri nixon +65572 calvin davidson +65572 calvin young +65572 katie young +65572 oscar ellison +65572 quinn garcia +65571 bob king +65571 irene polk +65571 katie ichabod +65571 mike steinbeck +65570 NULL +65570 bob ovid +65570 fred polk +65570 luke ellison +65570 mike hernandez +65570 yuri quirinius +65569 nick falkner +65568 bob ichabod +65568 holly thompson +65568 jessica thompson +65567 katie xylophone +65566 gabriella garcia +65566 rachel white +65565 katie young +65565 quinn young +65564 alice polk +65564 calvin carson +65564 calvin white +65564 ethan hernandez +65564 ethan quirinius +65564 jessica thompson +65564 katie hernandez +65563 calvin zipper +65563 priscilla young +65563 xavier davidson +65563 yuri steinbeck +65562 calvin falkner +65562 ethan xylophone +65562 luke white +65562 quinn allen +65562 rachel polk +65562 wendy polk +65561 bob davidson +65561 ethan polk +65561 jessica robinson +65560 fred white +65560 jessica johnson +65560 oscar thompson +65560 ulysses steinbeck +65560 zach brown +65559 NULL +65559 ethan laertes 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b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-3-34d9ee4120f21d0d0ae914fba0acc60c @@ -0,0 +1,1049 @@ + 7 + 11 + 38 +alice allen 48 +alice allen 78 +alice allen 79 +alice brown 91 +alice carson 28 +alice davidson 88 +alice falkner 117 +alice garcia 106 +alice hernandez 37 +alice hernandez 85 +alice johnson 65 +alice king 109 +alice king 113 +alice king 118 +alice laertes 6 +alice laertes 65 +alice miller 97 +alice nixon 15 +alice nixon 31 +alice nixon 56 +alice ovid 15 +alice polk 90 +alice quirinius 27 +alice quirinius 89 +alice robinson 5 +alice robinson 68 +alice steinbeck 45 +alice steinbeck 50 +alice steinbeck 77 +alice underhill 34 +alice van buren 31 +alice xylophone 0 +alice xylophone 5 +alice xylophone 113 +alice zipper 19 +alice zipper 51 +alice zipper 74 +bob brown 35 +bob brown 61 +bob brown 71 +bob carson 40 +bob davidson 44 +bob davidson 103 +bob davidson 124 +bob ellison 7 +bob ellison 46 +bob ellison 85 +bob ellison 103 +bob falkner 2 +bob garcia 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a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4 b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4 new file mode 100644 index 0000000000000..86ca4e49d21bd --- /dev/null +++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4 @@ -0,0 +1,1049 @@ + + + +alice allen +alice allen +alice allen +alice brown +alice carson +alice davidson +alice falkner +alice garcia +alice hernandez +alice hernandez +alice johnson +alice king +alice king +alice king +alice laertes +alice laertes +alice miller +alice nixon +alice nixon +alice nixon +alice ovid +alice polk +alice quirinius +alice quirinius +alice robinson +alice robinson +alice steinbeck +alice steinbeck +alice steinbeck +alice underhill +alice van buren +alice xylophone +alice xylophone +alice xylophone +alice zipper +alice zipper +alice zipper +bob brown +bob brown +bob brown 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b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7 @@ -0,0 +1,1049 @@ + 1 + 1 + 1 +alice allen 1 +alice allen 1 +alice allen 1 +alice brown 1 +alice carson 1 +alice davidson 1 +alice falkner 1 +alice garcia 2 +alice hernandez 1 +alice hernandez 1 +alice johnson 1 +alice king 1 +alice king 1 +alice king 1 +alice laertes 1 +alice laertes 1 +alice miller 1 +alice nixon 1 +alice nixon 1 +alice nixon 1 +alice ovid 1 +alice polk 1 +alice quirinius 1 +alice quirinius 1 +alice robinson 1 +alice robinson 1 +alice steinbeck 1 +alice steinbeck 1 +alice steinbeck 1 +alice underhill 1 +alice van buren 1 +alice xylophone 1 +alice xylophone 1 +alice xylophone 1 +alice zipper 1 +alice zipper 1 +alice zipper 1 +bob brown 1 +bob brown 1 +bob brown 2 +bob carson 1 +bob davidson 1 +bob davidson 1 +bob davidson 1 +bob ellison 1 +bob ellison 1 +bob ellison 1 +bob ellison 2 +bob falkner 1 +bob garcia 1 +bob garcia 1 +bob garcia 1 +bob garcia 1 +bob 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a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala new file mode 100644 index 0000000000000..52e782768cb75 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala @@ -0,0 +1,38 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import org.apache.spark.sql.{Row, QueryTest} +import org.apache.spark.sql.hive.test.TestHive.implicits._ + + +class HiveDataFrameJoinSuite extends QueryTest { + + // We should move this into SQL package if we make case sensitivity configurable in SQL. + test("join - self join auto resolve ambiguity with case insensitivity") { + val df = Seq((1, "1"), (2, "2")).toDF("key", "value") + checkAnswer( + df.join(df, df("key") === df("Key")), + Row(1, "1", 1, "1") :: Row(2, "2", 2, "2") :: Nil) + + checkAnswer( + df.join(df.filter($"value" === "2"), df("key") === df("Key")), + Row(2, "2", 2, "2") :: Nil) + } + +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala index a787fa5546e76..4990092df6a99 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala @@ -28,7 +28,7 @@ import org.apache.spark.util.Utils class QueryPartitionSuite extends QueryTest { import org.apache.spark.sql.hive.test.TestHive.implicits._ - test("SPARK-5068: query data when path doesn't exists"){ + test("SPARK-5068: query data when path doesn't exist"){ val testData = TestHive.sparkContext.parallelize( (1 to 10).map(i => TestData(i, i.toString))).toDF() testData.registerTempTable("testData") @@ -52,8 +52,9 @@ class QueryPartitionSuite extends QueryTest { ++ testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect) // delete the path of one partition - val folders = tmpDir.listFiles.filter(_.isDirectory) - Utils.deleteRecursively(folders(0)) + tmpDir.listFiles + .find { f => f.isDirectory && f.getName().startsWith("ds=") } + .foreach { f => Utils.deleteRecursively(f) } // test for after delete the path checkAnswer(sql("select key,value from table_with_partition"), diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala new file mode 100644 index 0000000000000..81e77ba257bf1 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala @@ -0,0 +1,105 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import org.apache.spark.Logging +import org.apache.spark.sql.catalyst.util.quietly +import org.apache.spark.util.Utils +import org.scalatest.FunSuite + +class VersionsSuite extends FunSuite with Logging { + val testType = "derby" + + private def buildConf() = { + lazy val warehousePath = Utils.createTempDir() + lazy val metastorePath = Utils.createTempDir() + metastorePath.delete() + Map( + "javax.jdo.option.ConnectionURL" -> s"jdbc:derby:;databaseName=$metastorePath;create=true", + "hive.metastore.warehouse.dir" -> warehousePath.toString) + } + + test("success sanity check") { + val badClient = IsolatedClientLoader.forVersion("13", buildConf()).client + val db = new HiveDatabase("default", "") + badClient.createDatabase(db) + } + + private def getNestedMessages(e: Throwable): String = { + var causes = "" + var lastException = e + while (lastException != null) { + causes += lastException.toString + "\n" + lastException = lastException.getCause + } + causes + } + + // Its actually pretty easy to mess things up and have all of your tests "pass" by accidentally + // connecting to an auto-populated, in-process metastore. Let's make sure we are getting the + // versions right by forcing a known compatibility failure. + // TODO: currently only works on mysql where we manually create the schema... + ignore("failure sanity check") { + val e = intercept[Throwable] { + val badClient = quietly { IsolatedClientLoader.forVersion("13", buildConf()).client } + } + assert(getNestedMessages(e) contains "Unknown column 'A0.OWNER_NAME' in 'field list'") + } + + private val versions = Seq("12", "13") + + private var client: ClientInterface = null + + versions.foreach { version => + test(s"$version: listTables") { + client = null + client = IsolatedClientLoader.forVersion(version, buildConf()).client + client.listTables("default") + } + + test(s"$version: createDatabase") { + val db = HiveDatabase("default", "") + client.createDatabase(db) + } + + test(s"$version: createTable") { + val table = + HiveTable( + specifiedDatabase = Option("default"), + name = "src", + schema = Seq(HiveColumn("key", "int", "")), + partitionColumns = Seq.empty, + properties = Map.empty, + serdeProperties = Map.empty, + tableType = ManagedTable, + location = None, + inputFormat = + Some(classOf[org.apache.hadoop.mapred.TextInputFormat].getName), + outputFormat = + Some(classOf[org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat[_, _]].getName), + serde = + Some(classOf[org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe].getName())) + + client.createTable(table) + } + + test(s"$version: getTable") { + client.getTable("default", "src") + } + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala index 027056d4b865f..5ead5f3c19908 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala @@ -300,7 +300,7 @@ abstract class HiveComparisonTest val hiveQueries = queryList.map(new TestHive.HiveQLQueryExecution(_)) // Make sure we can at least parse everything before attempting hive execution. - hiveQueries.foreach(_.analyzed) + hiveQueries.foreach(_.logical) val computedResults = (queryList.zipWithIndex, hiveQueries, hiveCacheFiles).zipped.map { case ((queryString, i), hiveQuery, cachedAnswerFile)=> try { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 4f8d0ac0e7656..616352d223c5b 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -18,14 +18,17 @@ package org.apache.spark.sql.hive.execution import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries -import org.apache.spark.sql.hive.{MetastoreRelation, HiveShim} +import org.apache.spark.sql.catalyst.errors.DialectException +import org.apache.spark.sql.DefaultDialect +import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} +import org.apache.spark.sql.hive.MetastoreRelation import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.apache.spark.sql.hive.{HiveQLDialect, HiveShim} import org.apache.spark.sql.parquet.ParquetRelation2 import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.types._ -import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} case class Nested1(f1: Nested2) case class Nested2(f2: Nested3) @@ -45,6 +48,13 @@ case class Order( state: String, month: Int) +case class WindowData( + month: Int, + area: String, + product: Int) +/** A SQL Dialect for testing purpose, and it can not be nested type */ +class MyDialect extends DefaultDialect + /** * A collection of hive query tests where we generate the answers ourselves instead of depending on * Hive to generate them (in contrast to HiveQuerySuite). Often this is because the query is @@ -229,6 +239,35 @@ class SQLQuerySuite extends QueryTest { setConf("spark.sql.hive.convertCTAS", originalConf) } + test("SQL Dialect Switching") { + assert(getSQLDialect().getClass === classOf[HiveQLDialect]) + setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName()) + assert(getSQLDialect().getClass === classOf[MyDialect]) + assert(sql("SELECT 1").collect() === Array(Row(1))) + + // set the dialect back to the DefaultSQLDialect + sql("SET spark.sql.dialect=sql") + assert(getSQLDialect().getClass === classOf[DefaultDialect]) + sql("SET spark.sql.dialect=hiveql") + assert(getSQLDialect().getClass === classOf[HiveQLDialect]) + + // set invalid dialect + sql("SET spark.sql.dialect.abc=MyTestClass") + sql("SET spark.sql.dialect=abc") + intercept[Exception] { + sql("SELECT 1") + } + // test if the dialect set back to HiveQLDialect + getSQLDialect().getClass === classOf[HiveQLDialect] + + sql("SET spark.sql.dialect=MyTestClass") + intercept[DialectException] { + sql("SELECT 1") + } + // test if the dialect set back to HiveQLDialect + assert(getSQLDialect().getClass === classOf[HiveQLDialect]) + } + test("CTAS with serde") { sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value").collect() sql( @@ -569,4 +608,147 @@ class SQLQuerySuite extends QueryTest { sql("SELECT TRANSFORM (d1, d2, d3) USING 'cat' AS (a,b,c) FROM script_trans") .queryExecution.toRdd.count()) } + + test("window function: udaf with aggregate expressin") { + val data = Seq( + WindowData(1, "a", 5), + WindowData(2, "a", 6), + WindowData(3, "b", 7), + WindowData(4, "b", 8), + WindowData(5, "c", 9), + WindowData(6, "c", 10) + ) + sparkContext.parallelize(data).toDF().registerTempTable("windowData") + + checkAnswer( + sql( + """ + |select area, sum(product), sum(sum(product)) over (partition by area) + |from windowData group by month, area + """.stripMargin), + Seq( + ("a", 5, 11), + ("a", 6, 11), + ("b", 7, 15), + ("b", 8, 15), + ("c", 9, 19), + ("c", 10, 19) + ).map(i => Row(i._1, i._2, i._3))) + + checkAnswer( + sql( + """ + |select area, sum(product) - 1, sum(sum(product)) over (partition by area) + |from windowData group by month, area + """.stripMargin), + Seq( + ("a", 4, 11), + ("a", 5, 11), + ("b", 6, 15), + ("b", 7, 15), + ("c", 8, 19), + ("c", 9, 19) + ).map(i => Row(i._1, i._2, i._3))) + + checkAnswer( + sql( + """ + |select area, sum(product), sum(product) / sum(sum(product)) over (partition by area) + |from windowData group by month, area + """.stripMargin), + Seq( + ("a", 5, 5d/11), + ("a", 6, 6d/11), + ("b", 7, 7d/15), + ("b", 8, 8d/15), + ("c", 10, 10d/19), + ("c", 9, 9d/19) + ).map(i => Row(i._1, i._2, i._3))) + + checkAnswer( + sql( + """ + |select area, sum(product), sum(product) / sum(sum(product) - 1) over (partition by area) + |from windowData group by month, area + """.stripMargin), + Seq( + ("a", 5, 5d/9), + ("a", 6, 6d/9), + ("b", 7, 7d/13), + ("b", 8, 8d/13), + ("c", 10, 10d/17), + ("c", 9, 9d/17) + ).map(i => Row(i._1, i._2, i._3))) + } + + test("window function: partition and order expressions") { + val data = Seq( + WindowData(1, "a", 5), + WindowData(2, "a", 6), + WindowData(3, "b", 7), + WindowData(4, "b", 8), + WindowData(5, "c", 9), + WindowData(6, "c", 10) + ) + sparkContext.parallelize(data).toDF().registerTempTable("windowData") + + checkAnswer( + sql( + """ + |select month, area, product, sum(product + 1) over (partition by 1 order by 2) + |from windowData + """.stripMargin), + Seq( + (1, "a", 5, 51), + (2, "a", 6, 51), + (3, "b", 7, 51), + (4, "b", 8, 51), + (5, "c", 9, 51), + (6, "c", 10, 51) + ).map(i => Row(i._1, i._2, i._3, i._4))) + + checkAnswer( + sql( + """ + |select month, area, product, sum(product) + |over (partition by month % 2 order by 10 - product) + |from windowData + """.stripMargin), + Seq( + (1, "a", 5, 21), + (2, "a", 6, 24), + (3, "b", 7, 16), + (4, "b", 8, 18), + (5, "c", 9, 9), + (6, "c", 10, 10) + ).map(i => Row(i._1, i._2, i._3, i._4))) + } + + test("window function: expressions in arguments of a window functions") { + val data = Seq( + WindowData(1, "a", 5), + WindowData(2, "a", 6), + WindowData(3, "b", 7), + WindowData(4, "b", 8), + WindowData(5, "c", 9), + WindowData(6, "c", 10) + ) + sparkContext.parallelize(data).toDF().registerTempTable("windowData") + + checkAnswer( + sql( + """ + |select month, area, month % 2, + |lag(product, 1 + 1, product) over (partition by month % 2 order by area) + |from windowData + """.stripMargin), + Seq( + (1, "a", 1, 5), + (2, "a", 0, 6), + (3, "b", 1, 7), + (4, "b", 0, 8), + (5, "c", 1, 5), + (6, "c", 0, 6) + ).map(i => Row(i._1, i._2, i._3, i._4))) + } } diff --git a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java new file mode 100644 index 0000000000000..8c0fdfa9c7478 --- /dev/null +++ b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java @@ -0,0 +1,60 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.util; + +import java.nio.ByteBuffer; +import java.util.Iterator; + +/** + * This abstract class represents a write ahead log (aka journal) that is used by Spark Streaming + * to save the received data (by receivers) and associated metadata to a reliable storage, so that + * they can be recovered after driver failures. See the Spark documentation for more information + * on how to plug in your own custom implementation of a write ahead log. + */ +@org.apache.spark.annotation.DeveloperApi +public abstract class WriteAheadLog { + /** + * Write the record to the log and return a record handle, which contains all the information + * necessary to read back the written record. The time is used to the index the record, + * such that it can be cleaned later. Note that implementations of this abstract class must + * ensure that the written data is durable and readable (using the record handle) by the + * time this function returns. + */ + abstract public WriteAheadLogRecordHandle write(ByteBuffer record, long time); + + /** + * Read a written record based on the given record handle. + */ + abstract public ByteBuffer read(WriteAheadLogRecordHandle handle); + + /** + * Read and return an iterator of all the records that have been written but not yet cleaned up. + */ + abstract public Iterator readAll(); + + /** + * Clean all the records that are older than the threshold time. It can wait for + * the completion of the deletion. + */ + abstract public void clean(long threshTime, boolean waitForCompletion); + + /** + * Close this log and release any resources. + */ + abstract public void close(); +} diff --git a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java new file mode 100644 index 0000000000000..02324189b7822 --- /dev/null +++ b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java @@ -0,0 +1,30 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.util; + +/** + * This abstract class represents a handle that refers to a record written in a + * {@link org.apache.spark.streaming.util.WriteAheadLog WriteAheadLog}. + * It must contain all the information necessary for the record to be read and returned by + * an implemenation of the WriteAheadLog class. + * + * @see org.apache.spark.streaming.util.WriteAheadLog + */ +@org.apache.spark.annotation.DeveloperApi +public abstract class WriteAheadLogRecordHandle implements java.io.Serializable { +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index 0a50485118588..7bfae253c3a0c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -77,7 +77,8 @@ object Checkpoint extends Logging { } /** Get checkpoint files present in the give directory, ordered by oldest-first */ - def getCheckpointFiles(checkpointDir: String, fs: FileSystem): Seq[Path] = { + def getCheckpointFiles(checkpointDir: String, fsOption: Option[FileSystem] = None): Seq[Path] = { + def sortFunc(path1: Path, path2: Path): Boolean = { val (time1, bk1) = path1.getName match { case REGEX(x, y) => (x.toLong, !y.isEmpty) } val (time2, bk2) = path2.getName match { case REGEX(x, y) => (x.toLong, !y.isEmpty) } @@ -85,6 +86,7 @@ object Checkpoint extends Logging { } val path = new Path(checkpointDir) + val fs = fsOption.getOrElse(path.getFileSystem(new Configuration())) if (fs.exists(path)) { val statuses = fs.listStatus(path) if (statuses != null) { @@ -160,7 +162,7 @@ class CheckpointWriter( } // Delete old checkpoint files - val allCheckpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs) + val allCheckpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, Some(fs)) if (allCheckpointFiles.size > 10) { allCheckpointFiles.take(allCheckpointFiles.size - 10).foreach(file => { logInfo("Deleting " + file) @@ -234,15 +236,24 @@ class CheckpointWriter( private[streaming] object CheckpointReader extends Logging { - def read(checkpointDir: String, conf: SparkConf, hadoopConf: Configuration): Option[Checkpoint] = - { + /** + * Read checkpoint files present in the given checkpoint directory. If there are no checkpoint + * files, then return None, else try to return the latest valid checkpoint object. If no + * checkpoint files could be read correctly, then return None (if ignoreReadError = true), + * or throw exception (if ignoreReadError = false). + */ + def read( + checkpointDir: String, + conf: SparkConf, + hadoopConf: Configuration, + ignoreReadError: Boolean = false): Option[Checkpoint] = { val checkpointPath = new Path(checkpointDir) // TODO(rxin): Why is this a def?! def fs: FileSystem = checkpointPath.getFileSystem(hadoopConf) // Try to find the checkpoint files - val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs).reverse + val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, Some(fs)).reverse if (checkpointFiles.isEmpty) { return None } @@ -282,7 +293,10 @@ object CheckpointReader extends Logging { }) // If none of checkpoint files could be read, then throw exception - throw new SparkException("Failed to read checkpoint from directory " + checkpointPath) + if (!ignoreReadError) { + throw new SparkException(s"Failed to read checkpoint from directory $checkpointPath") + } + None } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala index 175140481e5ae..9c7f698840778 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala @@ -110,6 +110,10 @@ final private[streaming] class DStreamGraph extends Serializable with Logging { .toArray } + def getInputStreamName(streamId: Int): Option[String] = synchronized { + inputStreams.find(_.id == streamId).map(_.name) + } + def generateJobs(time: Time): Seq[Job] = { logDebug("Generating jobs for time " + time) val jobs = this.synchronized { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index f57f295874645..b1ad0d42ec8a6 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -107,6 +107,19 @@ class StreamingContext private[streaming] ( */ def this(path: String) = this(path, new Configuration) + /** + * Recreate a StreamingContext from a checkpoint file using an existing SparkContext. + * @param path Path to the directory that was specified as the checkpoint directory + * @param sparkContext Existing SparkContext + */ + def this(path: String, sparkContext: SparkContext) = { + this( + sparkContext, + CheckpointReader.read(path, sparkContext.conf, sparkContext.hadoopConfiguration).get, + null) + } + + if (sc_ == null && cp_ == null) { throw new Exception("Spark Streaming cannot be initialized with " + "both SparkContext and checkpoint as null") @@ -115,10 +128,12 @@ class StreamingContext private[streaming] ( private[streaming] val isCheckpointPresent = (cp_ != null) private[streaming] val sc: SparkContext = { - if (isCheckpointPresent) { + if (sc_ != null) { + sc_ + } else if (isCheckpointPresent) { new SparkContext(cp_.createSparkConf()) } else { - sc_ + throw new SparkException("Cannot create StreamingContext without a SparkContext") } } @@ -129,7 +144,7 @@ class StreamingContext private[streaming] ( private[streaming] val conf = sc.conf - private[streaming] val env = SparkEnv.get + private[streaming] val env = sc.env private[streaming] val graph: DStreamGraph = { if (isCheckpointPresent) { @@ -144,7 +159,7 @@ class StreamingContext private[streaming] ( } } - private val nextReceiverInputStreamId = new AtomicInteger(0) + private val nextInputStreamId = new AtomicInteger(0) private[streaming] var checkpointDir: String = { if (isCheckpointPresent) { @@ -174,7 +189,9 @@ class StreamingContext private[streaming] ( /** Register streaming source to metrics system */ private val streamingSource = new StreamingSource(this) - SparkEnv.get.metricsSystem.registerSource(streamingSource) + assert(env != null) + assert(env.metricsSystem != null) + env.metricsSystem.registerSource(streamingSource) /** Enumeration to identify current state of the StreamingContext */ private[streaming] object StreamingContextState extends Enumeration { @@ -224,7 +241,7 @@ class StreamingContext private[streaming] ( if (isCheckpointPresent) cp_ else null } - private[streaming] def getNewReceiverStreamId() = nextReceiverInputStreamId.getAndIncrement() + private[streaming] def getNewInputStreamId() = nextInputStreamId.getAndIncrement() /** * Create an input stream with any arbitrary user implemented receiver. @@ -510,6 +527,7 @@ class StreamingContext private[streaming] ( validate() sparkContext.setCallSite(DStream.getCreationSite()) scheduler.start() + uiTab.foreach(_.attach()) state = Started } @@ -621,19 +639,59 @@ object StreamingContext extends Logging { hadoopConf: Configuration = new Configuration(), createOnError: Boolean = false ): StreamingContext = { - val checkpointOption = try { - CheckpointReader.read(checkpointPath, new SparkConf(), hadoopConf) - } catch { - case e: Exception => - if (createOnError) { - None - } else { - throw e - } - } + val checkpointOption = CheckpointReader.read( + checkpointPath, new SparkConf(), hadoopConf, createOnError) checkpointOption.map(new StreamingContext(null, _, null)).getOrElse(creatingFunc()) } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the StreamingContext + * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note + * that the SparkConf configuration in the checkpoint data will not be restored as the + * SparkContext has already been created. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new StreamingContext using the given SparkContext + * @param sparkContext SparkContext using which the StreamingContext will be created + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: SparkContext => StreamingContext, + sparkContext: SparkContext + ): StreamingContext = { + getOrCreate(checkpointPath, creatingFunc, sparkContext, createOnError = false) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the StreamingContext + * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note + * that the SparkConf configuration in the checkpoint data will not be restored as the + * SparkContext has already been created. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new StreamingContext using the given SparkContext + * @param sparkContext SparkContext using which the StreamingContext will be created + * @param createOnError Whether to create a new StreamingContext if there is an + * error in reading checkpoint data. By default, an exception will be + * thrown on error. + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: SparkContext => StreamingContext, + sparkContext: SparkContext, + createOnError: Boolean + ): StreamingContext = { + val checkpointOption = CheckpointReader.read( + checkpointPath, sparkContext.conf, sparkContext.hadoopConfiguration, createOnError) + checkpointOption.map(new StreamingContext(sparkContext, _, null)) + .getOrElse(creatingFunc(sparkContext)) + } + /** * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to StreamingContext. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index 4095a7cc84946..572d7d8e8753d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -32,13 +32,14 @@ import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext} import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} +import org.apache.spark.api.java.function.{Function0 => JFunction0} import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming._ import org.apache.spark.streaming.scheduler.StreamingListener -import org.apache.hadoop.conf.Configuration -import org.apache.spark.streaming.dstream.{PluggableInputDStream, ReceiverInputDStream, DStream} +import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.receiver.Receiver +import org.apache.hadoop.conf.Configuration /** * A Java-friendly version of [[org.apache.spark.streaming.StreamingContext]] which is the main @@ -655,6 +656,7 @@ object JavaStreamingContext { * @param checkpointPath Checkpoint directory used in an earlier JavaStreamingContext program * @param factory JavaStreamingContextFactory object to create a new JavaStreamingContext */ + @deprecated("use getOrCreate without JavaStreamingContextFactor", "1.4.0") def getOrCreate( checkpointPath: String, factory: JavaStreamingContextFactory @@ -676,6 +678,7 @@ object JavaStreamingContext { * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible * file system */ + @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( checkpointPath: String, hadoopConf: Configuration, @@ -700,6 +703,7 @@ object JavaStreamingContext { * @param createOnError Whether to create a new JavaStreamingContext if there is an * error in reading checkpoint data. */ + @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( checkpointPath: String, hadoopConf: Configuration, @@ -712,6 +716,117 @@ object JavaStreamingContext { new JavaStreamingContext(ssc) } + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier JavaStreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction0[JavaStreamingContext] + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, () => { + creatingFunc.call().ssc + }) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible + * file system + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction0[JavaStreamingContext], + hadoopConf: Configuration + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, () => { + creatingFunc.call().ssc + }, hadoopConf) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible + * file system + * @param createOnError Whether to create a new JavaStreamingContext if there is an + * error in reading checkpoint data. + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction0[JavaStreamingContext], + hadoopConf: Configuration, + createOnError: Boolean + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, () => { + creatingFunc.call().ssc + }, hadoopConf, createOnError) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param sparkContext SparkContext using which the StreamingContext will be created + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], + sparkContext: JavaSparkContext + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { + creatingFunc.call(new JavaSparkContext(sparkContext)).ssc + }, sparkContext.sc) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param sparkContext SparkContext using which the StreamingContext will be created + * @param createOnError Whether to create a new JavaStreamingContext if there is an + * error in reading checkpoint data. + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], + sparkContext: JavaSparkContext, + createOnError: Boolean + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { + creatingFunc.call(new JavaSparkContext(sparkContext)).ssc + }, sparkContext.sc, createOnError) + new JavaStreamingContext(ssc) + } + /** * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to StreamingContext. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala index 24f99a2b929f5..f1f8a70655996 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala @@ -553,7 +553,8 @@ abstract class DStream[T: ClassTag] ( // because the DStream is reachable from the outer object here, and because // DStreams can't be serialized with closures, we can't proactively check // it for serializability and so we pass the optional false to SparkContext.clean - transform((r: RDD[T], t: Time) => context.sparkContext.clean(transformFunc(r), false)) + val cleanedF = context.sparkContext.clean(transformFunc, false) + transform((r: RDD[T], t: Time) => cleanedF(r)) } /** @@ -626,7 +627,7 @@ abstract class DStream[T: ClassTag] ( println("Time: " + time) println("-------------------------------------------") firstNum.take(num).foreach(println) - if (firstNum.size > num) println("...") + if (firstNum.length > num) println("...") println() } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala index e652702e213ef..9716adb62817c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala @@ -41,6 +41,14 @@ abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext) ssc.graph.addInputStream(this) + /** This is an unique identifier for the input stream. */ + val id = ssc.getNewInputStreamId() + + /** + * The name of this InputDStream. By default, it's the class name with its id. + */ + private[streaming] def name: String = s"${getClass.getSimpleName}-$id" + /** * Checks whether the 'time' is valid wrt slideDuration for generating RDD. * Additionally it also ensures valid times are in strictly increasing order. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala index 8be04314c4285..5cfe43a1ce726 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala @@ -20,11 +20,12 @@ package org.apache.spark.streaming.dstream import scala.reflect.ClassTag import org.apache.spark.rdd.{BlockRDD, RDD} -import org.apache.spark.storage.{BlockId, StorageLevel} +import org.apache.spark.storage.BlockId import org.apache.spark.streaming._ import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD -import org.apache.spark.streaming.receiver.{Receiver, WriteAheadLogBasedStoreResult} -import org.apache.spark.streaming.scheduler.ReceivedBlockInfo +import org.apache.spark.streaming.receiver.Receiver +import org.apache.spark.streaming.scheduler.InputInfo +import org.apache.spark.streaming.util.WriteAheadLogUtils /** * Abstract class for defining any [[org.apache.spark.streaming.dstream.InputDStream]] @@ -39,9 +40,6 @@ import org.apache.spark.streaming.scheduler.ReceivedBlockInfo abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingContext) extends InputDStream[T](ssc_) { - /** This is an unique identifier for the receiver input stream. */ - val id = ssc.getNewReceiverStreamId() - /** * Gets the receiver object that will be sent to the worker nodes * to receive data. This method needs to defined by any specific implementation @@ -67,27 +65,34 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont } else { // Otherwise, ask the tracker for all the blocks that have been allocated to this stream // for this batch - val blockInfos = - ssc.scheduler.receiverTracker.getBlocksOfBatch(validTime).get(id).getOrElse(Seq.empty) - val blockStoreResults = blockInfos.map { _.blockStoreResult } - val blockIds = blockStoreResults.map { _.blockId.asInstanceOf[BlockId] }.toArray + val receiverTracker = ssc.scheduler.receiverTracker + val blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty) + val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray - // Check whether all the results are of the same type - val resultTypes = blockStoreResults.map { _.getClass }.distinct - if (resultTypes.size > 1) { - logWarning("Multiple result types in block information, WAL information will be ignored.") - } + // Register the input blocks information into InputInfoTracker + val inputInfo = InputInfo(id, blockInfos.map(_.numRecords).sum) + ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo) + + // Are WAL record handles present with all the blocks + val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty } - // If all the results are of type WriteAheadLogBasedStoreResult, then create - // WriteAheadLogBackedBlockRDD else create simple BlockRDD. - if (resultTypes.size == 1 && resultTypes.head == classOf[WriteAheadLogBasedStoreResult]) { - val logSegments = blockStoreResults.map { - _.asInstanceOf[WriteAheadLogBasedStoreResult].segment - }.toArray - // Since storeInBlockManager = false, the storage level does not matter. - new WriteAheadLogBackedBlockRDD[T](ssc.sparkContext, - blockIds, logSegments, storeInBlockManager = false, StorageLevel.MEMORY_ONLY_SER) + if (areWALRecordHandlesPresent) { + // If all the blocks have WAL record handle, then create a WALBackedBlockRDD + val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray + val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray + new WriteAheadLogBackedBlockRDD[T]( + ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid) } else { + // Else, create a BlockRDD. However, if there are some blocks with WAL info but not others + // then that is unexpected and log a warning accordingly. + if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) { + if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) { + logError("Some blocks do not have Write Ahead Log information; " + + "this is unexpected and data may not be recoverable after driver failures") + } else { + logWarning("Some blocks have Write Ahead Log information; this is unexpected") + } + } new BlockRDD[T](ssc.sc, blockIds) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala index 93caa4ba35c7f..ffce6a4c3c74c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala @@ -16,41 +16,59 @@ */ package org.apache.spark.streaming.rdd +import java.io.File +import java.nio.ByteBuffer +import java.util.UUID + import scala.reflect.ClassTag +import scala.util.control.NonFatal -import org.apache.hadoop.conf.Configuration +import org.apache.commons.io.FileUtils import org.apache.spark._ import org.apache.spark.rdd.BlockRDD import org.apache.spark.storage.{BlockId, StorageLevel} -import org.apache.spark.streaming.util.{HdfsUtils, WriteAheadLogFileSegment, WriteAheadLogRandomReader} +import org.apache.spark.streaming.util._ /** * Partition class for [[org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD]]. * It contains information about the id of the blocks having this partition's data and - * the segment of the write ahead log that backs the partition. + * the corresponding record handle in the write ahead log that backs the partition. * @param index index of the partition * @param blockId id of the block having the partition data - * @param segment segment of the write ahead log having the partition data + * @param isBlockIdValid Whether the block Ids are valid (i.e., the blocks are present in the Spark + * executors). If not, then block lookups by the block ids will be skipped. + * By default, this is an empty array signifying true for all the blocks. + * @param walRecordHandle Handle of the record in a write ahead log having the partition data */ private[streaming] class WriteAheadLogBackedBlockRDDPartition( val index: Int, val blockId: BlockId, - val segment: WriteAheadLogFileSegment) - extends Partition + val isBlockIdValid: Boolean, + val walRecordHandle: WriteAheadLogRecordHandle + ) extends Partition /** * This class represents a special case of the BlockRDD where the data blocks in - * the block manager are also backed by segments in write ahead logs. For reading + * the block manager are also backed by data in write ahead logs. For reading * the data, this RDD first looks up the blocks by their ids in the block manager. - * If it does not find them, it looks up the corresponding file segment. + * If it does not find them, it looks up the WAL using the corresponding record handle. + * The lookup of the blocks from the block manager can be skipped by setting the corresponding + * element in isBlockIdValid to false. This is a performance optimization which does not affect + * correctness, and it can be used in situations where it is known that the block + * does not exist in the Spark executors (e.g. after a failed driver is restarted). + * * * @param sc SparkContext * @param blockIds Ids of the blocks that contains this RDD's data - * @param segments Segments in write ahead logs that contain this RDD's data - * @param storeInBlockManager Whether to store in the block manager after reading from the segment + * @param walRecordHandles Record handles in write ahead logs that contain this RDD's data + * @param isBlockIdValid Whether the block Ids are valid (i.e., the blocks are present in the Spark + * executors). If not, then block lookups by the block ids will be skipped. + * By default, this is an empty array signifying true for all the blocks. + * @param storeInBlockManager Whether to store a block in the block manager + * after reading it from the WAL * @param storageLevel storage level to store when storing in block manager * (applicable when storeInBlockManager = true) */ @@ -58,30 +76,39 @@ private[streaming] class WriteAheadLogBackedBlockRDD[T: ClassTag]( @transient sc: SparkContext, @transient blockIds: Array[BlockId], - @transient segments: Array[WriteAheadLogFileSegment], - storeInBlockManager: Boolean, - storageLevel: StorageLevel) + @transient walRecordHandles: Array[WriteAheadLogRecordHandle], + @transient isBlockIdValid: Array[Boolean] = Array.empty, + storeInBlockManager: Boolean = false, + storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER) extends BlockRDD[T](sc, blockIds) { require( - blockIds.length == segments.length, - s"Number of block ids (${blockIds.length}) must be " + - s"the same as number of segments (${segments.length}})!") + blockIds.length == walRecordHandles.length, + s"Number of block Ids (${blockIds.length}) must be " + + s" same as number of WAL record handles (${walRecordHandles.length}})") + + require( + isBlockIdValid.isEmpty || isBlockIdValid.length == blockIds.length, + s"Number of elements in isBlockIdValid (${isBlockIdValid.length}) must be " + + s" same as number of block Ids (${blockIds.length})") // Hadoop configuration is not serializable, so broadcast it as a serializable. @transient private val hadoopConfig = sc.hadoopConfiguration private val broadcastedHadoopConf = new SerializableWritable(hadoopConfig) + override def isValid(): Boolean = true + override def getPartitions: Array[Partition] = { assertValid() - Array.tabulate(blockIds.size) { i => - new WriteAheadLogBackedBlockRDDPartition(i, blockIds(i), segments(i)) + Array.tabulate(blockIds.length) { i => + val isValid = if (isBlockIdValid.length == 0) true else isBlockIdValid(i) + new WriteAheadLogBackedBlockRDDPartition(i, blockIds(i), isValid, walRecordHandles(i)) } } /** * Gets the partition data by getting the corresponding block from the block manager. - * If the block does not exist, then the data is read from the corresponding segment + * If the block does not exist, then the data is read from the corresponding record * in write ahead log files. */ override def compute(split: Partition, context: TaskContext): Iterator[T] = { @@ -90,35 +117,87 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag]( val blockManager = SparkEnv.get.blockManager val partition = split.asInstanceOf[WriteAheadLogBackedBlockRDDPartition] val blockId = partition.blockId - blockManager.get(blockId) match { - case Some(block) => // Data is in Block Manager - val iterator = block.data.asInstanceOf[Iterator[T]] - logDebug(s"Read partition data of $this from block manager, block $blockId") - iterator - case None => // Data not found in Block Manager, grab it from write ahead log file - val reader = new WriteAheadLogRandomReader(partition.segment.path, hadoopConf) - val dataRead = reader.read(partition.segment) - reader.close() - logInfo(s"Read partition data of $this from write ahead log, segment ${partition.segment}") - if (storeInBlockManager) { - blockManager.putBytes(blockId, dataRead, storageLevel) - logDebug(s"Stored partition data of $this into block manager with level $storageLevel") - dataRead.rewind() + + def getBlockFromBlockManager(): Option[Iterator[T]] = { + blockManager.get(blockId).map(_.data.asInstanceOf[Iterator[T]]) + } + + def getBlockFromWriteAheadLog(): Iterator[T] = { + var dataRead: ByteBuffer = null + var writeAheadLog: WriteAheadLog = null + try { + // The WriteAheadLogUtils.createLog*** method needs a directory to create a + // WriteAheadLog object as the default FileBasedWriteAheadLog needs a directory for + // writing log data. However, the directory is not needed if data needs to be read, hence + // a dummy path is provided to satisfy the method parameter requirements. + // FileBasedWriteAheadLog will not create any file or directory at that path. + // FileBasedWriteAheadLog will not create any file or directory at that path. Also, + // this dummy directory should not already exist otherwise the WAL will try to recover + // past events from the directory and throw errors. + val nonExistentDirectory = new File( + System.getProperty("java.io.tmpdir"), UUID.randomUUID().toString).getAbsolutePath + writeAheadLog = WriteAheadLogUtils.createLogForReceiver( + SparkEnv.get.conf, nonExistentDirectory, hadoopConf) + dataRead = writeAheadLog.read(partition.walRecordHandle) + } catch { + case NonFatal(e) => + throw new SparkException( + s"Could not read data from write ahead log record ${partition.walRecordHandle}", e) + } finally { + if (writeAheadLog != null) { + writeAheadLog.close() + writeAheadLog = null } - blockManager.dataDeserialize(blockId, dataRead).asInstanceOf[Iterator[T]] + } + if (dataRead == null) { + throw new SparkException( + s"Could not read data from write ahead log record ${partition.walRecordHandle}, " + + s"read returned null") + } + logInfo(s"Read partition data of $this from write ahead log, record handle " + + partition.walRecordHandle) + if (storeInBlockManager) { + blockManager.putBytes(blockId, dataRead, storageLevel) + logDebug(s"Stored partition data of $this into block manager with level $storageLevel") + dataRead.rewind() + } + blockManager.dataDeserialize(blockId, dataRead).asInstanceOf[Iterator[T]] + } + + if (partition.isBlockIdValid) { + getBlockFromBlockManager().getOrElse { getBlockFromWriteAheadLog() } + } else { + getBlockFromWriteAheadLog() } } /** * Get the preferred location of the partition. This returns the locations of the block - * if it is present in the block manager, else it returns the location of the - * corresponding segment in HDFS. + * if it is present in the block manager, else if FileBasedWriteAheadLogSegment is used, + * it returns the location of the corresponding file segment in HDFS . */ override def getPreferredLocations(split: Partition): Seq[String] = { val partition = split.asInstanceOf[WriteAheadLogBackedBlockRDDPartition] - val blockLocations = getBlockIdLocations().get(partition.blockId) - blockLocations.getOrElse( - HdfsUtils.getFileSegmentLocations( - partition.segment.path, partition.segment.offset, partition.segment.length, hadoopConfig)) + val blockLocations = if (partition.isBlockIdValid) { + getBlockIdLocations().get(partition.blockId) + } else { + None + } + + blockLocations.getOrElse { + partition.walRecordHandle match { + case fileSegment: FileBasedWriteAheadLogSegment => + try { + HdfsUtils.getFileSegmentLocations( + fileSegment.path, fileSegment.offset, fileSegment.length, hadoopConfig) + } catch { + case NonFatal(e) => + logError("Error getting WAL file segment locations", e) + Seq.empty + } + case _ => + Seq.empty + } + } } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala index 297bf04c0c25e..651b534ac1900 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala @@ -17,18 +17,18 @@ package org.apache.spark.streaming.receiver -import scala.concurrent.{Await, ExecutionContext, Future} import scala.concurrent.duration._ +import scala.concurrent.{Await, ExecutionContext, Future} import scala.language.{existentials, postfixOps} -import WriteAheadLogBasedBlockHandler._ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage._ -import org.apache.spark.streaming.util.{WriteAheadLogFileSegment, WriteAheadLogManager} -import org.apache.spark.util.{ThreadUtils, Clock, SystemClock} +import org.apache.spark.streaming.receiver.WriteAheadLogBasedBlockHandler._ +import org.apache.spark.streaming.util.{WriteAheadLogRecordHandle, WriteAheadLogUtils} +import org.apache.spark.util.{Clock, SystemClock, ThreadUtils} +import org.apache.spark.{Logging, SparkConf, SparkException} /** Trait that represents the metadata related to storage of blocks */ private[streaming] trait ReceivedBlockStoreResult { @@ -96,7 +96,7 @@ private[streaming] class BlockManagerBasedBlockHandler( */ private[streaming] case class WriteAheadLogBasedStoreResult( blockId: StreamBlockId, - segment: WriteAheadLogFileSegment + walRecordHandle: WriteAheadLogRecordHandle ) extends ReceivedBlockStoreResult @@ -116,10 +116,6 @@ private[streaming] class WriteAheadLogBasedBlockHandler( private val blockStoreTimeout = conf.getInt( "spark.streaming.receiver.blockStoreTimeout", 30).seconds - private val rollingInterval = conf.getInt( - "spark.streaming.receiver.writeAheadLog.rollingInterval", 60) - private val maxFailures = conf.getInt( - "spark.streaming.receiver.writeAheadLog.maxFailures", 3) private val effectiveStorageLevel = { if (storageLevel.deserialized) { @@ -139,13 +135,9 @@ private[streaming] class WriteAheadLogBasedBlockHandler( s"$effectiveStorageLevel when write ahead log is enabled") } - // Manages rolling log files - private val logManager = new WriteAheadLogManager( - checkpointDirToLogDir(checkpointDir, streamId), - hadoopConf, rollingInterval, maxFailures, - callerName = this.getClass.getSimpleName, - clock = clock - ) + // Write ahead log manages + private val writeAheadLog = WriteAheadLogUtils.createLogForReceiver( + conf, checkpointDirToLogDir(checkpointDir, streamId), hadoopConf) // For processing futures used in parallel block storing into block manager and write ahead log // # threads = 2, so that both writing to BM and WAL can proceed in parallel @@ -183,21 +175,22 @@ private[streaming] class WriteAheadLogBasedBlockHandler( // Store the block in write ahead log val storeInWriteAheadLogFuture = Future { - logManager.writeToLog(serializedBlock) + writeAheadLog.write(serializedBlock, clock.getTimeMillis()) } - // Combine the futures, wait for both to complete, and return the write ahead log segment + // Combine the futures, wait for both to complete, and return the write ahead log record handle val combinedFuture = storeInBlockManagerFuture.zip(storeInWriteAheadLogFuture).map(_._2) - val segment = Await.result(combinedFuture, blockStoreTimeout) - WriteAheadLogBasedStoreResult(blockId, segment) + val walRecordHandle = Await.result(combinedFuture, blockStoreTimeout) + WriteAheadLogBasedStoreResult(blockId, walRecordHandle) } def cleanupOldBlocks(threshTime: Long) { - logManager.cleanupOldLogs(threshTime, waitForCompletion = false) + writeAheadLog.clean(threshTime, false) } def stop() { - logManager.stop() + writeAheadLog.close() + executionContext.shutdown() } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala index 89af40330b9d9..92938379b9c17 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala @@ -25,12 +25,13 @@ import scala.collection.mutable.ArrayBuffer import com.google.common.base.Throwables import org.apache.hadoop.conf.Configuration -import org.apache.spark.{Logging, SparkEnv, SparkException} import org.apache.spark.rpc.{RpcEnv, ThreadSafeRpcEndpoint} import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.Time import org.apache.spark.streaming.scheduler._ +import org.apache.spark.streaming.util.WriteAheadLogUtils import org.apache.spark.util.{RpcUtils, Utils} +import org.apache.spark.{Logging, SparkEnv, SparkException} /** * Concrete implementation of [[org.apache.spark.streaming.receiver.ReceiverSupervisor]] @@ -46,7 +47,7 @@ private[streaming] class ReceiverSupervisorImpl( ) extends ReceiverSupervisor(receiver, env.conf) with Logging { private val receivedBlockHandler: ReceivedBlockHandler = { - if (env.conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false)) { + if (WriteAheadLogUtils.enableReceiverLog(env.conf)) { if (checkpointDirOption.isEmpty) { throw new SparkException( "Cannot enable receiver write-ahead log without checkpoint directory set. " + @@ -145,8 +146,8 @@ private[streaming] class ReceiverSupervisorImpl( val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock) logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms") - val blockInfo = ReceivedBlockInfo(streamId, numRecords, blockStoreResult) - trackerEndpoint.askWithReply[Boolean](AddBlock(blockInfo)) + val blockInfo = ReceivedBlockInfo(streamId, numRecords, metadataOption, blockStoreResult) + trackerEndpoint.askWithRetry[Boolean](AddBlock(blockInfo)) logDebug(s"Reported block $blockId") } @@ -169,13 +170,13 @@ private[streaming] class ReceiverSupervisorImpl( override protected def onReceiverStart() { val msg = RegisterReceiver( streamId, receiver.getClass.getSimpleName, Utils.localHostName(), endpoint) - trackerEndpoint.askWithReply[Boolean](msg) + trackerEndpoint.askWithRetry[Boolean](msg) } override protected def onReceiverStop(message: String, error: Option[Throwable]) { logInfo("Deregistering receiver " + streamId) val errorString = error.map(Throwables.getStackTraceAsString).getOrElse("") - trackerEndpoint.askWithReply[Boolean](DeregisterReceiver(streamId, message, errorString)) + trackerEndpoint.askWithRetry[Boolean](DeregisterReceiver(streamId, message, errorString)) logInfo("Stopped receiver " + streamId) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala index 92dc113f397ca..5b9bfbf9b01e3 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala @@ -24,6 +24,7 @@ import org.apache.spark.streaming.Time * :: DeveloperApi :: * Class having information on completed batches. * @param batchTime Time of the batch + * @param streamIdToNumRecords A map of input stream id to record number * @param submissionTime Clock time of when jobs of this batch was submitted to * the streaming scheduler queue * @param processingStartTime Clock time of when the first job of this batch started processing @@ -32,7 +33,7 @@ import org.apache.spark.streaming.Time @DeveloperApi case class BatchInfo( batchTime: Time, - receivedBlockInfo: Map[Int, Array[ReceivedBlockInfo]], + streamIdToNumRecords: Map[Int, Long], submissionTime: Long, processingStartTime: Option[Long], processingEndTime: Option[Long] @@ -58,4 +59,9 @@ case class BatchInfo( */ def totalDelay: Option[Long] = schedulingDelay.zip(processingDelay) .map(x => x._1 + x._2).headOption + + /** + * The number of recorders received by the receivers in this batch. + */ + def numRecords: Long = streamIdToNumRecords.values.sum } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala new file mode 100644 index 0000000000000..a72efccf2f994 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala @@ -0,0 +1,62 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import scala.collection.mutable + +import org.apache.spark.Logging +import org.apache.spark.streaming.{Time, StreamingContext} + +/** To track the information of input stream at specified batch time. */ +private[streaming] case class InputInfo(inputStreamId: Int, numRecords: Long) + +/** + * This class manages all the input streams as well as their input data statistics. The information + * will be exposed through StreamingListener for monitoring. + */ +private[streaming] class InputInfoTracker(ssc: StreamingContext) extends Logging { + + // Map to track all the InputInfo related to specific batch time and input stream. + private val batchTimeToInputInfos = new mutable.HashMap[Time, mutable.HashMap[Int, InputInfo]] + + /** Report the input information with batch time to the tracker */ + def reportInfo(batchTime: Time, inputInfo: InputInfo): Unit = synchronized { + val inputInfos = batchTimeToInputInfos.getOrElseUpdate(batchTime, + new mutable.HashMap[Int, InputInfo]()) + + if (inputInfos.contains(inputInfo.inputStreamId)) { + throw new IllegalStateException(s"Input stream ${inputInfo.inputStreamId}} for batch" + + s"$batchTime is already added into InputInfoTracker, this is a illegal state") + } + inputInfos += ((inputInfo.inputStreamId, inputInfo)) + } + + /** Get the all the input stream's information of specified batch time */ + def getInfo(batchTime: Time): Map[Int, InputInfo] = synchronized { + val inputInfos = batchTimeToInputInfos.get(batchTime) + // Convert mutable HashMap to immutable Map for the caller + inputInfos.map(_.toMap).getOrElse(Map[Int, InputInfo]()) + } + + /** Cleanup the tracked input information older than threshold batch time */ + def cleanup(batchThreshTime: Time): Unit = synchronized { + val timesToCleanup = batchTimeToInputInfos.keys.filter(_ < batchThreshTime) + logInfo(s"remove old batch metadata: ${timesToCleanup.mkString(" ")}") + batchTimeToInputInfos --= timesToCleanup + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala index 30cf87f5b7dd1..3c481bf3491f9 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala @@ -25,15 +25,49 @@ import scala.util.Try */ private[streaming] class Job(val time: Time, func: () => _) { - var id: String = _ - var result: Try[_] = null + private var _id: String = _ + private var _outputOpId: Int = _ + private var isSet = false + private var _result: Try[_] = null def run() { - result = Try(func()) + _result = Try(func()) } - def setId(number: Int) { - id = "streaming job " + time + "." + number + def result: Try[_] = { + if (_result == null) { + throw new IllegalStateException("Cannot access result before job finishes") + } + _result + } + + /** + * @return the global unique id of this Job. + */ + def id: String = { + if (!isSet) { + throw new IllegalStateException("Cannot access id before calling setId") + } + _id + } + + /** + * @return the output op id of this Job. Each Job has a unique output op id in the same JobSet. + */ + def outputOpId: Int = { + if (!isSet) { + throw new IllegalStateException("Cannot access number before calling setId") + } + _outputOpId + } + + def setOutputOpId(outputOpId: Int) { + if (isSet) { + throw new IllegalStateException("Cannot call setOutputOpId more than once") + } + isSet = true + _id = s"streaming job $time.$outputOpId" + _outputOpId = outputOpId } override def toString: String = id diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala index 2467d50839add..9f93d6cbc3c20 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala @@ -243,9 +243,9 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { graph.generateJobs(time) // generate jobs using allocated block } match { case Success(jobs) => - val receivedBlockInfos = - jobScheduler.receiverTracker.getBlocksOfBatch(time).mapValues { _.toArray } - jobScheduler.submitJobSet(JobSet(time, jobs, receivedBlockInfos)) + val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time) + val streamIdToNumRecords = streamIdToInputInfos.mapValues(_.numRecords) + jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToNumRecords)) case Failure(e) => jobScheduler.reportError("Error generating jobs for time " + time, e) } @@ -266,6 +266,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // checkpointing of this batch to complete. val maxRememberDuration = graph.getMaxInputStreamRememberDuration() jobScheduler.receiverTracker.cleanupOldBlocksAndBatches(time - maxRememberDuration) + jobScheduler.inputInfoTracker.cleanup(time - maxRememberDuration) markBatchFullyProcessed(time) } } @@ -278,6 +279,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // been saved to checkpoints, so its safe to delete block metadata and data WAL files val maxRememberDuration = graph.getMaxInputStreamRememberDuration() jobScheduler.receiverTracker.cleanupOldBlocksAndBatches(time - maxRememberDuration) + jobScheduler.inputInfoTracker.cleanup(time - maxRememberDuration) markBatchFullyProcessed(time) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index 508b89278dcba..1d1ddaaccf217 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -50,6 +50,9 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { // These two are created only when scheduler starts. // eventLoop not being null means the scheduler has been started and not stopped var receiverTracker: ReceiverTracker = null + // A tracker to track all the input stream information as well as processed record number + var inputInfoTracker: InputInfoTracker = null + private var eventLoop: EventLoop[JobSchedulerEvent] = null def start(): Unit = synchronized { @@ -65,6 +68,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { listenerBus.start(ssc.sparkContext) receiverTracker = new ReceiverTracker(ssc) + inputInfoTracker = new InputInfoTracker(ssc) receiverTracker.start() jobGenerator.start() logInfo("Started JobScheduler") @@ -172,16 +176,28 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { ssc.waiter.notifyError(e) } - private class JobHandler(job: Job) extends Runnable { + private class JobHandler(job: Job) extends Runnable with Logging { def run() { - eventLoop.post(JobStarted(job)) - // Disable checks for existing output directories in jobs launched by the streaming scheduler, - // since we may need to write output to an existing directory during checkpoint recovery; - // see SPARK-4835 for more details. - PairRDDFunctions.disableOutputSpecValidation.withValue(true) { - job.run() + ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString) + ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString) + try { + eventLoop.post(JobStarted(job)) + // Disable checks for existing output directories in jobs launched by the streaming + // scheduler, since we may need to write output to an existing directory during checkpoint + // recovery; see SPARK-4835 for more details. + PairRDDFunctions.disableOutputSpecValidation.withValue(true) { + job.run() + } + eventLoop.post(JobCompleted(job)) + } finally { + ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null) + ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null) } - eventLoop.post(JobCompleted(job)) } } } + +private[streaming] object JobScheduler { + val BATCH_TIME_PROPERTY_KEY = "spark.streaming.internal.batchTime" + val OUTPUT_OP_ID_PROPERTY_KEY = "spark.streaming.internal.outputOpId" +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala index 5b134877d0b2d..e6be63b2ddbdc 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala @@ -28,14 +28,14 @@ private[streaming] case class JobSet( time: Time, jobs: Seq[Job], - receivedBlockInfo: Map[Int, Array[ReceivedBlockInfo]] = Map.empty) { + streamIdToNumRecords: Map[Int, Long] = Map.empty) { private val incompleteJobs = new HashSet[Job]() private val submissionTime = System.currentTimeMillis() // when this jobset was submitted private var processingStartTime = -1L // when the first job of this jobset started processing private var processingEndTime = -1L // when the last job of this jobset finished processing - jobs.zipWithIndex.foreach { case (job, i) => job.setId(i) } + jobs.zipWithIndex.foreach { case (job, i) => job.setOutputOpId(i) } incompleteJobs ++= jobs def handleJobStart(job: Job) { @@ -64,7 +64,7 @@ case class JobSet( def toBatchInfo: BatchInfo = { new BatchInfo( time, - receivedBlockInfo, + streamIdToNumRecords, submissionTime, if (processingStartTime >= 0 ) Some(processingStartTime) else None, if (processingEndTime >= 0 ) Some(processingEndTime) else None diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala index 94beb590f52d6..dc11e84f29965 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala @@ -17,12 +17,38 @@ package org.apache.spark.streaming.scheduler -import org.apache.spark.streaming.receiver.ReceivedBlockStoreResult +import org.apache.spark.storage.StreamBlockId +import org.apache.spark.streaming.receiver.{ReceivedBlockStoreResult, WriteAheadLogBasedStoreResult} +import org.apache.spark.streaming.util.WriteAheadLogRecordHandle /** Information about blocks received by the receiver */ private[streaming] case class ReceivedBlockInfo( streamId: Int, numRecords: Long, + metadataOption: Option[Any], blockStoreResult: ReceivedBlockStoreResult - ) + ) { + + @volatile private var _isBlockIdValid = true + + def blockId: StreamBlockId = blockStoreResult.blockId + + def walRecordHandleOption: Option[WriteAheadLogRecordHandle] = { + blockStoreResult match { + case walStoreResult: WriteAheadLogBasedStoreResult => Some(walStoreResult.walRecordHandle) + case _ => None + } + } + + /** Is the block ID valid, that is, is the block present in the Spark executors. */ + def isBlockIdValid(): Boolean = _isBlockIdValid + + /** + * Set the block ID as invalid. This is useful when it is known that the block is not present + * in the Spark executors. + */ + def setBlockIdInvalid(): Unit = { + _isBlockIdValid = false + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala index 200cf4ef4b0f1..a9f4147a5f020 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala @@ -25,10 +25,10 @@ import scala.language.implicitConversions import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.{SparkException, Logging, SparkConf} import org.apache.spark.streaming.Time -import org.apache.spark.streaming.util.WriteAheadLogManager +import org.apache.spark.streaming.util.{WriteAheadLog, WriteAheadLogUtils} import org.apache.spark.util.{Clock, Utils} +import org.apache.spark.{Logging, SparkConf, SparkException} /** Trait representing any event in the ReceivedBlockTracker that updates its state. */ private[streaming] sealed trait ReceivedBlockTrackerLogEvent @@ -45,7 +45,7 @@ private[streaming] case class BatchCleanupEvent(times: Seq[Time]) private[streaming] case class AllocatedBlocks(streamIdToAllocatedBlocks: Map[Int, Seq[ReceivedBlockInfo]]) { def getBlocksOfStream(streamId: Int): Seq[ReceivedBlockInfo] = { - streamIdToAllocatedBlocks.get(streamId).getOrElse(Seq.empty) + streamIdToAllocatedBlocks.getOrElse(streamId, Seq.empty) } } @@ -63,6 +63,7 @@ private[streaming] class ReceivedBlockTracker( hadoopConf: Configuration, streamIds: Seq[Int], clock: Clock, + recoverFromWriteAheadLog: Boolean, checkpointDirOption: Option[String]) extends Logging { @@ -70,12 +71,14 @@ private[streaming] class ReceivedBlockTracker( private val streamIdToUnallocatedBlockQueues = new mutable.HashMap[Int, ReceivedBlockQueue] private val timeToAllocatedBlocks = new mutable.HashMap[Time, AllocatedBlocks] - private val logManagerOption = createLogManager() + private val writeAheadLogOption = createWriteAheadLog() private var lastAllocatedBatchTime: Time = null // Recover block information from write ahead logs - recoverFromWriteAheadLogs() + if (recoverFromWriteAheadLog) { + recoverPastEvents() + } /** Add received block. This event will get written to the write ahead log (if enabled). */ def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = synchronized { @@ -155,22 +158,23 @@ private[streaming] class ReceivedBlockTracker( logInfo("Deleting batches " + timesToCleanup) writeToLog(BatchCleanupEvent(timesToCleanup)) timeToAllocatedBlocks --= timesToCleanup - logManagerOption.foreach(_.cleanupOldLogs(cleanupThreshTime.milliseconds, waitForCompletion)) + writeAheadLogOption.foreach(_.clean(cleanupThreshTime.milliseconds, waitForCompletion)) } /** Stop the block tracker. */ def stop() { - logManagerOption.foreach { _.stop() } + writeAheadLogOption.foreach { _.close() } } /** * Recover all the tracker actions from the write ahead logs to recover the state (unallocated * and allocated block info) prior to failure. */ - private def recoverFromWriteAheadLogs(): Unit = synchronized { + private def recoverPastEvents(): Unit = synchronized { // Insert the recovered block information def insertAddedBlock(receivedBlockInfo: ReceivedBlockInfo) { logTrace(s"Recovery: Inserting added block $receivedBlockInfo") + receivedBlockInfo.setBlockIdInvalid() getReceivedBlockQueue(receivedBlockInfo.streamId) += receivedBlockInfo } @@ -190,9 +194,10 @@ private[streaming] class ReceivedBlockTracker( timeToAllocatedBlocks --= batchTimes } - logManagerOption.foreach { logManager => + writeAheadLogOption.foreach { writeAheadLog => logInfo(s"Recovering from write ahead logs in ${checkpointDirOption.get}") - logManager.readFromLog().foreach { byteBuffer => + import scala.collection.JavaConversions._ + writeAheadLog.readAll().foreach { byteBuffer => logTrace("Recovering record " + byteBuffer) Utils.deserialize[ReceivedBlockTrackerLogEvent](byteBuffer.array) match { case BlockAdditionEvent(receivedBlockInfo) => @@ -208,10 +213,10 @@ private[streaming] class ReceivedBlockTracker( /** Write an update to the tracker to the write ahead log */ private def writeToLog(record: ReceivedBlockTrackerLogEvent) { - if (isLogManagerEnabled) { + if (isWriteAheadLogEnabled) { logDebug(s"Writing to log $record") - logManagerOption.foreach { logManager => - logManager.writeToLog(ByteBuffer.wrap(Utils.serialize(record))) + writeAheadLogOption.foreach { logManager => + logManager.write(ByteBuffer.wrap(Utils.serialize(record)), clock.getTimeMillis()) } } } @@ -222,28 +227,15 @@ private[streaming] class ReceivedBlockTracker( } /** Optionally create the write ahead log manager only if the feature is enabled */ - private def createLogManager(): Option[WriteAheadLogManager] = { - if (conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false)) { - if (checkpointDirOption.isEmpty) { - throw new SparkException( - "Cannot enable receiver write-ahead log without checkpoint directory set. " + - "Please use streamingContext.checkpoint() to set the checkpoint directory. " + - "See documentation for more details.") - } + private def createWriteAheadLog(): Option[WriteAheadLog] = { + checkpointDirOption.map { checkpointDir => val logDir = ReceivedBlockTracker.checkpointDirToLogDir(checkpointDirOption.get) - val rollingIntervalSecs = conf.getInt( - "spark.streaming.receivedBlockTracker.writeAheadLog.rotationIntervalSecs", 60) - val logManager = new WriteAheadLogManager(logDir, hadoopConf, - rollingIntervalSecs = rollingIntervalSecs, clock = clock, - callerName = "ReceivedBlockHandlerMaster") - Some(logManager) - } else { - None + WriteAheadLogUtils.createLogForDriver(conf, logDir, hadoopConf) } } - /** Check if the log manager is enabled. This is only used for testing purposes. */ - private[streaming] def isLogManagerEnabled: Boolean = logManagerOption.nonEmpty + /** Check if the write ahead log is enabled. This is only used for testing purposes. */ + private[streaming] def isWriteAheadLogEnabled: Boolean = writeAheadLogOption.nonEmpty } private[streaming] object ReceivedBlockTracker { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala index 52f08b9c9de68..de85f24dd988d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala @@ -32,6 +32,7 @@ case class ReceiverInfo( active: Boolean, location: String, lastErrorMessage: String = "", - lastError: String = "" + lastError: String = "", + lastErrorTime: Long = -1L ) { } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala index c4ead6f30a63d..f73f7e705ee0d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala @@ -20,6 +20,7 @@ package org.apache.spark.streaming.scheduler import scala.collection.mutable.{HashMap, SynchronizedMap} import scala.language.existentials +import org.apache.spark.streaming.util.WriteAheadLogUtils import org.apache.spark.{Logging, SerializableWritable, SparkEnv, SparkException} import org.apache.spark.rpc._ import org.apache.spark.streaming.{StreamingContext, Time} @@ -61,6 +62,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false ssc.sparkContext.hadoopConfiguration, receiverInputStreamIds, ssc.scheduler.clock, + ssc.isCheckpointPresent, Option(ssc.checkpointDir) ) private val listenerBus = ssc.scheduler.listenerBus @@ -125,7 +127,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false receivedBlockTracker.cleanupOldBatches(cleanupThreshTime, waitForCompletion = false) // Signal the receivers to delete old block data - if (ssc.conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false)) { + if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) { logInfo(s"Cleanup old received batch data: $cleanupThreshTime") receiverInfo.values.flatMap { info => Option(info.endpoint) } .foreach { _.send(CleanupOldBlocks(cleanupThreshTime)) } @@ -153,10 +155,16 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false private def deregisterReceiver(streamId: Int, message: String, error: String) { val newReceiverInfo = receiverInfo.get(streamId) match { case Some(oldInfo) => - oldInfo.copy(endpoint = null, active = false, lastErrorMessage = message, lastError = error) + val lastErrorTime = + if (error == null || error == "") -1 else ssc.scheduler.clock.getTimeMillis() + oldInfo.copy(endpoint = null, active = false, lastErrorMessage = message, + lastError = error, lastErrorTime = lastErrorTime) case None => logWarning("No prior receiver info") - ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message, lastError = error) + val lastErrorTime = + if (error == null || error == "") -1 else ssc.scheduler.clock.getTimeMillis() + ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message, + lastError = error, lastErrorTime = lastErrorTime) } receiverInfo -= streamId listenerBus.post(StreamingListenerReceiverStopped(newReceiverInfo)) @@ -180,7 +188,8 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false oldInfo.copy(lastErrorMessage = message, lastError = error) case None => logWarning("No prior receiver info") - ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message, lastError = error) + ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message, + lastError = error, lastErrorTime = ssc.scheduler.clock.getTimeMillis()) } receiverInfo(streamId) = newReceiverInfo listenerBus.post(StreamingListenerReceiverError(receiverInfo(streamId))) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala index df1c0a10704c3..2960b528d4c5e 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala @@ -19,8 +19,7 @@ package org.apache.spark.streaming.ui import scala.xml.Node -import org.apache.spark.streaming.scheduler.BatchInfo -import org.apache.spark.ui.UIUtils +import org.apache.spark.ui.{UIUtils => SparkUIUtils} private[ui] abstract class BatchTableBase(tableId: String) { @@ -31,18 +30,20 @@ private[ui] abstract class BatchTableBase(tableId: String) { Processing Time } - protected def baseRow(batch: BatchInfo): Seq[Node] = { + protected def baseRow(batch: BatchUIData): Seq[Node] = { val batchTime = batch.batchTime.milliseconds - val formattedBatchTime = UIUtils.formatDate(batch.batchTime.milliseconds) - val eventCount = batch.receivedBlockInfo.values.map { - receivers => receivers.map(_.numRecords).sum - }.sum + val formattedBatchTime = SparkUIUtils.formatDate(batch.batchTime.milliseconds) + val eventCount = batch.numRecords val schedulingDelay = batch.schedulingDelay - val formattedSchedulingDelay = schedulingDelay.map(UIUtils.formatDuration).getOrElse("-") + val formattedSchedulingDelay = schedulingDelay.map(SparkUIUtils.formatDuration).getOrElse("-") val processingTime = batch.processingDelay - val formattedProcessingTime = processingTime.map(UIUtils.formatDuration).getOrElse("-") + val formattedProcessingTime = processingTime.map(SparkUIUtils.formatDuration).getOrElse("-") - {formattedBatchTime} + + + {formattedBatchTime} + + {eventCount.toString} events {formattedSchedulingDelay} @@ -73,8 +74,9 @@ private[ui] abstract class BatchTableBase(tableId: String) { protected def renderRows: Seq[Node] } -private[ui] class ActiveBatchTable(runningBatches: Seq[BatchInfo], waitingBatches: Seq[BatchInfo]) - extends BatchTableBase("active-batches-table") { +private[ui] class ActiveBatchTable( + runningBatches: Seq[BatchUIData], + waitingBatches: Seq[BatchUIData]) extends BatchTableBase("active-batches-table") { override protected def columns: Seq[Node] = super.columns ++ Status @@ -85,16 +87,16 @@ private[ui] class ActiveBatchTable(runningBatches: Seq[BatchInfo], waitingBatche runningBatches.flatMap(batch => {runningBatchRow(batch)}) } - private def runningBatchRow(batch: BatchInfo): Seq[Node] = { + private def runningBatchRow(batch: BatchUIData): Seq[Node] = { baseRow(batch) ++ processing } - private def waitingBatchRow(batch: BatchInfo): Seq[Node] = { + private def waitingBatchRow(batch: BatchUIData): Seq[Node] = { baseRow(batch) ++ queued } } -private[ui] class CompletedBatchTable(batches: Seq[BatchInfo]) +private[ui] class CompletedBatchTable(batches: Seq[BatchUIData]) extends BatchTableBase("completed-batches-table") { override protected def columns: Seq[Node] = super.columns ++ Total Delay @@ -103,9 +105,9 @@ private[ui] class CompletedBatchTable(batches: Seq[BatchInfo]) batches.flatMap(batch => {completedBatchRow(batch)}) } - private def completedBatchRow(batch: BatchInfo): Seq[Node] = { + private def completedBatchRow(batch: BatchUIData): Seq[Node] = { val totalDelay = batch.totalDelay - val formattedTotalDelay = totalDelay.map(UIUtils.formatDuration).getOrElse("-") + val formattedTotalDelay = totalDelay.map(SparkUIUtils.formatDuration).getOrElse("-") baseRow(batch) ++ {formattedTotalDelay} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala new file mode 100644 index 0000000000000..3f1cab69068dc --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala @@ -0,0 +1,264 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.ui + +import javax.servlet.http.HttpServletRequest + +import scala.xml.{NodeSeq, Node} + +import org.apache.commons.lang3.StringEscapeUtils + +import org.apache.spark.streaming.Time +import org.apache.spark.ui.{UIUtils => SparkUIUtils, WebUIPage} +import org.apache.spark.streaming.ui.StreamingJobProgressListener.{SparkJobId, OutputOpId} +import org.apache.spark.ui.jobs.UIData.JobUIData + + +private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") { + private val streamingListener = parent.listener + private val sparkListener = parent.ssc.sc.jobProgressListener + + private def columns: Seq[Node] = { + Output Op Id + Description + Duration + Job Id + Duration + Stages: Succeeded/Total + Tasks (for all stages): Succeeded/Total + Error + } + + /** + * Generate a row for a Spark Job. Because duplicated output op infos needs to be collapsed into + * one cell, we use "rowspan" for the first row of a output op. + */ + def generateJobRow( + outputOpId: OutputOpId, + formattedOutputOpDuration: String, + numSparkJobRowsInOutputOp: Int, + isFirstRow: Boolean, + sparkJob: JobUIData): Seq[Node] = { + val lastStageInfo = Option(sparkJob.stageIds) + .filter(_.nonEmpty) + .flatMap { ids => sparkListener.stageIdToInfo.get(ids.max) } + val lastStageData = lastStageInfo.flatMap { s => + sparkListener.stageIdToData.get((s.stageId, s.attemptId)) + } + + val lastStageName = lastStageInfo.map(_.name).getOrElse("(Unknown Stage Name)") + val lastStageDescription = lastStageData.flatMap(_.description).getOrElse("") + val duration: Option[Long] = { + sparkJob.submissionTime.map { start => + val end = sparkJob.completionTime.getOrElse(System.currentTimeMillis()) + end - start + } + } + val lastFailureReason = + sparkJob.stageIds.sorted.reverse.flatMap(sparkListener.stageIdToInfo.get). + dropWhile(_.failureReason == None).take(1). // get the first info that contains failure + flatMap(info => info.failureReason).headOption.getOrElse("") + val formattedDuration = duration.map(d => SparkUIUtils.formatDuration(d)).getOrElse("-") + val detailUrl = s"${SparkUIUtils.prependBaseUri(parent.basePath)}/jobs/job?id=${sparkJob.jobId}" + + // In the first row, output op id and its information needs to be shown. In other rows, these + // cells will be taken up due to "rowspan". + // scalastyle:off + val prefixCells = + if (isFirstRow) { + {outputOpId.toString} + + + {lastStageDescription} + {lastStageName} + + {formattedOutputOpDuration} + } else { + Nil + } + // scalastyle:on + + + {prefixCells} + + + {sparkJob.jobId}{sparkJob.jobGroup.map(id => s"($id)").getOrElse("")} + + + + {formattedDuration} + + + {sparkJob.completedStageIndices.size}/{sparkJob.stageIds.size - sparkJob.numSkippedStages} + {if (sparkJob.numFailedStages > 0) s"(${sparkJob.numFailedStages} failed)"} + {if (sparkJob.numSkippedStages > 0) s"(${sparkJob.numSkippedStages} skipped)"} + + + { + SparkUIUtils.makeProgressBar( + started = sparkJob.numActiveTasks, + completed = sparkJob.numCompletedTasks, + failed = sparkJob.numFailedTasks, + skipped = sparkJob.numSkippedTasks, + total = sparkJob.numTasks - sparkJob.numSkippedTasks) + } + + {failureReasonCell(lastFailureReason)} + + } + + private def generateOutputOpIdRow( + outputOpId: OutputOpId, sparkJobs: Seq[JobUIData]): Seq[Node] = { + val sparkjobDurations = sparkJobs.map(sparkJob => { + sparkJob.submissionTime.map { start => + val end = sparkJob.completionTime.getOrElse(System.currentTimeMillis()) + end - start + } + }) + val formattedOutputOpDuration = + if (sparkjobDurations.exists(_ == None)) { + // If any job does not finish, set "formattedOutputOpDuration" to "-" + "-" + } else { + SparkUIUtils.formatDuration(sparkjobDurations.flatMap(x => x).sum) + } + generateJobRow(outputOpId, formattedOutputOpDuration, sparkJobs.size, true, sparkJobs.head) ++ + sparkJobs.tail.map { sparkJob => + generateJobRow(outputOpId, formattedOutputOpDuration, sparkJobs.size, false, sparkJob) + }.flatMap(x => x) + } + + private def failureReasonCell(failureReason: String): Seq[Node] = { + val isMultiline = failureReason.indexOf('\n') >= 0 + // Display the first line by default + val failureReasonSummary = StringEscapeUtils.escapeHtml4( + if (isMultiline) { + failureReason.substring(0, failureReason.indexOf('\n')) + } else { + failureReason + }) + val details = if (isMultiline) { + // scalastyle:off + + +details + ++ + + // scalastyle:on + } else { + "" + } + {failureReasonSummary}{details} + } + + private def getJobData(sparkJobId: SparkJobId): Option[JobUIData] = { + sparkListener.activeJobs.get(sparkJobId).orElse { + sparkListener.completedJobs.find(_.jobId == sparkJobId).orElse { + sparkListener.failedJobs.find(_.jobId == sparkJobId) + } + } + } + + /** + * Generate the job table for the batch. + */ + private def generateJobTable(batchUIData: BatchUIData): Seq[Node] = { + val outputOpIdToSparkJobIds = batchUIData.outputOpIdSparkJobIdPairs.groupBy(_.outputOpId).toSeq. + sortBy(_._1). // sorted by OutputOpId + map { case (outputOpId, outputOpIdAndSparkJobIds) => + // sort SparkJobIds for each OutputOpId + (outputOpId, outputOpIdAndSparkJobIds.map(_.sparkJobId).sorted) + } + sparkListener.synchronized { + val outputOpIdWithJobs: Seq[(OutputOpId, Seq[JobUIData])] = + outputOpIdToSparkJobIds.map { case (outputOpId, sparkJobIds) => + // Filter out spark Job ids that don't exist in sparkListener + (outputOpId, sparkJobIds.flatMap(getJobData)) + } + + + + {columns} + + + { + outputOpIdWithJobs.map { + case (outputOpId, jobs) => generateOutputOpIdRow(outputOpId, jobs) + } + } + +
+ } + } + + def render(request: HttpServletRequest): Seq[Node] = { + val batchTime = Option(request.getParameter("id")).map(id => Time(id.toLong)).getOrElse { + throw new IllegalArgumentException(s"Missing id parameter") + } + val formattedBatchTime = SparkUIUtils.formatDate(batchTime.milliseconds) + + val batchUIData = streamingListener.getBatchUIData(batchTime).getOrElse { + throw new IllegalArgumentException(s"Batch $formattedBatchTime does not exist") + } + + val formattedSchedulingDelay = + batchUIData.schedulingDelay.map(SparkUIUtils.formatDuration).getOrElse("-") + val formattedProcessingTime = + batchUIData.processingDelay.map(SparkUIUtils.formatDuration).getOrElse("-") + val formattedTotalDelay = batchUIData.totalDelay.map(SparkUIUtils.formatDuration).getOrElse("-") + + val summary: NodeSeq = +
+
    +
  • + Batch Duration: + {SparkUIUtils.formatDuration(streamingListener.batchDuration)} +
    • +
  • + Input data size: + {batchUIData.numRecords} records +
    • +
  • + Scheduling delay: + {formattedSchedulingDelay} +
    • +
  • + Processing time: + {formattedProcessingTime} +
    • +
  • + Total delay: + {formattedTotalDelay} +
    • +
+
+ + val jobTable = + if (batchUIData.outputOpIdSparkJobIdPairs.isEmpty) { +
Cannot find any job for Batch {formattedBatchTime}.
+ } else { + generateJobTable(batchUIData) + } + + val content = summary ++ jobTable + + SparkUIUtils.headerSparkPage(s"Details of batch at $formattedBatchTime", content, parent) + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala new file mode 100644 index 0000000000000..a5514dfd71c9f --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala @@ -0,0 +1,75 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package org.apache.spark.streaming.ui + +import org.apache.spark.streaming.Time +import org.apache.spark.streaming.scheduler.BatchInfo +import org.apache.spark.streaming.ui.StreamingJobProgressListener._ + +private[ui] case class OutputOpIdAndSparkJobId(outputOpId: OutputOpId, sparkJobId: SparkJobId) + +private[ui] case class BatchUIData( + val batchTime: Time, + val streamIdToNumRecords: Map[Int, Long], + val submissionTime: Long, + val processingStartTime: Option[Long], + val processingEndTime: Option[Long], + var outputOpIdSparkJobIdPairs: Seq[OutputOpIdAndSparkJobId] = Seq.empty) { + + /** + * Time taken for the first job of this batch to start processing from the time this batch + * was submitted to the streaming scheduler. Essentially, it is + * `processingStartTime` - `submissionTime`. + */ + def schedulingDelay: Option[Long] = processingStartTime.map(_ - submissionTime) + + /** + * Time taken for the all jobs of this batch to finish processing from the time they started + * processing. Essentially, it is `processingEndTime` - `processingStartTime`. + */ + def processingDelay: Option[Long] = { + for (start <- processingStartTime; + end <- processingEndTime) + yield end - start + } + + /** + * Time taken for all the jobs of this batch to finish processing from the time they + * were submitted. Essentially, it is `processingDelay` + `schedulingDelay`. + */ + def totalDelay: Option[Long] = processingEndTime.map(_ - submissionTime) + + /** + * The number of recorders received by the receivers in this batch. + */ + def numRecords: Long = streamIdToNumRecords.values.sum +} + +private[ui] object BatchUIData { + + def apply(batchInfo: BatchInfo): BatchUIData = { + new BatchUIData( + batchInfo.batchTime, + batchInfo.streamIdToNumRecords, + batchInfo.submissionTime, + batchInfo.processingStartTime, + batchInfo.processingEndTime + ) + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala index be1e8686cf9fa..68e8ce98945e0 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala @@ -17,29 +17,57 @@ package org.apache.spark.streaming.ui -import scala.collection.mutable.{Queue, HashMap} +import java.util.LinkedHashMap +import java.util.{Map => JMap} +import java.util.Properties +import scala.collection.mutable.{ArrayBuffer, Queue, HashMap, SynchronizedBuffer} + +import org.apache.spark.scheduler._ import org.apache.spark.streaming.{Time, StreamingContext} import org.apache.spark.streaming.scheduler._ import org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted import org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted -import org.apache.spark.streaming.scheduler.BatchInfo import org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted -import org.apache.spark.util.Distribution private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) - extends StreamingListener { + extends StreamingListener with SparkListener { - private val waitingBatchInfos = new HashMap[Time, BatchInfo] - private val runningBatchInfos = new HashMap[Time, BatchInfo] - private val completedBatchInfos = new Queue[BatchInfo] - private val batchInfoLimit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) + private val waitingBatchUIData = new HashMap[Time, BatchUIData] + private val runningBatchUIData = new HashMap[Time, BatchUIData] + private val completedBatchUIData = new Queue[BatchUIData] + private val batchUIDataLimit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000) private var totalCompletedBatches = 0L private var totalReceivedRecords = 0L private var totalProcessedRecords = 0L private val receiverInfos = new HashMap[Int, ReceiverInfo] + // Because onJobStart and onBatchXXX messages are processed in different threads, + // we may not be able to get the corresponding BatchUIData when receiving onJobStart. So here we + // cannot use a map of (Time, BatchUIData). + private[ui] val batchTimeToOutputOpIdSparkJobIdPair = + new LinkedHashMap[Time, SynchronizedBuffer[OutputOpIdAndSparkJobId]] { + override def removeEldestEntry( + p1: JMap.Entry[Time, SynchronizedBuffer[OutputOpIdAndSparkJobId]]): Boolean = { + // If a lot of "onBatchCompleted"s happen before "onJobStart" (image if + // SparkContext.listenerBus is very slow), "batchTimeToOutputOpIdToSparkJobIds" + // may add some information for a removed batch when processing "onJobStart". It will be a + // memory leak. + // + // To avoid the memory leak, we control the size of "batchTimeToOutputOpIdToSparkJobIds" and + // evict the eldest one. + // + // Note: if "onJobStart" happens before "onBatchSubmitted", the size of + // "batchTimeToOutputOpIdToSparkJobIds" may be greater than the number of the retained + // batches temporarily, so here we use "10" to handle such case. This is not a perfect + // solution, but at least it can handle most of cases. + size() > + waitingBatchUIData.size + runningBatchUIData.size + completedBatchUIData.size + 10 + } + } + + val batchDuration = ssc.graph.batchDuration.milliseconds override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted) { @@ -62,35 +90,62 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted): Unit = { synchronized { - waitingBatchInfos(batchSubmitted.batchInfo.batchTime) = batchSubmitted.batchInfo + waitingBatchUIData(batchSubmitted.batchInfo.batchTime) = + BatchUIData(batchSubmitted.batchInfo) } } override def onBatchStarted(batchStarted: StreamingListenerBatchStarted): Unit = synchronized { - runningBatchInfos(batchStarted.batchInfo.batchTime) = batchStarted.batchInfo - waitingBatchInfos.remove(batchStarted.batchInfo.batchTime) + val batchUIData = BatchUIData(batchStarted.batchInfo) + runningBatchUIData(batchStarted.batchInfo.batchTime) = BatchUIData(batchStarted.batchInfo) + waitingBatchUIData.remove(batchStarted.batchInfo.batchTime) - batchStarted.batchInfo.receivedBlockInfo.foreach { case (_, infos) => - totalReceivedRecords += infos.map(_.numRecords).sum - } + totalReceivedRecords += batchUIData.numRecords } override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted): Unit = { synchronized { - waitingBatchInfos.remove(batchCompleted.batchInfo.batchTime) - runningBatchInfos.remove(batchCompleted.batchInfo.batchTime) - completedBatchInfos.enqueue(batchCompleted.batchInfo) - if (completedBatchInfos.size > batchInfoLimit) completedBatchInfos.dequeue() + waitingBatchUIData.remove(batchCompleted.batchInfo.batchTime) + runningBatchUIData.remove(batchCompleted.batchInfo.batchTime) + val batchUIData = BatchUIData(batchCompleted.batchInfo) + completedBatchUIData.enqueue(batchUIData) + if (completedBatchUIData.size > batchUIDataLimit) { + val removedBatch = completedBatchUIData.dequeue() + batchTimeToOutputOpIdSparkJobIdPair.remove(removedBatch.batchTime) + } totalCompletedBatches += 1L - batchCompleted.batchInfo.receivedBlockInfo.foreach { case (_, infos) => - totalProcessedRecords += infos.map(_.numRecords).sum + totalProcessedRecords += batchUIData.numRecords + } + } + + override def onJobStart(jobStart: SparkListenerJobStart): Unit = synchronized { + getBatchTimeAndOutputOpId(jobStart.properties).foreach { case (batchTime, outputOpId) => + var outputOpIdToSparkJobIds = batchTimeToOutputOpIdSparkJobIdPair.get(batchTime) + if (outputOpIdToSparkJobIds == null) { + outputOpIdToSparkJobIds = + new ArrayBuffer[OutputOpIdAndSparkJobId]() + with SynchronizedBuffer[OutputOpIdAndSparkJobId] + batchTimeToOutputOpIdSparkJobIdPair.put(batchTime, outputOpIdToSparkJobIds) } + outputOpIdToSparkJobIds += OutputOpIdAndSparkJobId(outputOpId, jobStart.jobId) + } + } + + private def getBatchTimeAndOutputOpId(properties: Properties): Option[(Time, Int)] = { + val batchTime = properties.getProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY) + if (batchTime == null) { + // Not submitted from JobScheduler + None + } else { + val outputOpId = properties.getProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY) + assert(outputOpId != null) + Some(Time(batchTime.toLong) -> outputOpId.toInt) } } def numReceivers: Int = synchronized { - ssc.graph.getReceiverInputStreams().size + receiverInfos.size } def numTotalCompletedBatches: Long = synchronized { @@ -106,57 +161,57 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } def numUnprocessedBatches: Long = synchronized { - waitingBatchInfos.size + runningBatchInfos.size - } - - def waitingBatches: Seq[BatchInfo] = synchronized { - waitingBatchInfos.values.toSeq + waitingBatchUIData.size + runningBatchUIData.size } - def runningBatches: Seq[BatchInfo] = synchronized { - runningBatchInfos.values.toSeq + def waitingBatches: Seq[BatchUIData] = synchronized { + waitingBatchUIData.values.toSeq } - def retainedCompletedBatches: Seq[BatchInfo] = synchronized { - completedBatchInfos.toSeq + def runningBatches: Seq[BatchUIData] = synchronized { + runningBatchUIData.values.toSeq } - def processingDelayDistribution: Option[Distribution] = synchronized { - extractDistribution(_.processingDelay) + def retainedCompletedBatches: Seq[BatchUIData] = synchronized { + completedBatchUIData.toSeq } - def schedulingDelayDistribution: Option[Distribution] = synchronized { - extractDistribution(_.schedulingDelay) + def streamName(streamId: Int): Option[String] = { + ssc.graph.getInputStreamName(streamId) } - def totalDelayDistribution: Option[Distribution] = synchronized { - extractDistribution(_.totalDelay) - } + /** + * Return all InputDStream Ids + */ + def streamIds: Seq[Int] = ssc.graph.getInputStreams().map(_.id) - def receivedRecordsDistributions: Map[Int, Option[Distribution]] = synchronized { - val latestBatchInfos = retainedBatches.reverse.take(batchInfoLimit) - val latestBlockInfos = latestBatchInfos.map(_.receivedBlockInfo) - (0 until numReceivers).map { receiverId => - val blockInfoOfParticularReceiver = latestBlockInfos.map { batchInfo => - batchInfo.get(receiverId).getOrElse(Array.empty) - } - val recordsOfParticularReceiver = blockInfoOfParticularReceiver.map { blockInfo => - // calculate records per second for each batch - blockInfo.map(_.numRecords).sum.toDouble * 1000 / batchDuration + /** + * Return all of the event rates for each InputDStream in each batch. The key of the return value + * is the stream id, and the value is a sequence of batch time with its event rate. + */ + def receivedEventRateWithBatchTime: Map[Int, Seq[(Long, Double)]] = synchronized { + val _retainedBatches = retainedBatches + val latestBatches = _retainedBatches.map { batchUIData => + (batchUIData.batchTime.milliseconds, batchUIData.streamIdToNumRecords) + } + streamIds.map { streamId => + val eventRates = latestBatches.map { + case (batchTime, streamIdToNumRecords) => + val numRecords = streamIdToNumRecords.getOrElse(streamId, 0L) + (batchTime, numRecords * 1000.0 / batchDuration) } - val distributionOption = Distribution(recordsOfParticularReceiver) - (receiverId, distributionOption) + (streamId, eventRates) }.toMap } def lastReceivedBatchRecords: Map[Int, Long] = synchronized { - val lastReceivedBlockInfoOption = lastReceivedBatch.map(_.receivedBlockInfo) + val lastReceivedBlockInfoOption = lastReceivedBatch.map(_.streamIdToNumRecords) lastReceivedBlockInfoOption.map { lastReceivedBlockInfo => - (0 until numReceivers).map { receiverId => - (receiverId, lastReceivedBlockInfo(receiverId).map(_.numRecords).sum) + streamIds.map { streamId => + (streamId, lastReceivedBlockInfo.getOrElse(streamId, 0L)) }.toMap }.getOrElse { - (0 until numReceivers).map(receiverId => (receiverId, 0L)).toMap + streamIds.map(streamId => (streamId, 0L)).toMap } } @@ -164,20 +219,35 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) receiverInfos.get(receiverId) } - def lastCompletedBatch: Option[BatchInfo] = synchronized { - completedBatchInfos.sortBy(_.batchTime)(Time.ordering).lastOption + def lastCompletedBatch: Option[BatchUIData] = synchronized { + completedBatchUIData.sortBy(_.batchTime)(Time.ordering).lastOption } - def lastReceivedBatch: Option[BatchInfo] = synchronized { + def lastReceivedBatch: Option[BatchUIData] = synchronized { retainedBatches.lastOption } - private def retainedBatches: Seq[BatchInfo] = { - (waitingBatchInfos.values.toSeq ++ - runningBatchInfos.values.toSeq ++ completedBatchInfos).sortBy(_.batchTime)(Time.ordering) + def retainedBatches: Seq[BatchUIData] = synchronized { + (waitingBatchUIData.values.toSeq ++ + runningBatchUIData.values.toSeq ++ completedBatchUIData).sortBy(_.batchTime)(Time.ordering) } - private def extractDistribution(getMetric: BatchInfo => Option[Long]): Option[Distribution] = { - Distribution(completedBatchInfos.flatMap(getMetric(_)).map(_.toDouble)) + def getBatchUIData(batchTime: Time): Option[BatchUIData] = synchronized { + val batchUIData = waitingBatchUIData.get(batchTime).orElse { + runningBatchUIData.get(batchTime).orElse { + completedBatchUIData.find(batch => batch.batchTime == batchTime) + } + } + batchUIData.foreach { _batchUIData => + val outputOpIdToSparkJobIds = + Option(batchTimeToOutputOpIdSparkJobIdPair.get(batchTime)).getOrElse(Seq.empty) + _batchUIData.outputOpIdSparkJobIdPairs = outputOpIdToSparkJobIds + } + batchUIData } } + +private[streaming] object StreamingJobProgressListener { + type SparkJobId = Int + type OutputOpId = Int +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala index 07fa285642eec..ecbebe5c6c97b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala @@ -17,181 +17,454 @@ package org.apache.spark.streaming.ui -import java.util.Calendar +import java.text.SimpleDateFormat +import java.util.Date +import java.util.concurrent.TimeUnit import javax.servlet.http.HttpServletRequest -import scala.xml.Node +import scala.collection.mutable.ArrayBuffer +import scala.xml.{Node, Unparsed} import org.apache.spark.Logging import org.apache.spark.ui._ -import org.apache.spark.ui.UIUtils._ -import org.apache.spark.util.Distribution +import org.apache.spark.ui.{UIUtils => SparkUIUtils} + +/** + * A helper class to generate JavaScript and HTML for both timeline and histogram graphs. + * + * @param timelineDivId the timeline `id` used in the html `div` tag + * @param histogramDivId the timeline `id` used in the html `div` tag + * @param data the data for the graph + * @param minX the min value of X axis + * @param maxX the max value of X axis + * @param minY the min value of Y axis + * @param maxY the max value of Y axis + * @param unitY the unit of Y axis + * @param batchInterval if `batchInterval` is not None, we will draw a line for `batchInterval` in + * the graph + */ +private[ui] class GraphUIData( + timelineDivId: String, + histogramDivId: String, + data: Seq[(Long, Double)], + minX: Long, + maxX: Long, + minY: Double, + maxY: Double, + unitY: String, + batchInterval: Option[Double] = None) { + + private var dataJavaScriptName: String = _ + + def generateDataJs(jsCollector: JsCollector): Unit = { + val jsForData = data.map { case (x, y) => + s"""{"x": $x, "y": $y}""" + }.mkString("[", ",", "]") + dataJavaScriptName = jsCollector.nextVariableName + jsCollector.addPreparedStatement(s"var $dataJavaScriptName = $jsForData;") + } + + def generateTimelineHtml(jsCollector: JsCollector): Seq[Node] = { + jsCollector.addPreparedStatement(s"registerTimeline($minY, $maxY);") + if (batchInterval.isDefined) { + jsCollector.addStatement( + "drawTimeline(" + + s"'#$timelineDivId', $dataJavaScriptName, $minX, $maxX, $minY, $maxY, '$unitY'," + + s" ${batchInterval.get}" + + ");") + } else { + jsCollector.addStatement( + s"drawTimeline('#$timelineDivId', $dataJavaScriptName, $minX, $maxX, $minY, $maxY," + + s" '$unitY');") + } +
+ } + + def generateHistogramHtml(jsCollector: JsCollector): Seq[Node] = { + val histogramData = s"$dataJavaScriptName.map(function(d) { return d.y; })" + jsCollector.addPreparedStatement(s"registerHistogram($histogramData, $minY, $maxY);") + if (batchInterval.isDefined) { + jsCollector.addStatement( + "drawHistogram(" + + s"'#$histogramDivId', $histogramData, $minY, $maxY, '$unitY', ${batchInterval.get}" + + ");") + } else { + jsCollector.addStatement( + s"drawHistogram('#$histogramDivId', $histogramData, $minY, $maxY, '$unitY');") + } +
+ } +} + +/** + * A helper class for "scheduling delay", "processing time" and "total delay" to generate data that + * will be used in the timeline and histogram graphs. + * + * @param data (batchTime, milliseconds). "milliseconds" is something like "processing time". + */ +private[ui] class MillisecondsStatUIData(data: Seq[(Long, Long)]) { + + /** + * Converting the original data as per `unit`. + */ + def timelineData(unit: TimeUnit): Seq[(Long, Double)] = + data.map(x => x._1 -> UIUtils.convertToTimeUnit(x._2, unit)) + + /** + * Converting the original data as per `unit`. + */ + def histogramData(unit: TimeUnit): Seq[Double] = + data.map(x => UIUtils.convertToTimeUnit(x._2, unit)) + + val avg: Option[Long] = if (data.isEmpty) None else Some(data.map(_._2).sum / data.size) + + val formattedAvg: String = StreamingPage.formatDurationOption(avg) + + val max: Option[Long] = if (data.isEmpty) None else Some(data.map(_._2).max) +} + +/** + * A helper class for "input rate" to generate data that will be used in the timeline and histogram + * graphs. + * + * @param data (batchTime, event-rate). + */ +private[ui] class EventRateUIData(val data: Seq[(Long, Double)]) { + + val avg: Option[Double] = if (data.isEmpty) None else Some(data.map(_._2).sum / data.size) + + val formattedAvg: String = avg.map(_.formatted("%.2f")).getOrElse("-") + + val max: Option[Double] = if (data.isEmpty) None else Some(data.map(_._2).max) +} /** Page for Spark Web UI that shows statistics of a streaming job */ private[ui] class StreamingPage(parent: StreamingTab) extends WebUIPage("") with Logging { + import StreamingPage._ + private val listener = parent.listener private val startTime = System.currentTimeMillis() - private val emptyCell = "-" /** Render the page */ def render(request: HttpServletRequest): Seq[Node] = { - val content = listener.synchronized { - generateBasicStats() ++

++ -

Statistics over last {listener.retainedCompletedBatches.size} processed batches

++ - generateReceiverStats() ++ - generateBatchStatsTable() ++ - generateBatchListTables() - } - UIUtils.headerSparkPage("Streaming", content, parent, Some(5000)) + val resources = generateLoadResources() + val basicInfo = generateBasicInfo() + val content = resources ++ + basicInfo ++ + listener.synchronized { + generateStatTable() ++ + generateBatchListTables() + } + SparkUIUtils.headerSparkPage("Streaming Statistics", content, parent, Some(5000)) } - /** Generate basic stats of the streaming program */ - private def generateBasicStats(): Seq[Node] = { - val timeSinceStart = System.currentTimeMillis() - startTime + /** + * Generate html that will load css/js files for StreamingPage + */ + private def generateLoadResources(): Seq[Node] = { // scalastyle:off -
    -
  • - Started at: {UIUtils.formatDate(startTime)} -
    • -
  • - Time since start: {formatDurationVerbose(timeSinceStart)} -
    • -
  • - Network receivers: {listener.numReceivers} -
    • -
  • - Batch interval: {formatDurationVerbose(listener.batchDuration)} -
    • -
  • - Completed batches: {listener.numTotalCompletedBatches} -
    • -
  • - Active batches: {listener.numUnprocessedBatches} -
    • -
  • - Received events: {listener.numTotalReceivedRecords} -
    • -
  • - Processed events: {listener.numTotalProcessedRecords} -
    • -
+ + + // scalastyle:on } - /** Generate stats of data received by the receivers in the streaming program */ - private def generateReceiverStats(): Seq[Node] = { - val receivedRecordDistributions = listener.receivedRecordsDistributions - val lastBatchReceivedRecord = listener.lastReceivedBatchRecords - val table = if (receivedRecordDistributions.size > 0) { - val headerRow = Seq( - "Receiver", - "Status", - "Location", - "Events in last batch\n[" + formatDate(Calendar.getInstance().getTime()) + "]", - "Minimum rate\n[events/sec]", - "Median rate\n[events/sec]", - "Maximum rate\n[events/sec]", - "Last Error" - ) - val dataRows = (0 until listener.numReceivers).map { receiverId => - val receiverInfo = listener.receiverInfo(receiverId) - val receiverName = receiverInfo.map(_.name).getOrElse(s"Receiver-$receiverId") - val receiverActive = receiverInfo.map { info => - if (info.active) "ACTIVE" else "INACTIVE" - }.getOrElse(emptyCell) - val receiverLocation = receiverInfo.map(_.location).getOrElse(emptyCell) - val receiverLastBatchRecords = formatNumber(lastBatchReceivedRecord(receiverId)) - val receivedRecordStats = receivedRecordDistributions(receiverId).map { d => - d.getQuantiles(Seq(0.0, 0.5, 1.0)).map(r => formatNumber(r.toLong)) - }.getOrElse { - Seq(emptyCell, emptyCell, emptyCell, emptyCell, emptyCell) - } - val receiverLastError = listener.receiverInfo(receiverId).map { info => - val msg = s"${info.lastErrorMessage} - ${info.lastError}" - if (msg.size > 100) msg.take(97) + "..." else msg - }.getOrElse(emptyCell) - Seq(receiverName, receiverActive, receiverLocation, receiverLastBatchRecords) ++ - receivedRecordStats ++ Seq(receiverLastError) - } - Some(listingTable(headerRow, dataRows)) - } else { - None - } + /** Generate basic information of the streaming program */ + private def generateBasicInfo(): Seq[Node] = { + val timeSinceStart = System.currentTimeMillis() - startTime +
Running batches of + + {SparkUIUtils.formatDurationVerbose(listener.batchDuration)} + + for + + {SparkUIUtils.formatDurationVerbose(timeSinceStart)} + + since + + {SparkUIUtils.formatDate(startTime)} + +
+
+ } - val content = -
Receiver Statistics
++ -
{table.getOrElse("No receivers")}
+ /** + * Generate a global "timeFormat" dictionary in the JavaScript to store the time and its formatted + * string. Because we cannot specify a timezone in JavaScript, to make sure the server and client + * use the same timezone, we use the "timeFormat" dictionary to format all time values used in the + * graphs. + * + * @param times all time values that will be used in the graphs. + */ + private def generateTimeMap(times: Seq[Long]): Seq[Node] = { + val dateFormat = new SimpleDateFormat("HH:mm:ss") + val js = "var timeFormat = {};\n" + times.map { time => + val formattedTime = dateFormat.format(new Date(time)) + s"timeFormat[$time] = '$formattedTime';" + }.mkString("\n") - content + } - /** Generate stats of batch jobs of the streaming program */ - private def generateBatchStatsTable(): Seq[Node] = { - val numBatches = listener.retainedCompletedBatches.size - val lastCompletedBatch = listener.lastCompletedBatch - val table = if (numBatches > 0) { - val processingDelayQuantilesRow = { - Seq( - "Processing Time", - formatDurationOption(lastCompletedBatch.flatMap(_.processingDelay)) - ) ++ getQuantiles(listener.processingDelayDistribution) - } - val schedulingDelayQuantilesRow = { - Seq( - "Scheduling Delay", - formatDurationOption(lastCompletedBatch.flatMap(_.schedulingDelay)) - ) ++ getQuantiles(listener.schedulingDelayDistribution) - } - val totalDelayQuantilesRow = { - Seq( - "Total Delay", - formatDurationOption(lastCompletedBatch.flatMap(_.totalDelay)) - ) ++ getQuantiles(listener.totalDelayDistribution) - } - val headerRow = Seq("Metric", "Last batch", "Minimum", "25th percentile", - "Median", "75th percentile", "Maximum") - val dataRows: Seq[Seq[String]] = Seq( - processingDelayQuantilesRow, - schedulingDelayQuantilesRow, - totalDelayQuantilesRow - ) - Some(listingTable(headerRow, dataRows)) - } else { - None - } + private def generateStatTable(): Seq[Node] = { + val batches = listener.retainedBatches - val content = -
Batch Processing Statistics
++ -
-
    - {table.getOrElse("No statistics have been generated yet.")} -
-
+ val batchTimes = batches.map(_.batchTime.milliseconds) + val minBatchTime = if (batchTimes.isEmpty) startTime else batchTimes.min + val maxBatchTime = if (batchTimes.isEmpty) startTime else batchTimes.max - content - } + val eventRateForAllStreams = new EventRateUIData(batches.map { batchInfo => + (batchInfo.batchTime.milliseconds, batchInfo.numRecords * 1000.0 / listener.batchDuration) + }) + val schedulingDelay = new MillisecondsStatUIData(batches.flatMap { batchInfo => + batchInfo.schedulingDelay.map(batchInfo.batchTime.milliseconds -> _) + }) + val processingTime = new MillisecondsStatUIData(batches.flatMap { batchInfo => + batchInfo.processingDelay.map(batchInfo.batchTime.milliseconds -> _) + }) + val totalDelay = new MillisecondsStatUIData(batches.flatMap { batchInfo => + batchInfo.totalDelay.map(batchInfo.batchTime.milliseconds -> _) + }) - /** - * Returns a human-readable string representing a duration such as "5 second 35 ms" - */ - private def formatDurationOption(msOption: Option[Long]): String = { - msOption.map(formatDurationVerbose).getOrElse(emptyCell) + // Use the max value of "schedulingDelay", "processingTime", and "totalDelay" to make the + // Y axis ranges same. + val _maxTime = + (for (m1 <- schedulingDelay.max; m2 <- processingTime.max; m3 <- totalDelay.max) yield + m1 max m2 max m3).getOrElse(0L) + // Should start at 0 + val minTime = 0L + val (maxTime, normalizedUnit) = UIUtils.normalizeDuration(_maxTime) + val formattedUnit = UIUtils.shortTimeUnitString(normalizedUnit) + + // Use the max input rate for all InputDStreams' graphs to make the Y axis ranges same. + // If it's not an integral number, just use its ceil integral number. + val maxEventRate = eventRateForAllStreams.max.map(_.ceil.toLong).getOrElse(0L) + val minEventRate = 0L + + // JavaScript to show/hide the InputDStreams sub table. + val triangleJs = + s"""$$('#inputs-table').toggle('collapsed'); + |var status = false; + |if ($$(this).html() == '$BLACK_RIGHT_TRIANGLE_HTML') { + |$$(this).html('$BLACK_DOWN_TRIANGLE_HTML');status = true;} + |else {$$(this).html('$BLACK_RIGHT_TRIANGLE_HTML');status = false;} + |window.history.pushState('', + | document.title, window.location.pathname + '?show-streams-detail=' + status);""" + .stripMargin.replaceAll("\\n", "") // it must be only one single line + + val batchInterval = UIUtils.convertToTimeUnit(listener.batchDuration, normalizedUnit) + + val jsCollector = new JsCollector + + val graphUIDataForEventRateOfAllStreams = + new GraphUIData( + "all-stream-events-timeline", + "all-stream-events-histogram", + eventRateForAllStreams.data, + minBatchTime, + maxBatchTime, + minEventRate, + maxEventRate, + "events/sec") + graphUIDataForEventRateOfAllStreams.generateDataJs(jsCollector) + + val graphUIDataForSchedulingDelay = + new GraphUIData( + "scheduling-delay-timeline", + "scheduling-delay-histogram", + schedulingDelay.timelineData(normalizedUnit), + minBatchTime, + maxBatchTime, + minTime, + maxTime, + formattedUnit) + graphUIDataForSchedulingDelay.generateDataJs(jsCollector) + + val graphUIDataForProcessingTime = + new GraphUIData( + "processing-time-timeline", + "processing-time-histogram", + processingTime.timelineData(normalizedUnit), + minBatchTime, + maxBatchTime, + minTime, + maxTime, + formattedUnit, Some(batchInterval)) + graphUIDataForProcessingTime.generateDataJs(jsCollector) + + val graphUIDataForTotalDelay = + new GraphUIData( + "total-delay-timeline", + "total-delay-histogram", + totalDelay.timelineData(normalizedUnit), + minBatchTime, + maxBatchTime, + minTime, + maxTime, + formattedUnit) + graphUIDataForTotalDelay.generateDataJs(jsCollector) + + // It's false before the user registers the first InputDStream + val hasStream = listener.streamIds.nonEmpty + + val numCompletedBatches = listener.retainedCompletedBatches.size + val numActiveBatches = batchTimes.length - numCompletedBatches + val table = + // scalastyle:off + + + + + + + + + + + + + + {if (hasStream) { + + + + }} + + + + + + + + + + + + + + + + +
Timelines (Last {batchTimes.length} batches, {numActiveBatches} active, {numCompletedBatches} completed)Histograms
+
+
+ {if (hasStream) { + {Unparsed(BLACK_RIGHT_TRIANGLE_HTML)} + }} + Input Rate +
+
Avg: {eventRateForAllStreams.formattedAvg} events/sec
+
+ 		{graphUIDataForEventRateOfAllStreams.generateTimelineHtml(jsCollector)}{graphUIDataForEventRateOfAllStreams.generateHistogramHtml(jsCollector)}
+
+
Scheduling Delay
+
Avg: {schedulingDelay.formattedAvg}
+
+ 		{graphUIDataForSchedulingDelay.generateTimelineHtml(jsCollector)}{graphUIDataForSchedulingDelay.generateHistogramHtml(jsCollector)}
+
+
Processing Time
+
Avg: {processingTime.formattedAvg}
+
+ 		{graphUIDataForProcessingTime.generateTimelineHtml(jsCollector)}{graphUIDataForProcessingTime.generateHistogramHtml(jsCollector)}
+
+
Total Delay
+
Avg: {totalDelay.formattedAvg}
+
+ 		{graphUIDataForTotalDelay.generateTimelineHtml(jsCollector)}{graphUIDataForTotalDelay.generateHistogramHtml(jsCollector)}
+ // scalastyle:on + + generateTimeMap(batchTimes) ++ table ++ jsCollector.toHtml } - /** Get quantiles for any time distribution */ - private def getQuantiles(timeDistributionOption: Option[Distribution]) = { - timeDistributionOption.get.getQuantiles().map { ms => formatDurationVerbose(ms.toLong) } + private def generateInputDStreamsTable( + jsCollector: JsCollector, + minX: Long, + maxX: Long, + minY: Double, + maxY: Double): Seq[Node] = { + val content = listener.receivedEventRateWithBatchTime.map { case (streamId, eventRates) => + generateInputDStreamRow(jsCollector, streamId, eventRates, minX, maxX, minY, maxY) + }.foldLeft[Seq[Node]](Nil)(_ ++ _) + + // scalastyle:off + + + + + + + + + + + + {content} + +
Status
Location
Last Error Time
Last Error Message
+ // scalastyle:on } - /** Generate HTML table from string data */ - private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = { - def generateDataRow(data: Seq[String]): Seq[Node] = { - {data.map(d => {d})} - } - UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true) + private def generateInputDStreamRow( + jsCollector: JsCollector, + streamId: Int, + eventRates: Seq[(Long, Double)], + minX: Long, + maxX: Long, + minY: Double, + maxY: Double): Seq[Node] = { + // If this is a ReceiverInputDStream, we need to show the receiver info. Or we only need the + // InputDStream name. + val receiverInfo = listener.receiverInfo(streamId) + val receiverName = receiverInfo.map(_.name). + orElse(listener.streamName(streamId)).getOrElse(s"Stream-$streamId") + val receiverActive = receiverInfo.map { info => + if (info.active) "ACTIVE" else "INACTIVE" + }.getOrElse(emptyCell) + val receiverLocation = receiverInfo.map(_.location).getOrElse(emptyCell) + val receiverLastError = receiverInfo.map { info => + val msg = s"${info.lastErrorMessage} - ${info.lastError}" + if (msg.size > 100) msg.take(97) + "..." else msg + }.getOrElse(emptyCell) + val receiverLastErrorTime = receiverInfo.map { + r => if (r.lastErrorTime < 0) "-" else SparkUIUtils.formatDate(r.lastErrorTime) + }.getOrElse(emptyCell) + val receivedRecords = new EventRateUIData(eventRates) + + val graphUIDataForEventRate = + new GraphUIData( + s"stream-$streamId-events-timeline", + s"stream-$streamId-events-histogram", + receivedRecords.data, + minX, + maxX, + minY, + maxY, + "events/sec") + graphUIDataForEventRate.generateDataJs(jsCollector) + + + +
+
{receiverName}
+
Avg: {receivedRecords.formattedAvg} events/sec
+
+ + {receiverActive} + {receiverLocation} + {receiverLastErrorTime} +
{receiverLastError}
+ + + + {graphUIDataForEventRate.generateTimelineHtml(jsCollector)} + + {graphUIDataForEventRate.generateHistogramHtml(jsCollector)} + } private def generateBatchListTables(): Seq[Node] = { @@ -216,3 +489,67 @@ private[ui] class StreamingPage(parent: StreamingTab) } } +private[ui] object StreamingPage { + val BLACK_RIGHT_TRIANGLE_HTML = "▶" + val BLACK_DOWN_TRIANGLE_HTML = "▼" + + val emptyCell = "-" + + /** + * Returns a human-readable string representing a duration such as "5 second 35 ms" + */ + def formatDurationOption(msOption: Option[Long]): String = { + msOption.map(SparkUIUtils.formatDurationVerbose).getOrElse(emptyCell) + } + +} + +/** + * A helper class that allows the user to add JavaScript statements which will be executed when the + * DOM has finished loading. + */ +private[ui] class JsCollector { + + private var variableId = 0 + + /** + * Return the next unused JavaScript variable name + */ + def nextVariableName: String = { + variableId += 1 + "v" + variableId + } + + /** + * JavaScript statements that will execute before `statements` + */ + private val preparedStatements = ArrayBuffer[String]() + + /** + * JavaScript statements that will execute after `preparedStatements` + */ + private val statements = ArrayBuffer[String]() + + def addPreparedStatement(js: String): Unit = { + preparedStatements += js + } + + def addStatement(js: String): Unit = { + statements += js + } + + /** + * Generate a html snippet that will execute all scripts when the DOM has finished loading. + */ + def toHtml: Seq[Node] = { + val js = + s""" + |$$(document).ready(function(){ + | ${preparedStatements.mkString("\n")} + | ${statements.mkString("\n")} + |});""".stripMargin + + + } +} + diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala index 9a860ea4a6c68..f307b54bb9630 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala @@ -27,15 +27,20 @@ import StreamingTab._ * Spark Web UI tab that shows statistics of a streaming job. * This assumes the given SparkContext has enabled its SparkUI. */ -private[spark] class StreamingTab(ssc: StreamingContext) +private[spark] class StreamingTab(val ssc: StreamingContext) extends SparkUITab(getSparkUI(ssc), "streaming") with Logging { val parent = getSparkUI(ssc) val listener = ssc.progressListener ssc.addStreamingListener(listener) + ssc.sc.addSparkListener(listener) attachPage(new StreamingPage(this)) - parent.attachTab(this) + attachPage(new BatchPage(this)) + + def attach() { + getSparkUI(ssc).attachTab(this) + } def detach() { getSparkUI(ssc).detachTab(this) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala new file mode 100644 index 0000000000000..c206f973b2c66 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala @@ -0,0 +1,74 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.ui + +import java.util.concurrent.TimeUnit + +object UIUtils { + + /** + * Return the short string for a `TimeUnit`. + */ + def shortTimeUnitString(unit: TimeUnit): String = unit match { + case TimeUnit.NANOSECONDS => "ns" + case TimeUnit.MICROSECONDS => "us" + case TimeUnit.MILLISECONDS => "ms" + case TimeUnit.SECONDS => "sec" + case TimeUnit.MINUTES => "min" + case TimeUnit.HOURS => "hrs" + case TimeUnit.DAYS => "days" + } + + /** + * Find the best `TimeUnit` for converting milliseconds to a friendly string. Return the value + * after converting, also with its TimeUnit. + */ + def normalizeDuration(milliseconds: Long): (Double, TimeUnit) = { + if (milliseconds < 1000) { + return (milliseconds, TimeUnit.MILLISECONDS) + } + val seconds = milliseconds.toDouble / 1000 + if (seconds < 60) { + return (seconds, TimeUnit.SECONDS) + } + val minutes = seconds / 60 + if (minutes < 60) { + return (minutes, TimeUnit.MINUTES) + } + val hours = minutes / 60 + if (hours < 24) { + return (hours, TimeUnit.HOURS) + } + val days = hours / 24 + (days, TimeUnit.DAYS) + } + + /** + * Convert `milliseconds` to the specified `unit`. We cannot use `TimeUnit.convert` because it + * will discard the fractional part. + */ + def convertToTimeUnit(milliseconds: Long, unit: TimeUnit): Double = unit match { + case TimeUnit.NANOSECONDS => milliseconds * 1000 * 1000 + case TimeUnit.MICROSECONDS => milliseconds * 1000 + case TimeUnit.MILLISECONDS => milliseconds + case TimeUnit.SECONDS => milliseconds / 1000.0 + case TimeUnit.MINUTES => milliseconds / 1000.0 / 60.0 + case TimeUnit.HOURS => milliseconds / 1000.0 / 60.0 / 60.0 + case TimeUnit.DAYS => milliseconds / 1000.0 / 60.0 / 60.0 / 24.0 + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala similarity index 79% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala index 38a93cc3c9a1f..9985fedc35141 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala @@ -17,6 +17,7 @@ package org.apache.spark.streaming.util import java.nio.ByteBuffer +import java.util.{Iterator => JIterator} import scala.collection.mutable.ArrayBuffer import scala.concurrent.{Await, ExecutionContext, Future} @@ -24,9 +25,9 @@ import scala.language.postfixOps import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.Logging -import org.apache.spark.util.{ThreadUtils, Clock, SystemClock} -import WriteAheadLogManager._ + +import org.apache.spark.util.ThreadUtils +import org.apache.spark.{Logging, SparkConf} /** * This class manages write ahead log files. @@ -34,37 +35,32 @@ import WriteAheadLogManager._ * - Recovers the log files and the reads the recovered records upon failures. * - Cleans up old log files. * - * Uses [[org.apache.spark.streaming.util.WriteAheadLogWriter]] to write - * and [[org.apache.spark.streaming.util.WriteAheadLogReader]] to read. + * Uses [[org.apache.spark.streaming.util.FileBasedWriteAheadLogWriter]] to write + * and [[org.apache.spark.streaming.util.FileBasedWriteAheadLogReader]] to read. * * @param logDirectory Directory when rotating log files will be created. * @param hadoopConf Hadoop configuration for reading/writing log files. - * @param rollingIntervalSecs The interval in seconds with which logs will be rolled over. - * Default is one minute. - * @param maxFailures Max number of failures that is tolerated for every attempt to write to log. - * Default is three. - * @param callerName Optional name of the class who is using this manager. - * @param clock Optional clock that is used to check for rotation interval. */ -private[streaming] class WriteAheadLogManager( +private[streaming] class FileBasedWriteAheadLog( + conf: SparkConf, logDirectory: String, hadoopConf: Configuration, - rollingIntervalSecs: Int = 60, - maxFailures: Int = 3, - callerName: String = "", - clock: Clock = new SystemClock - ) extends Logging { + rollingIntervalSecs: Int, + maxFailures: Int + ) extends WriteAheadLog with Logging { + + import FileBasedWriteAheadLog._ private val pastLogs = new ArrayBuffer[LogInfo] - private val callerNameTag = - if (callerName.nonEmpty) s" for $callerName" else "" + private val callerNameTag = getCallerName.map(c => s" for $c").getOrElse("") + private val threadpoolName = s"WriteAheadLogManager $callerNameTag" implicit private val executionContext = ExecutionContext.fromExecutorService( ThreadUtils.newDaemonSingleThreadExecutor(threadpoolName)) override protected val logName = s"WriteAheadLogManager $callerNameTag" private var currentLogPath: Option[String] = None - private var currentLogWriter: WriteAheadLogWriter = null + private var currentLogWriter: FileBasedWriteAheadLogWriter = null private var currentLogWriterStartTime: Long = -1L private var currentLogWriterStopTime: Long = -1L @@ -75,14 +71,14 @@ private[streaming] class WriteAheadLogManager( * ByteBuffer to HDFS. When this method returns, the data is guaranteed to have been flushed * to HDFS, and will be available for readers to read. */ - def writeToLog(byteBuffer: ByteBuffer): WriteAheadLogFileSegment = synchronized { - var fileSegment: WriteAheadLogFileSegment = null + def write(byteBuffer: ByteBuffer, time: Long): FileBasedWriteAheadLogSegment = synchronized { + var fileSegment: FileBasedWriteAheadLogSegment = null var failures = 0 var lastException: Exception = null var succeeded = false while (!succeeded && failures < maxFailures) { try { - fileSegment = getLogWriter(clock.getTimeMillis()).write(byteBuffer) + fileSegment = getLogWriter(time).write(byteBuffer) succeeded = true } catch { case ex: Exception => @@ -99,6 +95,19 @@ private[streaming] class WriteAheadLogManager( fileSegment } + def read(segment: WriteAheadLogRecordHandle): ByteBuffer = { + val fileSegment = segment.asInstanceOf[FileBasedWriteAheadLogSegment] + var reader: FileBasedWriteAheadLogRandomReader = null + var byteBuffer: ByteBuffer = null + try { + reader = new FileBasedWriteAheadLogRandomReader(fileSegment.path, hadoopConf) + byteBuffer = reader.read(fileSegment) + } finally { + reader.close() + } + byteBuffer + } + /** * Read all the existing logs from the log directory. * @@ -108,12 +117,14 @@ private[streaming] class WriteAheadLogManager( * the latest the records. This does not deal with currently active log files, and * hence the implementation is kept simple. */ - def readFromLog(): Iterator[ByteBuffer] = synchronized { + def readAll(): JIterator[ByteBuffer] = synchronized { + import scala.collection.JavaConversions._ val logFilesToRead = pastLogs.map{ _.path} ++ currentLogPath logInfo("Reading from the logs: " + logFilesToRead.mkString("\n")) + logFilesToRead.iterator.map { file => logDebug(s"Creating log reader with $file") - new WriteAheadLogReader(file, hadoopConf) + new FileBasedWriteAheadLogReader(file, hadoopConf) } flatMap { x => x } } @@ -129,7 +140,7 @@ private[streaming] class WriteAheadLogManager( * deleted. This should be set to true only for testing. Else the files will be deleted * asynchronously. */ - def cleanupOldLogs(threshTime: Long, waitForCompletion: Boolean): Unit = { + def clean(threshTime: Long, waitForCompletion: Boolean): Unit = { val oldLogFiles = synchronized { pastLogs.filter { _.endTime < threshTime } } logInfo(s"Attempting to clear ${oldLogFiles.size} old log files in $logDirectory " + s"older than $threshTime: ${oldLogFiles.map { _.path }.mkString("\n")}") @@ -160,7 +171,7 @@ private[streaming] class WriteAheadLogManager( /** Stop the manager, close any open log writer */ - def stop(): Unit = synchronized { + def close(): Unit = synchronized { if (currentLogWriter != null) { currentLogWriter.close() } @@ -169,7 +180,7 @@ private[streaming] class WriteAheadLogManager( } /** Get the current log writer while taking care of rotation */ - private def getLogWriter(currentTime: Long): WriteAheadLogWriter = synchronized { + private def getLogWriter(currentTime: Long): FileBasedWriteAheadLogWriter = synchronized { if (currentLogWriter == null || currentTime > currentLogWriterStopTime) { resetWriter() currentLogPath.foreach { @@ -180,7 +191,7 @@ private[streaming] class WriteAheadLogManager( val newLogPath = new Path(logDirectory, timeToLogFile(currentLogWriterStartTime, currentLogWriterStopTime)) currentLogPath = Some(newLogPath.toString) - currentLogWriter = new WriteAheadLogWriter(currentLogPath.get, hadoopConf) + currentLogWriter = new FileBasedWriteAheadLogWriter(currentLogPath.get, hadoopConf) } currentLogWriter } @@ -207,7 +218,7 @@ private[streaming] class WriteAheadLogManager( } } -private[util] object WriteAheadLogManager { +private[streaming] object FileBasedWriteAheadLog { case class LogInfo(startTime: Long, endTime: Long, path: String) @@ -217,6 +228,11 @@ private[util] object WriteAheadLogManager { s"log-$startTime-$stopTime" } + def getCallerName(): Option[String] = { + val stackTraceClasses = Thread.currentThread.getStackTrace().map(_.getClassName) + stackTraceClasses.find(!_.contains("WriteAheadLog")).flatMap(_.split(".").lastOption) + } + /** Convert a sequence of files to a sequence of sorted LogInfo objects */ def logFilesTologInfo(files: Seq[Path]): Seq[LogInfo] = { files.flatMap { file => diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogRandomReader.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogRandomReader.scala similarity index 83% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogRandomReader.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogRandomReader.scala index 003989092a42a..f7168229ec15a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogRandomReader.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogRandomReader.scala @@ -23,16 +23,16 @@ import org.apache.hadoop.conf.Configuration /** * A random access reader for reading write ahead log files written using - * [[org.apache.spark.streaming.util.WriteAheadLogWriter]]. Given the file segment info, - * this reads the record (bytebuffer) from the log file. + * [[org.apache.spark.streaming.util.FileBasedWriteAheadLogWriter]]. Given the file segment info, + * this reads the record (ByteBuffer) from the log file. */ -private[streaming] class WriteAheadLogRandomReader(path: String, conf: Configuration) +private[streaming] class FileBasedWriteAheadLogRandomReader(path: String, conf: Configuration) extends Closeable { private val instream = HdfsUtils.getInputStream(path, conf) private var closed = false - def read(segment: WriteAheadLogFileSegment): ByteBuffer = synchronized { + def read(segment: FileBasedWriteAheadLogSegment): ByteBuffer = synchronized { assertOpen() instream.seek(segment.offset) val nextLength = instream.readInt() diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogReader.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogReader.scala similarity index 93% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogReader.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogReader.scala index 2afc0d1551acf..c3bb59f3fef94 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogReader.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogReader.scala @@ -24,11 +24,11 @@ import org.apache.spark.Logging /** * A reader for reading write ahead log files written using - * [[org.apache.spark.streaming.util.WriteAheadLogWriter]]. This reads + * [[org.apache.spark.streaming.util.FileBasedWriteAheadLogWriter]]. This reads * the records (bytebuffers) in the log file sequentially and return them as an * iterator of bytebuffers. */ -private[streaming] class WriteAheadLogReader(path: String, conf: Configuration) +private[streaming] class FileBasedWriteAheadLogReader(path: String, conf: Configuration) extends Iterator[ByteBuffer] with Closeable with Logging { private val instream = HdfsUtils.getInputStream(path, conf) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogFileSegment.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogSegment.scala similarity index 86% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogFileSegment.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogSegment.scala index 1005a2c8ec303..2e1f1528fad20 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogFileSegment.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogSegment.scala @@ -17,4 +17,5 @@ package org.apache.spark.streaming.util /** Class for representing a segment of data in a write ahead log file */ -private[streaming] case class WriteAheadLogFileSegment (path: String, offset: Long, length: Int) +private[streaming] case class FileBasedWriteAheadLogSegment(path: String, offset: Long, length: Int) + extends WriteAheadLogRecordHandle diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogWriter.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogWriter.scala similarity index 88% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogWriter.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogWriter.scala index 679f6a6dfd7c1..e146bec32a456 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogWriter.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogWriter.scala @@ -17,18 +17,17 @@ package org.apache.spark.streaming.util import java.io._ -import java.net.URI import java.nio.ByteBuffer import scala.util.Try import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FSDataOutputStream, FileSystem} +import org.apache.hadoop.fs.FSDataOutputStream /** * A writer for writing byte-buffers to a write ahead log file. */ -private[streaming] class WriteAheadLogWriter(path: String, hadoopConf: Configuration) +private[streaming] class FileBasedWriteAheadLogWriter(path: String, hadoopConf: Configuration) extends Closeable { private lazy val stream = HdfsUtils.getOutputStream(path, hadoopConf) @@ -43,11 +42,11 @@ private[streaming] class WriteAheadLogWriter(path: String, hadoopConf: Configura private var closed = false /** Write the bytebuffer to the log file */ - def write(data: ByteBuffer): WriteAheadLogFileSegment = synchronized { + def write(data: ByteBuffer): FileBasedWriteAheadLogSegment = synchronized { assertOpen() data.rewind() // Rewind to ensure all data in the buffer is retrieved val lengthToWrite = data.remaining() - val segment = new WriteAheadLogFileSegment(path, nextOffset, lengthToWrite) + val segment = new FileBasedWriteAheadLogSegment(path, nextOffset, lengthToWrite) stream.writeInt(lengthToWrite) if (data.hasArray) { stream.write(data.array()) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/HdfsUtils.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/HdfsUtils.scala index 858ba3c9eb4e5..f60688f173c44 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/HdfsUtils.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/HdfsUtils.scala @@ -27,7 +27,7 @@ private[streaming] object HdfsUtils { // If the file exists and we have append support, append instead of creating a new file val stream: FSDataOutputStream = { if (dfs.isFile(dfsPath)) { - if (conf.getBoolean("hdfs.append.support", false)) { + if (conf.getBoolean("hdfs.append.support", false) || dfs.isInstanceOf[RawLocalFileSystem]) { dfs.append(dfsPath) } else { throw new IllegalStateException("File exists and there is no append support!") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogUtils.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogUtils.scala new file mode 100644 index 0000000000000..7f6ff12c58d47 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogUtils.scala @@ -0,0 +1,129 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.util + +import scala.util.control.NonFatal + +import org.apache.hadoop.conf.Configuration + +import org.apache.spark.util.Utils +import org.apache.spark.{Logging, SparkConf, SparkException} + +/** A helper class with utility functions related to the WriteAheadLog interface */ +private[streaming] object WriteAheadLogUtils extends Logging { + val RECEIVER_WAL_ENABLE_CONF_KEY = "spark.streaming.receiver.writeAheadLog.enable" + val RECEIVER_WAL_CLASS_CONF_KEY = "spark.streaming.receiver.writeAheadLog.class" + val RECEIVER_WAL_ROLLING_INTERVAL_CONF_KEY = + "spark.streaming.receiver.writeAheadLog.rollingIntervalSecs" + val RECEIVER_WAL_MAX_FAILURES_CONF_KEY = "spark.streaming.receiver.writeAheadLog.maxFailures" + + val DRIVER_WAL_CLASS_CONF_KEY = "spark.streaming.driver.writeAheadLog.class" + val DRIVER_WAL_ROLLING_INTERVAL_CONF_KEY = + "spark.streaming.driver.writeAheadLog.rollingIntervalSecs" + val DRIVER_WAL_MAX_FAILURES_CONF_KEY = "spark.streaming.driver.writeAheadLog.maxFailures" + + val DEFAULT_ROLLING_INTERVAL_SECS = 60 + val DEFAULT_MAX_FAILURES = 3 + + def enableReceiverLog(conf: SparkConf): Boolean = { + conf.getBoolean(RECEIVER_WAL_ENABLE_CONF_KEY, false) + } + + def getRollingIntervalSecs(conf: SparkConf, isDriver: Boolean): Int = { + if (isDriver) { + conf.getInt(DRIVER_WAL_ROLLING_INTERVAL_CONF_KEY, DEFAULT_ROLLING_INTERVAL_SECS) + } else { + conf.getInt(RECEIVER_WAL_ROLLING_INTERVAL_CONF_KEY, DEFAULT_ROLLING_INTERVAL_SECS) + } + } + + def getMaxFailures(conf: SparkConf, isDriver: Boolean): Int = { + if (isDriver) { + conf.getInt(DRIVER_WAL_MAX_FAILURES_CONF_KEY, DEFAULT_MAX_FAILURES) + } else { + conf.getInt(RECEIVER_WAL_MAX_FAILURES_CONF_KEY, DEFAULT_MAX_FAILURES) + } + } + + /** + * Create a WriteAheadLog for the driver. If configured with custom WAL class, it will try + * to create instance of that class, otherwise it will create the default FileBasedWriteAheadLog. + */ + def createLogForDriver( + sparkConf: SparkConf, + fileWalLogDirectory: String, + fileWalHadoopConf: Configuration + ): WriteAheadLog = { + createLog(true, sparkConf, fileWalLogDirectory, fileWalHadoopConf) + } + + /** + * Create a WriteAheadLog for the receiver. If configured with custom WAL class, it will try + * to create instance of that class, otherwise it will create the default FileBasedWriteAheadLog. + */ + def createLogForReceiver( + sparkConf: SparkConf, + fileWalLogDirectory: String, + fileWalHadoopConf: Configuration + ): WriteAheadLog = { + createLog(false, sparkConf, fileWalLogDirectory, fileWalHadoopConf) + } + + /** + * Create a WriteAheadLog based on the value of the given config key. The config key is used + * to get the class name from the SparkConf. If the class is configured, it will try to + * create instance of that class by first trying `new CustomWAL(sparkConf, logDir)` then trying + * `new CustomWAL(sparkConf)`. If either fails, it will fail. If no class is configured, then + * it will create the default FileBasedWriteAheadLog. + */ + private def createLog( + isDriver: Boolean, + sparkConf: SparkConf, + fileWalLogDirectory: String, + fileWalHadoopConf: Configuration + ): WriteAheadLog = { + + val classNameOption = if (isDriver) { + sparkConf.getOption(DRIVER_WAL_CLASS_CONF_KEY) + } else { + sparkConf.getOption(RECEIVER_WAL_CLASS_CONF_KEY) + } + classNameOption.map { className => + try { + instantiateClass( + Utils.classForName(className).asInstanceOf[Class[_ <: WriteAheadLog]], sparkConf) + } catch { + case NonFatal(e) => + throw new SparkException(s"Could not create a write ahead log of class $className", e) + } + }.getOrElse { + new FileBasedWriteAheadLog(sparkConf, fileWalLogDirectory, fileWalHadoopConf, + getRollingIntervalSecs(sparkConf, isDriver), getMaxFailures(sparkConf, isDriver)) + } + } + + /** Instantiate the class, either using single arg constructor or zero arg constructor */ + private def instantiateClass(cls: Class[_ <: WriteAheadLog], conf: SparkConf): WriteAheadLog = { + try { + cls.getConstructor(classOf[SparkConf]).newInstance(conf) + } catch { + case nsme: NoSuchMethodException => + cls.getConstructor().newInstance() + } + } +} diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index 90340753a4eed..b1adf881dd0f5 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -21,11 +21,13 @@ import java.lang.Iterable; import java.nio.charset.Charset; import java.util.*; +import java.util.concurrent.atomic.AtomicBoolean; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; + import scala.Tuple2; import org.junit.Assert; @@ -45,6 +47,7 @@ import org.apache.spark.storage.StorageLevel; import org.apache.spark.streaming.api.java.*; import org.apache.spark.util.Utils; +import org.apache.spark.SparkConf; // The test suite itself is Serializable so that anonymous Function implementations can be // serialized, as an alternative to converting these anonymous classes to static inner classes; @@ -929,7 +932,7 @@ public void testPairMap() { // Maps pair -> pair of different type public Tuple2 call(Tuple2 in) throws Exception { return in.swap(); } - }); + }); JavaTestUtils.attachTestOutputStream(reversed); List>> result = JavaTestUtils.runStreams(ssc, 2, 2); @@ -987,12 +990,12 @@ public void testPairMap2() { // Maps pair -> single JavaDStream> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream pairStream = JavaPairDStream.fromJavaDStream(stream); JavaDStream reversed = pairStream.map( - new Function, Integer>() { - @Override - public Integer call(Tuple2 in) throws Exception { - return in._2(); - } - }); + new Function, Integer>() { + @Override + public Integer call(Tuple2 in) throws Exception { + return in._2(); + } + }); JavaTestUtils.attachTestOutputStream(reversed); List> result = JavaTestUtils.runStreams(ssc, 2, 2); @@ -1123,7 +1126,7 @@ public void testCombineByKey() { JavaPairDStream combined = pairStream.combineByKey( new Function() { - @Override + @Override public Integer call(Integer i) throws Exception { return i; } @@ -1144,14 +1147,14 @@ public void testCountByValue() { Arrays.asList("hello")); List>> expected = Arrays.asList( - Arrays.asList( - new Tuple2("hello", 1L), - new Tuple2("world", 1L)), - Arrays.asList( - new Tuple2("hello", 1L), - new Tuple2("moon", 1L)), - Arrays.asList( - new Tuple2("hello", 1L))); + Arrays.asList( + new Tuple2("hello", 1L), + new Tuple2("world", 1L)), + Arrays.asList( + new Tuple2("hello", 1L), + new Tuple2("moon", 1L)), + Arrays.asList( + new Tuple2("hello", 1L))); JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream counted = stream.countByValue(); @@ -1249,17 +1252,17 @@ public void testUpdateStateByKey() { JavaPairDStream updated = pairStream.updateStateByKey( new Function2, Optional, Optional>() { - @Override - public Optional call(List values, Optional state) { - int out = 0; - if (state.isPresent()) { - out = out + state.get(); - } - for (Integer v: values) { - out = out + v; + @Override + public Optional call(List values, Optional state) { + int out = 0; + if (state.isPresent()) { + out = out + state.get(); + } + for (Integer v : values) { + out = out + v; + } + return Optional.of(out); } - return Optional.of(out); - } }); JavaTestUtils.attachTestOutputStream(updated); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1292,17 +1295,17 @@ public void testUpdateStateByKeyWithInitial() { JavaPairDStream updated = pairStream.updateStateByKey( new Function2, Optional, Optional>() { - @Override - public Optional call(List values, Optional state) { - int out = 0; - if (state.isPresent()) { - out = out + state.get(); - } - for (Integer v: values) { - out = out + v; + @Override + public Optional call(List values, Optional state) { + int out = 0; + if (state.isPresent()) { + out = out + state.get(); + } + for (Integer v : values) { + out = out + v; + } + return Optional.of(out); } - return Optional.of(out); - } }, new HashPartitioner(1), initialRDD); JavaTestUtils.attachTestOutputStream(updated); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1328,7 +1331,7 @@ public void testReduceByKeyAndWindowWithInverse() { JavaPairDStream reduceWindowed = pairStream.reduceByKeyAndWindow(new IntegerSum(), new IntegerDifference(), - new Duration(2000), new Duration(1000)); + new Duration(2000), new Duration(1000)); JavaTestUtils.attachTestOutputStream(reduceWindowed); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1707,6 +1710,74 @@ public Integer call(String s) throws Exception { Utils.deleteRecursively(tempDir); } + @SuppressWarnings("unchecked") + @Test + public void testContextGetOrCreate() throws InterruptedException { + + final SparkConf conf = new SparkConf() + .setMaster("local[2]") + .setAppName("test") + .set("newContext", "true"); + + File emptyDir = Files.createTempDir(); + emptyDir.deleteOnExit(); + StreamingContextSuite contextSuite = new StreamingContextSuite(); + String corruptedCheckpointDir = contextSuite.createCorruptedCheckpoint(); + String checkpointDir = contextSuite.createValidCheckpoint(); + + // Function to create JavaStreamingContext without any output operations + // (used to detect the new context) + final AtomicBoolean newContextCreated = new AtomicBoolean(false); + Function0 creatingFunc = new Function0() { + public JavaStreamingContext call() { + newContextCreated.set(true); + return new JavaStreamingContext(conf, Seconds.apply(1)); + } + }; + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(emptyDir.getAbsolutePath(), creatingFunc); + Assert.assertTrue("new context not created", newContextCreated.get()); + ssc.stop(); + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc, + new org.apache.hadoop.conf.Configuration(), true); + Assert.assertTrue("new context not created", newContextCreated.get()); + ssc.stop(); + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc, + new org.apache.hadoop.conf.Configuration()); + Assert.assertTrue("old context not recovered", !newContextCreated.get()); + ssc.stop(); + + // Function to create JavaStreamingContext using existing JavaSparkContext + // without any output operations (used to detect the new context) + Function creatingFunc2 = + new Function() { + public JavaStreamingContext call(JavaSparkContext context) { + newContextCreated.set(true); + return new JavaStreamingContext(context, Seconds.apply(1)); + } + }; + + JavaSparkContext sc = new JavaSparkContext(conf); + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(emptyDir.getAbsolutePath(), creatingFunc2, sc); + Assert.assertTrue("new context not created", newContextCreated.get()); + ssc.stop(false); + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc2, sc, true); + Assert.assertTrue("new context not created", newContextCreated.get()); + ssc.stop(false); + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc2, sc); + Assert.assertTrue("old context not recovered", !newContextCreated.get()); + ssc.stop(); + } /* TEST DISABLED: Pending a discussion about checkpoint() semantics with TD @SuppressWarnings("unchecked") diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java new file mode 100644 index 0000000000000..50e8f9fc159c8 --- /dev/null +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java @@ -0,0 +1,129 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming; + +import java.util.ArrayList; +import java.nio.ByteBuffer; +import java.util.Arrays; +import java.util.Collection; + +import org.apache.commons.collections.CollectionUtils; +import org.apache.commons.collections.Transformer; +import org.apache.spark.SparkConf; +import org.apache.spark.streaming.util.WriteAheadLog; +import org.apache.spark.streaming.util.WriteAheadLogRecordHandle; +import org.apache.spark.streaming.util.WriteAheadLogUtils; + +import org.junit.Test; +import org.junit.Assert; + +class JavaWriteAheadLogSuiteHandle extends WriteAheadLogRecordHandle { + int index = -1; + public JavaWriteAheadLogSuiteHandle(int idx) { + index = idx; + } +} + +public class JavaWriteAheadLogSuite extends WriteAheadLog { + + class Record { + long time; + int index; + ByteBuffer buffer; + + public Record(long tym, int idx, ByteBuffer buf) { + index = idx; + time = tym; + buffer = buf; + } + } + private int index = -1; + private ArrayList records = new ArrayList(); + + + // Methods for WriteAheadLog + @Override + public WriteAheadLogRecordHandle write(java.nio.ByteBuffer record, long time) { + index += 1; + records.add(new org.apache.spark.streaming.JavaWriteAheadLogSuite.Record(time, index, record)); + return new JavaWriteAheadLogSuiteHandle(index); + } + + @Override + public java.nio.ByteBuffer read(WriteAheadLogRecordHandle handle) { + if (handle instanceof JavaWriteAheadLogSuiteHandle) { + int reqdIndex = ((JavaWriteAheadLogSuiteHandle) handle).index; + for (Record record: records) { + if (record.index == reqdIndex) { + return record.buffer; + } + } + } + return null; + } + + @Override + public java.util.Iterator readAll() { + Collection buffers = CollectionUtils.collect(records, new Transformer() { + @Override + public Object transform(Object input) { + return ((Record) input).buffer; + } + }); + return buffers.iterator(); + } + + @Override + public void clean(long threshTime, boolean waitForCompletion) { + for (int i = 0; i < records.size(); i++) { + if (records.get(i).time < threshTime) { + records.remove(i); + i--; + } + } + } + + @Override + public void close() { + records.clear(); + } + + @Test + public void testCustomWAL() { + SparkConf conf = new SparkConf(); + conf.set("spark.streaming.driver.writeAheadLog.class", JavaWriteAheadLogSuite.class.getName()); + WriteAheadLog wal = WriteAheadLogUtils.createLogForDriver(conf, null, null); + + String data1 = "data1"; + WriteAheadLogRecordHandle handle = wal.write(ByteBuffer.wrap(data1.getBytes()), 1234); + Assert.assertTrue(handle instanceof JavaWriteAheadLogSuiteHandle); + Assert.assertTrue(new String(wal.read(handle).array()).equals(data1)); + + wal.write(ByteBuffer.wrap("data2".getBytes()), 1235); + wal.write(ByteBuffer.wrap("data3".getBytes()), 1236); + wal.write(ByteBuffer.wrap("data4".getBytes()), 1237); + wal.clean(1236, false); + + java.util.Iterator dataIterator = wal.readAll(); + ArrayList readData = new ArrayList(); + while (dataIterator.hasNext()) { + readData.add(new String(dataIterator.next().array())); + } + Assert.assertTrue(readData.equals(Arrays.asList("data3", "data4"))); + } +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala index 54c30440a6e8d..6b0a3f91d4d06 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala @@ -430,9 +430,8 @@ class CheckpointSuite extends TestSuiteBase { assert(recordedFiles(ssc) === Seq(1, 2, 3) && batchCounter.getNumStartedBatches === 3) } // Wait for a checkpoint to be written - val fs = new Path(checkpointDir).getFileSystem(ssc.sc.hadoopConfiguration) eventually(eventuallyTimeout) { - assert(Checkpoint.getCheckpointFiles(checkpointDir, fs).size === 6) + assert(Checkpoint.getCheckpointFiles(checkpointDir).size === 6) } ssc.stop() // Check that we shut down while the third batch was being processed diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala index e6ac4975c5e68..93e6b0cd7c661 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -18,75 +18,90 @@ package org.apache.spark.streaming import java.io.{File, BufferedWriter, OutputStreamWriter} -import java.net.{SocketException, ServerSocket} +import java.net.{Socket, SocketException, ServerSocket} import java.nio.charset.Charset -import java.util.concurrent.{Executors, TimeUnit, ArrayBlockingQueue} +import java.util.concurrent.{CountDownLatch, Executors, TimeUnit, ArrayBlockingQueue} import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer, SynchronizedQueue} import scala.language.postfixOps import com.google.common.io.Files +import org.apache.hadoop.io.{Text, LongWritable} +import org.apache.hadoop.mapreduce.lib.input.TextInputFormat +import org.apache.hadoop.fs.Path import org.scalatest.BeforeAndAfter import org.scalatest.concurrent.Eventually._ import org.apache.spark.Logging +import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.scheduler.{StreamingListenerBatchCompleted, StreamingListener} import org.apache.spark.util.{ManualClock, Utils} +import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream} import org.apache.spark.streaming.receiver.Receiver -import org.apache.spark.rdd.RDD -import org.apache.hadoop.io.{Text, LongWritable} -import org.apache.hadoop.mapreduce.lib.input.TextInputFormat -import org.apache.hadoop.fs.Path class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { test("socket input stream") { - // Start the server - val testServer = new TestServer() - testServer.start() + withTestServer(new TestServer()) { testServer => + // Start the server + testServer.start() - // Set up the streaming context and input streams - val ssc = new StreamingContext(conf, batchDuration) - val networkStream = ssc.socketTextStream( - "localhost", testServer.port, StorageLevel.MEMORY_AND_DISK) - val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] - val outputStream = new TestOutputStream(networkStream, outputBuffer) - def output: ArrayBuffer[String] = outputBuffer.flatMap(x => x) - outputStream.register() - ssc.start() + // Set up the streaming context and input streams + withStreamingContext(new StreamingContext(conf, batchDuration)) { ssc => + ssc.addStreamingListener(ssc.progressListener) - // Feed data to the server to send to the network receiver - val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] - val input = Seq(1, 2, 3, 4, 5) - val expectedOutput = input.map(_.toString) - Thread.sleep(1000) - for (i <- 0 until input.size) { - testServer.send(input(i).toString + "\n") - Thread.sleep(500) - clock.advance(batchDuration.milliseconds) - } - Thread.sleep(1000) - logInfo("Stopping server") - testServer.stop() - logInfo("Stopping context") - ssc.stop() + val input = Seq(1, 2, 3, 4, 5) + // Use "batchCount" to make sure we check the result after all batches finish + val batchCounter = new BatchCounter(ssc) + val networkStream = ssc.socketTextStream( + "localhost", testServer.port, StorageLevel.MEMORY_AND_DISK) + val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] + val outputStream = new TestOutputStream(networkStream, outputBuffer) + outputStream.register() + ssc.start() - // Verify whether data received was as expected - logInfo("--------------------------------") - logInfo("output.size = " + outputBuffer.size) - logInfo("output") - outputBuffer.foreach(x => logInfo("[" + x.mkString(",") + "]")) - logInfo("expected output.size = " + expectedOutput.size) - logInfo("expected output") - expectedOutput.foreach(x => logInfo("[" + x.mkString(",") + "]")) - logInfo("--------------------------------") + // Feed data to the server to send to the network receiver + val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] + val expectedOutput = input.map(_.toString) + for (i <- 0 until input.size) { + testServer.send(input(i).toString + "\n") + Thread.sleep(500) + clock.advance(batchDuration.milliseconds) + } + // Make sure we finish all batches before "stop" + if (!batchCounter.waitUntilBatchesCompleted(input.size, 30000)) { + fail("Timeout: cannot finish all batches in 30 seconds") + } - // Verify whether all the elements received are as expected - // (whether the elements were received one in each interval is not verified) - assert(output.size === expectedOutput.size) - for (i <- 0 until output.size) { - assert(output(i) === expectedOutput(i)) + // Verify all "InputInfo"s have been reported + assert(ssc.progressListener.numTotalReceivedRecords === input.size) + assert(ssc.progressListener.numTotalProcessedRecords === input.size) + + logInfo("Stopping server") + testServer.stop() + logInfo("Stopping context") + ssc.stop() + + // Verify whether data received was as expected + logInfo("--------------------------------") + logInfo("output.size = " + outputBuffer.size) + logInfo("output") + outputBuffer.foreach(x => logInfo("[" + x.mkString(",") + "]")) + logInfo("expected output.size = " + expectedOutput.size) + logInfo("expected output") + expectedOutput.foreach(x => logInfo("[" + x.mkString(",") + "]")) + logInfo("--------------------------------") + + // Verify whether all the elements received are as expected + // (whether the elements were received one in each interval is not verified) + val output: ArrayBuffer[String] = outputBuffer.flatMap(x => x) + assert(output.size === expectedOutput.size) + for (i <- 0 until output.size) { + assert(output(i) === expectedOutput(i)) + } + } } } @@ -278,6 +293,30 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { } } + test("test track the number of input stream") { + val ssc = new StreamingContext(conf, batchDuration) + + class TestInputDStream extends InputDStream[String](ssc) { + def start() { } + def stop() { } + def compute(validTime: Time): Option[RDD[String]] = None + } + + class TestReceiverInputDStream extends ReceiverInputDStream[String](ssc) { + def getReceiver: Receiver[String] = null + } + + // Register input streams + val receiverInputStreams = Array(new TestReceiverInputDStream, new TestReceiverInputDStream) + val inputStreams = Array(new TestInputDStream, new TestInputDStream, new TestInputDStream) + + assert(ssc.graph.getInputStreams().length == receiverInputStreams.length + inputStreams.length) + assert(ssc.graph.getReceiverInputStreams().length == receiverInputStreams.length) + assert(ssc.graph.getReceiverInputStreams() === receiverInputStreams) + assert(ssc.graph.getInputStreams().map(_.id) === Array.tabulate(5)(i => i)) + assert(receiverInputStreams.map(_.id) === Array(0, 1)) + } + def testFileStream(newFilesOnly: Boolean) { val testDir: File = null try { @@ -343,31 +382,45 @@ class TestServer(portToBind: Int = 0) extends Logging { val serverSocket = new ServerSocket(portToBind) + private val startLatch = new CountDownLatch(1) + val servingThread = new Thread() { override def run() { try { while(true) { logInfo("Accepting connections on port " + port) val clientSocket = serverSocket.accept() - logInfo("New connection") - try { - clientSocket.setTcpNoDelay(true) - val outputStream = new BufferedWriter( - new OutputStreamWriter(clientSocket.getOutputStream)) - - while(clientSocket.isConnected) { - val msg = queue.poll(100, TimeUnit.MILLISECONDS) - if (msg != null) { - outputStream.write(msg) - outputStream.flush() - logInfo("Message '" + msg + "' sent") + if (startLatch.getCount == 1) { + // The first connection is a test connection to implement "waitForStart", so skip it + // and send a signal + if (!clientSocket.isClosed) { + clientSocket.close() + } + startLatch.countDown() + } else { + // Real connections + logInfo("New connection") + try { + clientSocket.setTcpNoDelay(true) + val outputStream = new BufferedWriter( + new OutputStreamWriter(clientSocket.getOutputStream)) + + while (clientSocket.isConnected) { + val msg = queue.poll(100, TimeUnit.MILLISECONDS) + if (msg != null) { + outputStream.write(msg) + outputStream.flush() + logInfo("Message '" + msg + "' sent") + } + } + } catch { + case e: SocketException => logError("TestServer error", e) + } finally { + logInfo("Connection closed") + if (!clientSocket.isClosed) { + clientSocket.close() } } - } catch { - case e: SocketException => logError("TestServer error", e) - } finally { - logInfo("Connection closed") - if (!clientSocket.isClosed) clientSocket.close() } } } catch { @@ -379,7 +432,29 @@ class TestServer(portToBind: Int = 0) extends Logging { } } - def start() { servingThread.start() } + def start(): Unit = { + servingThread.start() + if (!waitForStart(10000)) { + stop() + throw new AssertionError("Timeout: TestServer cannot start in 10 seconds") + } + } + + /** + * Wait until the server starts. Return true if the server starts in "millis" milliseconds. + * Otherwise, return false to indicate it's timeout. + */ + private def waitForStart(millis: Long): Boolean = { + // We will create a test connection to the server so that we can make sure it has started. + val socket = new Socket("localhost", port) + try { + startLatch.await(millis, TimeUnit.MILLISECONDS) + } finally { + if (!socket.isClosed) { + socket.close() + } + } + } def send(msg: String) { queue.put(msg) } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala index c090eaec2928d..23804237bda80 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala @@ -43,7 +43,7 @@ import WriteAheadLogSuite._ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matchers with Logging { - val conf = new SparkConf().set("spark.streaming.receiver.writeAheadLog.rollingInterval", "1") + val conf = new SparkConf().set("spark.streaming.receiver.writeAheadLog.rollingIntervalSecs", "1") val hadoopConf = new Configuration() val storageLevel = StorageLevel.MEMORY_ONLY_SER val streamId = 1 @@ -130,10 +130,13 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche "Unexpected store result type" ) // Verify the data in write ahead log files is correct - val fileSegments = storeResults.map { _.asInstanceOf[WriteAheadLogBasedStoreResult].segment} - val loggedData = fileSegments.flatMap { segment => - val reader = new WriteAheadLogRandomReader(segment.path, hadoopConf) - val bytes = reader.read(segment) + val walSegments = storeResults.map { result => + result.asInstanceOf[WriteAheadLogBasedStoreResult].walRecordHandle + } + val loggedData = walSegments.flatMap { walSegment => + val fileSegment = walSegment.asInstanceOf[FileBasedWriteAheadLogSegment] + val reader = new FileBasedWriteAheadLogRandomReader(fileSegment.path, hadoopConf) + val bytes = reader.read(fileSegment) reader.close() blockManager.dataDeserialize(generateBlockId(), bytes).toList } @@ -148,13 +151,13 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche } } - test("WriteAheadLogBasedBlockHandler - cleanup old blocks") { + test("WriteAheadLogBasedBlockHandler - clean old blocks") { withWriteAheadLogBasedBlockHandler { handler => val blocks = Seq.tabulate(10) { i => IteratorBlock(Iterator(1 to i)) } storeBlocks(handler, blocks) val preCleanupLogFiles = getWriteAheadLogFiles() - preCleanupLogFiles.size should be > 1 + require(preCleanupLogFiles.size > 1) // this depends on the number of blocks inserted using generateAndStoreData() manualClock.getTimeMillis() shouldEqual 5000L @@ -218,6 +221,7 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche /** Instantiate a WriteAheadLogBasedBlockHandler and run a code with it */ private def withWriteAheadLogBasedBlockHandler(body: WriteAheadLogBasedBlockHandler => Unit) { + require(WriteAheadLogUtils.getRollingIntervalSecs(conf, isDriver = false) === 1) val receivedBlockHandler = new WriteAheadLogBasedBlockHandler(blockManager, 1, storageLevel, conf, hadoopConf, tempDirectory.toString, manualClock) try { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala index b63b37d9f9cef..b1af8d5eaacfb 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala @@ -32,7 +32,7 @@ import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.receiver.BlockManagerBasedStoreResult import org.apache.spark.streaming.scheduler._ -import org.apache.spark.streaming.util.WriteAheadLogReader +import org.apache.spark.streaming.util.{WriteAheadLogUtils, FileBasedWriteAheadLogReader} import org.apache.spark.streaming.util.WriteAheadLogSuite._ import org.apache.spark.util.{Clock, ManualClock, SystemClock, Utils} @@ -59,7 +59,7 @@ class ReceivedBlockTrackerSuite test("block addition, and block to batch allocation") { val receivedBlockTracker = createTracker(setCheckpointDir = false) - receivedBlockTracker.isLogManagerEnabled should be (false) // should be disable by default + receivedBlockTracker.isWriteAheadLogEnabled should be (false) // should be disable by default receivedBlockTracker.getUnallocatedBlocks(streamId) shouldEqual Seq.empty val blockInfos = generateBlockInfos() @@ -67,15 +67,20 @@ class ReceivedBlockTrackerSuite // Verify added blocks are unallocated blocks receivedBlockTracker.getUnallocatedBlocks(streamId) shouldEqual blockInfos + receivedBlockTracker.hasUnallocatedReceivedBlocks should be (true) + // Allocate the blocks to a batch and verify that all of them have been allocated receivedBlockTracker.allocateBlocksToBatch(1) receivedBlockTracker.getBlocksOfBatchAndStream(1, streamId) shouldEqual blockInfos + receivedBlockTracker.getBlocksOfBatch(1) shouldEqual Map(streamId -> blockInfos) receivedBlockTracker.getUnallocatedBlocks(streamId) shouldBe empty + receivedBlockTracker.hasUnallocatedReceivedBlocks should be (false) // Allocate no blocks to another batch receivedBlockTracker.allocateBlocksToBatch(2) receivedBlockTracker.getBlocksOfBatchAndStream(2, streamId) shouldBe empty + receivedBlockTracker.getBlocksOfBatch(2) shouldEqual Map(streamId -> Seq.empty) // Verify that older batches have no operation on batch allocation, // will return the same blocks as previously allocated. @@ -88,7 +93,7 @@ class ReceivedBlockTrackerSuite receivedBlockTracker.getUnallocatedBlocks(streamId) shouldEqual blockInfos } - test("block addition, block to batch allocation and cleanup with write ahead log") { + test("recovery and cleanup with write ahead logs") { val manualClock = new ManualClock // Set the time increment level to twice the rotation interval so that every increment creates // a new log file @@ -113,11 +118,13 @@ class ReceivedBlockTrackerSuite logInfo(s"\n\n=====================\n$message\n$fileContents\n=====================\n") } + // Set WAL configuration + conf.set("spark.streaming.driver.writeAheadLog.rollingIntervalSecs", "1") + require(WriteAheadLogUtils.getRollingIntervalSecs(conf, isDriver = true) === 1) + // Start tracker and add blocks - conf.set("spark.streaming.receiver.writeAheadLog.enable", "true") - conf.set("spark.streaming.receivedBlockTracker.writeAheadLog.rotationIntervalSecs", "1") val tracker1 = createTracker(clock = manualClock) - tracker1.isLogManagerEnabled should be (true) + tracker1.isWriteAheadLogEnabled should be (true) val blockInfos1 = addBlockInfos(tracker1) tracker1.getUnallocatedBlocks(streamId).toList shouldEqual blockInfos1 @@ -127,15 +134,27 @@ class ReceivedBlockTrackerSuite getWrittenLogData() shouldEqual expectedWrittenData1 getWriteAheadLogFiles() should have size 1 - // Restart tracker and verify recovered list of unallocated blocks incrementTime() - val tracker2 = createTracker(clock = manualClock) - tracker2.getUnallocatedBlocks(streamId).toList shouldEqual blockInfos1 + + // Recovery without recovery from WAL and verify list of unallocated blocks is empty + val tracker1_ = createTracker(clock = manualClock, recoverFromWriteAheadLog = false) + tracker1_.getUnallocatedBlocks(streamId) shouldBe empty + tracker1_.hasUnallocatedReceivedBlocks should be (false) + + // Restart tracker and verify recovered list of unallocated blocks + val tracker2 = createTracker(clock = manualClock, recoverFromWriteAheadLog = true) + val unallocatedBlocks = tracker2.getUnallocatedBlocks(streamId).toList + unallocatedBlocks shouldEqual blockInfos1 + unallocatedBlocks.foreach { block => + block.isBlockIdValid() should be (false) + } + // Allocate blocks to batch and verify whether the unallocated blocks got allocated val batchTime1 = manualClock.getTimeMillis() tracker2.allocateBlocksToBatch(batchTime1) tracker2.getBlocksOfBatchAndStream(batchTime1, streamId) shouldEqual blockInfos1 + tracker2.getBlocksOfBatch(batchTime1) shouldEqual Map(streamId -> blockInfos1) // Add more blocks and allocate to another batch incrementTime() @@ -153,7 +172,7 @@ class ReceivedBlockTrackerSuite // Restart tracker and verify recovered state incrementTime() - val tracker3 = createTracker(clock = manualClock) + val tracker3 = createTracker(clock = manualClock, recoverFromWriteAheadLog = true) tracker3.getBlocksOfBatchAndStream(batchTime1, streamId) shouldEqual blockInfos1 tracker3.getBlocksOfBatchAndStream(batchTime2, streamId) shouldEqual blockInfos2 tracker3.getUnallocatedBlocks(streamId) shouldBe empty @@ -171,51 +190,41 @@ class ReceivedBlockTrackerSuite eventually(timeout(10 seconds), interval(10 millisecond)) { getWriteAheadLogFiles() should not contain oldestLogFile } - printLogFiles("After cleanup") + printLogFiles("After clean") // Restart tracker and verify recovered state, specifically whether info about the first // batch has been removed, but not the second batch incrementTime() - val tracker4 = createTracker(clock = manualClock) + val tracker4 = createTracker(clock = manualClock, recoverFromWriteAheadLog = true) tracker4.getUnallocatedBlocks(streamId) shouldBe empty tracker4.getBlocksOfBatchAndStream(batchTime1, streamId) shouldBe empty // should be cleaned tracker4.getBlocksOfBatchAndStream(batchTime2, streamId) shouldEqual blockInfos2 } - test("enabling write ahead log but not setting checkpoint dir") { - conf.set("spark.streaming.receiver.writeAheadLog.enable", "true") - intercept[SparkException] { - createTracker(setCheckpointDir = false) - } - } - - test("setting checkpoint dir but not enabling write ahead log") { - // When WAL config is not set, log manager should not be enabled - val tracker1 = createTracker(setCheckpointDir = true) - tracker1.isLogManagerEnabled should be (false) - - // When WAL is explicitly disabled, log manager should not be enabled - conf.set("spark.streaming.receiver.writeAheadLog.enable", "false") - val tracker2 = createTracker(setCheckpointDir = true) - tracker2.isLogManagerEnabled should be(false) + test("disable write ahead log when checkpoint directory is not set") { + // When checkpoint is disabled, then the write ahead log is disabled + val tracker1 = createTracker(setCheckpointDir = false) + tracker1.isWriteAheadLogEnabled should be (false) } /** * Create tracker object with the optional provided clock. Use fake clock if you - * want to control time by manually incrementing it to test log cleanup. + * want to control time by manually incrementing it to test log clean. */ def createTracker( setCheckpointDir: Boolean = true, + recoverFromWriteAheadLog: Boolean = false, clock: Clock = new SystemClock): ReceivedBlockTracker = { val cpDirOption = if (setCheckpointDir) Some(checkpointDirectory.toString) else None - val tracker = new ReceivedBlockTracker(conf, hadoopConf, Seq(streamId), clock, cpDirOption) + val tracker = new ReceivedBlockTracker( + conf, hadoopConf, Seq(streamId), clock, recoverFromWriteAheadLog, cpDirOption) allReceivedBlockTrackers += tracker tracker } /** Generate blocks infos using random ids */ def generateBlockInfos(): Seq[ReceivedBlockInfo] = { - List.fill(5)(ReceivedBlockInfo(streamId, 0, + List.fill(5)(ReceivedBlockInfo(streamId, 0, None, BlockManagerBasedStoreResult(StreamBlockId(streamId, math.abs(Random.nextInt))))) } @@ -231,7 +240,7 @@ class ReceivedBlockTrackerSuite def getWrittenLogData(logFiles: Seq[String] = getWriteAheadLogFiles) : Seq[ReceivedBlockTrackerLogEvent] = { logFiles.flatMap { - file => new WriteAheadLogReader(file, hadoopConf).toSeq + file => new FileBasedWriteAheadLogReader(file, hadoopConf).toSeq }.map { byteBuffer => Utils.deserialize[ReceivedBlockTrackerLogEvent](byteBuffer.array) }.toList @@ -250,7 +259,7 @@ class ReceivedBlockTrackerSuite BatchAllocationEvent(time, AllocatedBlocks(Map((streamId -> blockInfos)))) } - /** Create batch cleanup object from the given info */ + /** Create batch clean object from the given info */ def createBatchCleanup(time: Long, moreTimes: Long*): BatchCleanupEvent = { BatchCleanupEvent((Seq(time) ++ moreTimes).map(Time.apply)) } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala index b84129fd70dd4..5d7127627eea5 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala @@ -225,7 +225,7 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { .setAppName(framework) .set("spark.ui.enabled", "true") .set("spark.streaming.receiver.writeAheadLog.enable", "true") - .set("spark.streaming.receiver.writeAheadLog.rollingInterval", "1") + .set("spark.streaming.receiver.writeAheadLog.rollingIntervalSecs", "1") val batchDuration = Milliseconds(500) val tempDirectory = Utils.createTempDir() val logDirectory1 = new File(checkpointDirToLogDir(tempDirectory.getAbsolutePath, 0)) @@ -256,8 +256,8 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { } withStreamingContext(new StreamingContext(sparkConf, batchDuration)) { ssc => - val receiver1 = ssc.sparkContext.clean(new FakeReceiver(sendData = true)) - val receiver2 = ssc.sparkContext.clean(new FakeReceiver(sendData = true)) + val receiver1 = new FakeReceiver(sendData = true) + val receiver2 = new FakeReceiver(sendData = true) val receiverStream1 = ssc.receiverStream(receiver1) val receiverStream2 = ssc.receiverStream(receiver2) receiverStream1.register() diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index 58353a5f97c8a..5207b7109e69b 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -17,8 +17,10 @@ package org.apache.spark.streaming +import java.io.File import java.util.concurrent.atomic.AtomicInteger +import org.apache.commons.io.FileUtils import org.scalatest.{Assertions, BeforeAndAfter, FunSuite} import org.scalatest.concurrent.Timeouts import org.scalatest.concurrent.Eventually._ @@ -330,6 +332,139 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w } } + test("getOrCreate") { + val conf = new SparkConf().setMaster(master).setAppName(appName) + + // Function to create StreamingContext that has a config to identify it to be new context + var newContextCreated = false + def creatingFunction(): StreamingContext = { + newContextCreated = true + new StreamingContext(conf, batchDuration) + } + + // Call ssc.stop after a body of code + def testGetOrCreate(body: => Unit): Unit = { + newContextCreated = false + try { + body + } finally { + if (ssc != null) { + ssc.stop() + } + ssc = null + } + } + + val emptyPath = Utils.createTempDir().getAbsolutePath() + + // getOrCreate should create new context with empty path + testGetOrCreate { + ssc = StreamingContext.getOrCreate(emptyPath, creatingFunction _) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + } + + val corrutedCheckpointPath = createCorruptedCheckpoint() + + // getOrCreate should throw exception with fake checkpoint file and createOnError = false + intercept[Exception] { + ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _) + } + + // getOrCreate should throw exception with fake checkpoint file + intercept[Exception] { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, createOnError = false) + } + + // getOrCreate should create new context with fake checkpoint file and createOnError = true + testGetOrCreate { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, createOnError = true) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + } + + val checkpointPath = createValidCheckpoint() + + // getOrCreate should recover context with checkpoint path, and recover old configuration + testGetOrCreate { + ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _) + assert(ssc != null, "no context created") + assert(!newContextCreated, "old context not recovered") + assert(ssc.conf.get("someKey") === "someValue") + } + } + + test("getOrCreate with existing SparkContext") { + val conf = new SparkConf().setMaster(master).setAppName(appName) + sc = new SparkContext(conf) + + // Function to create StreamingContext that has a config to identify it to be new context + var newContextCreated = false + def creatingFunction(sparkContext: SparkContext): StreamingContext = { + newContextCreated = true + new StreamingContext(sparkContext, batchDuration) + } + + // Call ssc.stop(stopSparkContext = false) after a body of cody + def testGetOrCreate(body: => Unit): Unit = { + newContextCreated = false + try { + body + } finally { + if (ssc != null) { + ssc.stop(stopSparkContext = false) + } + ssc = null + } + } + + val emptyPath = Utils.createTempDir().getAbsolutePath() + + // getOrCreate should create new context with empty path + testGetOrCreate { + ssc = StreamingContext.getOrCreate(emptyPath, creatingFunction _, sc, createOnError = true) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") + } + + val corrutedCheckpointPath = createCorruptedCheckpoint() + + // getOrCreate should throw exception with fake checkpoint file and createOnError = false + intercept[Exception] { + ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _, sc) + } + + // getOrCreate should throw exception with fake checkpoint file + intercept[Exception] { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, sc, createOnError = false) + } + + // getOrCreate should create new context with fake checkpoint file and createOnError = true + testGetOrCreate { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, sc, createOnError = true) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") + } + + val checkpointPath = createValidCheckpoint() + + // StreamingContext.getOrCreate should recover context with checkpoint path + testGetOrCreate { + ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _, sc) + assert(ssc != null, "no context created") + assert(!newContextCreated, "old context not recovered") + assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") + assert(!ssc.conf.contains("someKey"), + "recovered StreamingContext unexpectedly has old config") + } + } + test("DStream and generated RDD creation sites") { testPackage.test() } @@ -339,6 +474,30 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w val inputStream = new TestInputStream(s, input, 1) inputStream } + + def createValidCheckpoint(): String = { + val testDirectory = Utils.createTempDir().getAbsolutePath() + val checkpointDirectory = Utils.createTempDir().getAbsolutePath() + val conf = new SparkConf().setMaster(master).setAppName(appName) + conf.set("someKey", "someValue") + ssc = new StreamingContext(conf, batchDuration) + ssc.checkpoint(checkpointDirectory) + ssc.textFileStream(testDirectory).foreachRDD { rdd => rdd.count() } + ssc.start() + eventually(timeout(10000 millis)) { + assert(Checkpoint.getCheckpointFiles(checkpointDirectory).size > 1) + } + ssc.stop() + checkpointDirectory + } + + def createCorruptedCheckpoint(): String = { + val checkpointDirectory = Utils.createTempDir().getAbsolutePath() + val fakeCheckpointFile = Checkpoint.checkpointFile(checkpointDirectory, Time(1000)) + FileUtils.write(new File(fakeCheckpointFile.toString()), "blablabla") + assert(Checkpoint.getCheckpointFiles(checkpointDirectory).nonEmpty) + checkpointDirectory + } } class TestException(msg: String) extends Exception(msg) @@ -363,7 +522,7 @@ class TestReceiver extends Receiver[Int](StorageLevel.MEMORY_ONLY) with Logging } def onStop() { - // no cleanup to be done, the receiving thread should stop on it own + // no clean to be done, the receiving thread should stop on it own } } @@ -396,7 +555,7 @@ class SlowTestReceiver(totalRecords: Int, recordsPerSecond: Int) def onStop() { // Simulate slow receiver by waiting for all records to be produced while(!SlowTestReceiver.receivedAllRecords) Thread.sleep(100) - // no cleanup to be done, the receiving thread should stop on it own + // no clean to be done, the receiving thread should stop on it own } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala index 7210439509541..312cce408cfe7 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.streaming -import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer} import scala.concurrent.Future import scala.concurrent.ExecutionContext.Implicits.global @@ -57,6 +57,11 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { info.totalDelay should be (None) }) + batchInfosSubmitted.foreach { info => + info.numRecords should be (1L) + info.streamIdToNumRecords should be (Map(0 -> 1L)) + } + isInIncreasingOrder(batchInfosSubmitted.map(_.submissionTime)) should be (true) // SPARK-6766: processingStartTime of batch info should not be None when starting @@ -70,6 +75,11 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { info.totalDelay should be (None) }) + batchInfosStarted.foreach { info => + info.numRecords should be (1L) + info.streamIdToNumRecords should be (Map(0 -> 1L)) + } + isInIncreasingOrder(batchInfosStarted.map(_.submissionTime)) should be (true) isInIncreasingOrder(batchInfosStarted.map(_.processingStartTime.get)) should be (true) @@ -86,6 +96,11 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { info.totalDelay.get should be >= 0L }) + batchInfosCompleted.foreach { info => + info.numRecords should be (1L) + info.streamIdToNumRecords should be (Map(0 -> 1L)) + } + isInIncreasingOrder(batchInfosCompleted.map(_.submissionTime)) should be (true) isInIncreasingOrder(batchInfosCompleted.map(_.processingStartTime.get)) should be (true) isInIncreasingOrder(batchInfosCompleted.map(_.processingEndTime.get)) should be (true) @@ -127,9 +142,9 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { /** Listener that collects information on processed batches */ class BatchInfoCollector extends StreamingListener { - val batchInfosCompleted = new ArrayBuffer[BatchInfo] - val batchInfosStarted = new ArrayBuffer[BatchInfo] - val batchInfosSubmitted = new ArrayBuffer[BatchInfo] + val batchInfosCompleted = new ArrayBuffer[BatchInfo] with SynchronizedBuffer[BatchInfo] + val batchInfosStarted = new ArrayBuffer[BatchInfo] with SynchronizedBuffer[BatchInfo] + val batchInfosSubmitted = new ArrayBuffer[BatchInfo] with SynchronizedBuffer[BatchInfo] override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted) { batchInfosSubmitted += batchSubmitted.batchInfo @@ -146,9 +161,10 @@ class BatchInfoCollector extends StreamingListener { /** Listener that collects information on processed batches */ class ReceiverInfoCollector extends StreamingListener { - val startedReceiverStreamIds = new ArrayBuffer[Int] - val stoppedReceiverStreamIds = new ArrayBuffer[Int]() - val receiverErrors = new ArrayBuffer[(Int, String, String)]() + val startedReceiverStreamIds = new ArrayBuffer[Int] with SynchronizedBuffer[Int] + val stoppedReceiverStreamIds = new ArrayBuffer[Int] with SynchronizedBuffer[Int] + val receiverErrors = + new ArrayBuffer[(Int, String, String)] with SynchronizedBuffer[(Int, String, String)] override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted) { startedReceiverStreamIds += receiverStarted.receiverInfo.streamId diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala index c3cae8aeb6d15..4d0cd7516f42e 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala @@ -29,10 +29,10 @@ import org.scalatest.time.{Span, Seconds => ScalaTestSeconds} import org.scalatest.concurrent.Eventually.timeout import org.scalatest.concurrent.PatienceConfiguration -import org.apache.spark.streaming.dstream.{DStream, InputDStream, ForEachDStream} -import org.apache.spark.streaming.scheduler.{StreamingListenerBatchStarted, StreamingListenerBatchCompleted, StreamingListener} import org.apache.spark.{SparkConf, Logging} import org.apache.spark.rdd.RDD +import org.apache.spark.streaming.dstream.{DStream, InputDStream, ForEachDStream} +import org.apache.spark.streaming.scheduler._ import org.apache.spark.util.{ManualClock, Utils} /** @@ -57,6 +57,10 @@ class TestInputStream[T: ClassTag](ssc_ : StreamingContext, input: Seq[Seq[T]], return None } + // Report the input data's information to InputInfoTracker for testing + val inputInfo = InputInfo(id, selectedInput.length.toLong) + ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo) + val rdd = ssc.sc.makeRDD(selectedInput, numPartitions) logInfo("Created RDD " + rdd.id + " with " + selectedInput) Some(rdd) @@ -142,6 +146,40 @@ class BatchCounter(ssc: StreamingContext) { def getNumStartedBatches: Int = this.synchronized { numStartedBatches } + + /** + * Wait until `expectedNumCompletedBatches` batches are completed, or timeout. Return true if + * `expectedNumCompletedBatches` batches are completed. Otherwise, return false to indicate it's + * timeout. + * + * @param expectedNumCompletedBatches the `expectedNumCompletedBatches` batches to wait + * @param timeout the maximum time to wait in milliseconds. + */ + def waitUntilBatchesCompleted(expectedNumCompletedBatches: Int, timeout: Long): Boolean = + waitUntilConditionBecomeTrue(numCompletedBatches >= expectedNumCompletedBatches, timeout) + + /** + * Wait until `expectedNumStartedBatches` batches are completed, or timeout. Return true if + * `expectedNumStartedBatches` batches are completed. Otherwise, return false to indicate it's + * timeout. + * + * @param expectedNumStartedBatches the `expectedNumStartedBatches` batches to wait + * @param timeout the maximum time to wait in milliseconds. + */ + def waitUntilBatchesStarted(expectedNumStartedBatches: Int, timeout: Long): Boolean = + waitUntilConditionBecomeTrue(numStartedBatches >= expectedNumStartedBatches, timeout) + + private def waitUntilConditionBecomeTrue(condition: => Boolean, timeout: Long): Boolean = { + synchronized { + var now = System.currentTimeMillis() + val timeoutTick = now + timeout + while (!condition && timeoutTick > now) { + wait(timeoutTick - now) + now = System.currentTimeMillis() + } + condition + } + } } /** diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala index 205ddf6dbe9b0..2211f62383ce8 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.streaming +import scala.collection.mutable.Queue + import org.openqa.selenium.WebDriver import org.openqa.selenium.htmlunit.HtmlUnitDriver import org.scalatest._ @@ -60,8 +62,28 @@ class UISeleniumSuite ssc } + private def setupStreams(ssc: StreamingContext): Unit = { + val rdds = Queue(ssc.sc.parallelize(1 to 4, 4)) + val inputStream = ssc.queueStream(rdds) + inputStream.foreachRDD { rdd => + rdd.foreach(_ => {}) + rdd.foreach(_ => {}) + } + inputStream.foreachRDD { rdd => + rdd.foreach(_ => {}) + try { + rdd.foreach(_ => throw new RuntimeException("Oops")) + } catch { + case e: SparkException if e.getMessage.contains("Oops") => + } + } + } + test("attaching and detaching a Streaming tab") { withStreamingContext(newSparkStreamingContext()) { ssc => + setupStreams(ssc) + ssc.start() + val sparkUI = ssc.sparkContext.ui.get eventually(timeout(10 seconds), interval(50 milliseconds)) { @@ -72,20 +94,92 @@ class UISeleniumSuite eventually(timeout(10 seconds), interval(50 milliseconds)) { // check whether streaming page exists go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming") - val statisticText = findAll(cssSelector("li strong")).map(_.text).toSeq - statisticText should contain("Network receivers:") - statisticText should contain("Batch interval:") - + val h3Text = findAll(cssSelector("h3")).map(_.text).toSeq + h3Text should contain("Streaming Statistics") + + // Check stat table + val statTableHeaders = findAll(cssSelector("#stat-table th")).map(_.text).toSeq + statTableHeaders.exists( + _.matches("Timelines \\(Last \\d+ batches, \\d+ active, \\d+ completed\\)")) should be + (true) + statTableHeaders should contain ("Histograms") + + val statTableCells = findAll(cssSelector("#stat-table td")).map(_.text).toSeq + statTableCells.exists(_.contains("Input Rate")) should be (true) + statTableCells.exists(_.contains("Scheduling Delay")) should be (true) + statTableCells.exists(_.contains("Processing Time")) should be (true) + statTableCells.exists(_.contains("Total Delay")) should be (true) + + // Check batch tables val h4Text = findAll(cssSelector("h4")).map(_.text).toSeq - h4Text should contain("Active Batches (0)") - h4Text should contain("Completed Batches (last 0 out of 0)") + h4Text.exists(_.matches("Active Batches \\(\\d+\\)")) should be (true) + h4Text.exists(_.matches("Completed Batches \\(last \\d+ out of \\d+\\)")) should be (true) findAll(cssSelector("""#active-batches-table th""")).map(_.text).toSeq should be { - List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Status") + List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", + "Status") } findAll(cssSelector("""#completed-batches-table th""")).map(_.text).toSeq should be { - List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Total Delay") + List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", + "Total Delay") + } + + val batchLinks = + findAll(cssSelector("""#completed-batches-table a""")).flatMap(_.attribute("href")).toSeq + batchLinks.size should be >= 1 + + // Check a normal batch page + go to (batchLinks.last) // Last should be the first batch, so it will have some jobs + val summaryText = findAll(cssSelector("li strong")).map(_.text).toSeq + summaryText should contain ("Batch Duration:") + summaryText should contain ("Input data size:") + summaryText should contain ("Scheduling delay:") + summaryText should contain ("Processing time:") + summaryText should contain ("Total delay:") + + findAll(cssSelector("""#batch-job-table th""")).map(_.text).toSeq should be { + List("Output Op Id", "Description", "Duration", "Job Id", "Duration", + "Stages: Succeeded/Total", "Tasks (for all stages): Succeeded/Total", "Error") } + + // Check we have 2 output op ids + val outputOpIds = findAll(cssSelector(".output-op-id-cell")).toSeq + outputOpIds.map(_.attribute("rowspan")) should be (List(Some("2"), Some("2"))) + outputOpIds.map(_.text) should be (List("0", "1")) + + // Check job ids + val jobIdCells = findAll(cssSelector( """#batch-job-table a""")).toSeq + jobIdCells.map(_.text) should be (List("0", "1", "2", "3")) + + val jobLinks = jobIdCells.flatMap(_.attribute("href")) + jobLinks.size should be (4) + + // Check stage progress + findAll(cssSelector(""".stage-progress-cell""")).map(_.text).toSeq should be + (List("1/1", "1/1", "1/1", "0/1 (1 failed)")) + + // Check job progress + findAll(cssSelector(""".progress-cell""")).map(_.text).toSeq should be + (List("1/1", "1/1", "1/1", "0/1 (1 failed)")) + + // Check stacktrace + val errorCells = findAll(cssSelector(""".stacktrace-details""")).map(_.text).toSeq + errorCells should have size 1 + errorCells(0) should include("java.lang.RuntimeException: Oops") + + // Check the job link in the batch page is right + go to (jobLinks(0)) + val jobDetails = findAll(cssSelector("li strong")).map(_.text).toSeq + jobDetails should contain("Status:") + jobDetails should contain("Completed Stages:") + + // Check a batch page without id + go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming/batch/") + webDriver.getPageSource should include ("Missing id parameter") + + // Check a non-exist batch + go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming/batch/?id=12345") + webDriver.getPageSource should include ("does not exist") } ssc.stop(false) @@ -97,9 +191,8 @@ class UISeleniumSuite eventually(timeout(10 seconds), interval(50 milliseconds)) { go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming") - val statisticText = findAll(cssSelector("li strong")).map(_.text).toSeq - statisticText should not contain ("Network receivers:") - statisticText should not contain ("Batch interval:") + val h3Text = findAll(cssSelector("h3")).map(_.text).toSeq + h3Text should not contain("Streaming Statistics") } } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala index c3602a5b73732..6859b65c7165f 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala @@ -21,12 +21,12 @@ import java.io.File import scala.util.Random import org.apache.hadoop.conf.Configuration -import org.scalatest.{BeforeAndAfterEach, BeforeAndAfterAll, FunSuite} +import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite} -import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.storage.{BlockId, BlockManager, StorageLevel, StreamBlockId} -import org.apache.spark.streaming.util.{WriteAheadLogFileSegment, WriteAheadLogWriter} +import org.apache.spark.streaming.util.{FileBasedWriteAheadLogSegment, FileBasedWriteAheadLogWriter} import org.apache.spark.util.Utils +import org.apache.spark.{SparkConf, SparkContext, SparkException} class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach { @@ -60,24 +60,35 @@ class WriteAheadLogBackedBlockRDDSuite System.clearProperty("spark.driver.port") } - test("Read data available in block manager and write ahead log") { - testRDD(5, 5) + test("Read data available in both block manager and write ahead log") { + testRDD(numPartitions = 5, numPartitionsInBM = 5, numPartitionsInWAL = 5) } test("Read data available only in block manager, not in write ahead log") { - testRDD(5, 0) + testRDD(numPartitions = 5, numPartitionsInBM = 5, numPartitionsInWAL = 0) } test("Read data available only in write ahead log, not in block manager") { - testRDD(0, 5) + testRDD(numPartitions = 5, numPartitionsInBM = 0, numPartitionsInWAL = 5) } - test("Read data available only in write ahead log, and test storing in block manager") { - testRDD(0, 5, testStoreInBM = true) + test("Read data with partially available in block manager, and rest in write ahead log") { + testRDD(numPartitions = 5, numPartitionsInBM = 3, numPartitionsInWAL = 2) } - test("Read data with partially available in block manager, and rest in write ahead log") { - testRDD(3, 2) + test("Test isBlockValid skips block fetching from BlockManager") { + testRDD( + numPartitions = 5, numPartitionsInBM = 5, numPartitionsInWAL = 0, testIsBlockValid = true) + } + + test("Test whether RDD is valid after removing blocks from block manager") { + testRDD( + numPartitions = 5, numPartitionsInBM = 5, numPartitionsInWAL = 5, testBlockRemove = true) + } + + test("Test storing of blocks recovered from write ahead log back into block manager") { + testRDD( + numPartitions = 5, numPartitionsInBM = 0, numPartitionsInWAL = 5, testStoreInBM = true) } /** @@ -85,24 +96,54 @@ class WriteAheadLogBackedBlockRDDSuite * and the rest to a write ahead log, and then reading reading it all back using the RDD. * It can also test if the partitions that were read from the log were again stored in * block manager. - * @param numPartitionsInBM Number of partitions to write to the Block Manager - * @param numPartitionsInWAL Number of partitions to write to the Write Ahead Log - * @param testStoreInBM Test whether blocks read from log are stored back into block manager + * + * + * + * @param numPartitions Number of partitions in RDD + * @param numPartitionsInBM Number of partitions to write to the BlockManager. + * Partitions 0 to (numPartitionsInBM-1) will be written to BlockManager + * @param numPartitionsInWAL Number of partitions to write to the Write Ahead Log. + * Partitions (numPartitions - 1 - numPartitionsInWAL) to + * (numPartitions - 1) will be written to WAL + * @param testIsBlockValid Test whether setting isBlockValid to false skips block fetching + * @param testBlockRemove Test whether calling rdd.removeBlock() makes the RDD still usable with + * reads falling back to the WAL + * @param testStoreInBM Test whether blocks read from log are stored back into block manager + * + * Example with numPartitions = 5, numPartitionsInBM = 3, and numPartitionsInWAL = 4 + * + * numPartitionsInBM = 3 + * |------------------| + * | | + * 0 1 2 3 4 + * | | + * |-------------------------| + * numPartitionsInWAL = 4 */ private def testRDD( - numPartitionsInBM: Int, numPartitionsInWAL: Int, testStoreInBM: Boolean = false) { - val numBlocks = numPartitionsInBM + numPartitionsInWAL - val data = Seq.fill(numBlocks, 10)(scala.util.Random.nextString(50)) + numPartitions: Int, + numPartitionsInBM: Int, + numPartitionsInWAL: Int, + testIsBlockValid: Boolean = false, + testBlockRemove: Boolean = false, + testStoreInBM: Boolean = false + ) { + require(numPartitionsInBM <= numPartitions, + "Can't put more partitions in BlockManager than that in RDD") + require(numPartitionsInWAL <= numPartitions, + "Can't put more partitions in write ahead log than that in RDD") + val data = Seq.fill(numPartitions, 10)(scala.util.Random.nextString(50)) // Put the necessary blocks in the block manager - val blockIds = Array.fill(numBlocks)(StreamBlockId(Random.nextInt(), Random.nextInt())) + val blockIds = Array.fill(numPartitions)(StreamBlockId(Random.nextInt(), Random.nextInt())) data.zip(blockIds).take(numPartitionsInBM).foreach { case(block, blockId) => blockManager.putIterator(blockId, block.iterator, StorageLevel.MEMORY_ONLY_SER) } - // Generate write ahead log segments - val segments = generateFakeSegments(numPartitionsInBM) ++ - writeLogSegments(data.takeRight(numPartitionsInWAL), blockIds.takeRight(numPartitionsInWAL)) + // Generate write ahead log record handles + val recordHandles = generateFakeRecordHandles(numPartitions - numPartitionsInWAL) ++ + generateWALRecordHandles(data.takeRight(numPartitionsInWAL), + blockIds.takeRight(numPartitionsInWAL)) // Make sure that the left `numPartitionsInBM` blocks are in block manager, and others are not require( @@ -110,30 +151,53 @@ class WriteAheadLogBackedBlockRDDSuite "Expected blocks not in BlockManager" ) require( - blockIds.takeRight(numPartitionsInWAL).forall(blockManager.get(_).isEmpty), + blockIds.takeRight(numPartitions - numPartitionsInBM).forall(blockManager.get(_).isEmpty), "Unexpected blocks in BlockManager" ) // Make sure that the right `numPartitionsInWAL` blocks are in WALs, and other are not require( - segments.takeRight(numPartitionsInWAL).forall(s => + recordHandles.takeRight(numPartitionsInWAL).forall(s => new File(s.path.stripPrefix("file://")).exists()), "Expected blocks not in write ahead log" ) require( - segments.take(numPartitionsInBM).forall(s => + recordHandles.take(numPartitions - numPartitionsInWAL).forall(s => !new File(s.path.stripPrefix("file://")).exists()), "Unexpected blocks in write ahead log" ) // Create the RDD and verify whether the returned data is correct val rdd = new WriteAheadLogBackedBlockRDD[String](sparkContext, blockIds.toArray, - segments.toArray, storeInBlockManager = false, StorageLevel.MEMORY_ONLY) + recordHandles.toArray, storeInBlockManager = false) assert(rdd.collect() === data.flatten) + // Verify that the block fetching is skipped when isBlockValid is set to false. + // This is done by using a RDD whose data is only in memory but is set to skip block fetching + // Using that RDD will throw exception, as it skips block fetching even if the blocks are in + // in BlockManager. + if (testIsBlockValid) { + require(numPartitionsInBM === numPartitions, "All partitions must be in BlockManager") + require(numPartitionsInWAL === 0, "No partitions must be in WAL") + val rdd2 = new WriteAheadLogBackedBlockRDD[String](sparkContext, blockIds.toArray, + recordHandles.toArray, isBlockIdValid = Array.fill(blockIds.length)(false)) + intercept[SparkException] { + rdd2.collect() + } + } + + // Verify that the RDD is not invalid after the blocks are removed and can still read data + // from write ahead log + if (testBlockRemove) { + require(numPartitions === numPartitionsInWAL, "All partitions must be in WAL for this test") + require(numPartitionsInBM > 0, "Some partitions must be in BlockManager for this test") + rdd.removeBlocks() + assert(rdd.collect() === data.flatten) + } + if (testStoreInBM) { val rdd2 = new WriteAheadLogBackedBlockRDD[String](sparkContext, blockIds.toArray, - segments.toArray, storeInBlockManager = true, StorageLevel.MEMORY_ONLY) + recordHandles.toArray, storeInBlockManager = true, storageLevel = StorageLevel.MEMORY_ONLY) assert(rdd2.collect() === data.flatten) assert( blockIds.forall(blockManager.get(_).nonEmpty), @@ -142,12 +206,12 @@ class WriteAheadLogBackedBlockRDDSuite } } - private def writeLogSegments( + private def generateWALRecordHandles( blockData: Seq[Seq[String]], blockIds: Seq[BlockId] - ): Seq[WriteAheadLogFileSegment] = { + ): Seq[FileBasedWriteAheadLogSegment] = { require(blockData.size === blockIds.size) - val writer = new WriteAheadLogWriter(new File(dir, "logFile").toString, hadoopConf) + val writer = new FileBasedWriteAheadLogWriter(new File(dir, "logFile").toString, hadoopConf) val segments = blockData.zip(blockIds).map { case (data, id) => writer.write(blockManager.dataSerialize(id, data.iterator)) } @@ -155,7 +219,7 @@ class WriteAheadLogBackedBlockRDDSuite segments } - private def generateFakeSegments(count: Int): Seq[WriteAheadLogFileSegment] = { - Array.fill(count)(new WriteAheadLogFileSegment("random", 0L, 0)) + private def generateFakeRecordHandles(count: Int): Seq[FileBasedWriteAheadLogSegment] = { + Array.fill(count)(new FileBasedWriteAheadLogSegment("random", 0L, 0)) } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala new file mode 100644 index 0000000000000..5478b41845943 --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala @@ -0,0 +1,79 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import org.scalatest.{BeforeAndAfter, FunSuite} + +import org.apache.spark.SparkConf +import org.apache.spark.streaming.{Time, Duration, StreamingContext} + +class InputInfoTrackerSuite extends FunSuite with BeforeAndAfter { + + private var ssc: StreamingContext = _ + + before { + val conf = new SparkConf().setMaster("local[2]").setAppName("DirectStreamTacker") + if (ssc == null) { + ssc = new StreamingContext(conf, Duration(1000)) + } + } + + after { + if (ssc != null) { + ssc.stop() + ssc = null + } + } + + test("test report and get InputInfo from InputInfoTracker") { + val inputInfoTracker = new InputInfoTracker(ssc) + + val streamId1 = 0 + val streamId2 = 1 + val time = Time(0L) + val inputInfo1 = InputInfo(streamId1, 100L) + val inputInfo2 = InputInfo(streamId2, 300L) + inputInfoTracker.reportInfo(time, inputInfo1) + inputInfoTracker.reportInfo(time, inputInfo2) + + val batchTimeToInputInfos = inputInfoTracker.getInfo(time) + assert(batchTimeToInputInfos.size == 2) + assert(batchTimeToInputInfos.keys === Set(streamId1, streamId2)) + assert(batchTimeToInputInfos(streamId1) === inputInfo1) + assert(batchTimeToInputInfos(streamId2) === inputInfo2) + assert(inputInfoTracker.getInfo(time)(streamId1) === inputInfo1) + } + + test("test cleanup InputInfo from InputInfoTracker") { + val inputInfoTracker = new InputInfoTracker(ssc) + + val streamId1 = 0 + val inputInfo1 = InputInfo(streamId1, 100L) + val inputInfo2 = InputInfo(streamId1, 300L) + inputInfoTracker.reportInfo(Time(0), inputInfo1) + inputInfoTracker.reportInfo(Time(1), inputInfo2) + + inputInfoTracker.cleanup(Time(0)) + assert(inputInfoTracker.getInfo(Time(0))(streamId1) === inputInfo1) + assert(inputInfoTracker.getInfo(Time(1))(streamId1) === inputInfo2) + + inputInfoTracker.cleanup(Time(1)) + assert(inputInfoTracker.getInfo(Time(0)).get(streamId1) === None) + assert(inputInfoTracker.getInfo(Time(1))(streamId1) === inputInfo2) + } +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala index 94b1985116feb..2a0f45830e03c 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala @@ -17,8 +17,11 @@ package org.apache.spark.streaming.ui +import java.util.Properties + import org.scalatest.Matchers +import org.apache.spark.scheduler.SparkListenerJobStart import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.scheduler._ import org.apache.spark.streaming.{Duration, Time, Milliseconds, TestSuiteBase} @@ -28,6 +31,17 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { val input = (1 to 4).map(Seq(_)).toSeq val operation = (d: DStream[Int]) => d.map(x => x) + private def createJobStart( + batchTime: Time, outputOpId: Int, jobId: Int): SparkListenerJobStart = { + val properties = new Properties() + properties.setProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, batchTime.milliseconds.toString) + properties.setProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, outputOpId.toString) + SparkListenerJobStart(jobId = jobId, + 0L, // unused + Nil, // unused + properties) + } + override def batchDuration: Duration = Milliseconds(100) test("onBatchSubmitted, onBatchStarted, onBatchCompleted, " + @@ -35,15 +49,12 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { val ssc = setupStreams(input, operation) val listener = new StreamingJobProgressListener(ssc) - val receivedBlockInfo = Map( - 0 -> Array(ReceivedBlockInfo(0, 100, null), ReceivedBlockInfo(0, 200, null)), - 1 -> Array(ReceivedBlockInfo(1, 300, null)) - ) + val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L) // onBatchSubmitted - val batchInfoSubmitted = BatchInfo(Time(1000), receivedBlockInfo, 1000, None, None) + val batchInfoSubmitted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, None, None) listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted)) - listener.waitingBatches should be (List(batchInfoSubmitted)) + listener.waitingBatches should be (List(BatchUIData(batchInfoSubmitted))) listener.runningBatches should be (Nil) listener.retainedCompletedBatches should be (Nil) listener.lastCompletedBatch should be (None) @@ -53,10 +64,10 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { listener.numTotalReceivedRecords should be (0) // onBatchStarted - val batchInfoStarted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + val batchInfoStarted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted)) listener.waitingBatches should be (Nil) - listener.runningBatches should be (List(batchInfoStarted)) + listener.runningBatches should be (List(BatchUIData(batchInfoStarted))) listener.retainedCompletedBatches should be (Nil) listener.lastCompletedBatch should be (None) listener.numUnprocessedBatches should be (1) @@ -64,13 +75,40 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { listener.numTotalProcessedRecords should be (0) listener.numTotalReceivedRecords should be (600) + // onJobStart + val jobStart1 = createJobStart(Time(1000), outputOpId = 0, jobId = 0) + listener.onJobStart(jobStart1) + + val jobStart2 = createJobStart(Time(1000), outputOpId = 0, jobId = 1) + listener.onJobStart(jobStart2) + + val jobStart3 = createJobStart(Time(1000), outputOpId = 1, jobId = 0) + listener.onJobStart(jobStart3) + + val jobStart4 = createJobStart(Time(1000), outputOpId = 1, jobId = 1) + listener.onJobStart(jobStart4) + + val batchUIData = listener.getBatchUIData(Time(1000)) + batchUIData should not be None + batchUIData.get.batchTime should be (batchInfoStarted.batchTime) + batchUIData.get.schedulingDelay should be (batchInfoStarted.schedulingDelay) + batchUIData.get.processingDelay should be (batchInfoStarted.processingDelay) + batchUIData.get.totalDelay should be (batchInfoStarted.totalDelay) + batchUIData.get.streamIdToNumRecords should be (Map(0 -> 300L, 1 -> 300L)) + batchUIData.get.numRecords should be(600) + batchUIData.get.outputOpIdSparkJobIdPairs should be + Seq(OutputOpIdAndSparkJobId(0, 0), + OutputOpIdAndSparkJobId(0, 1), + OutputOpIdAndSparkJobId(1, 0), + OutputOpIdAndSparkJobId(1, 1)) + // onBatchCompleted - val batchInfoCompleted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + val batchInfoCompleted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) listener.waitingBatches should be (Nil) listener.runningBatches should be (Nil) - listener.retainedCompletedBatches should be (List(batchInfoCompleted)) - listener.lastCompletedBatch should be (Some(batchInfoCompleted)) + listener.retainedCompletedBatches should be (List(BatchUIData(batchInfoCompleted))) + listener.lastCompletedBatch should be (Some(BatchUIData(batchInfoCompleted))) listener.numUnprocessedBatches should be (0) listener.numTotalCompletedBatches should be (1) listener.numTotalProcessedRecords should be (600) @@ -100,14 +138,12 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { test("Remove the old completed batches when exceeding the limit") { val ssc = setupStreams(input, operation) - val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) + val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000) val listener = new StreamingJobProgressListener(ssc) - val receivedBlockInfo = Map( - 0 -> Array(ReceivedBlockInfo(0, 100, null), ReceivedBlockInfo(0, 200, null)), - 1 -> Array(ReceivedBlockInfo(1, 300, null)) - ) - val batchInfoCompleted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L) + + val batchInfoCompleted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) for(_ <- 0 until (limit + 10)) { listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) @@ -116,4 +152,99 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { listener.retainedCompletedBatches.size should be (limit) listener.numTotalCompletedBatches should be(limit + 10) } + + test("out-of-order onJobStart and onBatchXXX") { + val ssc = setupStreams(input, operation) + val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000) + val listener = new StreamingJobProgressListener(ssc) + + // fulfill completedBatchInfos + for(i <- 0 until limit) { + val batchInfoCompleted = + BatchInfo(Time(1000 + i * 100), Map.empty, 1000 + i * 100, Some(2000 + i * 100), None) + listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) + val jobStart = createJobStart(Time(1000 + i * 100), outputOpId = 0, jobId = 1) + listener.onJobStart(jobStart) + } + + // onJobStart happens before onBatchSubmitted + val jobStart = createJobStart(Time(1000 + limit * 100), outputOpId = 0, jobId = 0) + listener.onJobStart(jobStart) + + val batchInfoSubmitted = + BatchInfo(Time(1000 + limit * 100), Map.empty, (1000 + limit * 100), None, None) + listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted)) + + // We still can see the info retrieved from onJobStart + val batchUIData = listener.getBatchUIData(Time(1000 + limit * 100)) + batchUIData should not be None + batchUIData.get.batchTime should be (batchInfoSubmitted.batchTime) + batchUIData.get.schedulingDelay should be (batchInfoSubmitted.schedulingDelay) + batchUIData.get.processingDelay should be (batchInfoSubmitted.processingDelay) + batchUIData.get.totalDelay should be (batchInfoSubmitted.totalDelay) + batchUIData.get.streamIdToNumRecords should be (Map.empty) + batchUIData.get.numRecords should be (0) + batchUIData.get.outputOpIdSparkJobIdPairs should be (Seq(OutputOpIdAndSparkJobId(0, 0))) + + // A lot of "onBatchCompleted"s happen before "onJobStart" + for(i <- limit + 1 to limit * 2) { + val batchInfoCompleted = + BatchInfo(Time(1000 + i * 100), Map.empty, 1000 + i * 100, Some(2000 + i * 100), None) + listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) + } + + for(i <- limit + 1 to limit * 2) { + val jobStart = createJobStart(Time(1000 + i * 100), outputOpId = 0, jobId = 1) + listener.onJobStart(jobStart) + } + + // We should not leak memory + listener.batchTimeToOutputOpIdSparkJobIdPair.size() should be <= + (listener.waitingBatches.size + listener.runningBatches.size + + listener.retainedCompletedBatches.size + 10) + } + + test("detect memory leak") { + val ssc = setupStreams(input, operation) + val listener = new StreamingJobProgressListener(ssc) + + val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000) + + for (_ <- 0 until 2 * limit) { + val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L) + + // onBatchSubmitted + val batchInfoSubmitted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, None, None) + listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted)) + + // onBatchStarted + val batchInfoStarted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) + listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted)) + + // onJobStart + val jobStart1 = createJobStart(Time(1000), outputOpId = 0, jobId = 0) + listener.onJobStart(jobStart1) + + val jobStart2 = createJobStart(Time(1000), outputOpId = 0, jobId = 1) + listener.onJobStart(jobStart2) + + val jobStart3 = createJobStart(Time(1000), outputOpId = 1, jobId = 0) + listener.onJobStart(jobStart3) + + val jobStart4 = createJobStart(Time(1000), outputOpId = 1, jobId = 1) + listener.onJobStart(jobStart4) + + // onBatchCompleted + val batchInfoCompleted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) + listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) + } + + listener.waitingBatches.size should be (0) + listener.runningBatches.size should be (0) + listener.retainedCompletedBatches.size should be (limit) + listener.batchTimeToOutputOpIdSparkJobIdPair.size() should be <= + (listener.waitingBatches.size + listener.runningBatches.size + + listener.retainedCompletedBatches.size + 10) + } + } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala new file mode 100644 index 0000000000000..6df1a63ab2e37 --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala @@ -0,0 +1,67 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.ui + +import java.util.concurrent.TimeUnit + +import org.scalatest.FunSuite +import org.scalatest.Matchers + +class UIUtilsSuite extends FunSuite with Matchers{ + + test("shortTimeUnitString") { + assert("ns" === UIUtils.shortTimeUnitString(TimeUnit.NANOSECONDS)) + assert("us" === UIUtils.shortTimeUnitString(TimeUnit.MICROSECONDS)) + assert("ms" === UIUtils.shortTimeUnitString(TimeUnit.MILLISECONDS)) + assert("sec" === UIUtils.shortTimeUnitString(TimeUnit.SECONDS)) + assert("min" === UIUtils.shortTimeUnitString(TimeUnit.MINUTES)) + assert("hrs" === UIUtils.shortTimeUnitString(TimeUnit.HOURS)) + assert("days" === UIUtils.shortTimeUnitString(TimeUnit.DAYS)) + } + + test("normalizeDuration") { + verifyNormalizedTime(900, TimeUnit.MILLISECONDS, 900) + verifyNormalizedTime(1.0, TimeUnit.SECONDS, 1000) + verifyNormalizedTime(1.0, TimeUnit.MINUTES, 60 * 1000) + verifyNormalizedTime(1.0, TimeUnit.HOURS, 60 * 60 * 1000) + verifyNormalizedTime(1.0, TimeUnit.DAYS, 24 * 60 * 60 * 1000) + } + + private def verifyNormalizedTime( + expectedTime: Double, expectedUnit: TimeUnit, input: Long): Unit = { + val (time, unit) = UIUtils.normalizeDuration(input) + time should be (expectedTime +- 1E-6) + unit should be (expectedUnit) + } + + test("convertToTimeUnit") { + verifyConvertToTimeUnit(60.0 * 1000 * 1000 * 1000, 60 * 1000, TimeUnit.NANOSECONDS) + verifyConvertToTimeUnit(60.0 * 1000 * 1000, 60 * 1000, TimeUnit.MICROSECONDS) + verifyConvertToTimeUnit(60 * 1000, 60 * 1000, TimeUnit.MILLISECONDS) + verifyConvertToTimeUnit(60, 60 * 1000, TimeUnit.SECONDS) + verifyConvertToTimeUnit(1, 60 * 1000, TimeUnit.MINUTES) + verifyConvertToTimeUnit(1.0 / 60, 60 * 1000, TimeUnit.HOURS) + verifyConvertToTimeUnit(1.0 / 60 / 24, 60 * 1000, TimeUnit.DAYS) + } + + private def verifyConvertToTimeUnit( + expectedTime: Double, milliseconds: Long, unit: TimeUnit): Unit = { + val convertedTime = UIUtils.convertToTimeUnit(milliseconds, unit) + convertedTime should be (expectedTime +- 1E-6) + } +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala index a3919c43b95b4..79098bcf4861c 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala @@ -18,33 +18,38 @@ package org.apache.spark.streaming.util import java.io._ import java.nio.ByteBuffer +import java.util import scala.collection.mutable.ArrayBuffer import scala.concurrent.duration._ import scala.language.{implicitConversions, postfixOps} +import scala.reflect.ClassTag -import WriteAheadLogSuite._ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.util.{ManualClock, Utils} -import org.scalatest.{BeforeAndAfter, FunSuite} import org.scalatest.concurrent.Eventually._ +import org.scalatest.{BeforeAndAfter, FunSuite} + +import org.apache.spark.util.{ManualClock, Utils} +import org.apache.spark.{SparkConf, SparkException} class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { + import WriteAheadLogSuite._ + val hadoopConf = new Configuration() var tempDir: File = null var testDir: String = null var testFile: String = null - var manager: WriteAheadLogManager = null + var writeAheadLog: FileBasedWriteAheadLog = null before { tempDir = Utils.createTempDir() testDir = tempDir.toString testFile = new File(tempDir, "testFile").toString - if (manager != null) { - manager.stop() - manager = null + if (writeAheadLog != null) { + writeAheadLog.close() + writeAheadLog = null } } @@ -52,16 +57,60 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { Utils.deleteRecursively(tempDir) } - test("WriteAheadLogWriter - writing data") { + test("WriteAheadLogUtils - log selection and creation") { + val logDir = Utils.createTempDir().getAbsolutePath() + + def assertDriverLogClass[T <: WriteAheadLog: ClassTag](conf: SparkConf): WriteAheadLog = { + val log = WriteAheadLogUtils.createLogForDriver(conf, logDir, hadoopConf) + assert(log.getClass === implicitly[ClassTag[T]].runtimeClass) + log + } + + def assertReceiverLogClass[T: ClassTag](conf: SparkConf): WriteAheadLog = { + val log = WriteAheadLogUtils.createLogForReceiver(conf, logDir, hadoopConf) + assert(log.getClass === implicitly[ClassTag[T]].runtimeClass) + log + } + + val emptyConf = new SparkConf() // no log configuration + assertDriverLogClass[FileBasedWriteAheadLog](emptyConf) + assertReceiverLogClass[FileBasedWriteAheadLog](emptyConf) + + // Verify setting driver WAL class + val conf1 = new SparkConf().set("spark.streaming.driver.writeAheadLog.class", + classOf[MockWriteAheadLog0].getName()) + assertDriverLogClass[MockWriteAheadLog0](conf1) + assertReceiverLogClass[FileBasedWriteAheadLog](conf1) + + // Verify setting receiver WAL class + val receiverWALConf = new SparkConf().set("spark.streaming.receiver.writeAheadLog.class", + classOf[MockWriteAheadLog0].getName()) + assertDriverLogClass[FileBasedWriteAheadLog](receiverWALConf) + assertReceiverLogClass[MockWriteAheadLog0](receiverWALConf) + + // Verify setting receiver WAL class with 1-arg constructor + val receiverWALConf2 = new SparkConf().set("spark.streaming.receiver.writeAheadLog.class", + classOf[MockWriteAheadLog1].getName()) + assertReceiverLogClass[MockWriteAheadLog1](receiverWALConf2) + + // Verify failure setting receiver WAL class with 2-arg constructor + intercept[SparkException] { + val receiverWALConf3 = new SparkConf().set("spark.streaming.receiver.writeAheadLog.class", + classOf[MockWriteAheadLog2].getName()) + assertReceiverLogClass[MockWriteAheadLog1](receiverWALConf3) + } + } + + test("FileBasedWriteAheadLogWriter - writing data") { val dataToWrite = generateRandomData() val segments = writeDataUsingWriter(testFile, dataToWrite) val writtenData = readDataManually(segments) assert(writtenData === dataToWrite) } - test("WriteAheadLogWriter - syncing of data by writing and reading immediately") { + test("FileBasedWriteAheadLogWriter - syncing of data by writing and reading immediately") { val dataToWrite = generateRandomData() - val writer = new WriteAheadLogWriter(testFile, hadoopConf) + val writer = new FileBasedWriteAheadLogWriter(testFile, hadoopConf) dataToWrite.foreach { data => val segment = writer.write(stringToByteBuffer(data)) val dataRead = readDataManually(Seq(segment)).head @@ -70,10 +119,10 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { writer.close() } - test("WriteAheadLogReader - sequentially reading data") { + test("FileBasedWriteAheadLogReader - sequentially reading data") { val writtenData = generateRandomData() writeDataManually(writtenData, testFile) - val reader = new WriteAheadLogReader(testFile, hadoopConf) + val reader = new FileBasedWriteAheadLogReader(testFile, hadoopConf) val readData = reader.toSeq.map(byteBufferToString) assert(readData === writtenData) assert(reader.hasNext === false) @@ -83,14 +132,14 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { reader.close() } - test("WriteAheadLogReader - sequentially reading data written with writer") { + test("FileBasedWriteAheadLogReader - sequentially reading data written with writer") { val dataToWrite = generateRandomData() writeDataUsingWriter(testFile, dataToWrite) val readData = readDataUsingReader(testFile) assert(readData === dataToWrite) } - test("WriteAheadLogReader - reading data written with writer after corrupted write") { + test("FileBasedWriteAheadLogReader - reading data written with writer after corrupted write") { // Write data manually for testing the sequential reader val dataToWrite = generateRandomData() writeDataUsingWriter(testFile, dataToWrite) @@ -113,38 +162,38 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { assert(readDataUsingReader(testFile) === (dataToWrite.dropRight(1))) } - test("WriteAheadLogRandomReader - reading data using random reader") { + test("FileBasedWriteAheadLogRandomReader - reading data using random reader") { // Write data manually for testing the random reader val writtenData = generateRandomData() val segments = writeDataManually(writtenData, testFile) // Get a random order of these segments and read them back val writtenDataAndSegments = writtenData.zip(segments).toSeq.permutations.take(10).flatten - val reader = new WriteAheadLogRandomReader(testFile, hadoopConf) + val reader = new FileBasedWriteAheadLogRandomReader(testFile, hadoopConf) writtenDataAndSegments.foreach { case (data, segment) => assert(data === byteBufferToString(reader.read(segment))) } reader.close() } - test("WriteAheadLogRandomReader - reading data using random reader written with writer") { + test("FileBasedWriteAheadLogRandomReader- reading data using random reader written with writer") { // Write data using writer for testing the random reader val data = generateRandomData() val segments = writeDataUsingWriter(testFile, data) // Read a random sequence of segments and verify read data val dataAndSegments = data.zip(segments).toSeq.permutations.take(10).flatten - val reader = new WriteAheadLogRandomReader(testFile, hadoopConf) + val reader = new FileBasedWriteAheadLogRandomReader(testFile, hadoopConf) dataAndSegments.foreach { case (data, segment) => assert(data === byteBufferToString(reader.read(segment))) } reader.close() } - test("WriteAheadLogManager - write rotating logs") { - // Write data using manager + test("FileBasedWriteAheadLog - write rotating logs") { + // Write data with rotation using WriteAheadLog class val dataToWrite = generateRandomData() - writeDataUsingManager(testDir, dataToWrite) + writeDataUsingWriteAheadLog(testDir, dataToWrite) // Read data manually to verify the written data val logFiles = getLogFilesInDirectory(testDir) @@ -153,8 +202,8 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { assert(writtenData === dataToWrite) } - test("WriteAheadLogManager - read rotating logs") { - // Write data manually for testing reading through manager + test("FileBasedWriteAheadLog - read rotating logs") { + // Write data manually for testing reading through WriteAheadLog val writtenData = (1 to 10).map { i => val data = generateRandomData() val file = testDir + s"/log-$i-$i" @@ -167,25 +216,25 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { assert(fileSystem.exists(logDirectoryPath) === true) // Read data using manager and verify - val readData = readDataUsingManager(testDir) + val readData = readDataUsingWriteAheadLog(testDir) assert(readData === writtenData) } - test("WriteAheadLogManager - recover past logs when creating new manager") { + test("FileBasedWriteAheadLog - recover past logs when creating new manager") { // Write data with manager, recover with new manager and verify val dataToWrite = generateRandomData() - writeDataUsingManager(testDir, dataToWrite) + writeDataUsingWriteAheadLog(testDir, dataToWrite) val logFiles = getLogFilesInDirectory(testDir) assert(logFiles.size > 1) - val readData = readDataUsingManager(testDir) + val readData = readDataUsingWriteAheadLog(testDir) assert(dataToWrite === readData) } - test("WriteAheadLogManager - cleanup old logs") { + test("FileBasedWriteAheadLog - clean old logs") { logCleanUpTest(waitForCompletion = false) } - test("WriteAheadLogManager - cleanup old logs synchronously") { + test("FileBasedWriteAheadLog - clean old logs synchronously") { logCleanUpTest(waitForCompletion = true) } @@ -193,11 +242,11 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { // Write data with manager, recover with new manager and verify val manualClock = new ManualClock val dataToWrite = generateRandomData() - manager = writeDataUsingManager(testDir, dataToWrite, manualClock, stopManager = false) + writeAheadLog = writeDataUsingWriteAheadLog(testDir, dataToWrite, manualClock, closeLog = false) val logFiles = getLogFilesInDirectory(testDir) assert(logFiles.size > 1) - manager.cleanupOldLogs(manualClock.getTimeMillis() / 2, waitForCompletion) + writeAheadLog.clean(manualClock.getTimeMillis() / 2, waitForCompletion) if (waitForCompletion) { assert(getLogFilesInDirectory(testDir).size < logFiles.size) @@ -208,11 +257,11 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { } } - test("WriteAheadLogManager - handling file errors while reading rotating logs") { + test("FileBasedWriteAheadLog - handling file errors while reading rotating logs") { // Generate a set of log files val manualClock = new ManualClock val dataToWrite1 = generateRandomData() - writeDataUsingManager(testDir, dataToWrite1, manualClock) + writeDataUsingWriteAheadLog(testDir, dataToWrite1, manualClock) val logFiles1 = getLogFilesInDirectory(testDir) assert(logFiles1.size > 1) @@ -220,12 +269,12 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { // Recover old files and generate a second set of log files val dataToWrite2 = generateRandomData() manualClock.advance(100000) - writeDataUsingManager(testDir, dataToWrite2, manualClock) + writeDataUsingWriteAheadLog(testDir, dataToWrite2, manualClock) val logFiles2 = getLogFilesInDirectory(testDir) assert(logFiles2.size > logFiles1.size) // Read the files and verify that all the written data can be read - val readData1 = readDataUsingManager(testDir) + val readData1 = readDataUsingWriteAheadLog(testDir) assert(readData1 === (dataToWrite1 ++ dataToWrite2)) // Corrupt the first set of files so that they are basically unreadable @@ -236,25 +285,51 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { } // Verify that the corrupted files do not prevent reading of the second set of data - val readData = readDataUsingManager(testDir) + val readData = readDataUsingWriteAheadLog(testDir) assert(readData === dataToWrite2) } + + test("FileBasedWriteAheadLog - do not create directories or files unless write") { + val nonexistentTempPath = File.createTempFile("test", "") + nonexistentTempPath.delete() + assert(!nonexistentTempPath.exists()) + + val writtenSegment = writeDataManually(generateRandomData(), testFile) + val wal = new FileBasedWriteAheadLog( + new SparkConf(), tempDir.getAbsolutePath, new Configuration(), 1, 1) + assert(!nonexistentTempPath.exists(), "Directory created just by creating log object") + wal.read(writtenSegment.head) + assert(!nonexistentTempPath.exists(), "Directory created just by attempting to read segment") + } } object WriteAheadLogSuite { + class MockWriteAheadLog0() extends WriteAheadLog { + override def write(record: ByteBuffer, time: Long): WriteAheadLogRecordHandle = { null } + override def read(handle: WriteAheadLogRecordHandle): ByteBuffer = { null } + override def readAll(): util.Iterator[ByteBuffer] = { null } + override def clean(threshTime: Long, waitForCompletion: Boolean): Unit = { } + override def close(): Unit = { } + } + + class MockWriteAheadLog1(val conf: SparkConf) extends MockWriteAheadLog0() + + class MockWriteAheadLog2(val conf: SparkConf, x: Int) extends MockWriteAheadLog0() + + private val hadoopConf = new Configuration() /** Write data to a file directly and return an array of the file segments written. */ - def writeDataManually(data: Seq[String], file: String): Seq[WriteAheadLogFileSegment] = { - val segments = new ArrayBuffer[WriteAheadLogFileSegment]() + def writeDataManually(data: Seq[String], file: String): Seq[FileBasedWriteAheadLogSegment] = { + val segments = new ArrayBuffer[FileBasedWriteAheadLogSegment]() val writer = HdfsUtils.getOutputStream(file, hadoopConf) data.foreach { item => val offset = writer.getPos val bytes = Utils.serialize(item) writer.writeInt(bytes.size) writer.write(bytes) - segments += WriteAheadLogFileSegment(file, offset, bytes.size) + segments += FileBasedWriteAheadLogSegment(file, offset, bytes.size) } writer.close() segments @@ -263,8 +338,11 @@ object WriteAheadLogSuite { /** * Write data to a file using the writer class and return an array of the file segments written. */ - def writeDataUsingWriter(filePath: String, data: Seq[String]): Seq[WriteAheadLogFileSegment] = { - val writer = new WriteAheadLogWriter(filePath, hadoopConf) + def writeDataUsingWriter( + filePath: String, + data: Seq[String] + ): Seq[FileBasedWriteAheadLogSegment] = { + val writer = new FileBasedWriteAheadLogWriter(filePath, hadoopConf) val segments = data.map { item => writer.write(item) } @@ -272,27 +350,27 @@ object WriteAheadLogSuite { segments } - /** Write data to rotating files in log directory using the manager class. */ - def writeDataUsingManager( + /** Write data to rotating files in log directory using the WriteAheadLog class. */ + def writeDataUsingWriteAheadLog( logDirectory: String, data: Seq[String], manualClock: ManualClock = new ManualClock, - stopManager: Boolean = true - ): WriteAheadLogManager = { + closeLog: Boolean = true + ): FileBasedWriteAheadLog = { if (manualClock.getTimeMillis() < 100000) manualClock.setTime(10000) - val manager = new WriteAheadLogManager(logDirectory, hadoopConf, - rollingIntervalSecs = 1, callerName = "WriteAheadLogSuite", clock = manualClock) + val wal = new FileBasedWriteAheadLog(new SparkConf(), logDirectory, hadoopConf, 1, 1) + // Ensure that 500 does not get sorted after 2000, so put a high base value. data.foreach { item => manualClock.advance(500) - manager.writeToLog(item) + wal.write(item, manualClock.getTimeMillis()) } - if (stopManager) manager.stop() - manager + if (closeLog) wal.close() + wal } /** Read data from a segments of a log file directly and return the list of byte buffers. */ - def readDataManually(segments: Seq[WriteAheadLogFileSegment]): Seq[String] = { + def readDataManually(segments: Seq[FileBasedWriteAheadLogSegment]): Seq[String] = { segments.map { segment => val reader = HdfsUtils.getInputStream(segment.path, hadoopConf) try { @@ -331,18 +409,18 @@ object WriteAheadLogSuite { /** Read all the data from a log file using reader class and return the list of byte buffers. */ def readDataUsingReader(file: String): Seq[String] = { - val reader = new WriteAheadLogReader(file, hadoopConf) + val reader = new FileBasedWriteAheadLogReader(file, hadoopConf) val readData = reader.toList.map(byteBufferToString) reader.close() readData } - /** Read all the data in the log file in a directory using the manager class. */ - def readDataUsingManager(logDirectory: String): Seq[String] = { - val manager = new WriteAheadLogManager(logDirectory, hadoopConf, - callerName = "WriteAheadLogSuite") - val data = manager.readFromLog().map(byteBufferToString).toSeq - manager.stop() + /** Read all the data in the log file in a directory using the WriteAheadLog class. */ + def readDataUsingWriteAheadLog(logDirectory: String): Seq[String] = { + import scala.collection.JavaConversions._ + val wal = new FileBasedWriteAheadLog(new SparkConf(), logDirectory, hadoopConf, 1, 1) + val data = wal.readAll().map(byteBufferToString).toSeq + wal.close() data } diff --git a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala index f2d135397ce2f..baa97616eaff3 100644 --- a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala +++ b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala @@ -46,7 +46,8 @@ object StoragePerfTester { val totalRecords = dataSizeMb * 1000 val recordsPerMap = totalRecords / numMaps - val writeData = "1" * recordLength + val writeKey = "1" * (recordLength / 2) + val writeValue = "1" * (recordLength / 2) val executor = Executors.newFixedThreadPool(numMaps) val conf = new SparkConf() @@ -63,7 +64,7 @@ object StoragePerfTester { new KryoSerializer(sc.conf), new ShuffleWriteMetrics()) val writers = shuffle.writers for (i <- 1 to recordsPerMap) { - writers(i % numOutputSplits).write(writeData) + writers(i % numOutputSplits).write(writeKey, writeValue) } writers.map { w => w.commitAndClose() diff --git a/unsafe/pom.xml b/unsafe/pom.xml new file mode 100644 index 0000000000000..5b0733206b2bc --- /dev/null +++ b/unsafe/pom.xml @@ -0,0 +1,93 @@ + + + + + 4.0.0 + + org.apache.spark + spark-parent_2.10 + 1.4.0-SNAPSHOT + ../pom.xml + + + org.apache.spark + spark-unsafe_2.10 + jar + Spark Project Unsafe + http://spark.apache.org/ + + unsafe + + + + + + + com.google.code.findbugs + jsr305 + + + + + org.slf4j + slf4j-api + provided + + + + + junit + junit + test + + + com.novocode + junit-interface + test + + + + target/scala-${scala.binary.version}/classes + target/scala-${scala.binary.version}/test-classes + + + + net.alchim31.maven + scala-maven-plugin + + + + -XDignore.symbol.file + + + + + org.apache.maven.plugins + maven-compiler-plugin + + + + -XDignore.symbol.file + + + + + + + diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java new file mode 100644 index 0000000000000..24b2892098059 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java @@ -0,0 +1,162 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe; + +import java.lang.reflect.Field; + +import sun.misc.Unsafe; + +public final class PlatformDependent { + + /** + * Facade in front of {@link sun.misc.Unsafe}, used to avoid directly exposing Unsafe outside of + * this package. This also lets us aovid accidental use of deprecated methods or methods that + * aren't present in Java 6. + */ + public static final class UNSAFE { + + private UNSAFE() { } + + public static int getInt(Object object, long offset) { + return _UNSAFE.getInt(object, offset); + } + + public static void putInt(Object object, long offset, int value) { + _UNSAFE.putInt(object, offset, value); + } + + public static boolean getBoolean(Object object, long offset) { + return _UNSAFE.getBoolean(object, offset); + } + + public static void putBoolean(Object object, long offset, boolean value) { + _UNSAFE.putBoolean(object, offset, value); + } + + public static byte getByte(Object object, long offset) { + return _UNSAFE.getByte(object, offset); + } + + public static void putByte(Object object, long offset, byte value) { + _UNSAFE.putByte(object, offset, value); + } + + public static short getShort(Object object, long offset) { + return _UNSAFE.getShort(object, offset); + } + + public static void putShort(Object object, long offset, short value) { + _UNSAFE.putShort(object, offset, value); + } + + public static long getLong(Object object, long offset) { + return _UNSAFE.getLong(object, offset); + } + + public static void putLong(Object object, long offset, long value) { + _UNSAFE.putLong(object, offset, value); + } + + public static float getFloat(Object object, long offset) { + return _UNSAFE.getFloat(object, offset); + } + + public static void putFloat(Object object, long offset, float value) { + _UNSAFE.putFloat(object, offset, value); + } + + public static double getDouble(Object object, long offset) { + return _UNSAFE.getDouble(object, offset); + } + + public static void putDouble(Object object, long offset, double value) { + _UNSAFE.putDouble(object, offset, value); + } + + public static long allocateMemory(long size) { + return _UNSAFE.allocateMemory(size); + } + + public static void freeMemory(long address) { + _UNSAFE.freeMemory(address); + } + + } + + private static final Unsafe _UNSAFE; + + public static final int BYTE_ARRAY_OFFSET; + + public static final int INT_ARRAY_OFFSET; + + public static final int LONG_ARRAY_OFFSET; + + public static final int DOUBLE_ARRAY_OFFSET; + + /** + * Limits the number of bytes to copy per {@link Unsafe#copyMemory(long, long, long)} to + * allow safepoint polling during a large copy. + */ + private static final long UNSAFE_COPY_THRESHOLD = 1024L * 1024L; + + static { + sun.misc.Unsafe unsafe; + try { + Field unsafeField = Unsafe.class.getDeclaredField("theUnsafe"); + unsafeField.setAccessible(true); + unsafe = (sun.misc.Unsafe) unsafeField.get(null); + } catch (Throwable cause) { + unsafe = null; + } + _UNSAFE = unsafe; + + if (_UNSAFE != null) { + BYTE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(byte[].class); + INT_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(int[].class); + LONG_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(long[].class); + DOUBLE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(double[].class); + } else { + BYTE_ARRAY_OFFSET = 0; + INT_ARRAY_OFFSET = 0; + LONG_ARRAY_OFFSET = 0; + DOUBLE_ARRAY_OFFSET = 0; + } + } + + static public void copyMemory( + Object src, + long srcOffset, + Object dst, + long dstOffset, + long length) { + while (length > 0) { + long size = Math.min(length, UNSAFE_COPY_THRESHOLD); + _UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size); + length -= size; + srcOffset += size; + dstOffset += size; + } + } + + /** + * Raises an exception bypassing compiler checks for checked exceptions. + */ + public static void throwException(Throwable t) { + _UNSAFE.throwException(t); + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java new file mode 100644 index 0000000000000..53eadf96a6b52 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.array; + +import org.apache.spark.unsafe.PlatformDependent; + +public class ByteArrayMethods { + + private ByteArrayMethods() { + // Private constructor, since this class only contains static methods. + } + + public static int roundNumberOfBytesToNearestWord(int numBytes) { + int remainder = numBytes & 0x07; // This is equivalent to `numBytes % 8` + if (remainder == 0) { + return numBytes; + } else { + return numBytes + (8 - remainder); + } + } + + /** + * Optimized byte array equality check for 8-byte-word-aligned byte arrays. + * @return true if the arrays are equal, false otherwise + */ + public static boolean wordAlignedArrayEquals( + Object leftBaseObject, + long leftBaseOffset, + Object rightBaseObject, + long rightBaseOffset, + long arrayLengthInBytes) { + for (int i = 0; i < arrayLengthInBytes; i += 8) { + final long left = + PlatformDependent.UNSAFE.getLong(leftBaseObject, leftBaseOffset + i); + final long right = + PlatformDependent.UNSAFE.getLong(rightBaseObject, rightBaseOffset + i); + if (left != right) return false; + } + return true; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java new file mode 100644 index 0000000000000..18d1f0d2d7eb2 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java @@ -0,0 +1,78 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.array; + +import org.apache.spark.unsafe.PlatformDependent; +import org.apache.spark.unsafe.memory.MemoryBlock; + +/** + * An array of long values. Compared with native JVM arrays, this: + *
    + *
  • supports using both in-heap and off-heap memory
    • + *
  • has no bound checking, and thus can crash the JVM process when assert is turned off
    • + *
+ */ +public final class LongArray { + + // This is a long so that we perform long multiplications when computing offsets. + private static final long WIDTH = 8; + + private final MemoryBlock memory; + private final Object baseObj; + private final long baseOffset; + + private final long length; + + public LongArray(MemoryBlock memory) { + assert memory.size() % WIDTH == 0 : "Memory not aligned (" + memory.size() + ")"; + assert memory.size() < (long) Integer.MAX_VALUE * 8: "Array size > 4 billion elements"; + this.memory = memory; + this.baseObj = memory.getBaseObject(); + this.baseOffset = memory.getBaseOffset(); + this.length = memory.size() / WIDTH; + } + + public MemoryBlock memoryBlock() { + return memory; + } + + /** + * Returns the number of elements this array can hold. + */ + public long size() { + return length; + } + + /** + * Sets the value at position {@code index}. + */ + public void set(int index, long value) { + assert index >= 0 : "index (" + index + ") should >= 0"; + assert index < length : "index (" + index + ") should < length (" + length + ")"; + PlatformDependent.UNSAFE.putLong(baseObj, baseOffset + index * WIDTH, value); + } + + /** + * Returns the value at position {@code index}. + */ + public long get(int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + assert index < length : "index (" + index + ") should < length (" + length + ")"; + return PlatformDependent.UNSAFE.getLong(baseObj, baseOffset + index * WIDTH); + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java new file mode 100644 index 0000000000000..f72e07fce92fd --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java @@ -0,0 +1,105 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.bitset; + +import org.apache.spark.unsafe.array.LongArray; +import org.apache.spark.unsafe.memory.MemoryBlock; + +/** + * A fixed size uncompressed bit set backed by a {@link LongArray}. + * + * Each bit occupies exactly one bit of storage. + */ +public final class BitSet { + + /** A long array for the bits. */ + private final LongArray words; + + /** Length of the long array. */ + private final int numWords; + + private final Object baseObject; + private final long baseOffset; + + /** + * Creates a new {@link BitSet} using the specified memory block. Size of the memory block must be + * multiple of 8 bytes (i.e. 64 bits). + */ + public BitSet(MemoryBlock memory) { + words = new LongArray(memory); + assert (words.size() <= Integer.MAX_VALUE); + numWords = (int) words.size(); + baseObject = words.memoryBlock().getBaseObject(); + baseOffset = words.memoryBlock().getBaseOffset(); + } + + public MemoryBlock memoryBlock() { + return words.memoryBlock(); + } + + /** + * Returns the number of bits in this {@code BitSet}. + */ + public long capacity() { + return numWords * 64; + } + + /** + * Sets the bit at the specified index to {@code true}. + */ + public void set(int index) { + assert index < numWords * 64 : "index (" + index + ") should < length (" + numWords * 64 + ")"; + BitSetMethods.set(baseObject, baseOffset, index); + } + + /** + * Sets the bit at the specified index to {@code false}. + */ + public void unset(int index) { + assert index < numWords * 64 : "index (" + index + ") should < length (" + numWords * 64 + ")"; + BitSetMethods.unset(baseObject, baseOffset, index); + } + + /** + * Returns {@code true} if the bit is set at the specified index. + */ + public boolean isSet(int index) { + assert index < numWords * 64 : "index (" + index + ") should < length (" + numWords * 64 + ")"; + return BitSetMethods.isSet(baseObject, baseOffset, index); + } + + /** + * Returns the index of the first bit that is set to true that occurs on or after the + * specified starting index. If no such bit exists then {@code -1} is returned. + *

+ * To iterate over the true bits in a BitSet, use the following loop: + * 

+   * 
+   *  for (long i = bs.nextSetBit(0); i >= 0; i = bs.nextSetBit(i + 1)) {
+   *    // operate on index i here
+   *  }
+   * 
+   *
+ * + * @param fromIndex the index to start checking from (inclusive) + * @return the index of the next set bit, or -1 if there is no such bit + */ + public int nextSetBit(int fromIndex) { + return BitSetMethods.nextSetBit(baseObject, baseOffset, fromIndex, numWords); + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java new file mode 100644 index 0000000000000..f30626d8f4317 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java @@ -0,0 +1,129 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.bitset; + +import org.apache.spark.unsafe.PlatformDependent; + +/** + * Methods for working with fixed-size uncompressed bitsets. + * + * We assume that the bitset data is word-aligned (that is, a multiple of 8 bytes in length). + * + * Each bit occupies exactly one bit of storage. + */ +public final class BitSetMethods { + + private static final long WORD_SIZE = 8; + + private BitSetMethods() { + // Make the default constructor private, since this only holds static methods. + } + + /** + * Sets the bit at the specified index to {@code true}. + */ + public static void set(Object baseObject, long baseOffset, int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + final long mask = 1L << (index & 0x3f); // mod 64 and shift + final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE; + final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset); + PlatformDependent.UNSAFE.putLong(baseObject, wordOffset, word | mask); + } + + /** + * Sets the bit at the specified index to {@code false}. + */ + public static void unset(Object baseObject, long baseOffset, int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + final long mask = 1L << (index & 0x3f); // mod 64 and shift + final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE; + final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset); + PlatformDependent.UNSAFE.putLong(baseObject, wordOffset, word & ~mask); + } + + /** + * Returns {@code true} if the bit is set at the specified index. + */ + public static boolean isSet(Object baseObject, long baseOffset, int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + final long mask = 1L << (index & 0x3f); // mod 64 and shift + final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE; + final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset); + return (word & mask) != 0; + } + + /** + * Returns {@code true} if any bit is set. + */ + public static boolean anySet(Object baseObject, long baseOffset, long bitSetWidthInBytes) { + for (int i = 0; i <= bitSetWidthInBytes; i++) { + if (PlatformDependent.UNSAFE.getByte(baseObject, baseOffset + i) != 0) { + return true; + } + } + return false; + } + + /** + * Returns the index of the first bit that is set to true that occurs on or after the + * specified starting index. If no such bit exists then {@code -1} is returned. + *

+ * To iterate over the true bits in a BitSet, use the following loop: + * 

+   * 
+   *  for (long i = bs.nextSetBit(0, sizeInWords); i >= 0; i = bs.nextSetBit(i + 1, sizeInWords)) {
+   *    // operate on index i here
+   *  }
+   * 
+   *
+ * + * @param fromIndex the index to start checking from (inclusive) + * @param bitsetSizeInWords the size of the bitset, measured in 8-byte words + * @return the index of the next set bit, or -1 if there is no such bit + */ + public static int nextSetBit( + Object baseObject, + long baseOffset, + int fromIndex, + int bitsetSizeInWords) { + int wi = fromIndex >> 6; + if (wi >= bitsetSizeInWords) { + return -1; + } + + // Try to find the next set bit in the current word + final int subIndex = fromIndex & 0x3f; + long word = + PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + wi * WORD_SIZE) >> subIndex; + if (word != 0) { + return (wi << 6) + subIndex + java.lang.Long.numberOfTrailingZeros(word); + } + + // Find the next set bit in the rest of the words + wi += 1; + while (wi < bitsetSizeInWords) { + word = PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + wi * WORD_SIZE); + if (word != 0) { + return (wi << 6) + java.lang.Long.numberOfTrailingZeros(word); + } + wi += 1; + } + + return -1; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java new file mode 100644 index 0000000000000..85cd02469adb7 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java @@ -0,0 +1,96 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.hash; + +import org.apache.spark.unsafe.PlatformDependent; + +/** + * 32-bit Murmur3 hasher. This is based on Guava's Murmur3_32HashFunction. + */ +public final class Murmur3_x86_32 { + private static final int C1 = 0xcc9e2d51; + private static final int C2 = 0x1b873593; + + private final int seed; + + public Murmur3_x86_32(int seed) { + this.seed = seed; + } + + @Override + public String toString() { + return "Murmur3_32(seed=" + seed + ")"; + } + + public int hashInt(int input) { + int k1 = mixK1(input); + int h1 = mixH1(seed, k1); + + return fmix(h1, 4); + } + + public int hashUnsafeWords(Object baseObject, long baseOffset, int lengthInBytes) { + // This is based on Guava's `Murmur32_Hasher.processRemaining(ByteBuffer)` method. + assert (lengthInBytes % 8 == 0): "lengthInBytes must be a multiple of 8 (word-aligned)"; + int h1 = seed; + for (int offset = 0; offset < lengthInBytes; offset += 4) { + int halfWord = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + offset); + int k1 = mixK1(halfWord); + h1 = mixH1(h1, k1); + } + return fmix(h1, lengthInBytes); + } + + public int hashLong(long input) { + int low = (int) input; + int high = (int) (input >>> 32); + + int k1 = mixK1(low); + int h1 = mixH1(seed, k1); + + k1 = mixK1(high); + h1 = mixH1(h1, k1); + + return fmix(h1, 8); + } + + private static int mixK1(int k1) { + k1 *= C1; + k1 = Integer.rotateLeft(k1, 15); + k1 *= C2; + return k1; + } + + private static int mixH1(int h1, int k1) { + h1 ^= k1; + h1 = Integer.rotateLeft(h1, 13); + h1 = h1 * 5 + 0xe6546b64; + return h1; + } + + // Finalization mix - force all bits of a hash block to avalanche + private static int fmix(int h1, int length) { + h1 ^= length; + h1 ^= h1 >>> 16; + h1 *= 0x85ebca6b; + h1 ^= h1 >>> 13; + h1 *= 0xc2b2ae35; + h1 ^= h1 >>> 16; + return h1; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java new file mode 100644 index 0000000000000..19d6a169fd2ad --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java @@ -0,0 +1,548 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.map; + +import java.lang.Override; +import java.lang.UnsupportedOperationException; +import java.util.Iterator; +import java.util.LinkedList; +import java.util.List; + +import org.apache.spark.unsafe.*; +import org.apache.spark.unsafe.array.ByteArrayMethods; +import org.apache.spark.unsafe.array.LongArray; +import org.apache.spark.unsafe.bitset.BitSet; +import org.apache.spark.unsafe.hash.Murmur3_x86_32; +import org.apache.spark.unsafe.memory.*; + +/** + * An append-only hash map where keys and values are contiguous regions of bytes. + *

+ * This is backed by a power-of-2-sized hash table, using quadratic probing with triangular numbers, + * which is guaranteed to exhaust the space. + *

+ * The map can support up to 2^31 keys because we use 32 bit MurmurHash. If the key cardinality is + * higher than this, you should probably be using sorting instead of hashing for better cache + * locality. + *

+ * This class is not thread safe. + */ +public final class BytesToBytesMap { + + private static final Murmur3_x86_32 HASHER = new Murmur3_x86_32(0); + + private static final HashMapGrowthStrategy growthStrategy = HashMapGrowthStrategy.DOUBLING; + + private final TaskMemoryManager memoryManager; + + /** + * A linked list for tracking all allocated data pages so that we can free all of our memory. + */ + private final List dataPages = new LinkedList(); + + /** + * The data page that will be used to store keys and values for new hashtable entries. When this + * page becomes full, a new page will be allocated and this pointer will change to point to that + * new page. + */ + private MemoryBlock currentDataPage = null; + + /** + * Offset into `currentDataPage` that points to the location where new data can be inserted into + * the page. + */ + private long pageCursor = 0; + + /** + * The size of the data pages that hold key and value data. Map entries cannot span multiple + * pages, so this limits the maximum entry size. + */ + private static final long PAGE_SIZE_BYTES = 1L << 26; // 64 megabytes + + // This choice of page table size and page size means that we can address up to 500 gigabytes + // of memory. + + /** + * A single array to store the key and value. + * + * Position {@code 2 * i} in the array is used to track a pointer to the key at index {@code i}, + * while position {@code 2 * i + 1} in the array holds key's full 32-bit hashcode. + */ + private LongArray longArray; + // TODO: we're wasting 32 bits of space here; we can probably store fewer bits of the hashcode + // and exploit word-alignment to use fewer bits to hold the address. This might let us store + // only one long per map entry, increasing the chance that this array will fit in cache at the + // expense of maybe performing more lookups if we have hash collisions. Say that we stored only + // 27 bits of the hashcode and 37 bits of the address. 37 bits is enough to address 1 terabyte + // of RAM given word-alignment. If we use 13 bits of this for our page table, that gives us a + // maximum page size of 2^24 * 8 = ~134 megabytes per page. This change will require us to store + // full base addresses in the page table for off-heap mode so that we can reconstruct the full + // absolute memory addresses. + + /** + * A {@link BitSet} used to track location of the map where the key is set. + * Size of the bitset should be half of the size of the long array. + */ + private BitSet bitset; + + private final double loadFactor; + + /** + * Number of keys defined in the map. + */ + private int size; + + /** + * The map will be expanded once the number of keys exceeds this threshold. + */ + private int growthThreshold; + + /** + * Mask for truncating hashcodes so that they do not exceed the long array's size. + * This is a strength reduction optimization; we're essentially performing a modulus operation, + * but doing so with a bitmask because this is a power-of-2-sized hash map. + */ + private int mask; + + /** + * Return value of {@link BytesToBytesMap#lookup(Object, long, int)}. + */ + private final Location loc; + + private final boolean enablePerfMetrics; + + private long timeSpentResizingNs = 0; + + private long numProbes = 0; + + private long numKeyLookups = 0; + + private long numHashCollisions = 0; + + public BytesToBytesMap( + TaskMemoryManager memoryManager, + int initialCapacity, + double loadFactor, + boolean enablePerfMetrics) { + this.memoryManager = memoryManager; + this.loadFactor = loadFactor; + this.loc = new Location(); + this.enablePerfMetrics = enablePerfMetrics; + allocate(initialCapacity); + } + + public BytesToBytesMap(TaskMemoryManager memoryManager, int initialCapacity) { + this(memoryManager, initialCapacity, 0.70, false); + } + + public BytesToBytesMap( + TaskMemoryManager memoryManager, + int initialCapacity, + boolean enablePerfMetrics) { + this(memoryManager, initialCapacity, 0.70, enablePerfMetrics); + } + + /** + * Returns the number of keys defined in the map. + */ + public int size() { return size; } + + /** + * Returns an iterator for iterating over the entries of this map. + * + * For efficiency, all calls to `next()` will return the same {@link Location} object. + * + * If any other lookups or operations are performed on this map while iterating over it, including + * `lookup()`, the behavior of the returned iterator is undefined. + */ + public Iterator iterator() { + return new Iterator() { + + private int nextPos = bitset.nextSetBit(0); + + @Override + public boolean hasNext() { + return nextPos != -1; + } + + @Override + public Location next() { + final int pos = nextPos; + nextPos = bitset.nextSetBit(nextPos + 1); + return loc.with(pos, 0, true); + } + + @Override + public void remove() { + throw new UnsupportedOperationException(); + } + }; + } + + /** + * Looks up a key, and return a {@link Location} handle that can be used to test existence + * and read/write values. + * + * This function always return the same {@link Location} instance to avoid object allocation. + */ + public Location lookup( + Object keyBaseObject, + long keyBaseOffset, + int keyRowLengthBytes) { + if (enablePerfMetrics) { + numKeyLookups++; + } + final int hashcode = HASHER.hashUnsafeWords(keyBaseObject, keyBaseOffset, keyRowLengthBytes); + int pos = hashcode & mask; + int step = 1; + while (true) { + if (enablePerfMetrics) { + numProbes++; + } + if (!bitset.isSet(pos)) { + // This is a new key. + return loc.with(pos, hashcode, false); + } else { + long stored = longArray.get(pos * 2 + 1); + if ((int) (stored) == hashcode) { + // Full hash code matches. Let's compare the keys for equality. + loc.with(pos, hashcode, true); + if (loc.getKeyLength() == keyRowLengthBytes) { + final MemoryLocation keyAddress = loc.getKeyAddress(); + final Object storedKeyBaseObject = keyAddress.getBaseObject(); + final long storedKeyBaseOffset = keyAddress.getBaseOffset(); + final boolean areEqual = ByteArrayMethods.wordAlignedArrayEquals( + keyBaseObject, + keyBaseOffset, + storedKeyBaseObject, + storedKeyBaseOffset, + keyRowLengthBytes + ); + if (areEqual) { + return loc; + } else { + if (enablePerfMetrics) { + numHashCollisions++; + } + } + } + } + } + pos = (pos + step) & mask; + step++; + } + } + + /** + * Handle returned by {@link BytesToBytesMap#lookup(Object, long, int)} function. + */ + public final class Location { + /** An index into the hash map's Long array */ + private int pos; + /** True if this location points to a position where a key is defined, false otherwise */ + private boolean isDefined; + /** + * The hashcode of the most recent key passed to + * {@link BytesToBytesMap#lookup(Object, long, int)}. Caching this hashcode here allows us to + * avoid re-hashing the key when storing a value for that key. + */ + private int keyHashcode; + private final MemoryLocation keyMemoryLocation = new MemoryLocation(); + private final MemoryLocation valueMemoryLocation = new MemoryLocation(); + private int keyLength; + private int valueLength; + + private void updateAddressesAndSizes(long fullKeyAddress) { + final Object page = memoryManager.getPage(fullKeyAddress); + final long keyOffsetInPage = memoryManager.getOffsetInPage(fullKeyAddress); + long position = keyOffsetInPage; + keyLength = (int) PlatformDependent.UNSAFE.getLong(page, position); + position += 8; // word used to store the key size + keyMemoryLocation.setObjAndOffset(page, position); + position += keyLength; + valueLength = (int) PlatformDependent.UNSAFE.getLong(page, position); + position += 8; // word used to store the key size + valueMemoryLocation.setObjAndOffset(page, position); + } + + Location with(int pos, int keyHashcode, boolean isDefined) { + this.pos = pos; + this.isDefined = isDefined; + this.keyHashcode = keyHashcode; + if (isDefined) { + final long fullKeyAddress = longArray.get(pos * 2); + updateAddressesAndSizes(fullKeyAddress); + } + return this; + } + + /** + * Returns true if the key is defined at this position, and false otherwise. + */ + public boolean isDefined() { + return isDefined; + } + + /** + * Returns the address of the key defined at this position. + * This points to the first byte of the key data. + * Unspecified behavior if the key is not defined. + * For efficiency reasons, calls to this method always returns the same MemoryLocation object. + */ + public MemoryLocation getKeyAddress() { + assert (isDefined); + return keyMemoryLocation; + } + + /** + * Returns the length of the key defined at this position. + * Unspecified behavior if the key is not defined. + */ + public int getKeyLength() { + assert (isDefined); + return keyLength; + } + + /** + * Returns the address of the value defined at this position. + * This points to the first byte of the value data. + * Unspecified behavior if the key is not defined. + * For efficiency reasons, calls to this method always returns the same MemoryLocation object. + */ + public MemoryLocation getValueAddress() { + assert (isDefined); + return valueMemoryLocation; + } + + /** + * Returns the length of the value defined at this position. + * Unspecified behavior if the key is not defined. + */ + public int getValueLength() { + assert (isDefined); + return valueLength; + } + + /** + * Store a new key and value. This method may only be called once for a given key; if you want + * to update the value associated with a key, then you can directly manipulate the bytes stored + * at the value address. + *

+ * It is only valid to call this method immediately after calling `lookup()` using the same key. + *

+ * After calling this method, calls to `get[Key|Value]Address()` and `get[Key|Value]Length` + * will return information on the data stored by this `putNewKey` call. + *

+ * As an example usage, here's the proper way to store a new key: + *

+ * 

+     *   Location loc = map.lookup(keyBaseObject, keyBaseOffset, keyLengthInBytes);
+     *   if (!loc.isDefined()) {
+     *     loc.putNewKey(keyBaseObject, keyBaseOffset, keyLengthInBytes, ...)
+     *   }
+     *
+ *

+ * Unspecified behavior if the key is not defined. + */ + public void putNewKey( + Object keyBaseObject, + long keyBaseOffset, + int keyLengthBytes, + Object valueBaseObject, + long valueBaseOffset, + int valueLengthBytes) { + assert (!isDefined) : "Can only set value once for a key"; + isDefined = true; + assert (keyLengthBytes % 8 == 0); + assert (valueLengthBytes % 8 == 0); + // Here, we'll copy the data into our data pages. Because we only store a relative offset from + // the key address instead of storing the absolute address of the value, the key and value + // must be stored in the same memory page. + // (8 byte key length) (key) (8 byte value length) (value) + final long requiredSize = 8 + keyLengthBytes + 8 + valueLengthBytes; + assert(requiredSize <= PAGE_SIZE_BYTES); + size++; + bitset.set(pos); + + // If there's not enough space in the current page, allocate a new page: + if (currentDataPage == null || PAGE_SIZE_BYTES - pageCursor < requiredSize) { + MemoryBlock newPage = memoryManager.allocatePage(PAGE_SIZE_BYTES); + dataPages.add(newPage); + pageCursor = 0; + currentDataPage = newPage; + } + + // Compute all of our offsets up-front: + final Object pageBaseObject = currentDataPage.getBaseObject(); + final long pageBaseOffset = currentDataPage.getBaseOffset(); + final long keySizeOffsetInPage = pageBaseOffset + pageCursor; + pageCursor += 8; // word used to store the key size + final long keyDataOffsetInPage = pageBaseOffset + pageCursor; + pageCursor += keyLengthBytes; + final long valueSizeOffsetInPage = pageBaseOffset + pageCursor; + pageCursor += 8; // word used to store the value size + final long valueDataOffsetInPage = pageBaseOffset + pageCursor; + pageCursor += valueLengthBytes; + + // Copy the key + PlatformDependent.UNSAFE.putLong(pageBaseObject, keySizeOffsetInPage, keyLengthBytes); + PlatformDependent.copyMemory( + keyBaseObject, keyBaseOffset, pageBaseObject, keyDataOffsetInPage, keyLengthBytes); + // Copy the value + PlatformDependent.UNSAFE.putLong(pageBaseObject, valueSizeOffsetInPage, valueLengthBytes); + PlatformDependent.copyMemory( + valueBaseObject, valueBaseOffset, pageBaseObject, valueDataOffsetInPage, valueLengthBytes); + + final long storedKeyAddress = memoryManager.encodePageNumberAndOffset( + currentDataPage, keySizeOffsetInPage); + longArray.set(pos * 2, storedKeyAddress); + longArray.set(pos * 2 + 1, keyHashcode); + updateAddressesAndSizes(storedKeyAddress); + isDefined = true; + if (size > growthThreshold) { + growAndRehash(); + } + } + } + + /** + * Allocate new data structures for this map. When calling this outside of the constructor, + * make sure to keep references to the old data structures so that you can free them. + * + * @param capacity the new map capacity + */ + private void allocate(int capacity) { + capacity = Math.max((int) Math.min(Integer.MAX_VALUE, nextPowerOf2(capacity)), 64); + longArray = new LongArray(memoryManager.allocate(capacity * 8 * 2)); + bitset = new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64])); + + this.growthThreshold = (int) (capacity * loadFactor); + this.mask = capacity - 1; + } + + /** + * Free all allocated memory associated with this map, including the storage for keys and values + * as well as the hash map array itself. + * + * This method is idempotent. + */ + public void free() { + if (longArray != null) { + memoryManager.free(longArray.memoryBlock()); + longArray = null; + } + if (bitset != null) { + // The bitset's heap memory isn't managed by a memory manager, so no need to free it here. + bitset = null; + } + Iterator dataPagesIterator = dataPages.iterator(); + while (dataPagesIterator.hasNext()) { + memoryManager.freePage(dataPagesIterator.next()); + dataPagesIterator.remove(); + } + assert(dataPages.isEmpty()); + } + + /** Returns the total amount of memory, in bytes, consumed by this map's managed structures. */ + public long getTotalMemoryConsumption() { + return ( + dataPages.size() * PAGE_SIZE_BYTES + + bitset.memoryBlock().size() + + longArray.memoryBlock().size()); + } + + /** + * Returns the total amount of time spent resizing this map (in nanoseconds). + */ + public long getTimeSpentResizingNs() { + if (!enablePerfMetrics) { + throw new IllegalStateException(); + } + return timeSpentResizingNs; + } + + + /** + * Returns the average number of probes per key lookup. + */ + public double getAverageProbesPerLookup() { + if (!enablePerfMetrics) { + throw new IllegalStateException(); + } + return (1.0 * numProbes) / numKeyLookups; + } + + public long getNumHashCollisions() { + if (!enablePerfMetrics) { + throw new IllegalStateException(); + } + return numHashCollisions; + } + + /** + * Grows the size of the hash table and re-hash everything. + */ + private void growAndRehash() { + long resizeStartTime = -1; + if (enablePerfMetrics) { + resizeStartTime = System.nanoTime(); + } + // Store references to the old data structures to be used when we re-hash + final LongArray oldLongArray = longArray; + final BitSet oldBitSet = bitset; + final int oldCapacity = (int) oldBitSet.capacity(); + + // Allocate the new data structures + allocate(Math.min(Integer.MAX_VALUE, growthStrategy.nextCapacity(oldCapacity))); + + // Re-mask (we don't recompute the hashcode because we stored all 32 bits of it) + for (int pos = oldBitSet.nextSetBit(0); pos >= 0; pos = oldBitSet.nextSetBit(pos + 1)) { + final long keyPointer = oldLongArray.get(pos * 2); + final int hashcode = (int) oldLongArray.get(pos * 2 + 1); + int newPos = hashcode & mask; + int step = 1; + boolean keepGoing = true; + + // No need to check for equality here when we insert so this has one less if branch than + // the similar code path in addWithoutResize. + while (keepGoing) { + if (!bitset.isSet(newPos)) { + bitset.set(newPos); + longArray.set(newPos * 2, keyPointer); + longArray.set(newPos * 2 + 1, hashcode); + keepGoing = false; + } else { + newPos = (newPos + step) & mask; + step++; + } + } + } + + // Deallocate the old data structures. + memoryManager.free(oldLongArray.memoryBlock()); + if (enablePerfMetrics) { + timeSpentResizingNs += System.nanoTime() - resizeStartTime; + } + } + + /** Returns the next number greater or equal num that is power of 2. */ + private static long nextPowerOf2(long num) { + final long highBit = Long.highestOneBit(num); + return (highBit == num) ? num : highBit << 1; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java new file mode 100644 index 0000000000000..7c321baffe82d --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java @@ -0,0 +1,39 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.map; + +/** + * Interface that defines how we can grow the size of a hash map when it is over a threshold. + */ +public interface HashMapGrowthStrategy { + + int nextCapacity(int currentCapacity); + + /** + * Double the size of the hash map every time. + */ + HashMapGrowthStrategy DOUBLING = new Doubling(); + + class Doubling implements HashMapGrowthStrategy { + @Override + public int nextCapacity(int currentCapacity) { + return currentCapacity * 2; + } + } + +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java new file mode 100644 index 0000000000000..62c29c8cc1e4d --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java @@ -0,0 +1,58 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +/** + * Manages memory for an executor. Individual operators / tasks allocate memory through + * {@link TaskMemoryManager} objects, which obtain their memory from ExecutorMemoryManager. + */ +public class ExecutorMemoryManager { + + /** + * Allocator, exposed for enabling untracked allocations of temporary data structures. + */ + public final MemoryAllocator allocator; + + /** + * Tracks whether memory will be allocated on the JVM heap or off-heap using sun.misc.Unsafe. + */ + final boolean inHeap; + + /** + * Construct a new ExecutorMemoryManager. + * + * @param allocator the allocator that will be used + */ + public ExecutorMemoryManager(MemoryAllocator allocator) { + this.inHeap = allocator instanceof HeapMemoryAllocator; + this.allocator = allocator; + } + + /** + * Allocates a contiguous block of memory. Note that the allocated memory is not guaranteed + * to be zeroed out (call `zero()` on the result if this is necessary). + */ + MemoryBlock allocate(long size) throws OutOfMemoryError { + return allocator.allocate(size); + } + + void free(MemoryBlock memory) { + allocator.free(memory); + } + +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java new file mode 100644 index 0000000000000..bbe83d36cf36b --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java @@ -0,0 +1,35 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +/** + * A simple {@link MemoryAllocator} that can allocate up to 16GB using a JVM long primitive array. + */ +public class HeapMemoryAllocator implements MemoryAllocator { + + @Override + public MemoryBlock allocate(long size) throws OutOfMemoryError { + long[] array = new long[(int) (size / 8)]; + return MemoryBlock.fromLongArray(array); + } + + @Override + public void free(MemoryBlock memory) { + // Do nothing + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryAllocator.java new file mode 100644 index 0000000000000..5192f68c862cf --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryAllocator.java @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +public interface MemoryAllocator { + + /** + * Allocates a contiguous block of memory. Note that the allocated memory is not guaranteed + * to be zeroed out (call `zero()` on the result if this is necessary). + */ + MemoryBlock allocate(long size) throws OutOfMemoryError; + + void free(MemoryBlock memory); + + MemoryAllocator UNSAFE = new UnsafeMemoryAllocator(); + + MemoryAllocator HEAP = new HeapMemoryAllocator(); +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java new file mode 100644 index 0000000000000..3dc82d8c2eb39 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java @@ -0,0 +1,55 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import javax.annotation.Nullable; + +import org.apache.spark.unsafe.PlatformDependent; + +/** + * A consecutive block of memory, starting at a {@link MemoryLocation} with a fixed size. + */ +public class MemoryBlock extends MemoryLocation { + + private final long length; + + /** + * Optional page number; used when this MemoryBlock represents a page allocated by a + * MemoryManager. This is package-private and is modified by MemoryManager. + */ + int pageNumber = -1; + + MemoryBlock(@Nullable Object obj, long offset, long length) { + super(obj, offset); + this.length = length; + } + + /** + * Returns the size of the memory block. + */ + public long size() { + return length; + } + + /** + * Creates a memory block pointing to the memory used by the long array. + */ + public static MemoryBlock fromLongArray(final long[] array) { + return new MemoryBlock(array, PlatformDependent.LONG_ARRAY_OFFSET, array.length * 8); + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryLocation.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryLocation.java new file mode 100644 index 0000000000000..74ebc87dc978c --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryLocation.java @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import javax.annotation.Nullable; + +/** + * A memory location. Tracked either by a memory address (with off-heap allocation), + * or by an offset from a JVM object (in-heap allocation). + */ +public class MemoryLocation { + + @Nullable + Object obj; + + long offset; + + public MemoryLocation(@Nullable Object obj, long offset) { + this.obj = obj; + this.offset = offset; + } + + public MemoryLocation() { + this(null, 0); + } + + public void setObjAndOffset(Object newObj, long newOffset) { + this.obj = newObj; + this.offset = newOffset; + } + + public final Object getBaseObject() { + return obj; + } + + public final long getBaseOffset() { + return offset; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java new file mode 100644 index 0000000000000..9224988e6ad69 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java @@ -0,0 +1,237 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import java.util.*; + +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/** + * Manages the memory allocated by an individual task. + *

+ * Most of the complexity in this class deals with encoding of off-heap addresses into 64-bit longs. + * In off-heap mode, memory can be directly addressed with 64-bit longs. In on-heap mode, memory is + * addressed by the combination of a base Object reference and a 64-bit offset within that object. + * This is a problem when we want to store pointers to data structures inside of other structures, + * such as record pointers inside hashmaps or sorting buffers. Even if we decided to use 128 bits + * to address memory, we can't just store the address of the base object since it's not guaranteed + * to remain stable as the heap gets reorganized due to GC. + *

+ * Instead, we use the following approach to encode record pointers in 64-bit longs: for off-heap + * mode, just store the raw address, and for on-heap mode use the upper 13 bits of the address to + * store a "page number" and the lower 51 bits to store an offset within this page. These page + * numbers are used to index into a "page table" array inside of the MemoryManager in order to + * retrieve the base object. + *

+ * This allows us to address 8192 pages. In on-heap mode, the maximum page size is limited by the + * maximum size of a long[] array, allowing us to address 8192 * 2^32 * 8 bytes, which is + * approximately 35 terabytes of memory. + */ +public final class TaskMemoryManager { + + private final Logger logger = LoggerFactory.getLogger(TaskMemoryManager.class); + + /** + * The number of entries in the page table. + */ + private static final int PAGE_TABLE_SIZE = 1 << 13; + + /** Bit mask for the lower 51 bits of a long. */ + private static final long MASK_LONG_LOWER_51_BITS = 0x7FFFFFFFFFFFFL; + + /** Bit mask for the upper 13 bits of a long */ + private static final long MASK_LONG_UPPER_13_BITS = ~MASK_LONG_LOWER_51_BITS; + + /** + * Similar to an operating system's page table, this array maps page numbers into base object + * pointers, allowing us to translate between the hashtable's internal 64-bit address + * representation and the baseObject+offset representation which we use to support both in- and + * off-heap addresses. When using an off-heap allocator, every entry in this map will be `null`. + * When using an in-heap allocator, the entries in this map will point to pages' base objects. + * Entries are added to this map as new data pages are allocated. + */ + private final MemoryBlock[] pageTable = new MemoryBlock[PAGE_TABLE_SIZE]; + + /** + * Bitmap for tracking free pages. + */ + private final BitSet allocatedPages = new BitSet(PAGE_TABLE_SIZE); + + /** + * Tracks memory allocated with {@link TaskMemoryManager#allocate(long)}, used to detect / clean + * up leaked memory. + */ + private final HashSet allocatedNonPageMemory = new HashSet(); + + private final ExecutorMemoryManager executorMemoryManager; + + /** + * Tracks whether we're in-heap or off-heap. For off-heap, we short-circuit most of these methods + * without doing any masking or lookups. Since this branching should be well-predicted by the JIT, + * this extra layer of indirection / abstraction hopefully shouldn't be too expensive. + */ + private final boolean inHeap; + + /** + * Construct a new MemoryManager. + */ + public TaskMemoryManager(ExecutorMemoryManager executorMemoryManager) { + this.inHeap = executorMemoryManager.inHeap; + this.executorMemoryManager = executorMemoryManager; + } + + /** + * Allocate a block of memory that will be tracked in the MemoryManager's page table; this is + * intended for allocating large blocks of memory that will be shared between operators. + */ + public MemoryBlock allocatePage(long size) { + if (logger.isTraceEnabled()) { + logger.trace("Allocating {} byte page", size); + } + if (size >= (1L << 51)) { + throw new IllegalArgumentException("Cannot allocate a page with more than 2^51 bytes"); + } + + final int pageNumber; + synchronized (this) { + pageNumber = allocatedPages.nextClearBit(0); + if (pageNumber >= PAGE_TABLE_SIZE) { + throw new IllegalStateException( + "Have already allocated a maximum of " + PAGE_TABLE_SIZE + " pages"); + } + allocatedPages.set(pageNumber); + } + final MemoryBlock page = executorMemoryManager.allocate(size); + page.pageNumber = pageNumber; + pageTable[pageNumber] = page; + if (logger.isDebugEnabled()) { + logger.debug("Allocate page number {} ({} bytes)", pageNumber, size); + } + return page; + } + + /** + * Free a block of memory allocated via {@link TaskMemoryManager#allocatePage(long)}. + */ + public void freePage(MemoryBlock page) { + if (logger.isTraceEnabled()) { + logger.trace("Freeing page number {} ({} bytes)", page.pageNumber, page.size()); + } + assert (page.pageNumber != -1) : + "Called freePage() on memory that wasn't allocated with allocatePage()"; + executorMemoryManager.free(page); + synchronized (this) { + allocatedPages.clear(page.pageNumber); + } + pageTable[page.pageNumber] = null; + if (logger.isDebugEnabled()) { + logger.debug("Freed page number {} ({} bytes)", page.pageNumber, page.size()); + } + } + + /** + * Allocates a contiguous block of memory. Note that the allocated memory is not guaranteed + * to be zeroed out (call `zero()` on the result if this is necessary). This method is intended + * to be used for allocating operators' internal data structures. For data pages that you want to + * exchange between operators, consider using {@link TaskMemoryManager#allocatePage(long)}, since + * that will enable intra-memory pointers (see + * {@link TaskMemoryManager#encodePageNumberAndOffset(MemoryBlock, long)} and this class's + * top-level Javadoc for more details). + */ + public MemoryBlock allocate(long size) throws OutOfMemoryError { + final MemoryBlock memory = executorMemoryManager.allocate(size); + allocatedNonPageMemory.add(memory); + return memory; + } + + /** + * Free memory allocated by {@link TaskMemoryManager#allocate(long)}. + */ + public void free(MemoryBlock memory) { + assert (memory.pageNumber == -1) : "Should call freePage() for pages, not free()"; + executorMemoryManager.free(memory); + final boolean wasAlreadyRemoved = !allocatedNonPageMemory.remove(memory); + assert (!wasAlreadyRemoved) : "Called free() on memory that was already freed!"; + } + + /** + * Given a memory page and offset within that page, encode this address into a 64-bit long. + * This address will remain valid as long as the corresponding page has not been freed. + */ + public long encodePageNumberAndOffset(MemoryBlock page, long offsetInPage) { + if (inHeap) { + assert (page.pageNumber != -1) : "encodePageNumberAndOffset called with invalid page"; + return (((long) page.pageNumber) << 51) | (offsetInPage & MASK_LONG_LOWER_51_BITS); + } else { + return offsetInPage; + } + } + + /** + * Get the page associated with an address encoded by + * {@link TaskMemoryManager#encodePageNumberAndOffset(MemoryBlock, long)} + */ + public Object getPage(long pagePlusOffsetAddress) { + if (inHeap) { + final int pageNumber = (int) ((pagePlusOffsetAddress & MASK_LONG_UPPER_13_BITS) >>> 51); + assert (pageNumber >= 0 && pageNumber < PAGE_TABLE_SIZE); + final Object page = pageTable[pageNumber].getBaseObject(); + assert (page != null); + return page; + } else { + return null; + } + } + + /** + * Get the offset associated with an address encoded by + * {@link TaskMemoryManager#encodePageNumberAndOffset(MemoryBlock, long)} + */ + public long getOffsetInPage(long pagePlusOffsetAddress) { + if (inHeap) { + return (pagePlusOffsetAddress & MASK_LONG_LOWER_51_BITS); + } else { + return pagePlusOffsetAddress; + } + } + + /** + * Clean up all allocated memory and pages. Returns the number of bytes freed. A non-zero return + * value can be used to detect memory leaks. + */ + public long cleanUpAllAllocatedMemory() { + long freedBytes = 0; + for (MemoryBlock page : pageTable) { + if (page != null) { + freedBytes += page.size(); + freePage(page); + } + } + final Iterator iter = allocatedNonPageMemory.iterator(); + while (iter.hasNext()) { + final MemoryBlock memory = iter.next(); + freedBytes += memory.size(); + // We don't call free() here because that calls Set.remove, which would lead to a + // ConcurrentModificationException here. + executorMemoryManager.free(memory); + iter.remove(); + } + return freedBytes; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java new file mode 100644 index 0000000000000..15898771fef25 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java @@ -0,0 +1,39 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import org.apache.spark.unsafe.PlatformDependent; + +/** + * A simple {@link MemoryAllocator} that uses {@code Unsafe} to allocate off-heap memory. + */ +public class UnsafeMemoryAllocator implements MemoryAllocator { + + @Override + public MemoryBlock allocate(long size) throws OutOfMemoryError { + long address = PlatformDependent.UNSAFE.allocateMemory(size); + return new MemoryBlock(null, address, size); + } + + @Override + public void free(MemoryBlock memory) { + assert (memory.obj == null) : + "baseObject not null; are you trying to use the off-heap allocator to free on-heap memory?"; + PlatformDependent.UNSAFE.freeMemory(memory.offset); + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/array/LongArraySuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/array/LongArraySuite.java new file mode 100644 index 0000000000000..5974cf91ff993 --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/array/LongArraySuite.java @@ -0,0 +1,38 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.array; + +import org.junit.Assert; +import org.junit.Test; + +import org.apache.spark.unsafe.memory.MemoryBlock; + +public class LongArraySuite { + + @Test + public void basicTest() { + long[] bytes = new long[2]; + LongArray arr = new LongArray(MemoryBlock.fromLongArray(bytes)); + arr.set(0, 1L); + arr.set(1, 2L); + arr.set(1, 3L); + Assert.assertEquals(2, arr.size()); + Assert.assertEquals(1L, arr.get(0)); + Assert.assertEquals(3L, arr.get(1)); + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java new file mode 100644 index 0000000000000..e3a824e29b768 --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.bitset; + +import junit.framework.Assert; +import org.apache.spark.unsafe.bitset.BitSet; +import org.junit.Test; + +import org.apache.spark.unsafe.memory.MemoryBlock; + +public class BitSetSuite { + + private static BitSet createBitSet(int capacity) { + assert capacity % 64 == 0; + return new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64])); + } + + @Test + public void basicOps() { + BitSet bs = createBitSet(64); + Assert.assertEquals(64, bs.capacity()); + + // Make sure the bit set starts empty. + for (int i = 0; i < bs.capacity(); i++) { + Assert.assertFalse(bs.isSet(i)); + } + + // Set every bit and check it. + for (int i = 0; i < bs.capacity(); i++) { + bs.set(i); + Assert.assertTrue(bs.isSet(i)); + } + + // Unset every bit and check it. + for (int i = 0; i < bs.capacity(); i++) { + Assert.assertTrue(bs.isSet(i)); + bs.unset(i); + Assert.assertFalse(bs.isSet(i)); + } + } + + @Test + public void traversal() { + BitSet bs = createBitSet(256); + + Assert.assertEquals(-1, bs.nextSetBit(0)); + Assert.assertEquals(-1, bs.nextSetBit(10)); + Assert.assertEquals(-1, bs.nextSetBit(64)); + + bs.set(10); + Assert.assertEquals(10, bs.nextSetBit(0)); + Assert.assertEquals(10, bs.nextSetBit(1)); + Assert.assertEquals(10, bs.nextSetBit(10)); + Assert.assertEquals(-1, bs.nextSetBit(11)); + + bs.set(11); + Assert.assertEquals(10, bs.nextSetBit(10)); + Assert.assertEquals(11, bs.nextSetBit(11)); + + // Skip a whole word and find it + bs.set(190); + Assert.assertEquals(190, bs.nextSetBit(12)); + + Assert.assertEquals(-1, bs.nextSetBit(191)); + Assert.assertEquals(-1, bs.nextSetBit(256)); + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java b/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java new file mode 100644 index 0000000000000..3b9175835229c --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.hash; + +import java.util.HashSet; +import java.util.Random; +import java.util.Set; + +import junit.framework.Assert; +import org.apache.spark.unsafe.PlatformDependent; +import org.junit.Test; + +/** + * Test file based on Guava's Murmur3Hash32Test. + */ +public class Murmur3_x86_32Suite { + + private static final Murmur3_x86_32 hasher = new Murmur3_x86_32(0); + + @Test + public void testKnownIntegerInputs() { + Assert.assertEquals(593689054, hasher.hashInt(0)); + Assert.assertEquals(-189366624, hasher.hashInt(-42)); + Assert.assertEquals(-1134849565, hasher.hashInt(42)); + Assert.assertEquals(-1718298732, hasher.hashInt(Integer.MIN_VALUE)); + Assert.assertEquals(-1653689534, hasher.hashInt(Integer.MAX_VALUE)); + } + + @Test + public void testKnownLongInputs() { + Assert.assertEquals(1669671676, hasher.hashLong(0L)); + Assert.assertEquals(-846261623, hasher.hashLong(-42L)); + Assert.assertEquals(1871679806, hasher.hashLong(42L)); + Assert.assertEquals(1366273829, hasher.hashLong(Long.MIN_VALUE)); + Assert.assertEquals(-2106506049, hasher.hashLong(Long.MAX_VALUE)); + } + + @Test + public void randomizedStressTest() { + int size = 65536; + Random rand = new Random(); + + // A set used to track collision rate. + Set hashcodes = new HashSet(); + for (int i = 0; i < size; i++) { + int vint = rand.nextInt(); + long lint = rand.nextLong(); + Assert.assertEquals(hasher.hashInt(vint), hasher.hashInt(vint)); + Assert.assertEquals(hasher.hashLong(lint), hasher.hashLong(lint)); + + hashcodes.add(hasher.hashLong(lint)); + } + + // A very loose bound. + Assert.assertTrue(hashcodes.size() > size * 0.95); + } + + @Test + public void randomizedStressTestBytes() { + int size = 65536; + Random rand = new Random(); + + // A set used to track collision rate. + Set hashcodes = new HashSet(); + for (int i = 0; i < size; i++) { + int byteArrSize = rand.nextInt(100) * 8; + byte[] bytes = new byte[byteArrSize]; + rand.nextBytes(bytes); + + Assert.assertEquals( + hasher.hashUnsafeWords(bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize), + hasher.hashUnsafeWords(bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize)); + + hashcodes.add(hasher.hashUnsafeWords( + bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize)); + } + + // A very loose bound. + Assert.assertTrue(hashcodes.size() > size * 0.95); + } + + @Test + public void randomizedStressTestPaddedStrings() { + int size = 64000; + // A set used to track collision rate. + Set hashcodes = new HashSet(); + for (int i = 0; i < size; i++) { + int byteArrSize = 8; + byte[] strBytes = ("" + i).getBytes(); + byte[] paddedBytes = new byte[byteArrSize]; + System.arraycopy(strBytes, 0, paddedBytes, 0, strBytes.length); + + Assert.assertEquals( + hasher.hashUnsafeWords(paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize), + hasher.hashUnsafeWords(paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize)); + + hashcodes.add(hasher.hashUnsafeWords( + paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize)); + } + + // A very loose bound. + Assert.assertTrue(hashcodes.size() > size * 0.95); + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java new file mode 100644 index 0000000000000..7a5c0622d1ffb --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java @@ -0,0 +1,250 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.map; + +import java.lang.Exception; +import java.nio.ByteBuffer; +import java.util.*; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.unsafe.array.ByteArrayMethods; +import org.apache.spark.unsafe.PlatformDependent; +import static org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET; +import org.apache.spark.unsafe.memory.ExecutorMemoryManager; +import org.apache.spark.unsafe.memory.MemoryAllocator; +import org.apache.spark.unsafe.memory.MemoryLocation; +import org.apache.spark.unsafe.memory.TaskMemoryManager; + +public abstract class AbstractBytesToBytesMapSuite { + + private final Random rand = new Random(42); + + private TaskMemoryManager memoryManager; + + @Before + public void setup() { + memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(getMemoryAllocator())); + } + + @After + public void tearDown() { + if (memoryManager != null) { + memoryManager.cleanUpAllAllocatedMemory(); + memoryManager = null; + } + } + + protected abstract MemoryAllocator getMemoryAllocator(); + + private static byte[] getByteArray(MemoryLocation loc, int size) { + final byte[] arr = new byte[size]; + PlatformDependent.copyMemory( + loc.getBaseObject(), + loc.getBaseOffset(), + arr, + BYTE_ARRAY_OFFSET, + size + ); + return arr; + } + + private byte[] getRandomByteArray(int numWords) { + Assert.assertTrue(numWords > 0); + final int lengthInBytes = numWords * 8; + final byte[] bytes = new byte[lengthInBytes]; + rand.nextBytes(bytes); + return bytes; + } + + /** + * Fast equality checking for byte arrays, since these comparisons are a bottleneck + * in our stress tests. + */ + private static boolean arrayEquals( + byte[] expected, + MemoryLocation actualAddr, + long actualLengthBytes) { + return (actualLengthBytes == expected.length) && ByteArrayMethods.wordAlignedArrayEquals( + expected, + BYTE_ARRAY_OFFSET, + actualAddr.getBaseObject(), + actualAddr.getBaseOffset(), + expected.length + ); + } + + @Test + public void emptyMap() { + BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64); + try { + Assert.assertEquals(0, map.size()); + final int keyLengthInWords = 10; + final int keyLengthInBytes = keyLengthInWords * 8; + final byte[] key = getRandomByteArray(keyLengthInWords); + Assert.assertFalse(map.lookup(key, BYTE_ARRAY_OFFSET, keyLengthInBytes).isDefined()); + } finally { + map.free(); + } + } + + @Test + public void setAndRetrieveAKey() { + BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64); + final int recordLengthWords = 10; + final int recordLengthBytes = recordLengthWords * 8; + final byte[] keyData = getRandomByteArray(recordLengthWords); + final byte[] valueData = getRandomByteArray(recordLengthWords); + try { + final BytesToBytesMap.Location loc = + map.lookup(keyData, BYTE_ARRAY_OFFSET, recordLengthBytes); + Assert.assertFalse(loc.isDefined()); + loc.putNewKey( + keyData, + BYTE_ARRAY_OFFSET, + recordLengthBytes, + valueData, + BYTE_ARRAY_OFFSET, + recordLengthBytes + ); + // After storing the key and value, the other location methods should return results that + // reflect the result of this store without us having to call lookup() again on the same key. + Assert.assertEquals(recordLengthBytes, loc.getKeyLength()); + Assert.assertEquals(recordLengthBytes, loc.getValueLength()); + Assert.assertArrayEquals(keyData, getByteArray(loc.getKeyAddress(), recordLengthBytes)); + Assert.assertArrayEquals(valueData, getByteArray(loc.getValueAddress(), recordLengthBytes)); + + // After calling lookup() the location should still point to the correct data. + Assert.assertTrue(map.lookup(keyData, BYTE_ARRAY_OFFSET, recordLengthBytes).isDefined()); + Assert.assertEquals(recordLengthBytes, loc.getKeyLength()); + Assert.assertEquals(recordLengthBytes, loc.getValueLength()); + Assert.assertArrayEquals(keyData, getByteArray(loc.getKeyAddress(), recordLengthBytes)); + Assert.assertArrayEquals(valueData, getByteArray(loc.getValueAddress(), recordLengthBytes)); + + try { + loc.putNewKey( + keyData, + BYTE_ARRAY_OFFSET, + recordLengthBytes, + valueData, + BYTE_ARRAY_OFFSET, + recordLengthBytes + ); + Assert.fail("Should not be able to set a new value for a key"); + } catch (AssertionError e) { + // Expected exception; do nothing. + } + } finally { + map.free(); + } + } + + @Test + public void iteratorTest() throws Exception { + final int size = 128; + BytesToBytesMap map = new BytesToBytesMap(memoryManager, size / 2); + try { + for (long i = 0; i < size; i++) { + final long[] value = new long[] { i }; + final BytesToBytesMap.Location loc = + map.lookup(value, PlatformDependent.LONG_ARRAY_OFFSET, 8); + Assert.assertFalse(loc.isDefined()); + loc.putNewKey( + value, + PlatformDependent.LONG_ARRAY_OFFSET, + 8, + value, + PlatformDependent.LONG_ARRAY_OFFSET, + 8 + ); + } + final java.util.BitSet valuesSeen = new java.util.BitSet(size); + final Iterator iter = map.iterator(); + while (iter.hasNext()) { + final BytesToBytesMap.Location loc = iter.next(); + Assert.assertTrue(loc.isDefined()); + final MemoryLocation keyAddress = loc.getKeyAddress(); + final MemoryLocation valueAddress = loc.getValueAddress(); + final long key = PlatformDependent.UNSAFE.getLong( + keyAddress.getBaseObject(), keyAddress.getBaseOffset()); + final long value = PlatformDependent.UNSAFE.getLong( + valueAddress.getBaseObject(), valueAddress.getBaseOffset()); + Assert.assertEquals(key, value); + valuesSeen.set((int) value); + } + Assert.assertEquals(size, valuesSeen.cardinality()); + } finally { + map.free(); + } + } + + @Test + public void randomizedStressTest() { + final int size = 65536; + // Java arrays' hashCodes() aren't based on the arrays' contents, so we need to wrap arrays + // into ByteBuffers in order to use them as keys here. + final Map expected = new HashMap(); + final BytesToBytesMap map = new BytesToBytesMap(memoryManager, size); + + try { + // Fill the map to 90% full so that we can trigger probing + for (int i = 0; i < size * 0.9; i++) { + final byte[] key = getRandomByteArray(rand.nextInt(256) + 1); + final byte[] value = getRandomByteArray(rand.nextInt(512) + 1); + if (!expected.containsKey(ByteBuffer.wrap(key))) { + expected.put(ByteBuffer.wrap(key), value); + final BytesToBytesMap.Location loc = map.lookup( + key, + BYTE_ARRAY_OFFSET, + key.length + ); + Assert.assertFalse(loc.isDefined()); + loc.putNewKey( + key, + BYTE_ARRAY_OFFSET, + key.length, + value, + BYTE_ARRAY_OFFSET, + value.length + ); + // After calling putNewKey, the following should be true, even before calling + // lookup(): + Assert.assertTrue(loc.isDefined()); + Assert.assertEquals(key.length, loc.getKeyLength()); + Assert.assertEquals(value.length, loc.getValueLength()); + Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), key.length)); + Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), value.length)); + } + } + + for (Map.Entry entry : expected.entrySet()) { + final byte[] key = entry.getKey().array(); + final byte[] value = entry.getValue(); + final BytesToBytesMap.Location loc = map.lookup(key, BYTE_ARRAY_OFFSET, key.length); + Assert.assertTrue(loc.isDefined()); + Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), loc.getKeyLength())); + Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), loc.getValueLength())); + } + } finally { + map.free(); + } + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala b/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java similarity index 63% rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala rename to unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java index f5fea3f015dc4..5a10de49f54fe 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala +++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java @@ -15,22 +15,15 @@ * limitations under the License. */ -package org.apache.spark.sql.catalyst.expressions +package org.apache.spark.unsafe.map; -import java.util.Random +import org.apache.spark.unsafe.memory.MemoryAllocator; -import org.apache.spark.sql.types.{DataType, DoubleType} +public class BytesToBytesMapOffHeapSuite extends AbstractBytesToBytesMapSuite { - -case object Rand extends LeafExpression { - override def dataType: DataType = DoubleType - override def nullable: Boolean = false - - private[this] lazy val rand = new Random - - override def eval(input: Row = null): EvaluatedType = { - rand.nextDouble().asInstanceOf[EvaluatedType] + @Override + protected MemoryAllocator getMemoryAllocator() { + return MemoryAllocator.UNSAFE; } - override def toString: String = "RAND()" } diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java new file mode 100644 index 0000000000000..12cc9b25d93b3 --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java @@ -0,0 +1,29 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.map; + +import org.apache.spark.unsafe.memory.MemoryAllocator; + +public class BytesToBytesMapOnHeapSuite extends AbstractBytesToBytesMapSuite { + + @Override + protected MemoryAllocator getMemoryAllocator() { + return MemoryAllocator.HEAP; + } + +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java new file mode 100644 index 0000000000000..932882f1ca248 --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import org.junit.Assert; +import org.junit.Test; + +public class TaskMemoryManagerSuite { + + @Test + public void leakedNonPageMemoryIsDetected() { + final TaskMemoryManager manager = + new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP)); + manager.allocate(1024); // leak memory + Assert.assertEquals(1024, manager.cleanUpAllAllocatedMemory()); + } + + @Test + public void leakedPageMemoryIsDetected() { + final TaskMemoryManager manager = + new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP)); + manager.allocatePage(4096); // leak memory + Assert.assertEquals(4096, manager.cleanUpAllAllocatedMemory()); + } + +} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala new file mode 100644 index 0000000000000..aaae6f9734a85 --- /dev/null +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala @@ -0,0 +1,205 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.deploy.yarn + +import java.security.PrivilegedExceptionAction +import java.util.concurrent.{Executors, TimeUnit} + +import scala.language.postfixOps + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.security.UserGroupInformation +import org.apache.spark.deploy.SparkHadoopUtil + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.util.ThreadUtils + +/* + * The following methods are primarily meant to make sure long-running apps like Spark + * Streaming apps can run without interruption while writing to secure HDFS. The + * scheduleLoginFromKeytab method is called on the driver when the + * CoarseGrainedScheduledBackend starts up. This method wakes up a thread that logs into the KDC + * once 75% of the renewal interval of the original delegation tokens used for the container + * has elapsed. It then creates new delegation tokens and writes them to HDFS in a + * pre-specified location - the prefix of which is specified in the sparkConf by + * spark.yarn.credentials.file (so the file(s) would be named c-1, c-2 etc. - each update goes + * to a new file, with a monotonically increasing suffix). After this, the credentials are + * updated once 75% of the new tokens renewal interval has elapsed. + * + * On the executor side, the updateCredentialsIfRequired method is called once 80% of the + * validity of the original tokens has elapsed. At that time the executor finds the + * credentials file with the latest timestamp and checks if it has read those credentials + * before (by keeping track of the suffix of the last file it read). If a new file has + * appeared, it will read the credentials and update the currently running UGI with it. This + * process happens again once 80% of the validity of this has expired. + */ +private[yarn] class AMDelegationTokenRenewer( + sparkConf: SparkConf, + hadoopConf: Configuration) extends Logging { + + private var lastCredentialsFileSuffix = 0 + + private val delegationTokenRenewer = + Executors.newSingleThreadScheduledExecutor( + ThreadUtils.namedThreadFactory("Delegation Token Refresh Thread")) + + private val hadoopUtil = YarnSparkHadoopUtil.get + + private val daysToKeepFiles = sparkConf.getInt("spark.yarn.credentials.file.retention.days", 5) + private val numFilesToKeep = sparkConf.getInt("spark.yarn.credentials.file.retention.count", 5) + + /** + * Schedule a login from the keytab and principal set using the --principal and --keytab + * arguments to spark-submit. This login happens only when the credentials of the current user + * are about to expire. This method reads spark.yarn.principal and spark.yarn.keytab from + * SparkConf to do the login. This method is a no-op in non-YARN mode. + * + */ + private[spark] def scheduleLoginFromKeytab(): Unit = { + val principal = sparkConf.get("spark.yarn.principal") + val keytab = sparkConf.get("spark.yarn.keytab") + + /** + * Schedule re-login and creation of new tokens. If tokens have already expired, this method + * will synchronously create new ones. + */ + def scheduleRenewal(runnable: Runnable): Unit = { + val credentials = UserGroupInformation.getCurrentUser.getCredentials + val renewalInterval = hadoopUtil.getTimeFromNowToRenewal(sparkConf, 0.75, credentials) + // Run now! + if (renewalInterval <= 0) { + logInfo("HDFS tokens have expired, creating new tokens now.") + runnable.run() + } else { + logInfo(s"Scheduling login from keytab in $renewalInterval millis.") + delegationTokenRenewer.schedule(runnable, renewalInterval, TimeUnit.MILLISECONDS) + } + } + + // This thread periodically runs on the driver to update the delegation tokens on HDFS. + val driverTokenRenewerRunnable = + new Runnable { + override def run(): Unit = { + try { + writeNewTokensToHDFS(principal, keytab) + cleanupOldFiles() + } catch { + case e: Exception => + // Log the error and try to write new tokens back in an hour + logWarning("Failed to write out new credentials to HDFS, will try again in an " + + "hour! If this happens too often tasks will fail.", e) + delegationTokenRenewer.schedule(this, 1, TimeUnit.HOURS) + return + } + scheduleRenewal(this) + } + } + // Schedule update of credentials. This handles the case of updating the tokens right now + // as well, since the renenwal interval will be 0, and the thread will get scheduled + // immediately. + scheduleRenewal(driverTokenRenewerRunnable) + } + + // Keeps only files that are newer than daysToKeepFiles days, and deletes everything else. At + // least numFilesToKeep files are kept for safety + private def cleanupOldFiles(): Unit = { + import scala.concurrent.duration._ + try { + val remoteFs = FileSystem.get(hadoopConf) + val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) + val thresholdTime = System.currentTimeMillis() - (daysToKeepFiles days).toMillis + hadoopUtil.listFilesSorted( + remoteFs, credentialsPath.getParent, + credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .dropRight(numFilesToKeep) + .takeWhile(_.getModificationTime < thresholdTime) + .foreach(x => remoteFs.delete(x.getPath, true)) + } catch { + // Such errors are not fatal, so don't throw. Make sure they are logged though + case e: Exception => + logWarning("Error while attempting to cleanup old tokens. If you are seeing many such " + + "warnings there may be an issue with your HDFS cluster.", e) + } + } + + private def writeNewTokensToHDFS(principal: String, keytab: String): Unit = { + // Keytab is copied by YARN to the working directory of the AM, so full path is + // not needed. + + // HACK: + // HDFS will not issue new delegation tokens, if the Credentials object + // passed in already has tokens for that FS even if the tokens are expired (it really only + // checks if there are tokens for the service, and not if they are valid). So the only real + // way to get new tokens is to make sure a different Credentials object is used each time to + // get new tokens and then the new tokens are copied over the the current user's Credentials. + // So: + // - we login as a different user and get the UGI + // - use that UGI to get the tokens (see doAs block below) + // - copy the tokens over to the current user's credentials (this will overwrite the tokens + // in the current user's Credentials object for this FS). + // The login to KDC happens each time new tokens are required, but this is rare enough to not + // have to worry about (like once every day or so). This makes this code clearer than having + // to login and then relogin every time (the HDFS API may not relogin since we don't use this + // UGI directly for HDFS communication. + logInfo(s"Attempting to login to KDC using principal: $principal") + val keytabLoggedInUGI = UserGroupInformation.loginUserFromKeytabAndReturnUGI(principal, keytab) + logInfo("Successfully logged into KDC.") + val tempCreds = keytabLoggedInUGI.getCredentials + val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) + val dst = credentialsPath.getParent + keytabLoggedInUGI.doAs(new PrivilegedExceptionAction[Void] { + // Get a copy of the credentials + override def run(): Void = { + val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + dst + hadoopUtil.obtainTokensForNamenodes(nns, hadoopConf, tempCreds) + null + } + }) + // Add the temp credentials back to the original ones. + UserGroupInformation.getCurrentUser.addCredentials(tempCreds) + val remoteFs = FileSystem.get(hadoopConf) + // If lastCredentialsFileSuffix is 0, then the AM is either started or restarted. If the AM + // was restarted, then the lastCredentialsFileSuffix might be > 0, so find the newest file + // and update the lastCredentialsFileSuffix. + if (lastCredentialsFileSuffix == 0) { + hadoopUtil.listFilesSorted( + remoteFs, credentialsPath.getParent, + credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .lastOption.foreach { status => + lastCredentialsFileSuffix = hadoopUtil.getSuffixForCredentialsPath(status.getPath) + } + } + val nextSuffix = lastCredentialsFileSuffix + 1 + val tokenPathStr = + sparkConf.get("spark.yarn.credentials.file") + + SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM + nextSuffix + val tokenPath = new Path(tokenPathStr) + val tempTokenPath = new Path(tokenPathStr + SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + logInfo("Writing out delegation tokens to " + tempTokenPath.toString) + val credentials = UserGroupInformation.getCurrentUser.getCredentials + credentials.writeTokenStorageFile(tempTokenPath, hadoopConf) + logInfo(s"Delegation Tokens written out successfully. Renaming file to $tokenPathStr") + remoteFs.rename(tempTokenPath, tokenPath) + logInfo("Delegation token file rename complete.") + lastCredentialsFileSuffix = nextSuffix + } + + def stop(): Unit = { + delegationTokenRenewer.shutdown() + } +} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 70cb57ffd8c69..29752969e6152 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -75,6 +75,8 @@ private[spark] class ApplicationMaster( // Fields used in cluster mode. private val sparkContextRef = new AtomicReference[SparkContext](null) + private var delegationTokenRenewerOption: Option[AMDelegationTokenRenewer] = None + final def run(): Int = { try { val appAttemptId = client.getAttemptId() @@ -89,6 +91,10 @@ private[spark] class ApplicationMaster( // Propagate the application ID so that YarnClusterSchedulerBackend can pick it up. System.setProperty("spark.yarn.app.id", appAttemptId.getApplicationId().toString()) + + // Propagate the attempt if, so that in case of event logging, + // different attempt's logs gets created in different directory + System.setProperty("spark.yarn.app.attemptId", appAttemptId.getAttemptId().toString()) } logInfo("ApplicationAttemptId: " + appAttemptId) @@ -125,6 +131,15 @@ private[spark] class ApplicationMaster( // doAs in order for the credentials to be passed on to the executor containers. val securityMgr = new SecurityManager(sparkConf) + // If the credentials file config is present, we must periodically renew tokens. So create + // a new AMDelegationTokenRenewer + if (sparkConf.contains("spark.yarn.credentials.file")) { + delegationTokenRenewerOption = Some(new AMDelegationTokenRenewer(sparkConf, yarnConf)) + // If a principal and keytab have been set, use that to create new credentials for executors + // periodically + delegationTokenRenewerOption.foreach(_.scheduleLoginFromKeytab()) + } + if (isClusterMode) { runDriver(securityMgr) } else { @@ -189,6 +204,7 @@ private[spark] class ApplicationMaster( logDebug("shutting down user thread") userClassThread.interrupt() } + if (!inShutdown) delegationTokenRenewerOption.foreach(_.stop()) } } } @@ -208,10 +224,11 @@ private[spark] class ApplicationMaster( val sc = sparkContextRef.get() val appId = client.getAttemptId().getApplicationId().toString() + val attemptId = client.getAttemptId().getAttemptId().toString() val historyAddress = sparkConf.getOption("spark.yarn.historyServer.address") .map { text => SparkHadoopUtil.get.substituteHadoopVariables(text, yarnConf) } - .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}" } + .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}/${attemptId}" } .getOrElse("") allocator = client.register(yarnConf, @@ -235,12 +252,12 @@ private[spark] class ApplicationMaster( host: String, port: String, isClusterMode: Boolean): Unit = { - val driverEndpont = rpcEnv.setupEndpointRef( + val driverEndpoint = rpcEnv.setupEndpointRef( SparkEnv.driverActorSystemName, RpcAddress(host, port.toInt), YarnSchedulerBackend.ENDPOINT_NAME) amEndpoint = - rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpont, isClusterMode)) + rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpoint, isClusterMode)) } private def runDriver(securityMgr: SecurityManager): Unit = { @@ -268,7 +285,8 @@ private[spark] class ApplicationMaster( } private def runExecutorLauncher(securityMgr: SecurityManager): Unit = { - rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, 0, sparkConf, securityMgr) + val port = sparkConf.getInt("spark.yarn.am.port", 0) + rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, port, sparkConf, securityMgr) waitForSparkDriver() addAmIpFilter() registerAM(sparkConf.get("spark.driver.appUIAddress", ""), securityMgr) @@ -494,6 +512,7 @@ private[spark] class ApplicationMaster( override def onStart(): Unit = { driver.send(RegisterClusterManager(self)) + } override def receive: PartialFunction[Any, Unit] = { diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 741239c953794..20ecaf092e3f8 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -17,9 +17,11 @@ package org.apache.spark.deploy.yarn -import java.io.{File, FileOutputStream} +import java.io.{ByteArrayInputStream, DataInputStream, File, FileOutputStream} import java.net.{InetAddress, UnknownHostException, URI, URISyntaxException} import java.nio.ByteBuffer +import java.security.PrivilegedExceptionAction +import java.util.UUID import java.util.zip.{ZipEntry, ZipOutputStream} import scala.collection.JavaConversions._ @@ -36,10 +38,9 @@ import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifie import org.apache.hadoop.fs._ import org.apache.hadoop.fs.permission.FsPermission import org.apache.hadoop.io.Text -import org.apache.hadoop.mapred.Master import org.apache.hadoop.mapreduce.MRJobConfig import org.apache.hadoop.security.{Credentials, UserGroupInformation} -import org.apache.hadoop.security.token.Token +import org.apache.hadoop.security.token.{TokenIdentifier, Token} import org.apache.hadoop.util.StringUtils import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.ApplicationConstants.Environment @@ -50,8 +51,8 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.exceptions.ApplicationNotFoundException import org.apache.hadoop.yarn.util.Records -import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkException} import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkException} import org.apache.spark.util.Utils private[spark] class Client( @@ -69,11 +70,13 @@ private[spark] class Client( private val yarnClient = YarnClient.createYarnClient private val yarnConf = new YarnConfiguration(hadoopConf) - private val credentials = UserGroupInformation.getCurrentUser.getCredentials + private var credentials: Credentials = null private val amMemoryOverhead = args.amMemoryOverhead // MB private val executorMemoryOverhead = args.executorMemoryOverhead // MB private val distCacheMgr = new ClientDistributedCacheManager() private val isClusterMode = args.isClusterMode + + private var loginFromKeytab = false private val fireAndForget = isClusterMode && !sparkConf.getBoolean("spark.yarn.submit.waitAppCompletion", true) @@ -88,6 +91,8 @@ private[spark] class Client( * available in the alpha API. */ def submitApplication(): ApplicationId = { + // Setup the credentials before doing anything else, so we have don't have issues at any point. + setupCredentials() yarnClient.init(yarnConf) yarnClient.start() @@ -219,13 +224,14 @@ private[spark] class Client( // and add them as local resources to the application master. val fs = FileSystem.get(hadoopConf) val dst = new Path(fs.getHomeDirectory(), appStagingDir) - val nns = getNameNodesToAccess(sparkConf) + dst + val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + dst + YarnSparkHadoopUtil.get.obtainTokensForNamenodes(nns, hadoopConf, credentials) // Used to keep track of URIs added to the distributed cache. If the same URI is added // multiple times, YARN will fail to launch containers for the app with an internal // error. val distributedUris = new HashSet[String] - obtainTokensForNamenodes(nns, hadoopConf, credentials) obtainTokenForHiveMetastore(hadoopConf, credentials) + obtainTokenForHBase(hadoopConf, credentials) val replication = sparkConf.getInt("spark.yarn.submit.file.replication", fs.getDefaultReplication(dst)).toShort @@ -242,6 +248,20 @@ private[spark] class Client( "for alternatives.") } + // If we passed in a keytab, make sure we copy the keytab to the staging directory on + // HDFS, and setup the relevant environment vars, so the AM can login again. + if (loginFromKeytab) { + logInfo("To enable the AM to login from keytab, credentials are being copied over to the AM" + + " via the YARN Secure Distributed Cache.") + val localUri = new URI(args.keytab) + val localPath = getQualifiedLocalPath(localUri, hadoopConf) + val destinationPath = copyFileToRemote(dst, localPath, replication) + val destFs = FileSystem.get(destinationPath.toUri(), hadoopConf) + distCacheMgr.addResource( + destFs, hadoopConf, destinationPath, localResources, LocalResourceType.FILE, + sparkConf.get("spark.yarn.keytab"), statCache, appMasterOnly = true) + } + def addDistributedUri(uri: URI): Boolean = { val uriStr = uri.toString() if (distributedUris.contains(uriStr)) { @@ -370,9 +390,11 @@ private[spark] class Client( try { hadoopConfStream.setLevel(0) hadoopConfFiles.foreach { case (name, file) => - hadoopConfStream.putNextEntry(new ZipEntry(name)) - Files.copy(file, hadoopConfStream) - hadoopConfStream.closeEntry() + if (file.canRead()) { + hadoopConfStream.putNextEntry(new ZipEntry(name)) + Files.copy(file, hadoopConfStream) + hadoopConfStream.closeEntry() + } } } finally { hadoopConfStream.close() @@ -384,6 +406,28 @@ private[spark] class Client( } } + /** + * Get the renewal interval for tokens. + */ + private def getTokenRenewalInterval(stagingDirPath: Path): Long = { + // We cannot use the tokens generated above since those have renewer yarn. Trying to renew + // those will fail with an access control issue. So create new tokens with the logged in + // user as renewer. + val creds = new Credentials() + val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + stagingDirPath + YarnSparkHadoopUtil.get.obtainTokensForNamenodes( + nns, hadoopConf, creds, Some(sparkConf.get("spark.yarn.principal"))) + val t = creds.getAllTokens + .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) + .head + val newExpiration = t.renew(hadoopConf) + val identifier = new DelegationTokenIdentifier() + identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) + val interval = newExpiration - identifier.getIssueDate + logInfo(s"Renewal Interval set to $interval") + interval + } + /** * Set up the environment for launching our ApplicationMaster container. */ @@ -395,7 +439,16 @@ private[spark] class Client( env("SPARK_YARN_MODE") = "true" env("SPARK_YARN_STAGING_DIR") = stagingDir env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName() - + if (loginFromKeytab) { + val remoteFs = FileSystem.get(hadoopConf) + val stagingDirPath = new Path(remoteFs.getHomeDirectory, stagingDir) + val credentialsFile = "credentials-" + UUID.randomUUID().toString + sparkConf.set( + "spark.yarn.credentials.file", new Path(stagingDirPath, credentialsFile).toString) + logInfo(s"Credentials file set to: $credentialsFile") + val renewalInterval = getTokenRenewalInterval(stagingDirPath) + sparkConf.set("spark.yarn.token.renewal.interval", renewalInterval.toString) + } // Set the environment variables to be passed on to the executors. distCacheMgr.setDistFilesEnv(env) distCacheMgr.setDistArchivesEnv(env) @@ -460,7 +513,6 @@ private[spark] class Client( private def createContainerLaunchContext(newAppResponse: GetNewApplicationResponse) : ContainerLaunchContext = { logInfo("Setting up container launch context for our AM") - val appId = newAppResponse.getApplicationId val appStagingDir = getAppStagingDir(appId) val localResources = prepareLocalResources(appStagingDir) @@ -541,6 +593,10 @@ private[spark] class Client( } javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) } + + sparkConf.getOption("spark.yarn.am.extraLibraryPath").foreach { paths => + prefixEnv = Some(Utils.libraryPathEnvPrefix(Seq(paths))) + } } // For log4j configuration to reference @@ -631,6 +687,24 @@ private[spark] class Client( amContainer } + def setupCredentials(): Unit = { + if (args.principal != null) { + require(args.keytab != null, "Keytab must be specified when principal is specified.") + logInfo("Attempting to login to the Kerberos" + + s" using principal: ${args.principal} and keytab: ${args.keytab}") + val f = new File(args.keytab) + // Generate a file name that can be used for the keytab file, that does not conflict + // with any user file. + val keytabFileName = f.getName + "-" + UUID.randomUUID().toString + UserGroupInformation.loginUserFromKeytab(args.principal, args.keytab) + loginFromKeytab = true + sparkConf.set("spark.yarn.keytab", keytabFileName) + sparkConf.set("spark.yarn.principal", args.principal) + logInfo("Successfully logged into the KDC.") + } + credentials = UserGroupInformation.getCurrentUser.getCredentials + } + /** * Report the state of an application until it has exited, either successfully or * due to some failure, then return a pair of the yarn application state (FINISHED, FAILED, @@ -986,46 +1060,6 @@ object Client extends Logging { private def addClasspathEntry(path: String, env: HashMap[String, String]): Unit = YarnSparkHadoopUtil.addPathToEnvironment(env, Environment.CLASSPATH.name, path) - /** - * Get the list of namenodes the user may access. - */ - private[yarn] def getNameNodesToAccess(sparkConf: SparkConf): Set[Path] = { - sparkConf.get("spark.yarn.access.namenodes", "") - .split(",") - .map(_.trim()) - .filter(!_.isEmpty) - .map(new Path(_)) - .toSet - } - - private[yarn] def getTokenRenewer(conf: Configuration): String = { - val delegTokenRenewer = Master.getMasterPrincipal(conf) - logDebug("delegation token renewer is: " + delegTokenRenewer) - if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { - val errorMessage = "Can't get Master Kerberos principal for use as renewer" - logError(errorMessage) - throw new SparkException(errorMessage) - } - delegTokenRenewer - } - - /** - * Obtains tokens for the namenodes passed in and adds them to the credentials. - */ - private def obtainTokensForNamenodes( - paths: Set[Path], - conf: Configuration, - creds: Credentials): Unit = { - if (UserGroupInformation.isSecurityEnabled()) { - val delegTokenRenewer = getTokenRenewer(conf) - paths.foreach { dst => - val dstFs = dst.getFileSystem(conf) - logDebug("getting token for namenode: " + dst) - dstFs.addDelegationTokens(delegTokenRenewer, creds) - } - } - } - /** * Obtains token for the Hive metastore and adds them to the credentials. */ @@ -1084,6 +1118,41 @@ object Client extends Logging { } } + /** + * Obtain security token for HBase. + */ + def obtainTokenForHBase(conf: Configuration, credentials: Credentials): Unit = { + if (UserGroupInformation.isSecurityEnabled) { + val mirror = universe.runtimeMirror(getClass.getClassLoader) + + try { + val confCreate = mirror.classLoader. + loadClass("org.apache.hadoop.hbase.HBaseConfiguration"). + getMethod("create", classOf[Configuration]) + val obtainToken = mirror.classLoader. + loadClass("org.apache.hadoop.hbase.security.token.TokenUtil"). + getMethod("obtainToken", classOf[Configuration]) + + logDebug("Attempting to fetch HBase security token.") + + val hbaseConf = confCreate.invoke(null, conf) + val token = obtainToken.invoke(null, hbaseConf).asInstanceOf[Token[TokenIdentifier]] + credentials.addToken(token.getService, token) + + logInfo("Added HBase security token to credentials.") + } catch { + case e:java.lang.NoSuchMethodException => + logInfo("HBase Method not found: " + e) + case e:java.lang.ClassNotFoundException => + logDebug("HBase Class not found: " + e) + case e:java.lang.NoClassDefFoundError => + logDebug("HBase Class not found: " + e) + case e:Exception => + logError("Exception when obtaining HBase security token: " + e) + } + } + } + /** * Return whether the two file systems are the same. */ diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index 1423533470fc0..5653c9f14dc6d 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -42,6 +42,8 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) var amCores: Int = 1 var appName: String = "Spark" var priority = 0 + var principal: String = null + var keytab: String = null def isClusterMode: Boolean = userClass != null private var driverMemory: Int = 512 // MB @@ -231,6 +233,14 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) archives = value args = tail + case ("--principal") :: value :: tail => + principal = value + args = tail + + case ("--keytab") :: value :: tail => + keytab = value + args = tail + case Nil => case _ => diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala new file mode 100644 index 0000000000000..229c2c4d5eb36 --- /dev/null +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala @@ -0,0 +1,106 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.deploy.yarn + +import java.util.concurrent.{Executors, TimeUnit} + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.security.{Credentials, UserGroupInformation} + +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.util.{ThreadUtils, Utils} + +import scala.util.control.NonFatal + +private[spark] class ExecutorDelegationTokenUpdater( + sparkConf: SparkConf, + hadoopConf: Configuration) extends Logging { + + @volatile private var lastCredentialsFileSuffix = 0 + + private val credentialsFile = sparkConf.get("spark.yarn.credentials.file") + + private val delegationTokenRenewer = + Executors.newSingleThreadScheduledExecutor( + ThreadUtils.namedThreadFactory("Delegation Token Refresh Thread")) + + // On the executor, this thread wakes up and picks up new tokens from HDFS, if any. + private val executorUpdaterRunnable = + new Runnable { + override def run(): Unit = Utils.logUncaughtExceptions(updateCredentialsIfRequired()) + } + + def updateCredentialsIfRequired(): Unit = { + try { + val credentialsFilePath = new Path(credentialsFile) + val remoteFs = FileSystem.get(hadoopConf) + SparkHadoopUtil.get.listFilesSorted( + remoteFs, credentialsFilePath.getParent, + credentialsFilePath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .lastOption.foreach { credentialsStatus => + val suffix = SparkHadoopUtil.get.getSuffixForCredentialsPath(credentialsStatus.getPath) + if (suffix > lastCredentialsFileSuffix) { + logInfo("Reading new delegation tokens from " + credentialsStatus.getPath) + val newCredentials = getCredentialsFromHDFSFile(remoteFs, credentialsStatus.getPath) + lastCredentialsFileSuffix = suffix + UserGroupInformation.getCurrentUser.addCredentials(newCredentials) + logInfo("Tokens updated from credentials file.") + } else { + // Check every hour to see if new credentials arrived. + logInfo("Updated delegation tokens were expected, but the driver has not updated the " + + "tokens yet, will check again in an hour.") + delegationTokenRenewer.schedule(executorUpdaterRunnable, 1, TimeUnit.HOURS) + return + } + } + val timeFromNowToRenewal = + SparkHadoopUtil.get.getTimeFromNowToRenewal( + sparkConf, 0.8, UserGroupInformation.getCurrentUser.getCredentials) + if (timeFromNowToRenewal <= 0) { + executorUpdaterRunnable.run() + } else { + logInfo(s"Scheduling token refresh from HDFS in $timeFromNowToRenewal millis.") + delegationTokenRenewer.schedule( + executorUpdaterRunnable, timeFromNowToRenewal, TimeUnit.MILLISECONDS) + } + } catch { + // Since the file may get deleted while we are reading it, catch the Exception and come + // back in an hour to try again + case NonFatal(e) => + logWarning("Error while trying to update credentials, will try again in 1 hour", e) + delegationTokenRenewer.schedule(executorUpdaterRunnable, 1, TimeUnit.HOURS) + } + } + + private def getCredentialsFromHDFSFile(remoteFs: FileSystem, tokenPath: Path): Credentials = { + val stream = remoteFs.open(tokenPath) + try { + val newCredentials = new Credentials() + newCredentials.readTokenStorageStream(stream) + newCredentials + } finally { + stream.close() + } + } + + def stop(): Unit = { + delegationTokenRenewer.shutdown() + } + +} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala index 5881dc5ffa3ad..ba91872107d0c 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -24,18 +24,19 @@ import java.util.regex.Pattern import scala.collection.mutable.HashMap import scala.util.Try +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path import org.apache.hadoop.io.Text -import org.apache.hadoop.mapred.JobConf +import org.apache.hadoop.mapred.{Master, JobConf} import org.apache.hadoop.security.Credentials import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.api.records.{Priority, ApplicationAccessType} -import org.apache.hadoop.conf.Configuration -import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.{SecurityManager, SparkConf, SparkException} import org.apache.spark.util.Utils /** @@ -43,6 +44,8 @@ import org.apache.spark.util.Utils */ class YarnSparkHadoopUtil extends SparkHadoopUtil { + private var tokenRenewer: Option[ExecutorDelegationTokenUpdater] = None + override def transferCredentials(source: UserGroupInformation, dest: UserGroupInformation) { dest.addCredentials(source.getCredentials()) } @@ -82,6 +85,57 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil { if (credentials != null) credentials.getSecretKey(new Text(key)) else null } + /** + * Get the list of namenodes the user may access. + */ + def getNameNodesToAccess(sparkConf: SparkConf): Set[Path] = { + sparkConf.get("spark.yarn.access.namenodes", "") + .split(",") + .map(_.trim()) + .filter(!_.isEmpty) + .map(new Path(_)) + .toSet + } + + def getTokenRenewer(conf: Configuration): String = { + val delegTokenRenewer = Master.getMasterPrincipal(conf) + logDebug("delegation token renewer is: " + delegTokenRenewer) + if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { + val errorMessage = "Can't get Master Kerberos principal for use as renewer" + logError(errorMessage) + throw new SparkException(errorMessage) + } + delegTokenRenewer + } + + /** + * Obtains tokens for the namenodes passed in and adds them to the credentials. + */ + def obtainTokensForNamenodes( + paths: Set[Path], + conf: Configuration, + creds: Credentials, + renewer: Option[String] = None + ): Unit = { + if (UserGroupInformation.isSecurityEnabled()) { + val delegTokenRenewer = renewer.getOrElse(getTokenRenewer(conf)) + paths.foreach { dst => + val dstFs = dst.getFileSystem(conf) + logInfo("getting token for namenode: " + dst) + dstFs.addDelegationTokens(delegTokenRenewer, creds) + } + } + } + + private[spark] override def startExecutorDelegationTokenRenewer(sparkConf: SparkConf): Unit = { + tokenRenewer = Some(new ExecutorDelegationTokenUpdater(sparkConf, conf)) + tokenRenewer.get.updateCredentialsIfRequired() + } + + private[spark] override def stopExecutorDelegationTokenRenewer(): Unit = { + tokenRenewer.foreach(_.stop()) + } + } object YarnSparkHadoopUtil { @@ -100,6 +154,14 @@ object YarnSparkHadoopUtil { // request types (like map/reduce in hadoop for example) val RM_REQUEST_PRIORITY = Priority.newInstance(1) + def get: YarnSparkHadoopUtil = { + val yarnMode = java.lang.Boolean.valueOf( + System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))) + if (!yarnMode) { + throw new SparkException("YarnSparkHadoopUtil is not available in non-YARN mode!") + } + SparkHadoopUtil.get.asInstanceOf[YarnSparkHadoopUtil] + } /** * Add a path variable to the given environment map. * If the map already contains this key, append the value to the existing value instead. @@ -212,3 +274,4 @@ object YarnSparkHadoopUtil { classPathSeparatorField.get(null).asInstanceOf[String] } } + diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala index b1de81e6a8b0f..aeb218a575455 100644 --- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala @@ -39,12 +39,18 @@ private[spark] class YarnClusterSchedulerBackend( } override def applicationId(): String = - // In YARN Cluster mode, spark.yarn.app.id is expect to be set - // before user application is launched. - // So, if spark.yarn.app.id is not set, it is something wrong. + // In YARN Cluster mode, the application ID is expected to be set, so log an error if it's + // not found. sc.getConf.getOption("spark.yarn.app.id").getOrElse { logError("Application ID is not set.") super.applicationId } + override def applicationAttemptId(): Option[String] = + // In YARN Cluster mode, the attempt ID is expected to be set, so log an error if it's + // not found. + sc.getConf.getOption("spark.yarn.app.attemptId").orElse { + logError("Application attempt ID is not set.") + super.applicationAttemptId + } } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala index a51c2005cb472..508819e242a26 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala @@ -151,57 +151,6 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { } } - test("check access nns empty") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set()) - } - - test("check access nns unset") { - val sparkConf = new SparkConf() - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set()) - } - - test("check access nns") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"))) - } - - test("check access nns space") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032, ") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"))) - } - - test("check access two nns") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032,hdfs://nn2:8032") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"), new Path("hdfs://nn2:8032"))) - } - - test("check token renewer") { - val hadoopConf = new Configuration() - hadoopConf.set("yarn.resourcemanager.address", "myrm:8033") - hadoopConf.set("yarn.resourcemanager.principal", "yarn/myrm:8032@SPARKTEST.COM") - val renewer = Client.getTokenRenewer(hadoopConf) - renewer should be ("yarn/myrm:8032@SPARKTEST.COM") - } - - test("check token renewer default") { - val hadoopConf = new Configuration() - val caught = - intercept[SparkException] { - Client.getTokenRenewer(hadoopConf) - } - assert(caught.getMessage === "Can't get Master Kerberos principal for use as renewer") - } - object Fixtures { val knownDefYarnAppCP: Seq[String] = diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index 3877da4120e7c..d3c606e0ed998 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -86,6 +86,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit tempDir = Utils.createTempDir() logConfDir = new File(tempDir, "log4j") logConfDir.mkdir() + System.setProperty("SPARK_YARN_MODE", "true") val logConfFile = new File(logConfDir, "log4j.properties") Files.write(LOG4J_CONF, logConfFile, UTF_8) @@ -128,6 +129,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit override def afterAll() { yarnCluster.stop() + System.clearProperty("SPARK_YARN_MODE") super.afterAll() } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala index 9395316b71ff4..e10b985c3c236 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala @@ -20,6 +20,8 @@ package org.apache.spark.deploy.yarn import java.io.{File, IOException} import com.google.common.io.{ByteStreams, Files} +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.conf.YarnConfiguration @@ -27,7 +29,7 @@ import org.scalatest.{FunSuite, Matchers} import org.apache.hadoop.yarn.api.records.ApplicationAccessType -import org.apache.spark.{Logging, SecurityManager, SparkConf} +import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException} import org.apache.spark.util.Utils @@ -173,4 +175,62 @@ class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging { YarnSparkHadoopUtil.getClassPathSeparator() should be (":") } } + + test("check access nns empty") { + val sparkConf = new SparkConf() + val util = new YarnSparkHadoopUtil + sparkConf.set("spark.yarn.access.namenodes", "") + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set()) + } + + test("check access nns unset") { + val sparkConf = new SparkConf() + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set()) + } + + test("check access nns") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032") + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"))) + } + + test("check access nns space") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032, ") + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"))) + } + + test("check access two nns") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032,hdfs://nn2:8032") + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"), new Path("hdfs://nn2:8032"))) + } + + test("check token renewer") { + val hadoopConf = new Configuration() + hadoopConf.set("yarn.resourcemanager.address", "myrm:8033") + hadoopConf.set("yarn.resourcemanager.principal", "yarn/myrm:8032@SPARKTEST.COM") + val util = new YarnSparkHadoopUtil + val renewer = util.getTokenRenewer(hadoopConf) + renewer should be ("yarn/myrm:8032@SPARKTEST.COM") + } + + test("check token renewer default") { + val hadoopConf = new Configuration() + val util = new YarnSparkHadoopUtil + val caught = + intercept[SparkException] { + util.getTokenRenewer(hadoopConf) + } + assert(caught.getMessage === "Can't get Master Kerberos principal for use as renewer") + } }