From 841721e03cc44ee7d8fe72c882db8c0f9f3af365 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Mon, 31 Mar 2014 12:07:14 -0700 Subject: [PATCH 01/78] SPARK-1352: Improve robustness of spark-submit script 1. Better error messages when required arguments are missing. 2. Support for unit testing cases where presented arguments are invalid. 3. Bug fix: Only use environment varaibles when they are set (otherwise will cause NPE). 4. A verbose mode to aid debugging. 5. Visibility of several variables is set to private. 6. Deprecation warning for existing scripts. Author: Patrick Wendell Closes #271 from pwendell/spark-submit and squashes the following commits: 9146def [Patrick Wendell] SPARK-1352: Improve robustness of spark-submit script --- .../org/apache/spark/deploy/Client.scala | 3 + .../org/apache/spark/deploy/SparkSubmit.scala | 67 +++++++++++------ .../spark/deploy/SparkSubmitArguments.scala | 74 +++++++++++++------ .../spark/deploy/SparkSubmitSuite.scala | 61 ++++++++++++++- .../org/apache/spark/deploy/yarn/Client.scala | 3 + .../org/apache/spark/deploy/yarn/Client.scala | 3 + 6 files changed, 163 insertions(+), 48 deletions(-) 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 d9e3035e1ab59..8fd2c7e95b966 100644 --- a/core/src/main/scala/org/apache/spark/deploy/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala @@ -128,6 +128,9 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends */ object Client { def main(args: Array[String]) { + println("WARNING: This client is deprecated and will be removed in a future version of Spark.") + println("Use ./bin/spark-submit with \"--master spark://host:port\"") + val conf = new SparkConf() val driverArgs = new ClientArguments(args) 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 24a9c98e188f6..1fa799190409f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -17,7 +17,7 @@ package org.apache.spark.deploy -import java.io.File +import java.io.{PrintStream, File} import java.net.URL import org.apache.spark.executor.ExecutorURLClassLoader @@ -32,38 +32,51 @@ import scala.collection.mutable.Map * modes that Spark supports. */ object SparkSubmit { - val YARN = 1 - val STANDALONE = 2 - val MESOS = 4 - val LOCAL = 8 - val ALL_CLUSTER_MGRS = YARN | STANDALONE | MESOS | LOCAL + private val YARN = 1 + private val STANDALONE = 2 + private val MESOS = 4 + private val LOCAL = 8 + private val ALL_CLUSTER_MGRS = YARN | STANDALONE | MESOS | LOCAL - var clusterManager: Int = LOCAL + private var clusterManager: Int = LOCAL def main(args: Array[String]) { val appArgs = new SparkSubmitArguments(args) + if (appArgs.verbose) { + printStream.println(appArgs) + } val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs) - launch(childArgs, classpath, sysProps, mainClass) + launch(childArgs, classpath, sysProps, mainClass, appArgs.verbose) } + // Exposed for testing + private[spark] var printStream: PrintStream = System.err + private[spark] var exitFn: () => Unit = () => System.exit(-1) + + private[spark] def printErrorAndExit(str: String) = { + printStream.println("error: " + str) + printStream.println("run with --help for more information or --verbose for debugging output") + exitFn() + } + private[spark] def printWarning(str: String) = printStream.println("warning: " + str) + /** * @return * a tuple containing the arguments for the child, a list of classpath * entries for the child, and the main class for the child */ - def createLaunchEnv(appArgs: SparkSubmitArguments): (ArrayBuffer[String], + private[spark] def createLaunchEnv(appArgs: SparkSubmitArguments): (ArrayBuffer[String], ArrayBuffer[String], Map[String, String], String) = { - if (appArgs.master.startsWith("yarn")) { + if (appArgs.master.startsWith("local")) { + clusterManager = LOCAL + } else if (appArgs.master.startsWith("yarn")) { clusterManager = YARN } else if (appArgs.master.startsWith("spark")) { clusterManager = STANDALONE } else if (appArgs.master.startsWith("mesos")) { clusterManager = MESOS - } else if (appArgs.master.startsWith("local")) { - clusterManager = LOCAL } else { - System.err.println("master must start with yarn, mesos, spark, or local") - System.exit(1) + printErrorAndExit("master must start with yarn, mesos, spark, or local") } // Because "yarn-standalone" and "yarn-client" encapsulate both the master @@ -73,12 +86,10 @@ object SparkSubmit { appArgs.deployMode = "cluster" } if (appArgs.deployMode == "cluster" && appArgs.master == "yarn-client") { - System.err.println("Deploy mode \"cluster\" and master \"yarn-client\" are at odds") - System.exit(1) + printErrorAndExit("Deploy mode \"cluster\" and master \"yarn-client\" are not compatible") } if (appArgs.deployMode == "client" && appArgs.master == "yarn-standalone") { - System.err.println("Deploy mode \"client\" and master \"yarn-standalone\" are at odds") - System.exit(1) + printErrorAndExit("Deploy mode \"client\" and master \"yarn-standalone\" are not compatible") } if (appArgs.deployMode == "cluster" && appArgs.master.startsWith("yarn")) { appArgs.master = "yarn-standalone" @@ -95,8 +106,7 @@ object SparkSubmit { var childMainClass = "" if (clusterManager == MESOS && deployOnCluster) { - System.err.println("Mesos does not support running the driver on the cluster") - System.exit(1) + printErrorAndExit("Mesos does not support running the driver on the cluster") } if (!deployOnCluster) { @@ -174,8 +184,17 @@ object SparkSubmit { (childArgs, childClasspath, sysProps, childMainClass) } - def launch(childArgs: ArrayBuffer[String], childClasspath: ArrayBuffer[String], - sysProps: Map[String, String], childMainClass: String) { + private def launch(childArgs: ArrayBuffer[String], childClasspath: ArrayBuffer[String], + sysProps: Map[String, String], childMainClass: String, verbose: Boolean = false) { + + if (verbose) { + System.err.println(s"Main class:\n$childMainClass") + System.err.println(s"Arguments:\n${childArgs.mkString("\n")}") + System.err.println(s"System properties:\n${sysProps.mkString("\n")}") + System.err.println(s"Classpath elements:\n${childClasspath.mkString("\n")}") + System.err.println("\n") + } + val loader = new ExecutorURLClassLoader(new Array[URL](0), Thread.currentThread.getContextClassLoader) Thread.currentThread.setContextClassLoader(loader) @@ -193,10 +212,10 @@ object SparkSubmit { mainMethod.invoke(null, childArgs.toArray) } - def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) { + private def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) { val localJarFile = new File(localJar) if (!localJarFile.exists()) { - System.err.println("Jar does not exist: " + localJar + ". Skipping.") + printWarning(s"Jar $localJar does not exist, skipping.") } val url = localJarFile.getAbsoluteFile.toURI.toURL 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 ff2aa68908e34..9c8f54ea6f77a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -40,25 +40,45 @@ private[spark] class SparkSubmitArguments(args: Array[String]) { var name: String = null var childArgs: ArrayBuffer[String] = new ArrayBuffer[String]() var jars: String = null + var verbose: Boolean = false loadEnvVars() - parseArgs(args.toList) - - def loadEnvVars() { - master = System.getenv("MASTER") - deployMode = System.getenv("DEPLOY_MODE") + parseOpts(args.toList) + + // Sanity checks + if (args.length == 0) printUsageAndExit(-1) + if (primaryResource == null) SparkSubmit.printErrorAndExit("Must specify a primary resource") + if (mainClass == null) SparkSubmit.printErrorAndExit("Must specify a main class with --class") + + override def toString = { + s"""Parsed arguments: + | master $master + | deployMode $deployMode + | executorMemory $executorMemory + | executorCores $executorCores + | totalExecutorCores $totalExecutorCores + | driverMemory $driverMemory + | drivercores $driverCores + | supervise $supervise + | queue $queue + | numExecutors $numExecutors + | files $files + | archives $archives + | mainClass $mainClass + | primaryResource $primaryResource + | name $name + | childArgs [${childArgs.mkString(" ")}] + | jars $jars + | verbose $verbose + """.stripMargin } - def parseArgs(args: List[String]) { - if (args.size == 0) { - printUsageAndExit(1) - System.exit(1) - } - primaryResource = args(0) - parseOpts(args.tail) + private def loadEnvVars() { + Option(System.getenv("MASTER")).map(master = _) + Option(System.getenv("DEPLOY_MODE")).map(deployMode = _) } - def parseOpts(opts: List[String]): Unit = opts match { + private def parseOpts(opts: List[String]): Unit = opts match { case ("--name") :: value :: tail => name = value parseOpts(tail) @@ -73,8 +93,7 @@ private[spark] class SparkSubmitArguments(args: Array[String]) { case ("--deploy-mode") :: value :: tail => if (value != "client" && value != "cluster") { - System.err.println("--deploy-mode must be either \"client\" or \"cluster\"") - System.exit(1) + SparkSubmit.printErrorAndExit("--deploy-mode must be either \"client\" or \"cluster\"") } deployMode = value parseOpts(tail) @@ -130,17 +149,28 @@ private[spark] class SparkSubmitArguments(args: Array[String]) { case ("--help" | "-h") :: tail => printUsageAndExit(0) - case Nil => + case ("--verbose" | "-v") :: tail => + verbose = true + parseOpts(tail) - case _ => - printUsageAndExit(1, opts) + case value :: tail => + if (primaryResource != null) { + val error = s"Found two conflicting resources, $value and $primaryResource." + + " Expecting only one resource." + SparkSubmit.printErrorAndExit(error) + } + primaryResource = value + parseOpts(tail) + + case Nil => } - def printUsageAndExit(exitCode: Int, unknownParam: Any = null) { + private def printUsageAndExit(exitCode: Int, unknownParam: Any = null) { + val outStream = SparkSubmit.printStream if (unknownParam != null) { - System.err.println("Unknown/unsupported param " + unknownParam) + outStream.println("Unknown/unsupported param " + unknownParam) } - System.err.println( + outStream.println( """Usage: spark-submit [options] |Options: | --master MASTER_URL spark://host:port, mesos://host:port, yarn, or local. @@ -171,6 +201,6 @@ private[spark] class SparkSubmitArguments(args: Array[String]) { | --archives ARCHIVES Comma separated list of archives to be extracted into the | working dir of each executor.""".stripMargin ) - System.exit(exitCode) + SparkSubmit.exitFn() } } 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 29fef2ed8c165..4e489cd9b66a6 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -17,14 +17,71 @@ package org.apache.spark.deploy +import java.io.{OutputStream, PrintStream} + +import scala.collection.mutable.ArrayBuffer + import org.scalatest.FunSuite import org.scalatest.matchers.ShouldMatchers + import org.apache.spark.deploy.SparkSubmit._ + class SparkSubmitSuite extends FunSuite with ShouldMatchers { + + val noOpOutputStream = new OutputStream { + def write(b: Int) = {} + } + + /** Simple PrintStream that reads data into a buffer */ + class BufferPrintStream extends PrintStream(noOpOutputStream) { + var lineBuffer = ArrayBuffer[String]() + override def println(line: String) { + lineBuffer += line + } + } + + /** Returns true if the script exits and the given search string is printed. */ + def testPrematureExit(input: Array[String], searchString: String): Boolean = { + val printStream = new BufferPrintStream() + SparkSubmit.printStream = printStream + + @volatile var exitedCleanly = false + SparkSubmit.exitFn = () => exitedCleanly = true + + val thread = new Thread { + override def run() = try { + SparkSubmit.main(input) + } catch { + // If exceptions occur after the "exit" has happened, fine to ignore them. + // These represent code paths not reachable during normal execution. + case e: Exception => if (!exitedCleanly) throw e + } + } + thread.start() + thread.join() + printStream.lineBuffer.find(s => s.contains(searchString)).size > 0 + } + test("prints usage on empty input") { - val clArgs = Array[String]() - // val appArgs = new SparkSubmitArguments(clArgs) + testPrematureExit(Array[String](), "Usage: spark-submit") should be (true) + } + + test("prints usage with only --help") { + testPrematureExit(Array("--help"), "Usage: spark-submit") should be (true) + } + + test("handles multiple binary definitions") { + val adjacentJars = Array("foo.jar", "bar.jar") + testPrematureExit(adjacentJars, "error: Found two conflicting resources") should be (true) + + val nonAdjacentJars = + Array("foo.jar", "--master", "123", "--class", "abc", "bar.jar") + testPrematureExit(nonAdjacentJars, "error: Found two conflicting resources") should be (true) + } + + test("handle binary specified but not class") { + testPrematureExit(Array("foo.jar"), "must specify a main class") } test("handles YARN cluster mode") { diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 71a64ecf5879a..0179b0600c61f 100644 --- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -167,6 +167,9 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa object Client { def main(argStrings: Array[String]) { + println("WARNING: This client is deprecated and will be removed in a future version of Spark.") + println("Use ./bin/spark-submit with \"--master yarn\"") + // Set an env variable indicating we are running in YARN mode. // Note that anything with SPARK prefix gets propagated to all (remote) processes System.setProperty("SPARK_YARN_MODE", "true") diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 837b7e12cb0de..77eb1276a0c4e 100644 --- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -173,6 +173,9 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa object Client { def main(argStrings: Array[String]) { + println("WARNING: This client is deprecated and will be removed in a future version of Spark.") + println("Use ./bin/spark-submit with \"--master yarn\"") + // Set an env variable indicating we are running in YARN mode. // Note: anything env variable with SPARK_ prefix gets propagated to all (remote) processes - // see Client#setupLaunchEnv(). From 5731af5be65ccac831445f351baf040a0d007687 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Mon, 31 Mar 2014 15:23:46 -0700 Subject: [PATCH 02/78] [SQL] Rewrite join implementation to allow streaming of one relation. Before we were materializing everything in memory. This also uses the projection interface so will be easier to plug in code gen (its ported from that branch). @rxin @liancheng Author: Michael Armbrust Closes #250 from marmbrus/hashJoin and squashes the following commits: 1ad873e [Michael Armbrust] Change hasNext logic back to the correct version. 8e6f2a2 [Michael Armbrust] Review comments. 1e9fb63 [Michael Armbrust] style bc0cb84 [Michael Armbrust] Rewrite join implementation to allow streaming of one relation. --- .../spark/sql/catalyst/expressions/Row.scala | 10 ++ .../sql/catalyst/expressions/predicates.scala | 6 + .../org/apache/spark/sql/SQLContext.scala | 2 +- .../spark/sql/execution/SparkStrategies.scala | 6 +- .../apache/spark/sql/execution/joins.scala | 127 +++++++++++++----- .../apache/spark/sql/hive/HiveContext.scala | 2 +- 6 files changed, 116 insertions(+), 37 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala index 31d42b9ee71a0..6f939e6c41f6b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala @@ -44,6 +44,16 @@ trait Row extends Seq[Any] with Serializable { s"[${this.mkString(",")}]" def copy(): Row + + /** Returns true if there are any NULL values in this row. */ + def anyNull: Boolean = { + var i = 0 + while (i < length) { + if (isNullAt(i)) { return true } + i += 1 + } + 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 722ff517d250e..02fedd16b8d4b 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 @@ -21,6 +21,12 @@ import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.analysis.UnresolvedException import org.apache.spark.sql.catalyst.types.{BooleanType, StringType} +object InterpretedPredicate { + def apply(expression: Expression): (Row => Boolean) = { + (r: Row) => expression.apply(r).asInstanceOf[Boolean] + } +} + trait Predicate extends Expression { self: Product => 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 cf3c06acce5b0..f950ea08ec57a 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 @@ -117,7 +117,7 @@ class SQLContext(@transient val sparkContext: SparkContext) val strategies: Seq[Strategy] = TopK :: PartialAggregation :: - SparkEquiInnerJoin :: + HashJoin :: ParquetOperations :: BasicOperators :: CartesianProduct :: 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 86f9d3e0fa954..e35ac0b6ca95a 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 @@ -28,7 +28,7 @@ import org.apache.spark.sql.parquet._ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { self: SQLContext#SparkPlanner => - object SparkEquiInnerJoin extends Strategy { + object HashJoin extends Strategy { def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match { case FilteredOperation(predicates, logical.Join(left, right, Inner, condition)) => logger.debug(s"Considering join: ${predicates ++ condition}") @@ -51,8 +51,8 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { val leftKeys = joinKeys.map(_._1) val rightKeys = joinKeys.map(_._2) - val joinOp = execution.SparkEquiInnerJoin( - leftKeys, rightKeys, planLater(left), planLater(right)) + val joinOp = execution.HashJoin( + leftKeys, rightKeys, BuildRight, planLater(left), planLater(right)) // Make sure other conditions are met if present. if (otherPredicates.nonEmpty) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala index f0d21143ba5d1..c89dae9358bf7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala @@ -17,21 +17,22 @@ package org.apache.spark.sql.execution -import scala.collection.mutable +import scala.collection.mutable.{ArrayBuffer, BitSet} -import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext -import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, Partitioning} -import org.apache.spark.rdd.PartitionLocalRDDFunctions._ +sealed abstract class BuildSide +case object BuildLeft extends BuildSide +case object BuildRight extends BuildSide -case class SparkEquiInnerJoin( +case class HashJoin( leftKeys: Seq[Expression], rightKeys: Seq[Expression], + buildSide: BuildSide, left: SparkPlan, right: SparkPlan) extends BinaryNode { @@ -40,33 +41,93 @@ case class SparkEquiInnerJoin( override def requiredChildDistribution = ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil + val (buildPlan, streamedPlan) = buildSide match { + case BuildLeft => (left, right) + case BuildRight => (right, left) + } + + val (buildKeys, streamedKeys) = buildSide match { + case BuildLeft => (leftKeys, rightKeys) + case BuildRight => (rightKeys, leftKeys) + } + def output = left.output ++ right.output - def execute() = attachTree(this, "execute") { - val leftWithKeys = left.execute().mapPartitions { iter => - val generateLeftKeys = new Projection(leftKeys, left.output) - iter.map(row => (generateLeftKeys(row), row.copy())) - } + @transient lazy val buildSideKeyGenerator = new Projection(buildKeys, buildPlan.output) + @transient lazy val streamSideKeyGenerator = + () => new MutableProjection(streamedKeys, streamedPlan.output) - val rightWithKeys = right.execute().mapPartitions { iter => - val generateRightKeys = new Projection(rightKeys, right.output) - iter.map(row => (generateRightKeys(row), row.copy())) - } + def execute() = { - // Do the join. - val joined = filterNulls(leftWithKeys).joinLocally(filterNulls(rightWithKeys)) - // Drop join keys and merge input tuples. - joined.map { case (_, (leftTuple, rightTuple)) => buildRow(leftTuple ++ rightTuple) } - } + buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) => + // TODO: Use Spark's HashMap implementation. + val hashTable = new java.util.HashMap[Row, ArrayBuffer[Row]]() + var currentRow: Row = null + + // Create a mapping of buildKeys -> rows + while (buildIter.hasNext) { + currentRow = buildIter.next() + val rowKey = buildSideKeyGenerator(currentRow) + if(!rowKey.anyNull) { + val existingMatchList = hashTable.get(rowKey) + val matchList = if (existingMatchList == null) { + val newMatchList = new ArrayBuffer[Row]() + hashTable.put(rowKey, newMatchList) + newMatchList + } else { + existingMatchList + } + matchList += currentRow.copy() + } + } + + new Iterator[Row] { + private[this] var currentStreamedRow: Row = _ + private[this] var currentHashMatches: ArrayBuffer[Row] = _ + private[this] var currentMatchPosition: Int = -1 - /** - * Filters any rows where the any of the join keys is null, ensuring three-valued - * logic for the equi-join conditions. - */ - protected def filterNulls(rdd: RDD[(Row, Row)]) = - rdd.filter { - case (key: Seq[_], _) => !key.exists(_ == null) + // Mutable per row objects. + private[this] val joinRow = new JoinedRow + + private[this] val joinKeys = streamSideKeyGenerator() + + override final def hasNext: Boolean = + (currentMatchPosition != -1 && currentMatchPosition < currentHashMatches.size) || + (streamIter.hasNext && fetchNext()) + + override final def next() = { + val ret = joinRow(currentStreamedRow, currentHashMatches(currentMatchPosition)) + currentMatchPosition += 1 + ret + } + + /** + * Searches the streamed iterator for the next row that has at least one match in hashtable. + * + * @return true if the search is successful, and false the streamed iterator runs out of + * tuples. + */ + private final def fetchNext(): Boolean = { + currentHashMatches = null + currentMatchPosition = -1 + + while (currentHashMatches == null && streamIter.hasNext) { + currentStreamedRow = streamIter.next() + if (!joinKeys(currentStreamedRow).anyNull) { + currentHashMatches = hashTable.get(joinKeys.currentValue) + } + } + + if (currentHashMatches == null) { + false + } else { + currentMatchPosition = 0 + true + } + } + } } + } } case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode { @@ -95,17 +156,19 @@ case class BroadcastNestedLoopJoin( def right = broadcast @transient lazy val boundCondition = - condition - .map(c => BindReferences.bindReference(c, left.output ++ right.output)) - .getOrElse(Literal(true)) + InterpretedPredicate( + condition + .map(c => BindReferences.bindReference(c, left.output ++ right.output)) + .getOrElse(Literal(true))) def execute() = { val broadcastedRelation = sc.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq) val streamedPlusMatches = streamed.execute().mapPartitions { streamedIter => - val matchedRows = new mutable.ArrayBuffer[Row] - val includedBroadcastTuples = new mutable.BitSet(broadcastedRelation.value.size) + val matchedRows = new ArrayBuffer[Row] + // TODO: Use Spark's BitSet. + val includedBroadcastTuples = new BitSet(broadcastedRelation.value.size) val joinedRow = new JoinedRow streamedIter.foreach { streamedRow => @@ -115,7 +178,7 @@ case class BroadcastNestedLoopJoin( while (i < broadcastedRelation.value.size) { // TODO: One bitset per partition instead of per row. val broadcastedRow = broadcastedRelation.value(i) - if (boundCondition(joinedRow(streamedRow, broadcastedRow)).asInstanceOf[Boolean]) { + if (boundCondition(joinedRow(streamedRow, broadcastedRow))) { matchedRows += buildRow(streamedRow ++ broadcastedRow) matched = true includedBroadcastTuples += i 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 fc5057b73fe24..197b557cba5f4 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 @@ -194,7 +194,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { DataSinks, Scripts, PartialAggregation, - SparkEquiInnerJoin, + HashJoin, BasicOperators, CartesianProduct, BroadcastNestedLoopJoin From 33b3c2a8c6c71b89744834017a183ea855e1697c Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Mon, 31 Mar 2014 16:25:43 -0700 Subject: [PATCH 03/78] SPARK-1365 [HOTFIX] Fix RateLimitedOutputStream test This test needs to be fixed. It currently depends on Thread.sleep() having exact-timing semantics, which is not a valid assumption. Author: Patrick Wendell Closes #277 from pwendell/rate-limited-stream and squashes the following commits: 6c0ff81 [Patrick Wendell] SPARK-1365: Fix RateLimitedOutputStream test --- .../spark/streaming/util/RateLimitedOutputStreamSuite.scala | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/util/RateLimitedOutputStreamSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/util/RateLimitedOutputStreamSuite.scala index 7d18a0fcf7ba8..9ebf7b484f421 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/util/RateLimitedOutputStreamSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/util/RateLimitedOutputStreamSuite.scala @@ -36,8 +36,9 @@ class RateLimitedOutputStreamSuite extends FunSuite { val stream = new RateLimitedOutputStream(underlying, desiredBytesPerSec = 10000) val elapsedNs = benchmark { stream.write(data.getBytes("UTF-8")) } - // We accept anywhere from 4.0 to 4.99999 seconds since the value is rounded down. - assert(SECONDS.convert(elapsedNs, NANOSECONDS) === 4) + val seconds = SECONDS.convert(elapsedNs, NANOSECONDS) + assert(seconds >= 4, s"Seconds value ($seconds) is less than 4.") + assert(seconds <= 30, s"Took more than 30 seconds ($seconds) to write data.") assert(underlying.toString("UTF-8") === data) } } From 564f1c137caf07bd1f073ec6c93551dcad935ee5 Mon Sep 17 00:00:00 2001 From: Sandy Ryza Date: Tue, 1 Apr 2014 08:26:31 +0530 Subject: [PATCH 04/78] SPARK-1376. In the yarn-cluster submitter, rename "args" option to "arg" Author: Sandy Ryza Closes #279 from sryza/sandy-spark-1376 and squashes the following commits: d8aebfa [Sandy Ryza] SPARK-1376. In the yarn-cluster submitter, rename "args" option to "arg" --- docs/running-on-yarn.md | 7 ++++--- .../org/apache/spark/deploy/yarn/ClientArguments.scala | 9 ++++++--- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index d8657c4bc7096..982514391ac00 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -61,7 +61,7 @@ The command to launch the Spark application on the cluster is as follows: SPARK_JAR= ./bin/spark-class org.apache.spark.deploy.yarn.Client \ --jar \ --class \ - --args \ + --arg \ --num-executors \ --driver-memory \ --executor-memory \ @@ -72,7 +72,7 @@ The command to launch the Spark application on the cluster is as follows: --files \ --archives -For example: +To pass multiple arguments the "arg" option can be specified multiple times. For example: # Build the Spark assembly JAR and the Spark examples JAR $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly @@ -85,7 +85,8 @@ For example: ./bin/spark-class org.apache.spark.deploy.yarn.Client \ --jar examples/target/scala-{{site.SCALA_BINARY_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ --class org.apache.spark.examples.SparkPi \ - --args yarn-cluster \ + --arg yarn-cluster \ + --arg 5 \ --num-executors 3 \ --driver-memory 4g \ --executor-memory 2g \ diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index c565f2dde24fc..3e4c739e34fe9 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -63,7 +63,10 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) { userClass = value args = tail - case ("--args") :: value :: tail => + case ("--args" | "--arg") :: value :: tail => + if (args(0) == "--args") { + println("--args is deprecated. Use --arg instead.") + } userArgsBuffer += value args = tail @@ -146,8 +149,8 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) { "Options:\n" + " --jar JAR_PATH Path to your application's JAR file (required in yarn-cluster mode)\n" + " --class CLASS_NAME Name of your application's main class (required)\n" + - " --args ARGS Arguments to be passed to your application's main class.\n" + - " Mutliple invocations are possible, each will be passed in order.\n" + + " --arg ARGS Argument to be passed to your application's main class.\n" + + " Multiple invocations are possible, each will be passed in order.\n" + " --num-executors NUM Number of executors to start (Default: 2)\n" + " --executor-cores NUM Number of cores for the executors (Default: 1).\n" + " --driver-memory MEM Memory for driver (e.g. 1000M, 2G) (Default: 512 Mb)\n" + From 94fe7fd4fa9749cb13e540e4f9caf28de47eaf32 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Mon, 31 Mar 2014 21:42:36 -0700 Subject: [PATCH 05/78] [SPARK-1377] Upgrade Jetty to 8.1.14v20131031 Previous version was 7.6.8v20121106. The only difference between Jetty 7 and Jetty 8 is that the former uses Servlet API 2.5, while the latter uses Servlet API 3.0. Author: Andrew Or Closes #280 from andrewor14/jetty-upgrade and squashes the following commits: dd57104 [Andrew Or] Merge github.com:apache/spark into jetty-upgrade e75fa85 [Andrew Or] Upgrade Jetty to 8.1.14v20131031 --- .../main/scala/org/apache/spark/ui/JettyUtils.scala | 3 ++- pom.xml | 8 ++++---- project/SparkBuild.scala | 12 ++++++------ 3 files changed, 12 insertions(+), 11 deletions(-) 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 6e1736f6fbc23..e1a1f209c9282 100644 --- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala @@ -18,13 +18,14 @@ package org.apache.spark.ui import java.net.{InetSocketAddress, URL} +import javax.servlet.DispatcherType import javax.servlet.http.{HttpServlet, HttpServletRequest, HttpServletResponse} import scala.annotation.tailrec import scala.util.{Failure, Success, Try} import scala.xml.Node -import org.eclipse.jetty.server.{DispatcherType, Server} +import org.eclipse.jetty.server.Server import org.eclipse.jetty.server.handler._ import org.eclipse.jetty.servlet._ import org.eclipse.jetty.util.thread.QueuedThreadPool diff --git a/pom.xml b/pom.xml index 72acf2b402703..09a449d81453f 100644 --- a/pom.xml +++ b/pom.xml @@ -192,22 +192,22 @@ org.eclipse.jetty jetty-util - 7.6.8.v20121106 + 8.1.14.v20131031 org.eclipse.jetty jetty-security - 7.6.8.v20121106 + 8.1.14.v20131031 org.eclipse.jetty jetty-plus - 7.6.8.v20121106 + 8.1.14.v20131031 org.eclipse.jetty jetty-server - 7.6.8.v20121106 + 8.1.14.v20131031 com.google.guava diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 2549bc9710f1f..7457ff456ade4 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -248,13 +248,13 @@ object SparkBuild extends Build { */ libraryDependencies ++= Seq( - "io.netty" % "netty-all" % "4.0.17.Final", - "org.eclipse.jetty" % "jetty-server" % "7.6.8.v20121106", - "org.eclipse.jetty" % "jetty-util" % "7.6.8.v20121106", - "org.eclipse.jetty" % "jetty-plus" % "7.6.8.v20121106", - "org.eclipse.jetty" % "jetty-security" % "7.6.8.v20121106", + "io.netty" % "netty-all" % "4.0.17.Final", + "org.eclipse.jetty" % "jetty-server" % "8.1.14.v20131031", + "org.eclipse.jetty" % "jetty-util" % "8.1.14.v20131031", + "org.eclipse.jetty" % "jetty-plus" % "8.1.14.v20131031", + "org.eclipse.jetty" % "jetty-security" % "8.1.14.v20131031", /** Workaround for SPARK-959. Dependency used by org.eclipse.jetty. Fixed in ivy 2.3.0. */ - "org.eclipse.jetty.orbit" % "javax.servlet" % "2.5.0.v201103041518" artifacts Artifact("javax.servlet", "jar", "jar"), + "org.eclipse.jetty.orbit" % "javax.servlet" % "3.0.0.v201112011016" artifacts Artifact("javax.servlet", "jar", "jar"), "org.scalatest" %% "scalatest" % "1.9.1" % "test", "org.scalacheck" %% "scalacheck" % "1.10.0" % "test", "com.novocode" % "junit-interface" % "0.10" % "test", From ada310a9d3d5419e101b24d9b41398f609da1ad3 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Mon, 31 Mar 2014 23:01:14 -0700 Subject: [PATCH 06/78] [Hot Fix #42] Persisted RDD disappears on storage page if re-used If a previously persisted RDD is re-used, its information disappears from the Storage page. This is because the tasks associated with re-using the RDD do not report the RDD's blocks as updated (which is correct). On stage submit, however, we overwrite any existing information regarding that RDD with a fresh one, whether or not the information for the RDD already exists. Author: Andrew Or Closes #281 from andrewor14/ui-storage-fix and squashes the following commits: 408585a [Andrew Or] Fix storage UI bug --- .../main/scala/org/apache/spark/ui/storage/BlockManagerUI.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/ui/storage/BlockManagerUI.scala b/core/src/main/scala/org/apache/spark/ui/storage/BlockManagerUI.scala index 4d8b01dbe6e1b..a7b24ff695214 100644 --- a/core/src/main/scala/org/apache/spark/ui/storage/BlockManagerUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/storage/BlockManagerUI.scala @@ -84,7 +84,7 @@ private[ui] class BlockManagerListener(storageStatusListener: StorageStatusListe override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = synchronized { val rddInfo = stageSubmitted.stageInfo.rddInfo - _rddInfoMap(rddInfo.id) = rddInfo + _rddInfoMap.getOrElseUpdate(rddInfo.id, rddInfo) } override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized { From f5c418da044ef7f3d7185cc5bb1bef79d7f4e25c Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 1 Apr 2014 14:45:44 -0700 Subject: [PATCH 07/78] [SQL] SPARK-1372 Support for caching and uncaching tables in a SQLContext. This doesn't yet support different databases in Hive (though you can probably workaround this by calling `USE `). However, given the time constraints for 1.0 I think its probably worth including this now and extending the functionality in the next release. Author: Michael Armbrust Closes #282 from marmbrus/cacheTables and squashes the following commits: 83785db [Michael Armbrust] Support for caching and uncaching tables in a SQLContext. --- .../spark/sql/catalyst/analysis/Catalog.scala | 11 +++- .../org/apache/spark/sql/SQLContext.scala | 32 +++++++++- .../apache/spark/sql/CachedTableSuite.scala | 61 +++++++++++++++++++ .../spark/sql/hive/HiveMetastoreCatalog.scala | 7 +++ ...d table-0-ce3797dc14a603cba2a5e58c8612de5b | 1 + ...d table-0-ce3797dc14a603cba2a5e58c8612de5b | 1 + .../spark/sql/hive/CachedTableSuite.scala | 58 ++++++++++++++++++ 7 files changed, 169 insertions(+), 2 deletions(-) create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala create mode 100644 sql/hive/src/test/resources/golden/read from cached table-0-ce3797dc14a603cba2a5e58c8612de5b create mode 100644 sql/hive/src/test/resources/golden/read from uncached table-0-ce3797dc14a603cba2a5e58c8612de5b create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala 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 e09182dd8d5df..6b58b9322c4bf 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 @@ -31,6 +31,7 @@ trait Catalog { alias: Option[String] = None): LogicalPlan def registerTable(databaseName: Option[String], tableName: String, plan: LogicalPlan): Unit + def unregisterTable(databaseName: Option[String], tableName: String): Unit } class SimpleCatalog extends Catalog { @@ -40,7 +41,7 @@ class SimpleCatalog extends Catalog { tables += ((tableName, plan)) } - def dropTable(tableName: String) = tables -= tableName + def unregisterTable(databaseName: Option[String], tableName: String) = { tables -= tableName } def lookupRelation( databaseName: Option[String], @@ -87,6 +88,10 @@ trait OverrideCatalog extends Catalog { plan: LogicalPlan): Unit = { overrides.put((databaseName, tableName), plan) } + + override def unregisterTable(databaseName: Option[String], tableName: String): Unit = { + overrides.remove((databaseName, tableName)) + } } /** @@ -104,4 +109,8 @@ object EmptyCatalog extends Catalog { def registerTable(databaseName: Option[String], tableName: String, plan: LogicalPlan): Unit = { throw new UnsupportedOperationException } + + def unregisterTable(databaseName: Option[String], tableName: String): Unit = { + throw new UnsupportedOperationException + } } 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 f950ea08ec57a..69bbbdc8943fa 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 @@ -26,8 +26,9 @@ import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.dsl import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.optimizer.Optimizer -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.plans.logical.{Subquery, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor +import org.apache.spark.sql.columnar.InMemoryColumnarTableScan import org.apache.spark.sql.execution._ /** @@ -111,6 +112,35 @@ class SQLContext(@transient val sparkContext: SparkContext) result } + /** Returns the specified table as a SchemaRDD */ + def table(tableName: String): SchemaRDD = + new SchemaRDD(this, catalog.lookupRelation(None, tableName)) + + /** Caches the specified table in-memory. */ + def cacheTable(tableName: String): Unit = { + val currentTable = catalog.lookupRelation(None, tableName) + val asInMemoryRelation = + InMemoryColumnarTableScan(currentTable.output, executePlan(currentTable).executedPlan) + + catalog.registerTable(None, tableName, SparkLogicalPlan(asInMemoryRelation)) + } + + /** Removes the specified table from the in-memory cache. */ + def uncacheTable(tableName: String): Unit = { + EliminateAnalysisOperators(catalog.lookupRelation(None, tableName)) match { + // This is kind of a hack to make sure that if this was just an RDD registered as a table, + // we reregister the RDD as a table. + case SparkLogicalPlan(inMem @ InMemoryColumnarTableScan(_, e: ExistingRdd)) => + inMem.cachedColumnBuffers.unpersist() + catalog.unregisterTable(None, tableName) + catalog.registerTable(None, tableName, SparkLogicalPlan(e)) + case SparkLogicalPlan(inMem: InMemoryColumnarTableScan) => + inMem.cachedColumnBuffers.unpersist() + catalog.unregisterTable(None, tableName) + case plan => throw new IllegalArgumentException(s"Table $tableName is not cached: $plan") + } + } + protected[sql] class SparkPlanner extends SparkStrategies { val sparkContext = self.sparkContext diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala new file mode 100644 index 0000000000000..e5902c3cae381 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala @@ -0,0 +1,61 @@ +/* + * 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.apache.spark.sql.TestData._ +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.execution.SparkLogicalPlan +import org.apache.spark.sql.columnar.InMemoryColumnarTableScan + +class CachedTableSuite extends QueryTest { + TestData // Load test tables. + + test("read from cached table and uncache") { + TestSQLContext.cacheTable("testData") + + checkAnswer( + TestSQLContext.table("testData"), + testData.collect().toSeq + ) + + TestSQLContext.table("testData").queryExecution.analyzed match { + case SparkLogicalPlan(_ : InMemoryColumnarTableScan) => // Found evidence of caching + case noCache => fail(s"No cache node found in plan $noCache") + } + + TestSQLContext.uncacheTable("testData") + + checkAnswer( + TestSQLContext.table("testData"), + testData.collect().toSeq + ) + + TestSQLContext.table("testData").queryExecution.analyzed match { + case cachePlan @ SparkLogicalPlan(_ : InMemoryColumnarTableScan) => + fail(s"Table still cached after uncache: $cachePlan") + case noCache => // Table uncached successfully + } + } + + test("correct error on uncache of non-cached table") { + intercept[IllegalArgumentException] { + TestSQLContext.uncacheTable("testData") + } + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 4f8353666a12b..29834a11f41dc 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -141,6 +141,13 @@ class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with Logging { */ override def registerTable( databaseName: Option[String], tableName: String, plan: LogicalPlan): Unit = ??? + + /** + * UNIMPLEMENTED: It needs to be decided how we will persist in-memory tables to the metastore. + * For now, if this functionality is desired mix in the in-memory [[OverrideCatalog]]. + */ + override def unregisterTable( + databaseName: Option[String], tableName: String): Unit = ??? } object HiveMetastoreTypes extends RegexParsers { diff --git a/sql/hive/src/test/resources/golden/read from cached table-0-ce3797dc14a603cba2a5e58c8612de5b b/sql/hive/src/test/resources/golden/read from cached table-0-ce3797dc14a603cba2a5e58c8612de5b new file mode 100644 index 0000000000000..60878ffb77064 --- /dev/null +++ b/sql/hive/src/test/resources/golden/read from cached table-0-ce3797dc14a603cba2a5e58c8612de5b @@ -0,0 +1 @@ +238 val_238 diff --git a/sql/hive/src/test/resources/golden/read from uncached table-0-ce3797dc14a603cba2a5e58c8612de5b b/sql/hive/src/test/resources/golden/read from uncached table-0-ce3797dc14a603cba2a5e58c8612de5b new file mode 100644 index 0000000000000..60878ffb77064 --- /dev/null +++ b/sql/hive/src/test/resources/golden/read from uncached table-0-ce3797dc14a603cba2a5e58c8612de5b @@ -0,0 +1 @@ +238 val_238 diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala new file mode 100644 index 0000000000000..68d45e53cdf26 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala @@ -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.sql.hive + +import org.apache.spark.sql.execution.SparkLogicalPlan +import org.apache.spark.sql.columnar.InMemoryColumnarTableScan +import org.apache.spark.sql.hive.execution.HiveComparisonTest + +class CachedTableSuite extends HiveComparisonTest { + TestHive.loadTestTable("src") + + test("cache table") { + TestHive.cacheTable("src") + } + + createQueryTest("read from cached table", + "SELECT * FROM src LIMIT 1") + + test("check that table is cached and uncache") { + TestHive.table("src").queryExecution.analyzed match { + case SparkLogicalPlan(_ : InMemoryColumnarTableScan) => // Found evidence of caching + case noCache => fail(s"No cache node found in plan $noCache") + } + TestHive.uncacheTable("src") + } + + createQueryTest("read from uncached table", + "SELECT * FROM src LIMIT 1") + + test("make sure table is uncached") { + TestHive.table("src").queryExecution.analyzed match { + case cachePlan @ SparkLogicalPlan(_ : InMemoryColumnarTableScan) => + fail(s"Table still cached after uncache: $cachePlan") + case noCache => // Table uncached successfully + } + } + + test("correct error on uncache of non-cached table") { + intercept[IllegalArgumentException] { + TestHive.uncacheTable("src") + } + } +} From 764353d2c5162352781c273dd3d4af6a309190c7 Mon Sep 17 00:00:00 2001 From: Mark Hamstra Date: Tue, 1 Apr 2014 18:35:50 -0700 Subject: [PATCH 08/78] [SPARK-1342] Scala 2.10.4 Just a Scala version increment Author: Mark Hamstra Closes #259 from markhamstra/scala-2.10.4 and squashes the following commits: fbec547 [Mark Hamstra] [SPARK-1342] Bumped Scala version to 2.10.4 --- core/pom.xml | 2 +- dev/audit-release/README.md | 2 +- dev/audit-release/audit_release.py | 2 +- docker/spark-test/base/Dockerfile | 2 +- docs/_config.yml | 2 +- pom.xml | 4 ++-- project/SparkBuild.scala | 2 +- project/plugins.sbt | 2 +- project/project/SparkPluginBuild.scala | 2 +- sql/README.md | 2 +- 10 files changed, 11 insertions(+), 11 deletions(-) diff --git a/core/pom.xml b/core/pom.xml index eb6cc4d3105e9..e4c32eff0cd77 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -150,7 +150,7 @@ json4s-jackson_${scala.binary.version} 3.2.6 diff --git a/dev/audit-release/README.md b/dev/audit-release/README.md index 2437a98672177..38becda0eae92 100644 --- a/dev/audit-release/README.md +++ b/dev/audit-release/README.md @@ -4,7 +4,7 @@ run them locally by setting appropriate environment variables. ``` $ cd sbt_app_core -$ SCALA_VERSION=2.10.3 \ +$ SCALA_VERSION=2.10.4 \ SPARK_VERSION=1.0.0-SNAPSHOT \ SPARK_RELEASE_REPOSITORY=file:///home/patrick/.ivy2/local \ sbt run diff --git a/dev/audit-release/audit_release.py b/dev/audit-release/audit_release.py index 52c367d9b030d..fa2f02dfecc75 100755 --- a/dev/audit-release/audit_release.py +++ b/dev/audit-release/audit_release.py @@ -35,7 +35,7 @@ RELEASE_KEY = "9E4FE3AF" RELEASE_REPOSITORY = "https://repository.apache.org/content/repositories/orgapachespark-1006/" RELEASE_VERSION = "1.0.0" -SCALA_VERSION = "2.10.3" +SCALA_VERSION = "2.10.4" SCALA_BINARY_VERSION = "2.10" ## diff --git a/docker/spark-test/base/Dockerfile b/docker/spark-test/base/Dockerfile index e543db6143e4d..5956d59130fbf 100644 --- a/docker/spark-test/base/Dockerfile +++ b/docker/spark-test/base/Dockerfile @@ -25,7 +25,7 @@ RUN apt-get update # install a few other useful packages plus Open Jdk 7 RUN apt-get install -y less openjdk-7-jre-headless net-tools vim-tiny sudo openssh-server -ENV SCALA_VERSION 2.10.3 +ENV SCALA_VERSION 2.10.4 ENV CDH_VERSION cdh4 ENV SCALA_HOME /opt/scala-$SCALA_VERSION ENV SPARK_HOME /opt/spark diff --git a/docs/_config.yml b/docs/_config.yml index aa5a5adbc1743..d585b8c5ea763 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -6,7 +6,7 @@ markdown: kramdown SPARK_VERSION: 1.0.0-SNAPSHOT SPARK_VERSION_SHORT: 1.0.0 SCALA_BINARY_VERSION: "2.10" -SCALA_VERSION: "2.10.3" +SCALA_VERSION: "2.10.4" MESOS_VERSION: 0.13.0 SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net SPARK_GITHUB_URL: https://github.com/apache/spark diff --git a/pom.xml b/pom.xml index 09a449d81453f..7d58060cba606 100644 --- a/pom.xml +++ b/pom.xml @@ -110,7 +110,7 @@ 1.6 - 2.10.3 + 2.10.4 2.10 0.13.0 org.spark-project.akka @@ -380,7 +380,7 @@ lift-json_${scala.binary.version} 2.5.1 diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 7457ff456ade4..c5c697e8e2427 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -152,7 +152,7 @@ object SparkBuild extends Build { def sharedSettings = Defaults.defaultSettings ++ MimaBuild.mimaSettings(file(sparkHome)) ++ Seq( organization := "org.apache.spark", version := SPARK_VERSION, - scalaVersion := "2.10.3", + scalaVersion := "2.10.4", scalacOptions := Seq("-Xmax-classfile-name", "120", "-unchecked", "-deprecation", "-target:" + SCALAC_JVM_VERSION), javacOptions := Seq("-target", JAVAC_JVM_VERSION, "-source", JAVAC_JVM_VERSION), diff --git a/project/plugins.sbt b/project/plugins.sbt index 5aa8a1ec2409b..d787237ddc540 100644 --- a/project/plugins.sbt +++ b/project/plugins.sbt @@ -1,4 +1,4 @@ -scalaVersion := "2.10.3" +scalaVersion := "2.10.4" resolvers += Resolver.url("artifactory", url("http://scalasbt.artifactoryonline.com/scalasbt/sbt-plugin-releases"))(Resolver.ivyStylePatterns) diff --git a/project/project/SparkPluginBuild.scala b/project/project/SparkPluginBuild.scala index 5a307044ba123..0142256e90fb7 100644 --- a/project/project/SparkPluginBuild.scala +++ b/project/project/SparkPluginBuild.scala @@ -32,7 +32,7 @@ object SparkPluginDef extends Build { name := "spark-style", organization := "org.apache.spark", version := sparkVersion, - scalaVersion := "2.10.3", + scalaVersion := "2.10.4", scalacOptions := Seq("-unchecked", "-deprecation"), libraryDependencies ++= Dependencies.scalaStyle ) diff --git a/sql/README.md b/sql/README.md index 4192fecb92fb0..14d5555f0c713 100644 --- a/sql/README.md +++ b/sql/README.md @@ -38,7 +38,7 @@ import org.apache.spark.sql.catalyst.util._ import org.apache.spark.sql.execution import org.apache.spark.sql.hive._ import org.apache.spark.sql.hive.TestHive._ -Welcome to Scala version 2.10.3 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_45). +Welcome to Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_45). Type in expressions to have them evaluated. Type :help for more information. From afb5ea62786e3ca055e247176def3e7ecf0d2c9d Mon Sep 17 00:00:00 2001 From: Diana Carroll Date: Tue, 1 Apr 2014 19:29:26 -0700 Subject: [PATCH 09/78] [Spark-1134] only call ipython if no arguments are given; remove IPYTHONOPTS from call see comments on Pull Request https://github.com/apache/spark/pull/38 (i couldn't figure out how to modify an existing pull request, so I'm hoping I can withdraw that one and replace it with this one.) Author: Diana Carroll Closes #227 from dianacarroll/spark-1134 and squashes the following commits: ffe47f2 [Diana Carroll] [spark-1134] remove ipythonopts from ipython command b673bf7 [Diana Carroll] Merge branch 'master' of github.com:apache/spark 0309cf9 [Diana Carroll] SPARK-1134 bug with ipython prevents non-interactive use with spark; only call ipython if no command line arguments were supplied --- bin/pyspark | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/bin/pyspark b/bin/pyspark index 67e1f61eeb1e5..7932a247b54d0 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -55,8 +55,9 @@ if [ -n "$IPYTHON_OPTS" ]; then IPYTHON=1 fi -if [[ "$IPYTHON" = "1" ]] ; then - exec ipython $IPYTHON_OPTS +# Only use ipython if no command line arguments were provided [SPARK-1134] +if [[ "$IPYTHON" = "1" && $# = 0 ]] ; then + exec ipython else exec "$PYSPARK_PYTHON" "$@" fi From 45df9127365f8942794273b8ada004bf6ea3ef10 Mon Sep 17 00:00:00 2001 From: Matei Zaharia Date: Tue, 1 Apr 2014 19:31:50 -0700 Subject: [PATCH 10/78] Revert "[Spark-1134] only call ipython if no arguments are given; remove IPYTHONOPTS from call" This reverts commit afb5ea62786e3ca055e247176def3e7ecf0d2c9d. --- bin/pyspark | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/bin/pyspark b/bin/pyspark index 7932a247b54d0..67e1f61eeb1e5 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -55,9 +55,8 @@ if [ -n "$IPYTHON_OPTS" ]; then IPYTHON=1 fi -# Only use ipython if no command line arguments were provided [SPARK-1134] -if [[ "$IPYTHON" = "1" && $# = 0 ]] ; then - exec ipython +if [[ "$IPYTHON" = "1" ]] ; then + exec ipython $IPYTHON_OPTS else exec "$PYSPARK_PYTHON" "$@" fi From 8b3045ceab591a3f3ca18823c7e2c5faca38a06e Mon Sep 17 00:00:00 2001 From: Manish Amde Date: Tue, 1 Apr 2014 21:40:49 -0700 Subject: [PATCH 11/78] MLI-1 Decision Trees Joint work with @hirakendu, @etrain, @atalwalkar and @harsha2010. Key features: + Supports binary classification and regression + Supports gini, entropy and variance for information gain calculation + Supports both continuous and categorical features The algorithm has gone through several development iterations over the last few months leading to a highly optimized implementation. Optimizations include: 1. Level-wise training to reduce passes over the entire dataset. 2. Bin-wise split calculation to reduce computation overhead. 3. Aggregation over partitions before combining to reduce communication overhead. Author: Manish Amde Author: manishamde Author: Xiangrui Meng Closes #79 from manishamde/tree and squashes the following commits: 1e8c704 [Manish Amde] remove numBins field in the Strategy class 7d54b4f [manishamde] Merge pull request #4 from mengxr/dtree f536ae9 [Xiangrui Meng] another pass on code style e1dd86f [Manish Amde] implementing code style suggestions 62dc723 [Manish Amde] updating javadoc and converting helper methods to package private to allow unit testing 201702f [Manish Amde] making some more methods private f963ef5 [Manish Amde] making methods private c487e6a [manishamde] Merge pull request #1 from mengxr/dtree 24500c5 [Xiangrui Meng] minor style updates 4576b64 [Manish Amde] documentation and for to while loop conversion ff363a7 [Manish Amde] binary search for bins and while loop for categorical feature bins 632818f [Manish Amde] removing threshold for classification predict method 2116360 [Manish Amde] removing dummy bin calculation for categorical variables 6068356 [Manish Amde] ensuring num bins is always greater than max number of categories 62c2562 [Manish Amde] fixing comment indentation ad1fc21 [Manish Amde] incorporated mengxr's code style suggestions d1ef4f6 [Manish Amde] more documentation 794ff4d [Manish Amde] minor improvements to docs and style eb8fcbe [Manish Amde] minor code style updates cd2c2b4 [Manish Amde] fixing code style based on feedback 63e786b [Manish Amde] added multiple train methods for java compatability d3023b3 [Manish Amde] adding more docs for nested methods 84f85d6 [Manish Amde] code documentation 9372779 [Manish Amde] code style: max line lenght <= 100 dd0c0d7 [Manish Amde] minor: some docs 0dd7659 [manishamde] basic doc 5841c28 [Manish Amde] unit tests for categorical features f067d68 [Manish Amde] minor cleanup c0e522b [Manish Amde] updated predict and split threshold logic b09dc98 [Manish Amde] minor refactoring 6b7de78 [Manish Amde] minor refactoring and tests d504eb1 [Manish Amde] more tests for categorical features dbb7ac1 [Manish Amde] categorical feature support 6df35b9 [Manish Amde] regression predict logic 53108ed [Manish Amde] fixing index for highest bin e23c2e5 [Manish Amde] added regression support c8f6d60 [Manish Amde] adding enum for feature type b0e3e76 [Manish Amde] adding enum for feature type 154aa77 [Manish Amde] enums for configurations 733d6dd [Manish Amde] fixed tests 02c595c [Manish Amde] added command line parsing 98ec8d5 [Manish Amde] tree building and prediction logic b0eb866 [Manish Amde] added logic to handle leaf nodes 80e8c66 [Manish Amde] working version of multi-level split calculation 4798aae [Manish Amde] added gain stats class dad0afc [Manish Amde] decison stump functionality working 03f534c [Manish Amde] some more tests 0012a77 [Manish Amde] basic stump working 8bca1e2 [Manish Amde] additional code for creating intermediate RDD 92cedce [Manish Amde] basic building blocks for intermediate RDD calculation. untested. cd53eae [Manish Amde] skeletal framework --- .../spark/mllib/tree/DecisionTree.scala | 1150 +++++++++++++++++ .../org/apache/spark/mllib/tree/README.md | 17 + .../spark/mllib/tree/configuration/Algo.scala | 26 + .../tree/configuration/FeatureType.scala | 26 + .../tree/configuration/QuantileStrategy.scala | 26 + .../mllib/tree/configuration/Strategy.scala | 43 + .../spark/mllib/tree/impurity/Entropy.scala | 47 + .../spark/mllib/tree/impurity/Gini.scala | 46 + .../spark/mllib/tree/impurity/Impurity.scala | 42 + .../spark/mllib/tree/impurity/Variance.scala | 37 + .../apache/spark/mllib/tree/model/Bin.scala | 33 + .../mllib/tree/model/DecisionTreeModel.scala | 49 + .../spark/mllib/tree/model/Filter.scala | 28 + .../tree/model/InformationGainStats.scala | 39 + .../apache/spark/mllib/tree/model/Node.scala | 90 ++ .../apache/spark/mllib/tree/model/Split.scala | 64 + .../spark/mllib/tree/DecisionTreeSuite.scala | 425 ++++++ 17 files changed, 2188 insertions(+) create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/README.md create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/model/Filter.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala new file mode 100644 index 0000000000000..33205b919db8f --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala @@ -0,0 +1,1150 @@ +/* + * 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.tree + +import scala.util.control.Breaks._ + +import org.apache.spark.{Logging, SparkContext} +import org.apache.spark.SparkContext._ +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.configuration.Strategy +import org.apache.spark.mllib.tree.configuration.Algo._ +import org.apache.spark.mllib.tree.configuration.FeatureType._ +import org.apache.spark.mllib.tree.configuration.QuantileStrategy._ +import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Impurity, Variance} +import org.apache.spark.mllib.tree.model._ +import org.apache.spark.rdd.RDD +import org.apache.spark.util.random.XORShiftRandom + +/** + * A class that implements a decision tree algorithm for classification and regression. It + * supports both continuous and categorical features. + * @param strategy The configuration parameters for the tree algorithm which specify the type + * of algorithm (classification, regression, etc.), feature type (continuous, + * categorical), depth of the tree, quantile calculation strategy, etc. + */ +class DecisionTree private(val strategy: Strategy) extends Serializable with Logging { + + /** + * Method to train a decision tree model over an RDD + * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data + * @return a DecisionTreeModel that can be used for prediction + */ + def train(input: RDD[LabeledPoint]): DecisionTreeModel = { + + // Cache input RDD for speedup during multiple passes. + input.cache() + logDebug("algo = " + strategy.algo) + + // Find the splits and the corresponding bins (interval between the splits) using a sample + // of the input data. + val (splits, bins) = DecisionTree.findSplitsBins(input, strategy) + logDebug("numSplits = " + bins(0).length) + + // depth of the decision tree + val maxDepth = strategy.maxDepth + // the max number of nodes possible given the depth of the tree + val maxNumNodes = scala.math.pow(2, maxDepth).toInt - 1 + // Initialize an array to hold filters applied to points for each node. + val filters = new Array[List[Filter]](maxNumNodes) + // The filter at the top node is an empty list. + filters(0) = List() + // Initialize an array to hold parent impurity calculations for each node. + val parentImpurities = new Array[Double](maxNumNodes) + // dummy value for top node (updated during first split calculation) + val nodes = new Array[Node](maxNumNodes) + + + /* + * The main idea here is to perform level-wise training of the decision tree nodes thus + * reducing the passes over the data from l to log2(l) where l is the total number of nodes. + * Each data sample is checked for validity w.r.t to each node at a given level -- i.e., + * the sample is only used for the split calculation at the node if the sampled would have + * still survived the filters of the parent nodes. + */ + + // TODO: Convert for loop to while loop + breakable { + for (level <- 0 until maxDepth) { + + logDebug("#####################################") + logDebug("level = " + level) + logDebug("#####################################") + + // Find best split for all nodes at a level. + val splitsStatsForLevel = DecisionTree.findBestSplits(input, parentImpurities, strategy, + level, filters, splits, bins) + + for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex) { + // Extract info for nodes at the current level. + extractNodeInfo(nodeSplitStats, level, index, nodes) + // Extract info for nodes at the next lower level. + extractInfoForLowerLevels(level, index, maxDepth, nodeSplitStats, parentImpurities, + filters) + logDebug("final best split = " + nodeSplitStats._1) + } + require(scala.math.pow(2, level) == splitsStatsForLevel.length) + // Check whether all the nodes at the current level at leaves. + val allLeaf = splitsStatsForLevel.forall(_._2.gain <= 0) + logDebug("all leaf = " + allLeaf) + if (allLeaf) break // no more tree construction + } + } + + // Initialize the top or root node of the tree. + val topNode = nodes(0) + // Build the full tree using the node info calculated in the level-wise best split calculations. + topNode.build(nodes) + + new DecisionTreeModel(topNode, strategy.algo) + } + + /** + * Extract the decision tree node information for the given tree level and node index + */ + private def extractNodeInfo( + nodeSplitStats: (Split, InformationGainStats), + level: Int, + index: Int, + nodes: Array[Node]): Unit = { + val split = nodeSplitStats._1 + val stats = nodeSplitStats._2 + val nodeIndex = scala.math.pow(2, level).toInt - 1 + index + val isLeaf = (stats.gain <= 0) || (level == strategy.maxDepth - 1) + val node = new Node(nodeIndex, stats.predict, isLeaf, Some(split), None, None, Some(stats)) + logDebug("Node = " + node) + nodes(nodeIndex) = node + } + + /** + * Extract the decision tree node information for the children of the node + */ + private def extractInfoForLowerLevels( + level: Int, + index: Int, + maxDepth: Int, + nodeSplitStats: (Split, InformationGainStats), + parentImpurities: Array[Double], + filters: Array[List[Filter]]): Unit = { + // 0 corresponds to the left child node and 1 corresponds to the right child node. + // TODO: Convert to while loop + for (i <- 0 to 1) { + // Calculate the index of the node from the node level and the index at the current level. + val nodeIndex = scala.math.pow(2, level + 1).toInt - 1 + 2 * index + i + if (level < maxDepth - 1) { + val impurity = if (i == 0) { + nodeSplitStats._2.leftImpurity + } else { + nodeSplitStats._2.rightImpurity + } + logDebug("nodeIndex = " + nodeIndex + ", impurity = " + impurity) + // noting the parent impurities + parentImpurities(nodeIndex) = impurity + // noting the parents filters for the child nodes + val childFilter = new Filter(nodeSplitStats._1, if (i == 0) -1 else 1) + filters(nodeIndex) = childFilter :: filters((nodeIndex - 1) / 2) + for (filter <- filters(nodeIndex)) { + logDebug("Filter = " + filter) + } + } + } + } +} + +object DecisionTree extends Serializable with Logging { + + /** + * Method to train a decision tree model where the instances are represented as an RDD of + * (label, features) pairs. The method supports binary classification and regression. For the + * binary classification, the label for each instance should either be 0 or 1 to denote the two + * classes. The parameters for the algorithm are specified using the strategy parameter. + * + * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data + * for DecisionTree + * @param strategy The configuration parameters for the tree algorithm which specify the type + * of algorithm (classification, regression, etc.), feature type (continuous, + * categorical), depth of the tree, quantile calculation strategy, etc. + * @return a DecisionTreeModel that can be used for prediction + */ + def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = { + new DecisionTree(strategy).train(input: RDD[LabeledPoint]) + } + + /** + * Method to train a decision tree model where the instances are represented as an RDD of + * (label, features) pairs. The method supports binary classification and regression. For the + * binary classification, the label for each instance should either be 0 or 1 to denote the two + * classes. + * + * @param input input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as + * training data + * @param algo algorithm, classification or regression + * @param impurity impurity criterion used for information gain calculation + * @param maxDepth maxDepth maximum depth of the tree + * @return a DecisionTreeModel that can be used for prediction + */ + def train( + input: RDD[LabeledPoint], + algo: Algo, + impurity: Impurity, + maxDepth: Int): DecisionTreeModel = { + val strategy = new Strategy(algo,impurity,maxDepth) + new DecisionTree(strategy).train(input: RDD[LabeledPoint]) + } + + + /** + * Method to train a decision tree model where the instances are represented as an RDD of + * (label, features) pairs. The decision tree method supports binary classification and + * regression. For the binary classification, the label for each instance should either be 0 or + * 1 to denote the two classes. The method also supports categorical features inputs where the + * number of categories can specified using the categoricalFeaturesInfo option. + * + * @param input input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as + * training data for DecisionTree + * @param algo classification or regression + * @param impurity criterion used for information gain calculation + * @param maxDepth maximum depth of the tree + * @param maxBins maximum number of bins used for splitting features + * @param quantileCalculationStrategy algorithm for calculating quantiles + * @param categoricalFeaturesInfo A map storing information about the categorical variables and + * the number of discrete values they take. For example, + * an entry (n -> k) implies the feature n is categorical with k + * categories 0, 1, 2, ... , k-1. It's important to note that + * features are zero-indexed. + * @return a DecisionTreeModel that can be used for prediction + */ + def train( + input: RDD[LabeledPoint], + algo: Algo, + impurity: Impurity, + maxDepth: Int, + maxBins: Int, + quantileCalculationStrategy: QuantileStrategy, + categoricalFeaturesInfo: Map[Int,Int]): DecisionTreeModel = { + val strategy = new Strategy(algo, impurity, maxDepth, maxBins, quantileCalculationStrategy, + categoricalFeaturesInfo) + new DecisionTree(strategy).train(input: RDD[LabeledPoint]) + } + + private val InvalidBinIndex = -1 + + /** + * Returns an array of optimal splits for all nodes at a given level + * + * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data + * for DecisionTree + * @param parentImpurities Impurities for all parent nodes for the current level + * @param strategy [[org.apache.spark.mllib.tree.configuration.Strategy]] instance containing + * parameters for construction the DecisionTree + * @param level Level of the tree + * @param filters Filters for all nodes at a given level + * @param splits possible splits for all features + * @param bins possible bins for all features + * @return array of splits with best splits for all nodes at a given level. + */ + protected[tree] def findBestSplits( + input: RDD[LabeledPoint], + parentImpurities: Array[Double], + strategy: Strategy, + level: Int, + filters: Array[List[Filter]], + splits: Array[Array[Split]], + bins: Array[Array[Bin]]): Array[(Split, InformationGainStats)] = { + + /* + * The high-level description for the best split optimizations are noted here. + * + * *Level-wise training* + * We perform bin calculations for all nodes at the given level to avoid making multiple + * passes over the data. Thus, for a slightly increased computation and storage cost we save + * several iterations over the data especially at higher levels of the decision tree. + * + * *Bin-wise computation* + * We use a bin-wise best split computation strategy instead of a straightforward best split + * computation strategy. Instead of analyzing each sample for contribution to the left/right + * child node impurity of every split, we first categorize each feature of a sample into a + * bin. Each bin is an interval between a low and high split. Since each splits, and thus bin, + * is ordered (read ordering for categorical variables in the findSplitsBins method), + * we exploit this structure to calculate aggregates for bins and then use these aggregates + * to calculate information gain for each split. + * + * *Aggregation over partitions* + * Instead of performing a flatMap/reduceByKey operation, we exploit the fact that we know + * the number of splits in advance. Thus, we store the aggregates (at the appropriate + * indices) in a single array for all bins and rely upon the RDD aggregate method to + * drastically reduce the communication overhead. + */ + + // common calculations for multiple nested methods + val numNodes = scala.math.pow(2, level).toInt + logDebug("numNodes = " + numNodes) + // Find the number of features by looking at the first sample. + val numFeatures = input.first().features.length + logDebug("numFeatures = " + numFeatures) + val numBins = bins(0).length + logDebug("numBins = " + numBins) + + /** Find the filters used before reaching the current code. */ + def findParentFilters(nodeIndex: Int): List[Filter] = { + if (level == 0) { + List[Filter]() + } else { + val nodeFilterIndex = scala.math.pow(2, level).toInt - 1 + nodeIndex + filters(nodeFilterIndex) + } + } + + /** + * Find whether the sample is valid input for the current node, i.e., whether it passes through + * all the filters for the current node. + */ + def isSampleValid(parentFilters: List[Filter], labeledPoint: LabeledPoint): Boolean = { + // leaf + if ((level > 0) & (parentFilters.length == 0)) { + return false + } + + // Apply each filter and check sample validity. Return false when invalid condition found. + for (filter <- parentFilters) { + val features = labeledPoint.features + val featureIndex = filter.split.feature + val threshold = filter.split.threshold + val comparison = filter.comparison + val categories = filter.split.categories + val isFeatureContinuous = filter.split.featureType == Continuous + val feature = features(featureIndex) + if (isFeatureContinuous) { + comparison match { + case -1 => if (feature > threshold) return false + case 1 => if (feature <= threshold) return false + } + } else { + val containsFeature = categories.contains(feature) + comparison match { + case -1 => if (!containsFeature) return false + case 1 => if (containsFeature) return false + } + + } + } + + // Return true when the sample is valid for all filters. + true + } + + /** + * Find bin for one feature. + */ + def findBin( + featureIndex: Int, + labeledPoint: LabeledPoint, + isFeatureContinuous: Boolean): Int = { + val binForFeatures = bins(featureIndex) + val feature = labeledPoint.features(featureIndex) + + /** + * Binary search helper method for continuous feature. + */ + def binarySearchForBins(): Int = { + var left = 0 + var right = binForFeatures.length - 1 + while (left <= right) { + val mid = left + (right - left) / 2 + val bin = binForFeatures(mid) + val lowThreshold = bin.lowSplit.threshold + val highThreshold = bin.highSplit.threshold + if ((lowThreshold < feature) & (highThreshold >= feature)){ + return mid + } + else if (lowThreshold >= feature) { + right = mid - 1 + } + else { + left = mid + 1 + } + } + -1 + } + + /** + * Sequential search helper method to find bin for categorical feature. + */ + def sequentialBinSearchForCategoricalFeature(): Int = { + val numCategoricalBins = strategy.categoricalFeaturesInfo(featureIndex) + var binIndex = 0 + while (binIndex < numCategoricalBins) { + val bin = bins(featureIndex)(binIndex) + val category = bin.category + val features = labeledPoint.features + if (category == features(featureIndex)) { + return binIndex + } + binIndex += 1 + } + -1 + } + + if (isFeatureContinuous) { + // Perform binary search for finding bin for continuous features. + val binIndex = binarySearchForBins() + if (binIndex == -1){ + throw new UnknownError("no bin was found for continuous variable.") + } + binIndex + } else { + // Perform sequential search to find bin for categorical features. + val binIndex = sequentialBinSearchForCategoricalFeature() + if (binIndex == -1){ + throw new UnknownError("no bin was found for categorical variable.") + } + binIndex + } + } + + /** + * Finds bins for all nodes (and all features) at a given level. + * For l nodes, k features the storage is as follows: + * label, b_11, b_12, .. , b_1k, b_21, b_22, .. , b_2k, b_l1, b_l2, .. , b_lk, + * where b_ij is an integer between 0 and numBins - 1. + * Invalid sample is denoted by noting bin for feature 1 as -1. + */ + def findBinsForLevel(labeledPoint: LabeledPoint): Array[Double] = { + // Calculate bin index and label per feature per node. + val arr = new Array[Double](1 + (numFeatures * numNodes)) + arr(0) = labeledPoint.label + var nodeIndex = 0 + while (nodeIndex < numNodes) { + val parentFilters = findParentFilters(nodeIndex) + // Find out whether the sample qualifies for the particular node. + val sampleValid = isSampleValid(parentFilters, labeledPoint) + val shift = 1 + numFeatures * nodeIndex + if (!sampleValid) { + // Mark one bin as -1 is sufficient. + arr(shift) = InvalidBinIndex + } else { + var featureIndex = 0 + while (featureIndex < numFeatures) { + val isFeatureContinuous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty + arr(shift + featureIndex) = findBin(featureIndex, labeledPoint,isFeatureContinuous) + featureIndex += 1 + } + } + nodeIndex += 1 + } + arr + } + + /** + * Performs a sequential aggregation over a partition for classification. For l nodes, + * k features, either the left count or the right count of one of the p bins is + * incremented based upon whether the feature is classified as 0 or 1. + * + * @param agg Array[Double] storing aggregate calculation of size + * 2 * numSplits * numFeatures*numNodes for classification + * @param arr Array[Double] of size 1 + (numFeatures * numNodes) + * @return Array[Double] storing aggregate calculation of size + * 2 * numSplits * numFeatures * numNodes for classification + */ + def classificationBinSeqOp(arr: Array[Double], agg: Array[Double]) { + // Iterate over all nodes. + var nodeIndex = 0 + while (nodeIndex < numNodes) { + // Check whether the instance was valid for this nodeIndex. + val validSignalIndex = 1 + numFeatures * nodeIndex + val isSampleValidForNode = arr(validSignalIndex) != InvalidBinIndex + if (isSampleValidForNode) { + // actual class label + val label = arr(0) + // Iterate over all features. + var featureIndex = 0 + while (featureIndex < numFeatures) { + // Find the bin index for this feature. + val arrShift = 1 + numFeatures * nodeIndex + val arrIndex = arrShift + featureIndex + // Update the left or right count for one bin. + val aggShift = 2 * numBins * numFeatures * nodeIndex + val aggIndex = aggShift + 2 * featureIndex * numBins + arr(arrIndex).toInt * 2 + label match { + case 0.0 => agg(aggIndex) = agg(aggIndex) + 1 + case 1.0 => agg(aggIndex + 1) = agg(aggIndex + 1) + 1 + } + featureIndex += 1 + } + } + nodeIndex += 1 + } + } + + /** + * Performs a sequential aggregation over a partition for regression. For l nodes, k features, + * the count, sum, sum of squares of one of the p bins is incremented. + * + * @param agg Array[Double] storing aggregate calculation of size + * 3 * numSplits * numFeatures * numNodes for classification + * @param arr Array[Double] of size 1 + (numFeatures * numNodes) + * @return Array[Double] storing aggregate calculation of size + * 3 * numSplits * numFeatures * numNodes for regression + */ + def regressionBinSeqOp(arr: Array[Double], agg: Array[Double]) { + // Iterate over all nodes. + var nodeIndex = 0 + while (nodeIndex < numNodes) { + // Check whether the instance was valid for this nodeIndex. + val validSignalIndex = 1 + numFeatures * nodeIndex + val isSampleValidForNode = arr(validSignalIndex) != InvalidBinIndex + if (isSampleValidForNode) { + // actual class label + val label = arr(0) + // Iterate over all features. + var featureIndex = 0 + while (featureIndex < numFeatures) { + // Find the bin index for this feature. + val arrShift = 1 + numFeatures * nodeIndex + val arrIndex = arrShift + featureIndex + // Update count, sum, and sum^2 for one bin. + val aggShift = 3 * numBins * numFeatures * nodeIndex + val aggIndex = aggShift + 3 * featureIndex * numBins + arr(arrIndex).toInt * 3 + agg(aggIndex) = agg(aggIndex) + 1 + agg(aggIndex + 1) = agg(aggIndex + 1) + label + agg(aggIndex + 2) = agg(aggIndex + 2) + label*label + featureIndex += 1 + } + } + nodeIndex += 1 + } + } + + /** + * Performs a sequential aggregation over a partition. + */ + def binSeqOp(agg: Array[Double], arr: Array[Double]): Array[Double] = { + strategy.algo match { + case Classification => classificationBinSeqOp(arr, agg) + case Regression => regressionBinSeqOp(arr, agg) + } + agg + } + + // Calculate bin aggregate length for classification or regression. + val binAggregateLength = strategy.algo match { + case Classification => 2 * numBins * numFeatures * numNodes + case Regression => 3 * numBins * numFeatures * numNodes + } + logDebug("binAggregateLength = " + binAggregateLength) + + /** + * Combines the aggregates from partitions. + * @param agg1 Array containing aggregates from one or more partitions + * @param agg2 Array containing aggregates from one or more partitions + * @return Combined aggregate from agg1 and agg2 + */ + def binCombOp(agg1: Array[Double], agg2: Array[Double]): Array[Double] = { + var index = 0 + val combinedAggregate = new Array[Double](binAggregateLength) + while (index < binAggregateLength) { + combinedAggregate(index) = agg1(index) + agg2(index) + index += 1 + } + combinedAggregate + } + + // Find feature bins for all nodes at a level. + val binMappedRDD = input.map(x => findBinsForLevel(x)) + + // Calculate bin aggregates. + val binAggregates = { + binMappedRDD.aggregate(Array.fill[Double](binAggregateLength)(0))(binSeqOp,binCombOp) + } + logDebug("binAggregates.length = " + binAggregates.length) + + /** + * Calculates the information gain for all splits based upon left/right split aggregates. + * @param leftNodeAgg left node aggregates + * @param featureIndex feature index + * @param splitIndex split index + * @param rightNodeAgg right node aggregate + * @param topImpurity impurity of the parent node + * @return information gain and statistics for all splits + */ + def calculateGainForSplit( + leftNodeAgg: Array[Array[Double]], + featureIndex: Int, + splitIndex: Int, + rightNodeAgg: Array[Array[Double]], + topImpurity: Double): InformationGainStats = { + strategy.algo match { + case Classification => + val left0Count = leftNodeAgg(featureIndex)(2 * splitIndex) + val left1Count = leftNodeAgg(featureIndex)(2 * splitIndex + 1) + val leftCount = left0Count + left1Count + + val right0Count = rightNodeAgg(featureIndex)(2 * splitIndex) + val right1Count = rightNodeAgg(featureIndex)(2 * splitIndex + 1) + val rightCount = right0Count + right1Count + + val impurity = { + if (level > 0) { + topImpurity + } else { + // Calculate impurity for root node. + strategy.impurity.calculate(left0Count + right0Count, left1Count + right1Count) + } + } + + if (leftCount == 0) { + return new InformationGainStats(0, topImpurity, Double.MinValue, topImpurity,1) + } + if (rightCount == 0) { + return new InformationGainStats(0, topImpurity, topImpurity, Double.MinValue,0) + } + + val leftImpurity = strategy.impurity.calculate(left0Count, left1Count) + val rightImpurity = strategy.impurity.calculate(right0Count, right1Count) + + val leftWeight = leftCount.toDouble / (leftCount + rightCount) + val rightWeight = rightCount.toDouble / (leftCount + rightCount) + + val gain = { + if (level > 0) { + impurity - leftWeight * leftImpurity - rightWeight * rightImpurity + } else { + impurity - leftWeight * leftImpurity - rightWeight * rightImpurity + } + } + + val predict = (left1Count + right1Count) / (leftCount + rightCount) + + new InformationGainStats(gain, impurity, leftImpurity, rightImpurity, predict) + case Regression => + val leftCount = leftNodeAgg(featureIndex)(3 * splitIndex) + val leftSum = leftNodeAgg(featureIndex)(3 * splitIndex + 1) + val leftSumSquares = leftNodeAgg(featureIndex)(3 * splitIndex + 2) + + val rightCount = rightNodeAgg(featureIndex)(3 * splitIndex) + val rightSum = rightNodeAgg(featureIndex)(3 * splitIndex + 1) + val rightSumSquares = rightNodeAgg(featureIndex)(3 * splitIndex + 2) + + val impurity = { + if (level > 0) { + topImpurity + } else { + // Calculate impurity for root node. + val count = leftCount + rightCount + val sum = leftSum + rightSum + val sumSquares = leftSumSquares + rightSumSquares + strategy.impurity.calculate(count, sum, sumSquares) + } + } + + if (leftCount == 0) { + return new InformationGainStats(0, topImpurity, Double.MinValue, topImpurity, + rightSum / rightCount) + } + if (rightCount == 0) { + return new InformationGainStats(0, topImpurity ,topImpurity, + Double.MinValue, leftSum / leftCount) + } + + val leftImpurity = strategy.impurity.calculate(leftCount, leftSum, leftSumSquares) + val rightImpurity = strategy.impurity.calculate(rightCount, rightSum, rightSumSquares) + + val leftWeight = leftCount.toDouble / (leftCount + rightCount) + val rightWeight = rightCount.toDouble / (leftCount + rightCount) + + val gain = { + if (level > 0) { + impurity - leftWeight * leftImpurity - rightWeight * rightImpurity + } else { + impurity - leftWeight * leftImpurity - rightWeight * rightImpurity + } + } + + val predict = (leftSum + rightSum) / (leftCount + rightCount) + new InformationGainStats(gain, impurity, leftImpurity, rightImpurity, predict) + } + } + + /** + * Extracts left and right split aggregates. + * @param binData Array[Double] of size 2*numFeatures*numSplits + * @return (leftNodeAgg, rightNodeAgg) tuple of type (Array[Double], + * Array[Double]) where each array is of size(numFeature,2*(numSplits-1)) + */ + def extractLeftRightNodeAggregates( + binData: Array[Double]): (Array[Array[Double]], Array[Array[Double]]) = { + strategy.algo match { + case Classification => + // Initialize left and right split aggregates. + val leftNodeAgg = Array.ofDim[Double](numFeatures, 2 * (numBins - 1)) + val rightNodeAgg = Array.ofDim[Double](numFeatures, 2 * (numBins - 1)) + // Iterate over all features. + var featureIndex = 0 + while (featureIndex < numFeatures) { + // shift for this featureIndex + val shift = 2 * featureIndex * numBins + + // left node aggregate for the lowest split + leftNodeAgg(featureIndex)(0) = binData(shift + 0) + leftNodeAgg(featureIndex)(1) = binData(shift + 1) + + // right node aggregate for the highest split + rightNodeAgg(featureIndex)(2 * (numBins - 2)) + = binData(shift + (2 * (numBins - 1))) + rightNodeAgg(featureIndex)(2 * (numBins - 2) + 1) + = binData(shift + (2 * (numBins - 1)) + 1) + + // Iterate over all splits. + var splitIndex = 1 + while (splitIndex < numBins - 1) { + // calculating left node aggregate for a split as a sum of left node aggregate of a + // lower split and the left bin aggregate of a bin where the split is a high split + leftNodeAgg(featureIndex)(2 * splitIndex) = binData(shift + 2 * splitIndex) + + leftNodeAgg(featureIndex)(2 * splitIndex - 2) + leftNodeAgg(featureIndex)(2 * splitIndex + 1) = binData(shift + 2 * splitIndex + 1) + + leftNodeAgg(featureIndex)(2 * splitIndex - 2 + 1) + + // calculating right node aggregate for a split as a sum of right node aggregate of a + // higher split and the right bin aggregate of a bin where the split is a low split + rightNodeAgg(featureIndex)(2 * (numBins - 2 - splitIndex)) = + binData(shift + (2 *(numBins - 2 - splitIndex))) + + rightNodeAgg(featureIndex)(2 * (numBins - 1 - splitIndex)) + rightNodeAgg(featureIndex)(2 * (numBins - 2 - splitIndex) + 1) = + binData(shift + (2* (numBins - 2 - splitIndex) + 1)) + + rightNodeAgg(featureIndex)(2 * (numBins - 1 - splitIndex) + 1) + + splitIndex += 1 + } + featureIndex += 1 + } + (leftNodeAgg, rightNodeAgg) + case Regression => + // Initialize left and right split aggregates. + val leftNodeAgg = Array.ofDim[Double](numFeatures, 3 * (numBins - 1)) + val rightNodeAgg = Array.ofDim[Double](numFeatures, 3 * (numBins - 1)) + // Iterate over all features. + var featureIndex = 0 + while (featureIndex < numFeatures) { + // shift for this featureIndex + val shift = 3 * featureIndex * numBins + // left node aggregate for the lowest split + leftNodeAgg(featureIndex)(0) = binData(shift + 0) + leftNodeAgg(featureIndex)(1) = binData(shift + 1) + leftNodeAgg(featureIndex)(2) = binData(shift + 2) + + // right node aggregate for the highest split + rightNodeAgg(featureIndex)(3 * (numBins - 2)) = + binData(shift + (3 * (numBins - 1))) + rightNodeAgg(featureIndex)(3 * (numBins - 2) + 1) = + binData(shift + (3 * (numBins - 1)) + 1) + rightNodeAgg(featureIndex)(3 * (numBins - 2) + 2) = + binData(shift + (3 * (numBins - 1)) + 2) + + // Iterate over all splits. + var splitIndex = 1 + while (splitIndex < numBins - 1) { + // calculating left node aggregate for a split as a sum of left node aggregate of a + // lower split and the left bin aggregate of a bin where the split is a high split + leftNodeAgg(featureIndex)(3 * splitIndex) = binData(shift + 3 * splitIndex) + + leftNodeAgg(featureIndex)(3 * splitIndex - 3) + leftNodeAgg(featureIndex)(3 * splitIndex + 1) = binData(shift + 3 * splitIndex + 1) + + leftNodeAgg(featureIndex)(3 * splitIndex - 3 + 1) + leftNodeAgg(featureIndex)(3 * splitIndex + 2) = binData(shift + 3 * splitIndex + 2) + + leftNodeAgg(featureIndex)(3 * splitIndex - 3 + 2) + + // calculating right node aggregate for a split as a sum of right node aggregate of a + // higher split and the right bin aggregate of a bin where the split is a low split + rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex)) = + binData(shift + (3 * (numBins - 2 - splitIndex))) + + rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex)) + rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex) + 1) = + binData(shift + (3 * (numBins - 2 - splitIndex) + 1)) + + rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex) + 1) + rightNodeAgg(featureIndex)(3 * (numBins - 2 - splitIndex) + 2) = + binData(shift + (3 * (numBins - 2 - splitIndex) + 2)) + + rightNodeAgg(featureIndex)(3 * (numBins - 1 - splitIndex) + 2) + + splitIndex += 1 + } + featureIndex += 1 + } + (leftNodeAgg, rightNodeAgg) + } + } + + /** + * Calculates information gain for all nodes splits. + */ + def calculateGainsForAllNodeSplits( + leftNodeAgg: Array[Array[Double]], + rightNodeAgg: Array[Array[Double]], + nodeImpurity: Double): Array[Array[InformationGainStats]] = { + val gains = Array.ofDim[InformationGainStats](numFeatures, numBins - 1) + + for (featureIndex <- 0 until numFeatures) { + for (splitIndex <- 0 until numBins - 1) { + gains(featureIndex)(splitIndex) = calculateGainForSplit(leftNodeAgg, featureIndex, + splitIndex, rightNodeAgg, nodeImpurity) + } + } + gains + } + + /** + * Find the best split for a node. + * @param binData Array[Double] of size 2 * numSplits * numFeatures + * @param nodeImpurity impurity of the top node + * @return tuple of split and information gain + */ + def binsToBestSplit( + binData: Array[Double], + nodeImpurity: Double): (Split, InformationGainStats) = { + + logDebug("node impurity = " + nodeImpurity) + + // Extract left right node aggregates. + val (leftNodeAgg, rightNodeAgg) = extractLeftRightNodeAggregates(binData) + + // Calculate gains for all splits. + val gains = calculateGainsForAllNodeSplits(leftNodeAgg, rightNodeAgg, nodeImpurity) + + val (bestFeatureIndex,bestSplitIndex, gainStats) = { + // Initialize with infeasible values. + var bestFeatureIndex = Int.MinValue + var bestSplitIndex = Int.MinValue + var bestGainStats = new InformationGainStats(Double.MinValue, -1.0, -1.0, -1.0, -1.0) + // Iterate over features. + var featureIndex = 0 + while (featureIndex < numFeatures) { + // Iterate over all splits. + var splitIndex = 0 + while (splitIndex < numBins - 1) { + val gainStats = gains(featureIndex)(splitIndex) + if (gainStats.gain > bestGainStats.gain) { + bestGainStats = gainStats + bestFeatureIndex = featureIndex + bestSplitIndex = splitIndex + } + splitIndex += 1 + } + featureIndex += 1 + } + (bestFeatureIndex, bestSplitIndex, bestGainStats) + } + + logDebug("best split bin = " + bins(bestFeatureIndex)(bestSplitIndex)) + logDebug("best split bin = " + splits(bestFeatureIndex)(bestSplitIndex)) + + (splits(bestFeatureIndex)(bestSplitIndex), gainStats) + } + + /** + * Get bin data for one node. + */ + def getBinDataForNode(node: Int): Array[Double] = { + strategy.algo match { + case Classification => + val shift = 2 * node * numBins * numFeatures + val binsForNode = binAggregates.slice(shift, shift + 2 * numBins * numFeatures) + binsForNode + case Regression => + val shift = 3 * node * numBins * numFeatures + val binsForNode = binAggregates.slice(shift, shift + 3 * numBins * numFeatures) + binsForNode + } + } + + // Calculate best splits for all nodes at a given level + val bestSplits = new Array[(Split, InformationGainStats)](numNodes) + // Iterating over all nodes at this level + var node = 0 + while (node < numNodes) { + val nodeImpurityIndex = scala.math.pow(2, level).toInt - 1 + node + val binsForNode: Array[Double] = getBinDataForNode(node) + logDebug("nodeImpurityIndex = " + nodeImpurityIndex) + val parentNodeImpurity = parentImpurities(nodeImpurityIndex) + logDebug("node impurity = " + parentNodeImpurity) + bestSplits(node) = binsToBestSplit(binsForNode, parentNodeImpurity) + node += 1 + } + + bestSplits + } + + /** + * Returns split and bins for decision tree calculation. + * @param input RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] used as training data + * for DecisionTree + * @param strategy [[org.apache.spark.mllib.tree.configuration.Strategy]] instance containing + * parameters for construction the DecisionTree + * @return a tuple of (splits,bins) where splits is an Array of [org.apache.spark.mllib.tree + * .model.Split] of size (numFeatures, numSplits-1) and bins is an Array of [org.apache + * .spark.mllib.tree.model.Bin] of size (numFeatures, numSplits1) + */ + protected[tree] def findSplitsBins( + input: RDD[LabeledPoint], + strategy: Strategy): (Array[Array[Split]], Array[Array[Bin]]) = { + val count = input.count() + + // Find the number of features by looking at the first sample + val numFeatures = input.take(1)(0).features.length + + val maxBins = strategy.maxBins + val numBins = if (maxBins <= count) maxBins else count.toInt + logDebug("numBins = " + numBins) + + /* + * TODO: Add a require statement ensuring #bins is always greater than the categories. + * It's a limitation of the current implementation but a reasonable trade-off since features + * with large number of categories get favored over continuous features. + */ + if (strategy.categoricalFeaturesInfo.size > 0) { + val maxCategoriesForFeatures = strategy.categoricalFeaturesInfo.maxBy(_._2)._2 + require(numBins >= maxCategoriesForFeatures) + } + + // Calculate the number of sample for approximate quantile calculation. + val requiredSamples = numBins*numBins + val fraction = if (requiredSamples < count) requiredSamples.toDouble / count else 1.0 + logDebug("fraction of data used for calculating quantiles = " + fraction) + + // sampled input for RDD calculation + val sampledInput = input.sample(false, fraction, new XORShiftRandom().nextInt()).collect() + val numSamples = sampledInput.length + + val stride: Double = numSamples.toDouble / numBins + logDebug("stride = " + stride) + + strategy.quantileCalculationStrategy match { + case Sort => + val splits = Array.ofDim[Split](numFeatures, numBins - 1) + val bins = Array.ofDim[Bin](numFeatures, numBins) + + // Find all splits. + + // Iterate over all features. + var featureIndex = 0 + while (featureIndex < numFeatures){ + // Check whether the feature is continuous. + val isFeatureContinuous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty + if (isFeatureContinuous) { + val featureSamples = sampledInput.map(lp => lp.features(featureIndex)).sorted + val stride: Double = numSamples.toDouble / numBins + logDebug("stride = " + stride) + for (index <- 0 until numBins - 1) { + val sampleIndex = (index + 1) * stride.toInt + val split = new Split(featureIndex, featureSamples(sampleIndex), Continuous, List()) + splits(featureIndex)(index) = split + } + } else { + val maxFeatureValue = strategy.categoricalFeaturesInfo(featureIndex) + require(maxFeatureValue < numBins, "number of categories should be less than number " + + "of bins") + + // For categorical variables, each bin is a category. The bins are sorted and they + // are ordered by calculating the centroid of their corresponding labels. + val centroidForCategories = + sampledInput.map(lp => (lp.features(featureIndex),lp.label)) + .groupBy(_._1) + .mapValues(x => x.map(_._2).sum / x.map(_._1).length) + + // Check for missing categorical variables and putting them last in the sorted list. + val fullCentroidForCategories = scala.collection.mutable.Map[Double,Double]() + for (i <- 0 until maxFeatureValue) { + if (centroidForCategories.contains(i)) { + fullCentroidForCategories(i) = centroidForCategories(i) + } else { + fullCentroidForCategories(i) = Double.MaxValue + } + } + + // bins sorted by centroids + val categoriesSortedByCentroid = fullCentroidForCategories.toList.sortBy(_._2) + + logDebug("centriod for categorical variable = " + categoriesSortedByCentroid) + + var categoriesForSplit = List[Double]() + categoriesSortedByCentroid.iterator.zipWithIndex.foreach { + case ((key, value), index) => + categoriesForSplit = key :: categoriesForSplit + splits(featureIndex)(index) = new Split(featureIndex, Double.MinValue, Categorical, + categoriesForSplit) + bins(featureIndex)(index) = { + if (index == 0) { + new Bin(new DummyCategoricalSplit(featureIndex, Categorical), + splits(featureIndex)(0), Categorical, key) + } else { + new Bin(splits(featureIndex)(index-1), splits(featureIndex)(index), + Categorical, key) + } + } + } + } + featureIndex += 1 + } + + // Find all bins. + featureIndex = 0 + while (featureIndex < numFeatures) { + val isFeatureContinuous = strategy.categoricalFeaturesInfo.get(featureIndex).isEmpty + if (isFeatureContinuous) { // Bins for categorical variables are already assigned. + bins(featureIndex)(0) = new Bin(new DummyLowSplit(featureIndex, Continuous), + splits(featureIndex)(0), Continuous, Double.MinValue) + for (index <- 1 until numBins - 1){ + val bin = new Bin(splits(featureIndex)(index-1), splits(featureIndex)(index), + Continuous, Double.MinValue) + bins(featureIndex)(index) = bin + } + bins(featureIndex)(numBins-1) = new Bin(splits(featureIndex)(numBins-2), + new DummyHighSplit(featureIndex, Continuous), Continuous, Double.MinValue) + } + featureIndex += 1 + } + (splits,bins) + case MinMax => + throw new UnsupportedOperationException("minmax not supported yet.") + case ApproxHist => + throw new UnsupportedOperationException("approximate histogram not supported yet.") + } + } + + val usage = """ + Usage: DecisionTreeRunner [slices] --algo --trainDataDir path --testDataDir path --maxDepth num [--impurity ] [--maxBins num] + """ + + def main(args: Array[String]) { + + if (args.length < 2) { + System.err.println(usage) + System.exit(1) + } + + val sc = new SparkContext(args(0), "DecisionTree") + + val argList = args.toList.drop(1) + type OptionMap = Map[Symbol, Any] + + def nextOption(map : OptionMap, list: List[String]): OptionMap = { + list match { + case Nil => map + case "--algo" :: string :: tail => nextOption(map ++ Map('algo -> string), tail) + case "--impurity" :: string :: tail => nextOption(map ++ Map('impurity -> string), tail) + case "--maxDepth" :: string :: tail => nextOption(map ++ Map('maxDepth -> string), tail) + case "--maxBins" :: string :: tail => nextOption(map ++ Map('maxBins -> string), tail) + case "--trainDataDir" :: string :: tail => nextOption(map ++ Map('trainDataDir -> string) + , tail) + case "--testDataDir" :: string :: tail => nextOption(map ++ Map('testDataDir -> string), + tail) + case string :: Nil => nextOption(map ++ Map('infile -> string), list.tail) + case option :: tail => logError("Unknown option " + option) + sys.exit(1) + } + } + val options = nextOption(Map(), argList) + logDebug(options.toString()) + + // Load training data. + val trainData = loadLabeledData(sc, options.get('trainDataDir).get.toString) + + // Identify the type of algorithm. + val algoStr = options.get('algo).get.toString + val algo = algoStr match { + case "Classification" => Classification + case "Regression" => Regression + } + + // Identify the type of impurity. + val impurityStr = options.getOrElse('impurity, + if (algo == Classification) "Gini" else "Variance").toString + val impurity = impurityStr match { + case "Gini" => Gini + case "Entropy" => Entropy + case "Variance" => Variance + } + + val maxDepth = options.getOrElse('maxDepth, "1").toString.toInt + val maxBins = options.getOrElse('maxBins, "100").toString.toInt + + val strategy = new Strategy(algo, impurity, maxDepth, maxBins) + val model = DecisionTree.train(trainData, strategy) + + // Load test data. + val testData = loadLabeledData(sc, options.get('testDataDir).get.toString) + + // Measure algorithm accuracy + if (algo == Classification) { + val accuracy = accuracyScore(model, testData) + logDebug("accuracy = " + accuracy) + } + + if (algo == Regression) { + val mse = meanSquaredError(model, testData) + logDebug("mean square error = " + mse) + } + + sc.stop() + } + + /** + * Load labeled data from a file. The data format used here is + * , ..., + * where , are feature values in Double and is the corresponding label as Double. + * + * @param sc SparkContext + * @param dir Directory to the input data files. + * @return An RDD of LabeledPoint. Each labeled point has two elements: the first element is + * the label, and the second element represents the feature values (an array of Double). + */ + def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = { + sc.textFile(dir).map { line => + val parts = line.trim().split(",") + val label = parts(0).toDouble + val features = parts.slice(1,parts.length).map(_.toDouble) + LabeledPoint(label, features) + } + } + + // TODO: Port this method to a generic metrics package. + /** + * Calculates the classifier accuracy. + */ + private def accuracyScore(model: DecisionTreeModel, data: RDD[LabeledPoint], + threshold: Double = 0.5): Double = { + def predictedValue(features: Array[Double]) = { + if (model.predict(features) < threshold) 0.0 else 1.0 + } + val correctCount = data.filter(y => predictedValue(y.features) == y.label).count() + val count = data.count() + logDebug("correct prediction count = " + correctCount) + logDebug("data count = " + count) + correctCount.toDouble / count + } + + // TODO: Port this method to a generic metrics package + /** + * Calculates the mean squared error for regression. + */ + private def meanSquaredError(tree: DecisionTreeModel, data: RDD[LabeledPoint]): Double = { + data.map { y => + val err = tree.predict(y.features) - y.label + err * err + }.mean() + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/README.md b/mllib/src/main/scala/org/apache/spark/mllib/tree/README.md new file mode 100644 index 0000000000000..0fd71aa9735bc --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/README.md @@ -0,0 +1,17 @@ +This package contains the default implementation of the decision tree algorithm. + +The decision tree algorithm supports: ++ Binary classification ++ Regression ++ Information loss calculation with entropy and gini for classification and variance for regression ++ Both continuous and categorical features + +# Tree improvements ++ Node model pruning ++ Printing to dot files + +# Future Ensemble Extensions + ++ Random forests ++ Boosting ++ Extremely randomized trees diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala new file mode 100644 index 0000000000000..2dd1f0f27b8f5 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala @@ -0,0 +1,26 @@ +/* + * 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.tree.configuration + +/** + * Enum to select the algorithm for the decision tree + */ +object Algo extends Enumeration { + type Algo = Value + val Classification, Regression = Value +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala new file mode 100644 index 0000000000000..09ee0586c58fa --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala @@ -0,0 +1,26 @@ +/* + * 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.tree.configuration + +/** + * Enum to describe whether a feature is "continuous" or "categorical" + */ +object FeatureType extends Enumeration { + type FeatureType = Value + val Continuous, Categorical = Value +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala new file mode 100644 index 0000000000000..2457a480c2a14 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala @@ -0,0 +1,26 @@ +/* + * 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.tree.configuration + +/** + * Enum for selecting the quantile calculation strategy + */ +object QuantileStrategy extends Enumeration { + type QuantileStrategy = Value + val Sort, MinMax, ApproxHist = Value +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala new file mode 100644 index 0000000000000..df565f3eb8859 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala @@ -0,0 +1,43 @@ +/* + * 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.tree.configuration + +import org.apache.spark.mllib.tree.impurity.Impurity +import org.apache.spark.mllib.tree.configuration.Algo._ +import org.apache.spark.mllib.tree.configuration.QuantileStrategy._ + +/** + * Stores all the configuration options for tree construction + * @param algo classification or regression + * @param impurity criterion used for information gain calculation + * @param maxDepth maximum depth of the tree + * @param maxBins maximum number of bins used for splitting features + * @param quantileCalculationStrategy algorithm for calculating quantiles + * @param categoricalFeaturesInfo A map storing information about the categorical variables and the + * number of discrete values they take. For example, an entry (n -> + * k) implies the feature n is categorical with k categories 0, + * 1, 2, ... , k-1. It's important to note that features are + * zero-indexed. + */ +class Strategy ( + val algo: Algo, + val impurity: Impurity, + val maxDepth: Int, + val maxBins: Int = 100, + val quantileCalculationStrategy: QuantileStrategy = Sort, + val categoricalFeaturesInfo: Map[Int,Int] = Map[Int,Int]()) extends Serializable diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala new file mode 100644 index 0000000000000..b93995fcf9441 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.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.tree.impurity + +/** + * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during + * binary classification. + */ +object Entropy extends Impurity { + + def log2(x: Double) = scala.math.log(x) / scala.math.log(2) + + /** + * entropy calculation + * @param c0 count of instances with label 0 + * @param c1 count of instances with label 1 + * @return entropy value + */ + def calculate(c0: Double, c1: Double): Double = { + if (c0 == 0 || c1 == 0) { + 0 + } else { + val total = c0 + c1 + val f0 = c0 / total + val f1 = c1 / total + -(f0 * log2(f0)) - (f1 * log2(f1)) + } + } + + def calculate(count: Double, sum: Double, sumSquares: Double): Double = + throw new UnsupportedOperationException("Entropy.calculate") +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala new file mode 100644 index 0000000000000..c0407554a91b3 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala @@ -0,0 +1,46 @@ +/* + * 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.tree.impurity + +/** + * Class for calculating the + * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]] + * during binary classification. + */ +object Gini extends Impurity { + + /** + * Gini coefficient calculation + * @param c0 count of instances with label 0 + * @param c1 count of instances with label 1 + * @return Gini coefficient value + */ + override def calculate(c0: Double, c1: Double): Double = { + if (c0 == 0 || c1 == 0) { + 0 + } else { + val total = c0 + c1 + val f0 = c0 / total + val f1 = c1 / total + 1 - f0 * f0 - f1 * f1 + } + } + + def calculate(count: Double, sum: Double, sumSquares: Double): Double = + throw new UnsupportedOperationException("Gini.calculate") +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala new file mode 100644 index 0000000000000..a4069063af2ad --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala @@ -0,0 +1,42 @@ +/* + * 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.tree.impurity + +/** + * Trait for calculating information gain. + */ +trait Impurity extends Serializable { + + /** + * information calculation for binary classification + * @param c0 count of instances with label 0 + * @param c1 count of instances with label 1 + * @return information value + */ + def calculate(c0 : Double, c1 : Double): Double + + /** + * information calculation for regression + * @param count number of instances + * @param sum sum of labels + * @param sumSquares summation of squares of the labels + * @return information value + */ + def calculate(count: Double, sum: Double, sumSquares: Double): Double + +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala new file mode 100644 index 0000000000000..b74577dcec167 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala @@ -0,0 +1,37 @@ +/* + * 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.tree.impurity + +/** + * Class for calculating variance during regression + */ +object Variance extends Impurity { + override def calculate(c0: Double, c1: Double): Double = + throw new UnsupportedOperationException("Variance.calculate") + + /** + * variance calculation + * @param count number of instances + * @param sum sum of labels + * @param sumSquares summation of squares of the labels + */ + override def calculate(count: Double, sum: Double, sumSquares: Double): Double = { + val squaredLoss = sumSquares - (sum * sum) / count + squaredLoss / count + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala new file mode 100644 index 0000000000000..a57faa13745f7 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.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.mllib.tree.model + +import org.apache.spark.mllib.tree.configuration.FeatureType._ + +/** + * Used for "binning" the features bins for faster best split calculation. For a continuous + * feature, a bin is determined by a low and a high "split". For a categorical feature, + * the a bin is determined using a single label value (category). + * @param lowSplit signifying the lower threshold for the continuous feature to be + * accepted in the bin + * @param highSplit signifying the upper threshold for the continuous feature to be + * accepted in the bin + * @param featureType type of feature -- categorical or continuous + * @param category categorical label value accepted in the bin + */ +case class Bin(lowSplit: Split, highSplit: Split, featureType: FeatureType, category: Double) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala new file mode 100644 index 0000000000000..a8bbf21daec01 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.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.tree.model + +import org.apache.spark.mllib.tree.configuration.Algo._ +import org.apache.spark.rdd.RDD + +/** + * Model to store the decision tree parameters + * @param topNode root node + * @param algo algorithm type -- classification or regression + */ +class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable { + + /** + * Predict values for a single data point using the model trained. + * + * @param features array representing a single data point + * @return Double prediction from the trained model + */ + def predict(features: Array[Double]): Double = { + topNode.predictIfLeaf(features) + } + + /** + * Predict values for the given data set using the model trained. + * + * @param features RDD representing data points to be predicted + * @return RDD[Int] where each entry contains the corresponding prediction + */ + def predict(features: RDD[Array[Double]]): RDD[Double] = { + features.map(x => predict(x)) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Filter.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Filter.scala new file mode 100644 index 0000000000000..ebc9595eafef3 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Filter.scala @@ -0,0 +1,28 @@ +/* + * 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.tree.model + +/** + * Filter specifying a split and type of comparison to be applied on features + * @param split split specifying the feature index, type and threshold + * @param comparison integer specifying <,=,> + */ +case class Filter(split: Split, comparison: Int) { + // Comparison -1,0,1 signifies <.=,> + override def toString = " split = " + split + "comparison = " + comparison +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala new file mode 100644 index 0000000000000..99bf79cf12e45 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala @@ -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.mllib.tree.model + +/** + * Information gain statistics for each split + * @param gain information gain value + * @param impurity current node impurity + * @param leftImpurity left node impurity + * @param rightImpurity right node impurity + * @param predict predicted value + */ +class InformationGainStats( + val gain: Double, + val impurity: Double, + val leftImpurity: Double, + val rightImpurity: Double, + val predict: Double) extends Serializable { + + override def toString = { + "gain = %f, impurity = %f, left impurity = %f, right impurity = %f, predict = %f" + .format(gain, impurity, leftImpurity, rightImpurity, predict) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala new file mode 100644 index 0000000000000..ea4693c5c2f4e --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.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.tree.model + +import org.apache.spark.Logging +import org.apache.spark.mllib.tree.configuration.FeatureType._ + +/** + * Node in a decision tree + * @param id integer node id + * @param predict predicted value at the node + * @param isLeaf whether the leaf is a node + * @param split split to calculate left and right nodes + * @param leftNode left child + * @param rightNode right child + * @param stats information gain stats + */ +class Node ( + val id: Int, + val predict: Double, + val isLeaf: Boolean, + val split: Option[Split], + var leftNode: Option[Node], + var rightNode: Option[Node], + val stats: Option[InformationGainStats]) extends Serializable with Logging { + + override def toString = "id = " + id + ", isLeaf = " + isLeaf + ", predict = " + predict + ", " + + "split = " + split + ", stats = " + stats + + /** + * build the left node and right nodes if not leaf + * @param nodes array of nodes + */ + def build(nodes: Array[Node]): Unit = { + + logDebug("building node " + id + " at level " + + (scala.math.log(id + 1)/scala.math.log(2)).toInt ) + logDebug("id = " + id + ", split = " + split) + logDebug("stats = " + stats) + logDebug("predict = " + predict) + if (!isLeaf) { + val leftNodeIndex = id*2 + 1 + val rightNodeIndex = id*2 + 2 + leftNode = Some(nodes(leftNodeIndex)) + rightNode = Some(nodes(rightNodeIndex)) + leftNode.get.build(nodes) + rightNode.get.build(nodes) + } + } + + /** + * predict value if node is not leaf + * @param feature feature value + * @return predicted value + */ + def predictIfLeaf(feature: Array[Double]) : Double = { + if (isLeaf) { + predict + } else{ + if (split.get.featureType == Continuous) { + if (feature(split.get.feature) <= split.get.threshold) { + leftNode.get.predictIfLeaf(feature) + } else { + rightNode.get.predictIfLeaf(feature) + } + } else { + if (split.get.categories.contains(feature(split.get.feature))) { + leftNode.get.predictIfLeaf(feature) + } else { + rightNode.get.predictIfLeaf(feature) + } + } + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala new file mode 100644 index 0000000000000..4e64a81dda74e --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.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.tree.model + +import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType + +/** + * Split applied to a feature + * @param feature feature index + * @param threshold threshold for continuous feature + * @param featureType type of feature -- categorical or continuous + * @param categories accepted values for categorical variables + */ +case class Split( + feature: Int, + threshold: Double, + featureType: FeatureType, + categories: List[Double]){ + + override def toString = + "Feature = " + feature + ", threshold = " + threshold + ", featureType = " + featureType + + ", categories = " + categories +} + +/** + * Split with minimum threshold for continuous features. Helps with the smallest bin creation. + * @param feature feature index + * @param featureType type of feature -- categorical or continuous + */ +class DummyLowSplit(feature: Int, featureType: FeatureType) + extends Split(feature, Double.MinValue, featureType, List()) + +/** + * Split with maximum threshold for continuous features. Helps with the highest bin creation. + * @param feature feature index + * @param featureType type of feature -- categorical or continuous + */ +class DummyHighSplit(feature: Int, featureType: FeatureType) + extends Split(feature, Double.MaxValue, featureType, List()) + +/** + * Split with no acceptable feature values for categorical features. Helps with the first bin + * creation. + * @param feature feature index + * @param featureType type of feature -- categorical or continuous + */ +class DummyCategoricalSplit(feature: Int, featureType: FeatureType) + extends Split(feature, Double.MaxValue, featureType, List()) + diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala new file mode 100644 index 0000000000000..4349c7000a0ae --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala @@ -0,0 +1,425 @@ +/* + * 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.tree + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import org.apache.spark.SparkContext +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance} +import org.apache.spark.mllib.tree.model.Filter +import org.apache.spark.mllib.tree.configuration.Strategy +import org.apache.spark.mllib.tree.configuration.Algo._ +import org.apache.spark.mllib.tree.configuration.FeatureType._ + +class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll { + + @transient private var sc: SparkContext = _ + + override def beforeAll() { + sc = new SparkContext("local", "test") + } + + override def afterAll() { + sc.stop() + System.clearProperty("spark.driver.port") + } + + test("split and bin calculation") { + val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy(Classification, Gini, 3, 100) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy) + assert(splits.length === 2) + assert(bins.length === 2) + assert(splits(0).length === 99) + assert(bins(0).length === 100) + } + + test("split and bin calculation for categorical variables") { + val arr = DecisionTreeSuite.generateCategoricalDataPoints() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy( + Classification, + Gini, + maxDepth = 3, + maxBins = 100, + categoricalFeaturesInfo = Map(0 -> 2, 1-> 2)) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy) + assert(splits.length === 2) + assert(bins.length === 2) + assert(splits(0).length === 99) + assert(bins(0).length === 100) + + // Check splits. + + assert(splits(0)(0).feature === 0) + assert(splits(0)(0).threshold === Double.MinValue) + assert(splits(0)(0).featureType === Categorical) + assert(splits(0)(0).categories.length === 1) + assert(splits(0)(0).categories.contains(1.0)) + + assert(splits(0)(1).feature === 0) + assert(splits(0)(1).threshold === Double.MinValue) + assert(splits(0)(1).featureType === Categorical) + assert(splits(0)(1).categories.length === 2) + assert(splits(0)(1).categories.contains(1.0)) + assert(splits(0)(1).categories.contains(0.0)) + + assert(splits(0)(2) === null) + + assert(splits(1)(0).feature === 1) + assert(splits(1)(0).threshold === Double.MinValue) + assert(splits(1)(0).featureType === Categorical) + assert(splits(1)(0).categories.length === 1) + assert(splits(1)(0).categories.contains(0.0)) + + assert(splits(1)(1).feature === 1) + assert(splits(1)(1).threshold === Double.MinValue) + assert(splits(1)(1).featureType === Categorical) + assert(splits(1)(1).categories.length === 2) + assert(splits(1)(1).categories.contains(1.0)) + assert(splits(1)(1).categories.contains(0.0)) + + assert(splits(1)(2) === null) + + // Check bins. + + assert(bins(0)(0).category === 1.0) + assert(bins(0)(0).lowSplit.categories.length === 0) + assert(bins(0)(0).highSplit.categories.length === 1) + assert(bins(0)(0).highSplit.categories.contains(1.0)) + + assert(bins(0)(1).category === 0.0) + assert(bins(0)(1).lowSplit.categories.length === 1) + assert(bins(0)(1).lowSplit.categories.contains(1.0)) + assert(bins(0)(1).highSplit.categories.length === 2) + assert(bins(0)(1).highSplit.categories.contains(1.0)) + assert(bins(0)(1).highSplit.categories.contains(0.0)) + + assert(bins(0)(2) === null) + + assert(bins(1)(0).category === 0.0) + assert(bins(1)(0).lowSplit.categories.length === 0) + assert(bins(1)(0).highSplit.categories.length === 1) + assert(bins(1)(0).highSplit.categories.contains(0.0)) + + assert(bins(1)(1).category === 1.0) + assert(bins(1)(1).lowSplit.categories.length === 1) + assert(bins(1)(1).lowSplit.categories.contains(0.0)) + assert(bins(1)(1).highSplit.categories.length === 2) + assert(bins(1)(1).highSplit.categories.contains(0.0)) + assert(bins(1)(1).highSplit.categories.contains(1.0)) + + assert(bins(1)(2) === null) + } + + test("split and bin calculations for categorical variables with no sample for one category") { + val arr = DecisionTreeSuite.generateCategoricalDataPoints() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy( + Classification, + Gini, + maxDepth = 3, + maxBins = 100, + categoricalFeaturesInfo = Map(0 -> 3, 1-> 3)) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy) + + // Check splits. + + assert(splits(0)(0).feature === 0) + assert(splits(0)(0).threshold === Double.MinValue) + assert(splits(0)(0).featureType === Categorical) + assert(splits(0)(0).categories.length === 1) + assert(splits(0)(0).categories.contains(1.0)) + + assert(splits(0)(1).feature === 0) + assert(splits(0)(1).threshold === Double.MinValue) + assert(splits(0)(1).featureType === Categorical) + assert(splits(0)(1).categories.length === 2) + assert(splits(0)(1).categories.contains(1.0)) + assert(splits(0)(1).categories.contains(0.0)) + + assert(splits(0)(2).feature === 0) + assert(splits(0)(2).threshold === Double.MinValue) + assert(splits(0)(2).featureType === Categorical) + assert(splits(0)(2).categories.length === 3) + assert(splits(0)(2).categories.contains(1.0)) + assert(splits(0)(2).categories.contains(0.0)) + assert(splits(0)(2).categories.contains(2.0)) + + assert(splits(0)(3) === null) + + assert(splits(1)(0).feature === 1) + assert(splits(1)(0).threshold === Double.MinValue) + assert(splits(1)(0).featureType === Categorical) + assert(splits(1)(0).categories.length === 1) + assert(splits(1)(0).categories.contains(0.0)) + + assert(splits(1)(1).feature === 1) + assert(splits(1)(1).threshold === Double.MinValue) + assert(splits(1)(1).featureType === Categorical) + assert(splits(1)(1).categories.length === 2) + assert(splits(1)(1).categories.contains(1.0)) + assert(splits(1)(1).categories.contains(0.0)) + + assert(splits(1)(2).feature === 1) + assert(splits(1)(2).threshold === Double.MinValue) + assert(splits(1)(2).featureType === Categorical) + assert(splits(1)(2).categories.length === 3) + assert(splits(1)(2).categories.contains(1.0)) + assert(splits(1)(2).categories.contains(0.0)) + assert(splits(1)(2).categories.contains(2.0)) + + assert(splits(1)(3) === null) + + // Check bins. + + assert(bins(0)(0).category === 1.0) + assert(bins(0)(0).lowSplit.categories.length === 0) + assert(bins(0)(0).highSplit.categories.length === 1) + assert(bins(0)(0).highSplit.categories.contains(1.0)) + + assert(bins(0)(1).category === 0.0) + assert(bins(0)(1).lowSplit.categories.length === 1) + assert(bins(0)(1).lowSplit.categories.contains(1.0)) + assert(bins(0)(1).highSplit.categories.length === 2) + assert(bins(0)(1).highSplit.categories.contains(1.0)) + assert(bins(0)(1).highSplit.categories.contains(0.0)) + + assert(bins(0)(2).category === 2.0) + assert(bins(0)(2).lowSplit.categories.length === 2) + assert(bins(0)(2).lowSplit.categories.contains(1.0)) + assert(bins(0)(2).lowSplit.categories.contains(0.0)) + assert(bins(0)(2).highSplit.categories.length === 3) + assert(bins(0)(2).highSplit.categories.contains(1.0)) + assert(bins(0)(2).highSplit.categories.contains(0.0)) + assert(bins(0)(2).highSplit.categories.contains(2.0)) + + assert(bins(0)(3) === null) + + assert(bins(1)(0).category === 0.0) + assert(bins(1)(0).lowSplit.categories.length === 0) + assert(bins(1)(0).highSplit.categories.length === 1) + assert(bins(1)(0).highSplit.categories.contains(0.0)) + + assert(bins(1)(1).category === 1.0) + assert(bins(1)(1).lowSplit.categories.length === 1) + assert(bins(1)(1).lowSplit.categories.contains(0.0)) + assert(bins(1)(1).highSplit.categories.length === 2) + assert(bins(1)(1).highSplit.categories.contains(0.0)) + assert(bins(1)(1).highSplit.categories.contains(1.0)) + + assert(bins(1)(2).category === 2.0) + assert(bins(1)(2).lowSplit.categories.length === 2) + assert(bins(1)(2).lowSplit.categories.contains(0.0)) + assert(bins(1)(2).lowSplit.categories.contains(1.0)) + assert(bins(1)(2).highSplit.categories.length === 3) + assert(bins(1)(2).highSplit.categories.contains(0.0)) + assert(bins(1)(2).highSplit.categories.contains(1.0)) + assert(bins(1)(2).highSplit.categories.contains(2.0)) + + assert(bins(1)(3) === null) + } + + test("classification stump with all categorical variables") { + val arr = DecisionTreeSuite.generateCategoricalDataPoints() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy( + Classification, + Gini, + maxDepth = 3, + maxBins = 100, + categoricalFeaturesInfo = Map(0 -> 3, 1-> 3)) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy) + val bestSplits = DecisionTree.findBestSplits(rdd, new Array(7), strategy, 0, + Array[List[Filter]](), splits, bins) + + val split = bestSplits(0)._1 + assert(split.categories.length === 1) + assert(split.categories.contains(1.0)) + assert(split.featureType === Categorical) + assert(split.threshold === Double.MinValue) + + val stats = bestSplits(0)._2 + assert(stats.gain > 0) + assert(stats.predict > 0.4) + assert(stats.predict < 0.5) + assert(stats.impurity > 0.2) + } + + test("regression stump with all categorical variables") { + val arr = DecisionTreeSuite.generateCategoricalDataPoints() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy( + Regression, + Variance, + maxDepth = 3, + maxBins = 100, + categoricalFeaturesInfo = Map(0 -> 3, 1-> 3)) + val (splits, bins) = DecisionTree.findSplitsBins(rdd,strategy) + val bestSplits = DecisionTree.findBestSplits(rdd, new Array(7), strategy, 0, + Array[List[Filter]](), splits, bins) + + val split = bestSplits(0)._1 + assert(split.categories.length === 1) + assert(split.categories.contains(1.0)) + assert(split.featureType === Categorical) + assert(split.threshold === Double.MinValue) + + val stats = bestSplits(0)._2 + assert(stats.gain > 0) + assert(stats.predict > 0.4) + assert(stats.predict < 0.5) + assert(stats.impurity > 0.2) + } + + test("stump with fixed label 0 for Gini") { + val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel0() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy(Classification, Gini, 3, 100) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy) + assert(splits.length === 2) + assert(splits(0).length === 99) + assert(bins.length === 2) + assert(bins(0).length === 100) + assert(splits(0).length === 99) + assert(bins(0).length === 100) + + val bestSplits = DecisionTree.findBestSplits(rdd, new Array(7), strategy, 0, + Array[List[Filter]](), splits, bins) + assert(bestSplits.length === 1) + assert(bestSplits(0)._1.feature === 0) + assert(bestSplits(0)._1.threshold === 10) + assert(bestSplits(0)._2.gain === 0) + assert(bestSplits(0)._2.leftImpurity === 0) + assert(bestSplits(0)._2.rightImpurity === 0) + } + + test("stump with fixed label 1 for Gini") { + val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy(Classification, Gini, 3, 100) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy) + assert(splits.length === 2) + assert(splits(0).length === 99) + assert(bins.length === 2) + assert(bins(0).length === 100) + assert(splits(0).length === 99) + assert(bins(0).length === 100) + + val bestSplits = DecisionTree.findBestSplits(rdd, Array(0.0), strategy, 0, + Array[List[Filter]](), splits, bins) + assert(bestSplits.length === 1) + assert(bestSplits(0)._1.feature === 0) + assert(bestSplits(0)._1.threshold === 10) + assert(bestSplits(0)._2.gain === 0) + assert(bestSplits(0)._2.leftImpurity === 0) + assert(bestSplits(0)._2.rightImpurity === 0) + assert(bestSplits(0)._2.predict === 1) + } + + test("stump with fixed label 0 for Entropy") { + val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel0() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy(Classification, Entropy, 3, 100) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy) + assert(splits.length === 2) + assert(splits(0).length === 99) + assert(bins.length === 2) + assert(bins(0).length === 100) + assert(splits(0).length === 99) + assert(bins(0).length === 100) + + val bestSplits = DecisionTree.findBestSplits(rdd, Array(0.0), strategy, 0, + Array[List[Filter]](), splits, bins) + assert(bestSplits.length === 1) + assert(bestSplits(0)._1.feature === 0) + assert(bestSplits(0)._1.threshold === 10) + assert(bestSplits(0)._2.gain === 0) + assert(bestSplits(0)._2.leftImpurity === 0) + assert(bestSplits(0)._2.rightImpurity === 0) + assert(bestSplits(0)._2.predict === 0) + } + + test("stump with fixed label 1 for Entropy") { + val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy(Classification, Entropy, 3, 100) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, strategy) + assert(splits.length === 2) + assert(splits(0).length === 99) + assert(bins.length === 2) + assert(bins(0).length === 100) + assert(splits(0).length === 99) + assert(bins(0).length === 100) + + val bestSplits = DecisionTree.findBestSplits(rdd, Array(0.0), strategy, 0, + Array[List[Filter]](), splits, bins) + assert(bestSplits.length === 1) + assert(bestSplits(0)._1.feature === 0) + assert(bestSplits(0)._1.threshold === 10) + assert(bestSplits(0)._2.gain === 0) + assert(bestSplits(0)._2.leftImpurity === 0) + assert(bestSplits(0)._2.rightImpurity === 0) + assert(bestSplits(0)._2.predict === 1) + } +} + +object DecisionTreeSuite { + + def generateOrderedLabeledPointsWithLabel0(): Array[LabeledPoint] = { + val arr = new Array[LabeledPoint](1000) + for (i <- 0 until 1000){ + val lp = new LabeledPoint(0.0,Array(i.toDouble,1000.0-i)) + arr(i) = lp + } + arr + } + + def generateOrderedLabeledPointsWithLabel1(): Array[LabeledPoint] = { + val arr = new Array[LabeledPoint](1000) + for (i <- 0 until 1000){ + val lp = new LabeledPoint(1.0,Array(i.toDouble,999.0-i)) + arr(i) = lp + } + arr + } + + def generateCategoricalDataPoints(): Array[LabeledPoint] = { + val arr = new Array[LabeledPoint](1000) + for (i <- 0 until 1000){ + if (i < 600){ + arr(i) = new LabeledPoint(1.0,Array(0.0,1.0)) + } else { + arr(i) = new LabeledPoint(0.0,Array(1.0,0.0)) + } + } + arr + } +} From ea9de658a365dca2b7403d8fab68a8a87c4e06c8 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 1 Apr 2014 23:54:38 -0700 Subject: [PATCH 12/78] Remove * from test case golden filename. @rxin mentioned this might cause issues on windows machines. Author: Michael Armbrust Closes #297 from marmbrus/noStars and squashes the following commits: 263122a [Michael Armbrust] Remove * from test case golden filename. --- .../resources/golden/alias.*-0-7bdb861d11e895aaea545810cdac316d | 1 - .../golden/alias.star-0-7bdb861d11e895aaea545810cdac316d | 1 + .../apache/spark/sql/hive/execution/HiveResolutionSuite.scala | 2 +- 3 files changed, 2 insertions(+), 2 deletions(-) delete mode 100644 sql/hive/src/test/resources/golden/alias.*-0-7bdb861d11e895aaea545810cdac316d create mode 100644 sql/hive/src/test/resources/golden/alias.star-0-7bdb861d11e895aaea545810cdac316d diff --git a/sql/hive/src/test/resources/golden/alias.*-0-7bdb861d11e895aaea545810cdac316d b/sql/hive/src/test/resources/golden/alias.*-0-7bdb861d11e895aaea545810cdac316d deleted file mode 100644 index 5f4de85940513..0000000000000 --- a/sql/hive/src/test/resources/golden/alias.*-0-7bdb861d11e895aaea545810cdac316d +++ /dev/null @@ -1 +0,0 @@ -0 val_0 \ No newline at end of file diff --git a/sql/hive/src/test/resources/golden/alias.star-0-7bdb861d11e895aaea545810cdac316d b/sql/hive/src/test/resources/golden/alias.star-0-7bdb861d11e895aaea545810cdac316d new file mode 100644 index 0000000000000..016f64cc26f2a --- /dev/null +++ b/sql/hive/src/test/resources/golden/alias.star-0-7bdb861d11e895aaea545810cdac316d @@ -0,0 +1 @@ +0 val_0 diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala index d77900ddc950c..40c4e23f90fb8 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala @@ -48,7 +48,7 @@ class HiveResolutionSuite extends HiveComparisonTest { createQueryTest("attr", "SELECT key FROM src a ORDER BY key LIMIT 1") - createQueryTest("alias.*", + createQueryTest("alias.star", "SELECT a.* FROM src a ORDER BY key LIMIT 1") test("case insensitivity with scala reflection") { From 11973a7bdad58fdb759033c232d87f0b279c83b4 Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Wed, 2 Apr 2014 10:35:52 -0700 Subject: [PATCH 13/78] Renamed stageIdToActiveJob to jobIdToActiveJob. This data structure was misused and, as a result, later renamed to an incorrect name. This data structure seems to have gotten into this tangled state as a result of @henrydavidge using the stageID instead of the job Id to index into it and later @andrewor14 renaming the data structure to reflect this misunderstanding. This patch renames it and removes an incorrect indexing into it. The incorrect indexing into it meant that the code added by @henrydavidge to warn when a task size is too large (added here https://github.com/apache/spark/commit/57579934f0454f258615c10e69ac2adafc5b9835) was not always executed; this commit fixes that. Author: Kay Ousterhout Closes #301 from kayousterhout/fixCancellation and squashes the following commits: bd3d3a4 [Kay Ousterhout] Renamed stageIdToActiveJob to jobIdToActiveJob. --- .../apache/spark/scheduler/DAGScheduler.scala | 21 +++++++++---------- .../spark/scheduler/DAGSchedulerSuite.scala | 2 +- 2 files changed, 11 insertions(+), 12 deletions(-) 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 4fce47e1ee8de..ef3d24d746829 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -84,7 +84,7 @@ class DAGScheduler( private[scheduler] val stageIdToJobIds = new TimeStampedHashMap[Int, HashSet[Int]] private[scheduler] val stageIdToStage = new TimeStampedHashMap[Int, Stage] private[scheduler] val shuffleToMapStage = new TimeStampedHashMap[Int, Stage] - private[scheduler] val stageIdToActiveJob = new HashMap[Int, ActiveJob] + private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob] private[scheduler] val resultStageToJob = new HashMap[Stage, ActiveJob] private[spark] val stageToInfos = new TimeStampedHashMap[Stage, StageInfo] @@ -536,7 +536,7 @@ class DAGScheduler( listenerBus.post(SparkListenerJobStart(job.jobId, Array[Int](), properties)) runLocally(job) } else { - stageIdToActiveJob(jobId) = job + jobIdToActiveJob(jobId) = job activeJobs += job resultStageToJob(finalStage) = job listenerBus.post( @@ -559,7 +559,7 @@ class DAGScheduler( // Cancel all running jobs. runningStages.map(_.jobId).foreach(handleJobCancellation) activeJobs.clear() // These should already be empty by this point, - stageIdToActiveJob.clear() // but just in case we lost track of some jobs... + jobIdToActiveJob.clear() // but just in case we lost track of some jobs... case ExecutorAdded(execId, host) => handleExecutorAdded(execId, host) @@ -569,7 +569,6 @@ class DAGScheduler( case BeginEvent(task, taskInfo) => for ( - job <- stageIdToActiveJob.get(task.stageId); stage <- stageIdToStage.get(task.stageId); stageInfo <- stageToInfos.get(stage) ) { @@ -697,7 +696,7 @@ class DAGScheduler( private def activeJobForStage(stage: Stage): Option[Int] = { if (stageIdToJobIds.contains(stage.id)) { val jobsThatUseStage: Array[Int] = stageIdToJobIds(stage.id).toArray.sorted - jobsThatUseStage.find(stageIdToActiveJob.contains) + jobsThatUseStage.find(jobIdToActiveJob.contains) } else { None } @@ -750,8 +749,8 @@ class DAGScheduler( } } - val properties = if (stageIdToActiveJob.contains(jobId)) { - stageIdToActiveJob(stage.jobId).properties + val properties = if (jobIdToActiveJob.contains(jobId)) { + jobIdToActiveJob(stage.jobId).properties } else { // this stage will be assigned to "default" pool null @@ -827,7 +826,7 @@ class DAGScheduler( job.numFinished += 1 // If the whole job has finished, remove it if (job.numFinished == job.numPartitions) { - stageIdToActiveJob -= stage.jobId + jobIdToActiveJob -= stage.jobId activeJobs -= job resultStageToJob -= stage markStageAsFinished(stage) @@ -986,11 +985,11 @@ class DAGScheduler( val independentStages = removeJobAndIndependentStages(jobId) independentStages.foreach(taskScheduler.cancelTasks) val error = new SparkException("Job %d cancelled".format(jobId)) - val job = stageIdToActiveJob(jobId) + val job = jobIdToActiveJob(jobId) job.listener.jobFailed(error) jobIdToStageIds -= jobId activeJobs -= job - stageIdToActiveJob -= jobId + jobIdToActiveJob -= jobId listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error, job.finalStage.id))) } } @@ -1011,7 +1010,7 @@ class DAGScheduler( val error = new SparkException("Job aborted: " + reason) job.listener.jobFailed(error) jobIdToStageIdsRemove(job.jobId) - stageIdToActiveJob -= resultStage.jobId + jobIdToActiveJob -= resultStage.jobId activeJobs -= job resultStageToJob -= resultStage listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error, failedStage.id))) 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 c97543f57d8f3..ce567b0cde85d 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -428,7 +428,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont assert(scheduler.pendingTasks.isEmpty) assert(scheduler.activeJobs.isEmpty) assert(scheduler.failedStages.isEmpty) - assert(scheduler.stageIdToActiveJob.isEmpty) + assert(scheduler.jobIdToActiveJob.isEmpty) assert(scheduler.jobIdToStageIds.isEmpty) assert(scheduler.stageIdToJobIds.isEmpty) assert(scheduler.stageIdToStage.isEmpty) From de8eefa804e229635eaa29a78b9e9ce161ac58e1 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Wed, 2 Apr 2014 10:43:09 -0700 Subject: [PATCH 14/78] [SPARK-1385] Use existing code for JSON de/serialization of BlockId `BlockId.scala` offers a way to reconstruct a BlockId from a string through regex matching. `util/JsonProtocol.scala` duplicates this functionality by explicitly matching on the BlockId type. With this PR, the de/serialization of BlockIds will go through the first (older) code path. (Most of the line changes in this PR involve changing `==` to `===` in `JsonProtocolSuite.scala`) Author: Andrew Or Closes #289 from andrewor14/blockid-json and squashes the following commits: 409d226 [Andrew Or] Simplify JSON de/serialization for BlockId --- .../org/apache/spark/util/JsonProtocol.scala | 77 +--------- .../apache/spark/util/JsonProtocolSuite.scala | 141 +++++++++--------- 2 files changed, 72 insertions(+), 146 deletions(-) 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 346f2b7856791..d9a6af61872d1 100644 --- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala @@ -195,7 +195,7 @@ private[spark] object JsonProtocol { taskMetrics.shuffleWriteMetrics.map(shuffleWriteMetricsToJson).getOrElse(JNothing) val updatedBlocks = taskMetrics.updatedBlocks.map { blocks => JArray(blocks.toList.map { case (id, status) => - ("Block ID" -> blockIdToJson(id)) ~ + ("Block ID" -> id.toString) ~ ("Status" -> blockStatusToJson(status)) }) }.getOrElse(JNothing) @@ -284,35 +284,6 @@ private[spark] object JsonProtocol { ("Replication" -> storageLevel.replication) } - def blockIdToJson(blockId: BlockId): JValue = { - val blockType = Utils.getFormattedClassName(blockId) - val json: JObject = blockId match { - case rddBlockId: RDDBlockId => - ("RDD ID" -> rddBlockId.rddId) ~ - ("Split Index" -> rddBlockId.splitIndex) - case shuffleBlockId: ShuffleBlockId => - ("Shuffle ID" -> shuffleBlockId.shuffleId) ~ - ("Map ID" -> shuffleBlockId.mapId) ~ - ("Reduce ID" -> shuffleBlockId.reduceId) - case broadcastBlockId: BroadcastBlockId => - "Broadcast ID" -> broadcastBlockId.broadcastId - case broadcastHelperBlockId: BroadcastHelperBlockId => - ("Broadcast Block ID" -> blockIdToJson(broadcastHelperBlockId.broadcastId)) ~ - ("Helper Type" -> broadcastHelperBlockId.hType) - case taskResultBlockId: TaskResultBlockId => - "Task ID" -> taskResultBlockId.taskId - case streamBlockId: StreamBlockId => - ("Stream ID" -> streamBlockId.streamId) ~ - ("Unique ID" -> streamBlockId.uniqueId) - case tempBlockId: TempBlockId => - val uuid = UUIDToJson(tempBlockId.id) - "Temp ID" -> uuid - case testBlockId: TestBlockId => - "Test ID" -> testBlockId.id - } - ("Type" -> blockType) ~ json - } - def blockStatusToJson(blockStatus: BlockStatus): JValue = { val storageLevel = storageLevelToJson(blockStatus.storageLevel) ("Storage Level" -> storageLevel) ~ @@ -513,7 +484,7 @@ private[spark] object JsonProtocol { Utils.jsonOption(json \ "Shuffle Write Metrics").map(shuffleWriteMetricsFromJson) metrics.updatedBlocks = Utils.jsonOption(json \ "Updated Blocks").map { value => value.extract[List[JValue]].map { block => - val id = blockIdFromJson(block \ "Block ID") + val id = BlockId((block \ "Block ID").extract[String]) val status = blockStatusFromJson(block \ "Status") (id, status) } @@ -616,50 +587,6 @@ private[spark] object JsonProtocol { StorageLevel(useDisk, useMemory, deserialized, replication) } - def blockIdFromJson(json: JValue): BlockId = { - val rddBlockId = Utils.getFormattedClassName(RDDBlockId) - val shuffleBlockId = Utils.getFormattedClassName(ShuffleBlockId) - val broadcastBlockId = Utils.getFormattedClassName(BroadcastBlockId) - val broadcastHelperBlockId = Utils.getFormattedClassName(BroadcastHelperBlockId) - val taskResultBlockId = Utils.getFormattedClassName(TaskResultBlockId) - val streamBlockId = Utils.getFormattedClassName(StreamBlockId) - val tempBlockId = Utils.getFormattedClassName(TempBlockId) - val testBlockId = Utils.getFormattedClassName(TestBlockId) - - (json \ "Type").extract[String] match { - case `rddBlockId` => - val rddId = (json \ "RDD ID").extract[Int] - val splitIndex = (json \ "Split Index").extract[Int] - new RDDBlockId(rddId, splitIndex) - case `shuffleBlockId` => - val shuffleId = (json \ "Shuffle ID").extract[Int] - val mapId = (json \ "Map ID").extract[Int] - val reduceId = (json \ "Reduce ID").extract[Int] - new ShuffleBlockId(shuffleId, mapId, reduceId) - case `broadcastBlockId` => - val broadcastId = (json \ "Broadcast ID").extract[Long] - new BroadcastBlockId(broadcastId) - case `broadcastHelperBlockId` => - val broadcastBlockId = - blockIdFromJson(json \ "Broadcast Block ID").asInstanceOf[BroadcastBlockId] - val hType = (json \ "Helper Type").extract[String] - new BroadcastHelperBlockId(broadcastBlockId, hType) - case `taskResultBlockId` => - val taskId = (json \ "Task ID").extract[Long] - new TaskResultBlockId(taskId) - case `streamBlockId` => - val streamId = (json \ "Stream ID").extract[Int] - val uniqueId = (json \ "Unique ID").extract[Long] - new StreamBlockId(streamId, uniqueId) - case `tempBlockId` => - val tempId = UUIDFromJson(json \ "Temp ID") - new TempBlockId(tempId) - case `testBlockId` => - val testId = (json \ "Test ID").extract[String] - new TestBlockId(testId) - } - } - def blockStatusFromJson(json: JValue): BlockStatus = { val storageLevel = storageLevelFromJson(json \ "Storage Level") val memorySize = (json \ "Memory Size").extract[Long] 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 67c0a434c9b52..40c29014c4b59 100644 --- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala @@ -112,7 +112,6 @@ class JsonProtocolSuite extends FunSuite { testBlockId(BroadcastHelperBlockId(BroadcastBlockId(2L), "Spark")) testBlockId(TaskResultBlockId(1L)) testBlockId(StreamBlockId(1, 2L)) - testBlockId(TempBlockId(UUID.randomUUID())) } @@ -168,8 +167,8 @@ class JsonProtocolSuite extends FunSuite { } private def testBlockId(blockId: BlockId) { - val newBlockId = JsonProtocol.blockIdFromJson(JsonProtocol.blockIdToJson(blockId)) - blockId == newBlockId + val newBlockId = BlockId(blockId.toString) + assert(blockId === newBlockId) } @@ -180,90 +179,90 @@ class JsonProtocolSuite extends FunSuite { private def assertEquals(event1: SparkListenerEvent, event2: SparkListenerEvent) { (event1, event2) match { case (e1: SparkListenerStageSubmitted, e2: SparkListenerStageSubmitted) => - assert(e1.properties == e2.properties) + assert(e1.properties === e2.properties) assertEquals(e1.stageInfo, e2.stageInfo) case (e1: SparkListenerStageCompleted, e2: SparkListenerStageCompleted) => assertEquals(e1.stageInfo, e2.stageInfo) case (e1: SparkListenerTaskStart, e2: SparkListenerTaskStart) => - assert(e1.stageId == e2.stageId) + assert(e1.stageId === e2.stageId) assertEquals(e1.taskInfo, e2.taskInfo) case (e1: SparkListenerTaskGettingResult, e2: SparkListenerTaskGettingResult) => assertEquals(e1.taskInfo, e2.taskInfo) case (e1: SparkListenerTaskEnd, e2: SparkListenerTaskEnd) => - assert(e1.stageId == e2.stageId) - assert(e1.taskType == e2.taskType) + assert(e1.stageId === e2.stageId) + assert(e1.taskType === e2.taskType) assertEquals(e1.reason, e2.reason) assertEquals(e1.taskInfo, e2.taskInfo) assertEquals(e1.taskMetrics, e2.taskMetrics) case (e1: SparkListenerJobStart, e2: SparkListenerJobStart) => - assert(e1.jobId == e2.jobId) - assert(e1.properties == e2.properties) - assertSeqEquals(e1.stageIds, e2.stageIds, (i1: Int, i2: Int) => assert(i1 == i2)) + assert(e1.jobId === e2.jobId) + assert(e1.properties === e2.properties) + assertSeqEquals(e1.stageIds, e2.stageIds, (i1: Int, i2: Int) => assert(i1 === i2)) case (e1: SparkListenerJobEnd, e2: SparkListenerJobEnd) => - assert(e1.jobId == e2.jobId) + assert(e1.jobId === e2.jobId) assertEquals(e1.jobResult, e2.jobResult) case (e1: SparkListenerEnvironmentUpdate, e2: SparkListenerEnvironmentUpdate) => assertEquals(e1.environmentDetails, e2.environmentDetails) case (e1: SparkListenerBlockManagerAdded, e2: SparkListenerBlockManagerAdded) => - assert(e1.maxMem == e2.maxMem) + assert(e1.maxMem === e2.maxMem) assertEquals(e1.blockManagerId, e2.blockManagerId) case (e1: SparkListenerBlockManagerRemoved, e2: SparkListenerBlockManagerRemoved) => assertEquals(e1.blockManagerId, e2.blockManagerId) case (e1: SparkListenerUnpersistRDD, e2: SparkListenerUnpersistRDD) => - assert(e1.rddId == e2.rddId) + assert(e1.rddId === e2.rddId) case (SparkListenerShutdown, SparkListenerShutdown) => case _ => fail("Events don't match in types!") } } private def assertEquals(info1: StageInfo, info2: StageInfo) { - assert(info1.stageId == info2.stageId) - assert(info1.name == info2.name) - assert(info1.numTasks == info2.numTasks) - assert(info1.submissionTime == info2.submissionTime) - assert(info1.completionTime == info2.completionTime) - assert(info1.emittedTaskSizeWarning == info2.emittedTaskSizeWarning) + assert(info1.stageId === info2.stageId) + assert(info1.name === info2.name) + assert(info1.numTasks === info2.numTasks) + assert(info1.submissionTime === info2.submissionTime) + assert(info1.completionTime === info2.completionTime) + assert(info1.emittedTaskSizeWarning === info2.emittedTaskSizeWarning) assertEquals(info1.rddInfo, info2.rddInfo) } private def assertEquals(info1: RDDInfo, info2: RDDInfo) { - assert(info1.id == info2.id) - assert(info1.name == info2.name) - assert(info1.numPartitions == info2.numPartitions) - assert(info1.numCachedPartitions == info2.numCachedPartitions) - assert(info1.memSize == info2.memSize) - assert(info1.diskSize == info2.diskSize) + assert(info1.id === info2.id) + assert(info1.name === info2.name) + assert(info1.numPartitions === info2.numPartitions) + assert(info1.numCachedPartitions === info2.numCachedPartitions) + assert(info1.memSize === info2.memSize) + assert(info1.diskSize === info2.diskSize) assertEquals(info1.storageLevel, info2.storageLevel) } private def assertEquals(level1: StorageLevel, level2: StorageLevel) { - assert(level1.useDisk == level2.useDisk) - assert(level1.useMemory == level2.useMemory) - assert(level1.deserialized == level2.deserialized) - assert(level1.replication == level2.replication) + assert(level1.useDisk === level2.useDisk) + assert(level1.useMemory === level2.useMemory) + assert(level1.deserialized === level2.deserialized) + assert(level1.replication === level2.replication) } private def assertEquals(info1: TaskInfo, info2: TaskInfo) { - assert(info1.taskId == info2.taskId) - assert(info1.index == info2.index) - assert(info1.launchTime == info2.launchTime) - assert(info1.executorId == info2.executorId) - assert(info1.host == info2.host) - assert(info1.taskLocality == info2.taskLocality) - assert(info1.gettingResultTime == info2.gettingResultTime) - assert(info1.finishTime == info2.finishTime) - assert(info1.failed == info2.failed) - assert(info1.serializedSize == info2.serializedSize) + assert(info1.taskId === info2.taskId) + assert(info1.index === info2.index) + assert(info1.launchTime === info2.launchTime) + assert(info1.executorId === info2.executorId) + assert(info1.host === info2.host) + assert(info1.taskLocality === info2.taskLocality) + assert(info1.gettingResultTime === info2.gettingResultTime) + assert(info1.finishTime === info2.finishTime) + assert(info1.failed === info2.failed) + assert(info1.serializedSize === info2.serializedSize) } private def assertEquals(metrics1: TaskMetrics, metrics2: TaskMetrics) { - assert(metrics1.hostname == metrics2.hostname) - assert(metrics1.executorDeserializeTime == metrics2.executorDeserializeTime) - assert(metrics1.resultSize == metrics2.resultSize) - assert(metrics1.jvmGCTime == metrics2.jvmGCTime) - assert(metrics1.resultSerializationTime == metrics2.resultSerializationTime) - assert(metrics1.memoryBytesSpilled == metrics2.memoryBytesSpilled) - assert(metrics1.diskBytesSpilled == metrics2.diskBytesSpilled) + assert(metrics1.hostname === metrics2.hostname) + assert(metrics1.executorDeserializeTime === metrics2.executorDeserializeTime) + assert(metrics1.resultSize === metrics2.resultSize) + assert(metrics1.jvmGCTime === metrics2.jvmGCTime) + assert(metrics1.resultSerializationTime === metrics2.resultSerializationTime) + assert(metrics1.memoryBytesSpilled === metrics2.memoryBytesSpilled) + assert(metrics1.diskBytesSpilled === metrics2.diskBytesSpilled) assertOptionEquals( metrics1.shuffleReadMetrics, metrics2.shuffleReadMetrics, assertShuffleReadEquals) assertOptionEquals( @@ -272,31 +271,31 @@ class JsonProtocolSuite extends FunSuite { } private def assertEquals(metrics1: ShuffleReadMetrics, metrics2: ShuffleReadMetrics) { - assert(metrics1.shuffleFinishTime == metrics2.shuffleFinishTime) - assert(metrics1.totalBlocksFetched == metrics2.totalBlocksFetched) - assert(metrics1.remoteBlocksFetched == metrics2.remoteBlocksFetched) - assert(metrics1.localBlocksFetched == metrics2.localBlocksFetched) - assert(metrics1.fetchWaitTime == metrics2.fetchWaitTime) - assert(metrics1.remoteBytesRead == metrics2.remoteBytesRead) + assert(metrics1.shuffleFinishTime === metrics2.shuffleFinishTime) + assert(metrics1.totalBlocksFetched === metrics2.totalBlocksFetched) + assert(metrics1.remoteBlocksFetched === metrics2.remoteBlocksFetched) + assert(metrics1.localBlocksFetched === metrics2.localBlocksFetched) + assert(metrics1.fetchWaitTime === metrics2.fetchWaitTime) + assert(metrics1.remoteBytesRead === metrics2.remoteBytesRead) } private def assertEquals(metrics1: ShuffleWriteMetrics, metrics2: ShuffleWriteMetrics) { - assert(metrics1.shuffleBytesWritten == metrics2.shuffleBytesWritten) - assert(metrics1.shuffleWriteTime == metrics2.shuffleWriteTime) + assert(metrics1.shuffleBytesWritten === metrics2.shuffleBytesWritten) + assert(metrics1.shuffleWriteTime === metrics2.shuffleWriteTime) } private def assertEquals(bm1: BlockManagerId, bm2: BlockManagerId) { - assert(bm1.executorId == bm2.executorId) - assert(bm1.host == bm2.host) - assert(bm1.port == bm2.port) - assert(bm1.nettyPort == bm2.nettyPort) + assert(bm1.executorId === bm2.executorId) + assert(bm1.host === bm2.host) + assert(bm1.port === bm2.port) + assert(bm1.nettyPort === bm2.nettyPort) } private def assertEquals(result1: JobResult, result2: JobResult) { (result1, result2) match { case (JobSucceeded, JobSucceeded) => case (r1: JobFailed, r2: JobFailed) => - assert(r1.failedStageId == r2.failedStageId) + assert(r1.failedStageId === r2.failedStageId) assertEquals(r1.exception, r2.exception) case _ => fail("Job results don't match in types!") } @@ -307,13 +306,13 @@ class JsonProtocolSuite extends FunSuite { case (Success, Success) => case (Resubmitted, Resubmitted) => case (r1: FetchFailed, r2: FetchFailed) => - assert(r1.shuffleId == r2.shuffleId) - assert(r1.mapId == r2.mapId) - assert(r1.reduceId == r2.reduceId) + assert(r1.shuffleId === r2.shuffleId) + assert(r1.mapId === r2.mapId) + assert(r1.reduceId === r2.reduceId) assertEquals(r1.bmAddress, r2.bmAddress) case (r1: ExceptionFailure, r2: ExceptionFailure) => - assert(r1.className == r2.className) - assert(r1.description == r2.description) + assert(r1.className === r2.className) + assert(r1.description === r2.description) assertSeqEquals(r1.stackTrace, r2.stackTrace, assertStackTraceElementEquals) assertOptionEquals(r1.metrics, r2.metrics, assertTaskMetricsEquals) case (TaskResultLost, TaskResultLost) => @@ -329,13 +328,13 @@ class JsonProtocolSuite extends FunSuite { details2: Map[String, Seq[(String, String)]]) { details1.zip(details2).foreach { case ((key1, values1: Seq[(String, String)]), (key2, values2: Seq[(String, String)])) => - assert(key1 == key2) - values1.zip(values2).foreach { case (v1, v2) => assert(v1 == v2) } + assert(key1 === key2) + values1.zip(values2).foreach { case (v1, v2) => assert(v1 === v2) } } } private def assertEquals(exception1: Exception, exception2: Exception) { - assert(exception1.getMessage == exception2.getMessage) + assert(exception1.getMessage === exception2.getMessage) assertSeqEquals( exception1.getStackTrace, exception2.getStackTrace, @@ -344,11 +343,11 @@ class JsonProtocolSuite extends FunSuite { private def assertJsonStringEquals(json1: String, json2: String) { val formatJsonString = (json: String) => json.replaceAll("[\\s|]", "") - formatJsonString(json1) == formatJsonString(json2) + formatJsonString(json1) === formatJsonString(json2) } private def assertSeqEquals[T](seq1: Seq[T], seq2: Seq[T], assertEquals: (T, T) => Unit) { - assert(seq1.length == seq2.length) + assert(seq1.length === seq2.length) seq1.zip(seq2).foreach { case (t1, t2) => assertEquals(t1, t2) } @@ -389,11 +388,11 @@ class JsonProtocolSuite extends FunSuite { } private def assertBlockEquals(b1: (BlockId, BlockStatus), b2: (BlockId, BlockStatus)) { - assert(b1 == b2) + assert(b1 === b2) } private def assertStackTraceElementEquals(ste1: StackTraceElement, ste2: StackTraceElement) { - assert(ste1 == ste2) + assert(ste1 === ste2) } From 78236334e4ca7518b6d7d9b38464dbbda854a777 Mon Sep 17 00:00:00 2001 From: Daniel Darabos Date: Wed, 2 Apr 2014 12:27:37 -0700 Subject: [PATCH 15/78] Do not re-use objects in the EdgePartition/EdgeTriplet iterators. This avoids a silent data corruption issue (https://spark-project.atlassian.net/browse/SPARK-1188) and has no performance impact by my measurements. It also simplifies the code. As far as I can tell the object re-use was nothing but premature optimization. I did actual benchmarks for all the included changes, and there is no performance difference. I am not sure where to put the benchmarks. Does Spark not have a benchmark suite? This is an example benchmark I did: test("benchmark") { val builder = new EdgePartitionBuilder[Int] for (i <- (1 to 10000000)) { builder.add(i.toLong, i.toLong, i) } val p = builder.toEdgePartition p.map(_.attr + 1).iterator.toList } It ran for 10 seconds both before and after this change. Author: Daniel Darabos Closes #276 from darabos/spark-1188 and squashes the following commits: 574302b [Daniel Darabos] Restore "manual" copying in EdgePartition.map(Iterator). Add comment to discourage novices like myself from trying to simplify the code. 4117a64 [Daniel Darabos] Revert EdgePartitionSuite. 4955697 [Daniel Darabos] Create a copy of the Edge objects in EdgeRDD.compute(). This avoids exposing the object re-use, while still enables the more efficient behavior for internal code. 4ec77f8 [Daniel Darabos] Add comments about object re-use to the affected functions. 2da5e87 [Daniel Darabos] Restore object re-use in EdgePartition. 0182f2b [Daniel Darabos] Do not re-use objects in the EdgePartition/EdgeTriplet iterators. This avoids a silent data corruption issue (SPARK-1188) and has no performance impact in my measurements. It also simplifies the code. c55f52f [Daniel Darabos] Tests that reproduce the problems from SPARK-1188. --- .../org/apache/spark/graphx/EdgeRDD.scala | 3 +- .../spark/graphx/impl/EdgePartition.scala | 15 +++++-- .../graphx/impl/EdgeTripletIterator.scala | 7 +-- .../impl/EdgeTripletIteratorSuite.scala | 43 +++++++++++++++++++ 4 files changed, 58 insertions(+), 10 deletions(-) create mode 100644 graphx/src/test/scala/org/apache/spark/graphx/impl/EdgeTripletIteratorSuite.scala diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala index f2296a865e1b3..6d04bf790e3a5 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala @@ -45,7 +45,8 @@ class EdgeRDD[@specialized ED: ClassTag]( partitionsRDD.partitioner.orElse(Some(Partitioner.defaultPartitioner(partitionsRDD))) override def compute(part: Partition, context: TaskContext): Iterator[Edge[ED]] = { - firstParent[(PartitionID, EdgePartition[ED])].iterator(part, context).next._2.iterator + val p = firstParent[(PartitionID, EdgePartition[ED])].iterator(part, context) + p.next._2.iterator.map(_.copy()) } override def collect(): Array[Edge[ED]] = this.map(_.copy()).collect() diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala index 57fa5eefd5e09..2e05f5d4e4969 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala @@ -56,6 +56,9 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) * Construct a new edge partition by applying the function f to all * edges in this partition. * + * Be careful not to keep references to the objects passed to `f`. + * To improve GC performance the same object is re-used for each call. + * * @param f a function from an edge to a new attribute * @tparam ED2 the type of the new attribute * @return a new edge partition with the result of the function `f` @@ -84,12 +87,12 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) * order of the edges returned by `EdgePartition.iterator` and * should return attributes equal to the number of edges. * - * @param f a function from an edge to a new attribute + * @param iter an iterator for the new attribute values * @tparam ED2 the type of the new attribute - * @return a new edge partition with the result of the function `f` - * applied to each edge + * @return a new edge partition with the attribute values replaced */ def map[ED2: ClassTag](iter: Iterator[ED2]): EdgePartition[ED2] = { + // Faster than iter.toArray, because the expected size is known. val newData = new Array[ED2](data.size) var i = 0 while (iter.hasNext) { @@ -188,6 +191,9 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) /** * Get an iterator over the edges in this partition. * + * Be careful not to keep references to the objects from this iterator. + * To improve GC performance the same object is re-used in `next()`. + * * @return an iterator over edges in the partition */ def iterator = new Iterator[Edge[ED]] { @@ -216,6 +222,9 @@ class EdgePartition[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) /** * Get an iterator over the cluster of edges in this partition with source vertex id `srcId`. The * cluster must start at position `index`. + * + * Be careful not to keep references to the objects from this iterator. To improve GC performance + * the same object is re-used in `next()`. */ private def clusterIterator(srcId: VertexId, index: Int) = new Iterator[Edge[ED]] { private[this] val edge = new Edge[ED] diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala index 886c250d7cffd..220a89d73d711 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeTripletIterator.scala @@ -37,20 +37,15 @@ class EdgeTripletIterator[VD: ClassTag, ED: ClassTag]( // Current position in the array. private var pos = 0 - // A triplet object that this iterator.next() call returns. We reuse this object to avoid - // allocating too many temporary Java objects. - private val triplet = new EdgeTriplet[VD, ED] - private val vmap = new PrimitiveKeyOpenHashMap[VertexId, VD](vidToIndex, vertexArray) override def hasNext: Boolean = pos < edgePartition.size override def next() = { + val triplet = new EdgeTriplet[VD, ED] triplet.srcId = edgePartition.srcIds(pos) - // assert(vmap.containsKey(e.src.id)) triplet.srcAttr = vmap(triplet.srcId) triplet.dstId = edgePartition.dstIds(pos) - // assert(vmap.containsKey(e.dst.id)) triplet.dstAttr = vmap(triplet.dstId) triplet.attr = edgePartition.data(pos) pos += 1 diff --git a/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgeTripletIteratorSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgeTripletIteratorSuite.scala new file mode 100644 index 0000000000000..9cbb2d2acdc2d --- /dev/null +++ b/graphx/src/test/scala/org/apache/spark/graphx/impl/EdgeTripletIteratorSuite.scala @@ -0,0 +1,43 @@ +/* + * 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.graphx.impl + +import scala.reflect.ClassTag +import scala.util.Random + +import org.scalatest.FunSuite + +import org.apache.spark.graphx._ + +class EdgeTripletIteratorSuite extends FunSuite { + test("iterator.toList") { + val builder = new EdgePartitionBuilder[Int] + builder.add(1, 2, 0) + builder.add(1, 3, 0) + builder.add(1, 4, 0) + val vidmap = new VertexIdToIndexMap + vidmap.add(1) + vidmap.add(2) + vidmap.add(3) + vidmap.add(4) + val vs = Array.fill(vidmap.capacity)(0) + val iter = new EdgeTripletIterator[Int, Int](vidmap, vs, builder.toEdgePartition) + val result = iter.toList.map(et => (et.srcId, et.dstId)) + assert(result === Seq((1, 2), (1, 3), (1, 4))) + } +} From 1faa57971192226837bea32eb29eae5bfb425a7e Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Wed, 2 Apr 2014 12:47:22 -0700 Subject: [PATCH 16/78] [SPARK-1371][WIP] Compression support for Spark SQL in-memory columnar storage JIRA issue: [SPARK-1373](https://issues.apache.org/jira/browse/SPARK-1373) (Although tagged as WIP, this PR is structurally complete. The only things left unimplemented are 3 more compression algorithms: `BooleanBitSet`, `IntDelta` and `LongDelta`, which are trivial to add later in this or another separate PR.) This PR contains compression support for Spark SQL in-memory columnar storage. Main interfaces include: * `CompressionScheme` Each `CompressionScheme` represents a concrete compression algorithm, which basically consists of an `Encoder` for compression and a `Decoder` for decompression. Algorithms implemented include: * `RunLengthEncoding` * `DictionaryEncoding` Algorithms to be implemented include: * `BooleanBitSet` * `IntDelta` * `LongDelta` * `CompressibleColumnBuilder` A stackable `ColumnBuilder` trait used to build byte buffers for compressible columns. A best `CompressionScheme` that exhibits lowest compression ratio is chosen for each column according to statistical information gathered while elements are appended into the `ColumnBuilder`. However, if no `CompressionScheme` can achieve a compression ratio better than 80%, no compression will be done for this column to save CPU time. Memory layout of the final byte buffer is showed below: ``` .--------------------------- Column type ID (4 bytes) | .----------------------- Null count N (4 bytes) | | .------------------- Null positions (4 x N bytes, empty if null count is zero) | | | .------------- Compression scheme ID (4 bytes) | | | | .--------- Compressed non-null elements V V V V V +---+---+-----+---+---------+ | | | ... | | ... ... | +---+---+-----+---+---------+ \-----------/ \-----------/ header body ``` * `CompressibleColumnAccessor` A stackable `ColumnAccessor` trait used to iterate (possibly) compressed data column. * `ColumnStats` Used to collect statistical information while loading data into in-memory columnar table. Optimizations like partition pruning rely on this information. Strictly speaking, `ColumnStats` related code is not part of the compression support. It's contained in this PR to ensure and validate the row-based API design (which is used to avoid boxing/unboxing cost whenever possible). A major refactoring change since PR #205 is: * Refactored all getter/setter methods for primitive types in various places into `ColumnType` classes to remove duplicated code. Author: Cheng Lian Closes #285 from liancheng/memColumnarCompression and squashes the following commits: ed71bbd [Cheng Lian] Addressed all PR comments by @marmbrus d3a4fa9 [Cheng Lian] Removed Ordering[T] in ColumnStats for better performance 5034453 [Cheng Lian] Bug fix, more tests, and more refactoring c298b76 [Cheng Lian] Test suites refactored 2780d6a [Cheng Lian] [WIP] in-memory columnar compression support 211331c [Cheng Lian] WIP: in-memory columnar compression support 85cc59b [Cheng Lian] Refactored ColumnAccessors & ColumnBuilders to remove duplicate code --- .../spark/sql/columnar/ColumnAccessor.scala | 103 ++--- .../spark/sql/columnar/ColumnBuilder.scala | 125 +++--- .../spark/sql/columnar/ColumnStats.scala | 360 ++++++++++++++++++ .../spark/sql/columnar/ColumnType.scala | 87 ++++- ....scala => InMemoryColumnarTableScan.scala} | 7 +- .../sql/columnar/NullableColumnAccessor.scala | 2 +- .../sql/columnar/NullableColumnBuilder.scala | 29 +- .../CompressibleColumnAccessor.scala | 36 ++ .../CompressibleColumnBuilder.scala | 95 +++++ .../compression/CompressionScheme.scala | 94 +++++ .../compression/compressionSchemes.scala | 288 ++++++++++++++ .../spark/sql/columnar/ColumnStatsSuite.scala | 61 +++ .../spark/sql/columnar/ColumnTypeSuite.scala | 216 +++++------ .../sql/columnar/ColumnarQuerySuite.scala | 4 +- .../spark/sql/columnar/ColumnarTestData.scala | 55 --- .../sql/columnar/ColumnarTestUtils.scala | 100 +++++ .../NullableColumnAccessorSuite.scala | 43 ++- .../columnar/NullableColumnBuilderSuite.scala | 61 +-- .../compression/DictionaryEncodingSuite.scala | 113 ++++++ .../compression/RunLengthEncodingSuite.scala | 130 +++++++ .../TestCompressibleColumnBuilder.scala | 43 +++ 21 files changed, 1644 insertions(+), 408 deletions(-) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala rename sql/core/src/main/scala/org/apache/spark/sql/columnar/{inMemoryColumnarOperators.scala => InMemoryColumnarTableScan.scala} (93%) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestData.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala index e0c98ecdf8f22..ffd4894b5213d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala @@ -21,7 +21,7 @@ import java.nio.{ByteOrder, ByteBuffer} import org.apache.spark.sql.catalyst.types.{BinaryType, NativeType, DataType} import org.apache.spark.sql.catalyst.expressions.MutableRow -import org.apache.spark.sql.execution.SparkSqlSerializer +import org.apache.spark.sql.columnar.compression.CompressibleColumnAccessor /** * An `Iterator` like trait used to extract values from columnar byte buffer. When a value is @@ -41,121 +41,66 @@ private[sql] trait ColumnAccessor { protected def underlyingBuffer: ByteBuffer } -private[sql] abstract class BasicColumnAccessor[T <: DataType, JvmType](buffer: ByteBuffer) +private[sql] abstract class BasicColumnAccessor[T <: DataType, JvmType]( + protected val buffer: ByteBuffer, + protected val columnType: ColumnType[T, JvmType]) extends ColumnAccessor { protected def initialize() {} - def columnType: ColumnType[T, JvmType] - def hasNext = buffer.hasRemaining def extractTo(row: MutableRow, ordinal: Int) { - doExtractTo(row, ordinal) + columnType.setField(row, ordinal, extractSingle(buffer)) } - protected def doExtractTo(row: MutableRow, ordinal: Int) + def extractSingle(buffer: ByteBuffer): JvmType = columnType.extract(buffer) protected def underlyingBuffer = buffer } private[sql] abstract class NativeColumnAccessor[T <: NativeType]( - buffer: ByteBuffer, - val columnType: NativeColumnType[T]) - extends BasicColumnAccessor[T, T#JvmType](buffer) + override protected val buffer: ByteBuffer, + override protected val columnType: NativeColumnType[T]) + extends BasicColumnAccessor(buffer, columnType) with NullableColumnAccessor + with CompressibleColumnAccessor[T] private[sql] class BooleanColumnAccessor(buffer: ByteBuffer) - extends NativeColumnAccessor(buffer, BOOLEAN) { - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - row.setBoolean(ordinal, columnType.extract(buffer)) - } -} + extends NativeColumnAccessor(buffer, BOOLEAN) private[sql] class IntColumnAccessor(buffer: ByteBuffer) - extends NativeColumnAccessor(buffer, INT) { - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - row.setInt(ordinal, columnType.extract(buffer)) - } -} + extends NativeColumnAccessor(buffer, INT) private[sql] class ShortColumnAccessor(buffer: ByteBuffer) - extends NativeColumnAccessor(buffer, SHORT) { - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - row.setShort(ordinal, columnType.extract(buffer)) - } -} + extends NativeColumnAccessor(buffer, SHORT) private[sql] class LongColumnAccessor(buffer: ByteBuffer) - extends NativeColumnAccessor(buffer, LONG) { - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - row.setLong(ordinal, columnType.extract(buffer)) - } -} + extends NativeColumnAccessor(buffer, LONG) private[sql] class ByteColumnAccessor(buffer: ByteBuffer) - extends NativeColumnAccessor(buffer, BYTE) { - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - row.setByte(ordinal, columnType.extract(buffer)) - } -} + extends NativeColumnAccessor(buffer, BYTE) private[sql] class DoubleColumnAccessor(buffer: ByteBuffer) - extends NativeColumnAccessor(buffer, DOUBLE) { - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - row.setDouble(ordinal, columnType.extract(buffer)) - } -} + extends NativeColumnAccessor(buffer, DOUBLE) private[sql] class FloatColumnAccessor(buffer: ByteBuffer) - extends NativeColumnAccessor(buffer, FLOAT) { - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - row.setFloat(ordinal, columnType.extract(buffer)) - } -} + extends NativeColumnAccessor(buffer, FLOAT) private[sql] class StringColumnAccessor(buffer: ByteBuffer) - extends NativeColumnAccessor(buffer, STRING) { - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - row.setString(ordinal, columnType.extract(buffer)) - } -} + extends NativeColumnAccessor(buffer, STRING) private[sql] class BinaryColumnAccessor(buffer: ByteBuffer) - extends BasicColumnAccessor[BinaryType.type, Array[Byte]](buffer) - with NullableColumnAccessor { - - def columnType = BINARY - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - row(ordinal) = columnType.extract(buffer) - } -} + extends BasicColumnAccessor[BinaryType.type, Array[Byte]](buffer, BINARY) + with NullableColumnAccessor private[sql] class GenericColumnAccessor(buffer: ByteBuffer) - extends BasicColumnAccessor[DataType, Array[Byte]](buffer) - with NullableColumnAccessor { - - def columnType = GENERIC - - override protected def doExtractTo(row: MutableRow, ordinal: Int) { - val serialized = columnType.extract(buffer) - row(ordinal) = SparkSqlSerializer.deserialize[Any](serialized) - } -} + extends BasicColumnAccessor[DataType, Array[Byte]](buffer, GENERIC) + with NullableColumnAccessor private[sql] object ColumnAccessor { - def apply(b: ByteBuffer): ColumnAccessor = { - // The first 4 bytes in the buffer indicates the column type. - val buffer = b.duplicate().order(ByteOrder.nativeOrder()) + def apply(buffer: ByteBuffer): ColumnAccessor = { + // The first 4 bytes in the buffer indicate the column type. val columnTypeId = buffer.getInt() columnTypeId match { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala index 3e622adfd3d6a..048ee66bff44b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala @@ -22,7 +22,7 @@ import java.nio.{ByteBuffer, ByteOrder} import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.types._ import org.apache.spark.sql.columnar.ColumnBuilder._ -import org.apache.spark.sql.execution.SparkSqlSerializer +import org.apache.spark.sql.columnar.compression.{AllCompressionSchemes, CompressibleColumnBuilder} private[sql] trait ColumnBuilder { /** @@ -30,37 +30,44 @@ private[sql] trait ColumnBuilder { */ def initialize(initialSize: Int, columnName: String = "") + /** + * Appends `row(ordinal)` to the column builder. + */ def appendFrom(row: Row, ordinal: Int) + /** + * Column statistics information + */ + def columnStats: ColumnStats[_, _] + + /** + * Returns the final columnar byte buffer. + */ def build(): ByteBuffer } -private[sql] abstract class BasicColumnBuilder[T <: DataType, JvmType] extends ColumnBuilder { +private[sql] class BasicColumnBuilder[T <: DataType, JvmType]( + val columnStats: ColumnStats[T, JvmType], + val columnType: ColumnType[T, JvmType]) + extends ColumnBuilder { - private var columnName: String = _ - protected var buffer: ByteBuffer = _ + protected var columnName: String = _ - def columnType: ColumnType[T, JvmType] + protected var buffer: ByteBuffer = _ override def initialize(initialSize: Int, columnName: String = "") = { val size = if (initialSize == 0) DEFAULT_INITIAL_BUFFER_SIZE else initialSize this.columnName = columnName - buffer = ByteBuffer.allocate(4 + 4 + size * columnType.defaultSize) + + // Reserves 4 bytes for column type ID + buffer = ByteBuffer.allocate(4 + size * columnType.defaultSize) buffer.order(ByteOrder.nativeOrder()).putInt(columnType.typeId) } - // Have to give a concrete implementation to make mixin possible override def appendFrom(row: Row, ordinal: Int) { - doAppendFrom(row, ordinal) - } - - // Concrete `ColumnBuilder`s can override this method to append values - protected def doAppendFrom(row: Row, ordinal: Int) - - // Helper method to append primitive values (to avoid boxing cost) - protected def appendValue(v: JvmType) { - buffer = ensureFreeSpace(buffer, columnType.actualSize(v)) - columnType.append(v, buffer) + val field = columnType.getField(row, ordinal) + buffer = ensureFreeSpace(buffer, columnType.actualSize(field)) + columnType.append(field, buffer) } override def build() = { @@ -69,83 +76,39 @@ private[sql] abstract class BasicColumnBuilder[T <: DataType, JvmType] extends C } } -private[sql] abstract class NativeColumnBuilder[T <: NativeType]( - val columnType: NativeColumnType[T]) - extends BasicColumnBuilder[T, T#JvmType] +private[sql] abstract class ComplexColumnBuilder[T <: DataType, JvmType]( + columnType: ColumnType[T, JvmType]) + extends BasicColumnBuilder[T, JvmType](new NoopColumnStats[T, JvmType], columnType) with NullableColumnBuilder -private[sql] class BooleanColumnBuilder extends NativeColumnBuilder(BOOLEAN) { - override def doAppendFrom(row: Row, ordinal: Int) { - appendValue(row.getBoolean(ordinal)) - } -} - -private[sql] class IntColumnBuilder extends NativeColumnBuilder(INT) { - override def doAppendFrom(row: Row, ordinal: Int) { - appendValue(row.getInt(ordinal)) - } -} +private[sql] abstract class NativeColumnBuilder[T <: NativeType]( + override val columnStats: NativeColumnStats[T], + override val columnType: NativeColumnType[T]) + extends BasicColumnBuilder[T, T#JvmType](columnStats, columnType) + with NullableColumnBuilder + with AllCompressionSchemes + with CompressibleColumnBuilder[T] -private[sql] class ShortColumnBuilder extends NativeColumnBuilder(SHORT) { - override def doAppendFrom(row: Row, ordinal: Int) { - appendValue(row.getShort(ordinal)) - } -} +private[sql] class BooleanColumnBuilder extends NativeColumnBuilder(new BooleanColumnStats, BOOLEAN) -private[sql] class LongColumnBuilder extends NativeColumnBuilder(LONG) { - override def doAppendFrom(row: Row, ordinal: Int) { - appendValue(row.getLong(ordinal)) - } -} +private[sql] class IntColumnBuilder extends NativeColumnBuilder(new IntColumnStats, INT) -private[sql] class ByteColumnBuilder extends NativeColumnBuilder(BYTE) { - override def doAppendFrom(row: Row, ordinal: Int) { - appendValue(row.getByte(ordinal)) - } -} +private[sql] class ShortColumnBuilder extends NativeColumnBuilder(new ShortColumnStats, SHORT) -private[sql] class DoubleColumnBuilder extends NativeColumnBuilder(DOUBLE) { - override def doAppendFrom(row: Row, ordinal: Int) { - appendValue(row.getDouble(ordinal)) - } -} +private[sql] class LongColumnBuilder extends NativeColumnBuilder(new LongColumnStats, LONG) -private[sql] class FloatColumnBuilder extends NativeColumnBuilder(FLOAT) { - override def doAppendFrom(row: Row, ordinal: Int) { - appendValue(row.getFloat(ordinal)) - } -} +private[sql] class ByteColumnBuilder extends NativeColumnBuilder(new ByteColumnStats, BYTE) -private[sql] class StringColumnBuilder extends NativeColumnBuilder(STRING) { - override def doAppendFrom(row: Row, ordinal: Int) { - appendValue(row.getString(ordinal)) - } -} +private[sql] class DoubleColumnBuilder extends NativeColumnBuilder(new DoubleColumnStats, DOUBLE) -private[sql] class BinaryColumnBuilder - extends BasicColumnBuilder[BinaryType.type, Array[Byte]] - with NullableColumnBuilder { +private[sql] class FloatColumnBuilder extends NativeColumnBuilder(new FloatColumnStats, FLOAT) - def columnType = BINARY +private[sql] class StringColumnBuilder extends NativeColumnBuilder(new StringColumnStats, STRING) - override def doAppendFrom(row: Row, ordinal: Int) { - appendValue(row(ordinal).asInstanceOf[Array[Byte]]) - } -} +private[sql] class BinaryColumnBuilder extends ComplexColumnBuilder(BINARY) // TODO (lian) Add support for array, struct and map -private[sql] class GenericColumnBuilder - extends BasicColumnBuilder[DataType, Array[Byte]] - with NullableColumnBuilder { - - def columnType = GENERIC - - override def doAppendFrom(row: Row, ordinal: Int) { - val serialized = SparkSqlSerializer.serialize(row(ordinal)) - buffer = ColumnBuilder.ensureFreeSpace(buffer, columnType.actualSize(serialized)) - columnType.append(serialized, buffer) - } -} +private[sql] class GenericColumnBuilder extends ComplexColumnBuilder(GENERIC) private[sql] object ColumnBuilder { val DEFAULT_INITIAL_BUFFER_SIZE = 10 * 1024 * 104 diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala new file mode 100644 index 0000000000000..30c6bdc7912fc --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala @@ -0,0 +1,360 @@ +/* + * 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.columnar + +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.types._ + +private[sql] sealed abstract class ColumnStats[T <: DataType, JvmType] extends Serializable { + /** + * Closed lower bound of this column. + */ + def lowerBound: JvmType + + /** + * Closed upper bound of this column. + */ + def upperBound: JvmType + + /** + * Gathers statistics information from `row(ordinal)`. + */ + def gatherStats(row: Row, ordinal: Int) + + /** + * Returns `true` if `lower <= row(ordinal) <= upper`. + */ + def contains(row: Row, ordinal: Int): Boolean + + /** + * Returns `true` if `row(ordinal) < upper` holds. + */ + def isAbove(row: Row, ordinal: Int): Boolean + + /** + * Returns `true` if `lower < row(ordinal)` holds. + */ + def isBelow(row: Row, ordinal: Int): Boolean + + /** + * Returns `true` if `row(ordinal) <= upper` holds. + */ + def isAtOrAbove(row: Row, ordinal: Int): Boolean + + /** + * Returns `true` if `lower <= row(ordinal)` holds. + */ + def isAtOrBelow(row: Row, ordinal: Int): Boolean +} + +private[sql] sealed abstract class NativeColumnStats[T <: NativeType] + extends ColumnStats[T, T#JvmType] { + + type JvmType = T#JvmType + + protected var (_lower, _upper) = initialBounds + + def initialBounds: (JvmType, JvmType) + + protected def columnType: NativeColumnType[T] + + override def lowerBound: T#JvmType = _lower + + override def upperBound: T#JvmType = _upper + + override def isAtOrAbove(row: Row, ordinal: Int) = { + contains(row, ordinal) || isAbove(row, ordinal) + } + + override def isAtOrBelow(row: Row, ordinal: Int) = { + contains(row, ordinal) || isBelow(row, ordinal) + } +} + +private[sql] class NoopColumnStats[T <: DataType, JvmType] extends ColumnStats[T, JvmType] { + override def isAtOrBelow(row: Row, ordinal: Int) = true + + override def isAtOrAbove(row: Row, ordinal: Int) = true + + override def isBelow(row: Row, ordinal: Int) = true + + override def isAbove(row: Row, ordinal: Int) = true + + override def contains(row: Row, ordinal: Int) = true + + override def gatherStats(row: Row, ordinal: Int) {} + + override def upperBound = null.asInstanceOf[JvmType] + + override def lowerBound = null.asInstanceOf[JvmType] +} + +private[sql] abstract class BasicColumnStats[T <: NativeType]( + protected val columnType: NativeColumnType[T]) + extends NativeColumnStats[T] + +private[sql] class BooleanColumnStats extends BasicColumnStats(BOOLEAN) { + override def initialBounds = (true, false) + + override def isBelow(row: Row, ordinal: Int) = { + lowerBound < columnType.getField(row, ordinal) + } + + override def isAbove(row: Row, ordinal: Int) = { + columnType.getField(row, ordinal) < upperBound + } + + override def contains(row: Row, ordinal: Int) = { + val field = columnType.getField(row, ordinal) + lowerBound <= field && field <= upperBound + } + + override def gatherStats(row: Row, ordinal: Int) { + val field = columnType.getField(row, ordinal) + if (field > upperBound) _upper = field + if (field < lowerBound) _lower = field + } +} + +private[sql] class ByteColumnStats extends BasicColumnStats(BYTE) { + override def initialBounds = (Byte.MaxValue, Byte.MinValue) + + override def isBelow(row: Row, ordinal: Int) = { + lowerBound < columnType.getField(row, ordinal) + } + + override def isAbove(row: Row, ordinal: Int) = { + columnType.getField(row, ordinal) < upperBound + } + + override def contains(row: Row, ordinal: Int) = { + val field = columnType.getField(row, ordinal) + lowerBound <= field && field <= upperBound + } + + override def gatherStats(row: Row, ordinal: Int) { + val field = columnType.getField(row, ordinal) + if (field > upperBound) _upper = field + if (field < lowerBound) _lower = field + } +} + +private[sql] class ShortColumnStats extends BasicColumnStats(SHORT) { + override def initialBounds = (Short.MaxValue, Short.MinValue) + + override def isBelow(row: Row, ordinal: Int) = { + lowerBound < columnType.getField(row, ordinal) + } + + override def isAbove(row: Row, ordinal: Int) = { + columnType.getField(row, ordinal) < upperBound + } + + override def contains(row: Row, ordinal: Int) = { + val field = columnType.getField(row, ordinal) + lowerBound <= field && field <= upperBound + } + + override def gatherStats(row: Row, ordinal: Int) { + val field = columnType.getField(row, ordinal) + if (field > upperBound) _upper = field + if (field < lowerBound) _lower = field + } +} + +private[sql] class LongColumnStats extends BasicColumnStats(LONG) { + override def initialBounds = (Long.MaxValue, Long.MinValue) + + override def isBelow(row: Row, ordinal: Int) = { + lowerBound < columnType.getField(row, ordinal) + } + + override def isAbove(row: Row, ordinal: Int) = { + columnType.getField(row, ordinal) < upperBound + } + + override def contains(row: Row, ordinal: Int) = { + val field = columnType.getField(row, ordinal) + lowerBound <= field && field <= upperBound + } + + override def gatherStats(row: Row, ordinal: Int) { + val field = columnType.getField(row, ordinal) + if (field > upperBound) _upper = field + if (field < lowerBound) _lower = field + } +} + +private[sql] class DoubleColumnStats extends BasicColumnStats(DOUBLE) { + override def initialBounds = (Double.MaxValue, Double.MinValue) + + override def isBelow(row: Row, ordinal: Int) = { + lowerBound < columnType.getField(row, ordinal) + } + + override def isAbove(row: Row, ordinal: Int) = { + columnType.getField(row, ordinal) < upperBound + } + + override def contains(row: Row, ordinal: Int) = { + val field = columnType.getField(row, ordinal) + lowerBound <= field && field <= upperBound + } + + override def gatherStats(row: Row, ordinal: Int) { + val field = columnType.getField(row, ordinal) + if (field > upperBound) _upper = field + if (field < lowerBound) _lower = field + } +} + +private[sql] class FloatColumnStats extends BasicColumnStats(FLOAT) { + override def initialBounds = (Float.MaxValue, Float.MinValue) + + override def isBelow(row: Row, ordinal: Int) = { + lowerBound < columnType.getField(row, ordinal) + } + + override def isAbove(row: Row, ordinal: Int) = { + columnType.getField(row, ordinal) < upperBound + } + + override def contains(row: Row, ordinal: Int) = { + val field = columnType.getField(row, ordinal) + lowerBound <= field && field <= upperBound + } + + override def gatherStats(row: Row, ordinal: Int) { + val field = columnType.getField(row, ordinal) + if (field > upperBound) _upper = field + if (field < lowerBound) _lower = field + } +} + +private[sql] object IntColumnStats { + val UNINITIALIZED = 0 + val INITIALIZED = 1 + val ASCENDING = 2 + val DESCENDING = 3 + val UNORDERED = 4 +} + +/** + * Statistical information for `Int` columns. More information is collected since `Int` is + * frequently used. Extra information include: + * + * - Ordering state (ascending/descending/unordered), may be used to decide whether binary search + * is applicable when searching elements. + * - Maximum delta between adjacent elements, may be used to guide the `IntDelta` compression + * scheme. + * + * (This two kinds of information are not used anywhere yet and might be removed later.) + */ +private[sql] class IntColumnStats extends BasicColumnStats(INT) { + import IntColumnStats._ + + private var orderedState = UNINITIALIZED + private var lastValue: Int = _ + private var _maxDelta: Int = _ + + def isAscending = orderedState != DESCENDING && orderedState != UNORDERED + def isDescending = orderedState != ASCENDING && orderedState != UNORDERED + def isOrdered = isAscending || isDescending + def maxDelta = _maxDelta + + override def initialBounds = (Int.MaxValue, Int.MinValue) + + override def isBelow(row: Row, ordinal: Int) = { + lowerBound < columnType.getField(row, ordinal) + } + + override def isAbove(row: Row, ordinal: Int) = { + columnType.getField(row, ordinal) < upperBound + } + + override def contains(row: Row, ordinal: Int) = { + val field = columnType.getField(row, ordinal) + lowerBound <= field && field <= upperBound + } + + override def gatherStats(row: Row, ordinal: Int) { + val field = columnType.getField(row, ordinal) + + if (field > upperBound) _upper = field + if (field < lowerBound) _lower = field + + orderedState = orderedState match { + case UNINITIALIZED => + lastValue = field + INITIALIZED + + case INITIALIZED => + // If all the integers in the column are the same, ordered state is set to Ascending. + // TODO (lian) Confirm whether this is the standard behaviour. + val nextState = if (field >= lastValue) ASCENDING else DESCENDING + _maxDelta = math.abs(field - lastValue) + lastValue = field + nextState + + case ASCENDING if field < lastValue => + UNORDERED + + case DESCENDING if field > lastValue => + UNORDERED + + case state @ (ASCENDING | DESCENDING) => + _maxDelta = _maxDelta.max(field - lastValue) + lastValue = field + state + + case _ => + orderedState + } + } +} + +private[sql] class StringColumnStats extends BasicColumnStats(STRING) { + override def initialBounds = (null, null) + + override def gatherStats(row: Row, ordinal: Int) { + val field = columnType.getField(row, ordinal) + if ((upperBound eq null) || field.compareTo(upperBound) > 0) _upper = field + if ((lowerBound eq null) || field.compareTo(lowerBound) < 0) _lower = field + } + + override def contains(row: Row, ordinal: Int) = { + !(upperBound eq null) && { + val field = columnType.getField(row, ordinal) + lowerBound.compareTo(field) <= 0 && field.compareTo(upperBound) <= 0 + } + } + + override def isAbove(row: Row, ordinal: Int) = { + !(upperBound eq null) && { + val field = columnType.getField(row, ordinal) + field.compareTo(upperBound) < 0 + } + } + + override def isBelow(row: Row, ordinal: Int) = { + !(lowerBound eq null) && { + val field = columnType.getField(row, ordinal) + lowerBound.compareTo(field) < 0 + } + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala index a452b86f0cda3..5be76890afe31 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala @@ -19,7 +19,12 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer +import scala.reflect.runtime.universe.TypeTag + +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.expressions.MutableRow import org.apache.spark.sql.catalyst.types._ +import org.apache.spark.sql.execution.SparkSqlSerializer /** * An abstract class that represents type of a column. Used to append/extract Java objects into/from @@ -50,10 +55,24 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType]( */ def actualSize(v: JvmType): Int = defaultSize + /** + * Returns `row(ordinal)`. Subclasses should override this method to avoid boxing/unboxing costs + * whenever possible. + */ + def getField(row: Row, ordinal: Int): JvmType + + /** + * Sets `row(ordinal)` to `field`. Subclasses should override this method to avoid boxing/unboxing + * costs whenever possible. + */ + def setField(row: MutableRow, ordinal: Int, value: JvmType) + /** * Creates a duplicated copy of the value. */ def clone(v: JvmType): JvmType = v + + override def toString = getClass.getSimpleName.stripSuffix("$") } private[sql] abstract class NativeColumnType[T <: NativeType]( @@ -65,7 +84,7 @@ private[sql] abstract class NativeColumnType[T <: NativeType]( /** * Scala TypeTag. Can be used to create primitive arrays and hash tables. */ - def scalaTag = dataType.tag + def scalaTag: TypeTag[dataType.JvmType] = dataType.tag } private[sql] object INT extends NativeColumnType(IntegerType, 0, 4) { @@ -76,6 +95,12 @@ private[sql] object INT extends NativeColumnType(IntegerType, 0, 4) { def extract(buffer: ByteBuffer) = { buffer.getInt() } + + override def setField(row: MutableRow, ordinal: Int, value: Int) { + row.setInt(ordinal, value) + } + + override def getField(row: Row, ordinal: Int) = row.getInt(ordinal) } private[sql] object LONG extends NativeColumnType(LongType, 1, 8) { @@ -86,6 +111,12 @@ private[sql] object LONG extends NativeColumnType(LongType, 1, 8) { override def extract(buffer: ByteBuffer) = { buffer.getLong() } + + override def setField(row: MutableRow, ordinal: Int, value: Long) { + row.setLong(ordinal, value) + } + + override def getField(row: Row, ordinal: Int) = row.getLong(ordinal) } private[sql] object FLOAT extends NativeColumnType(FloatType, 2, 4) { @@ -96,6 +127,12 @@ private[sql] object FLOAT extends NativeColumnType(FloatType, 2, 4) { override def extract(buffer: ByteBuffer) = { buffer.getFloat() } + + override def setField(row: MutableRow, ordinal: Int, value: Float) { + row.setFloat(ordinal, value) + } + + override def getField(row: Row, ordinal: Int) = row.getFloat(ordinal) } private[sql] object DOUBLE extends NativeColumnType(DoubleType, 3, 8) { @@ -106,6 +143,12 @@ private[sql] object DOUBLE extends NativeColumnType(DoubleType, 3, 8) { override def extract(buffer: ByteBuffer) = { buffer.getDouble() } + + override def setField(row: MutableRow, ordinal: Int, value: Double) { + row.setDouble(ordinal, value) + } + + override def getField(row: Row, ordinal: Int) = row.getDouble(ordinal) } private[sql] object BOOLEAN extends NativeColumnType(BooleanType, 4, 1) { @@ -116,6 +159,12 @@ private[sql] object BOOLEAN extends NativeColumnType(BooleanType, 4, 1) { override def extract(buffer: ByteBuffer) = { if (buffer.get() == 1) true else false } + + override def setField(row: MutableRow, ordinal: Int, value: Boolean) { + row.setBoolean(ordinal, value) + } + + override def getField(row: Row, ordinal: Int) = row.getBoolean(ordinal) } private[sql] object BYTE extends NativeColumnType(ByteType, 5, 1) { @@ -126,6 +175,12 @@ private[sql] object BYTE extends NativeColumnType(ByteType, 5, 1) { override def extract(buffer: ByteBuffer) = { buffer.get() } + + override def setField(row: MutableRow, ordinal: Int, value: Byte) { + row.setByte(ordinal, value) + } + + override def getField(row: Row, ordinal: Int) = row.getByte(ordinal) } private[sql] object SHORT extends NativeColumnType(ShortType, 6, 2) { @@ -136,6 +191,12 @@ private[sql] object SHORT extends NativeColumnType(ShortType, 6, 2) { override def extract(buffer: ByteBuffer) = { buffer.getShort() } + + override def setField(row: MutableRow, ordinal: Int, value: Short) { + row.setShort(ordinal, value) + } + + override def getField(row: Row, ordinal: Int) = row.getShort(ordinal) } private[sql] object STRING extends NativeColumnType(StringType, 7, 8) { @@ -152,6 +213,12 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) { buffer.get(stringBytes, 0, length) new String(stringBytes) } + + override def setField(row: MutableRow, ordinal: Int, value: String) { + row.setString(ordinal, value) + } + + override def getField(row: Row, ordinal: Int) = row.getString(ordinal) } private[sql] sealed abstract class ByteArrayColumnType[T <: DataType]( @@ -173,15 +240,27 @@ private[sql] sealed abstract class ByteArrayColumnType[T <: DataType]( } } -private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](8, 16) +private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](8, 16) { + override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]) { + row(ordinal) = value + } + + override def getField(row: Row, ordinal: Int) = row(ordinal).asInstanceOf[Array[Byte]] +} // Used to process generic objects (all types other than those listed above). Objects should be // serialized first before appending to the column `ByteBuffer`, and is also extracted as serialized // byte array. -private[sql] object GENERIC extends ByteArrayColumnType[DataType](9, 16) +private[sql] object GENERIC extends ByteArrayColumnType[DataType](9, 16) { + override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]) { + row(ordinal) = SparkSqlSerializer.deserialize[Any](value) + } + + override def getField(row: Row, ordinal: Int) = SparkSqlSerializer.serialize(row(ordinal)) +} private[sql] object ColumnType { - implicit def dataTypeToColumnType(dataType: DataType): ColumnType[_, _] = { + def apply(dataType: DataType): ColumnType[_, _] = { dataType match { case IntegerType => INT case LongType => LONG diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/inMemoryColumnarOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala similarity index 93% rename from sql/core/src/main/scala/org/apache/spark/sql/columnar/inMemoryColumnarOperators.scala rename to sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala index f853759e5a306..8a24733047423 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/inMemoryColumnarOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala @@ -21,9 +21,6 @@ import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, Attribute} import org.apache.spark.sql.execution.{SparkPlan, LeafNode} import org.apache.spark.sql.Row -/* Implicit conversions */ -import org.apache.spark.sql.columnar.ColumnType._ - private[sql] case class InMemoryColumnarTableScan(attributes: Seq[Attribute], child: SparkPlan) extends LeafNode { @@ -32,8 +29,8 @@ private[sql] case class InMemoryColumnarTableScan(attributes: Seq[Attribute], ch lazy val cachedColumnBuffers = { val output = child.output val cached = child.execute().mapPartitions { iterator => - val columnBuilders = output.map { a => - ColumnBuilder(a.dataType.typeId, 0, a.name) + val columnBuilders = output.map { attribute => + ColumnBuilder(ColumnType(attribute.dataType).typeId, 0, attribute.name) }.toArray var row: Row = null diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala index 2970c609b928d..7d49ab07f7a53 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala @@ -29,7 +29,7 @@ private[sql] trait NullableColumnAccessor extends ColumnAccessor { private var nextNullIndex: Int = _ private var pos: Int = 0 - abstract override def initialize() { + abstract override protected def initialize() { nullsBuffer = underlyingBuffer.duplicate().order(ByteOrder.nativeOrder()) nullCount = nullsBuffer.getInt() nextNullIndex = if (nullCount > 0) nullsBuffer.getInt() else -1 diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala index 048d1f05c7df2..2a3b6fc1e46d3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala @@ -22,10 +22,18 @@ import java.nio.{ByteBuffer, ByteOrder} import org.apache.spark.sql.Row /** - * Builds a nullable column. The byte buffer of a nullable column contains: - * - 4 bytes for the null count (number of nulls) - * - positions for each null, in ascending order - * - the non-null data (column data type, compression type, data...) + * A stackable trait used for building byte buffer for a column containing null values. Memory + * layout of the final byte buffer is: + * {{{ + * .----------------------- Column type ID (4 bytes) + * | .------------------- Null count N (4 bytes) + * | | .--------------- Null positions (4 x N bytes, empty if null count is zero) + * | | | .--------- Non-null elements + * V V V V + * +---+---+-----+---------+ + * | | | ... | ... ... | + * +---+---+-----+---------+ + * }}} */ private[sql] trait NullableColumnBuilder extends ColumnBuilder { private var nulls: ByteBuffer = _ @@ -59,19 +67,8 @@ private[sql] trait NullableColumnBuilder extends ColumnBuilder { nulls.limit(nullDataLen) nulls.rewind() - // Column type ID is moved to the front, follows the null count, then non-null data - // - // +---------+ - // | 4 bytes | Column type ID - // +---------+ - // | 4 bytes | Null count - // +---------+ - // | ... | Null positions (if null count is not zero) - // +---------+ - // | ... | Non-null part (without column type ID) - // +---------+ val buffer = ByteBuffer - .allocate(4 + nullDataLen + nonNulls.limit) + .allocate(4 + 4 + nullDataLen + nonNulls.remaining()) .order(ByteOrder.nativeOrder()) .putInt(typeId) .putInt(nullCount) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala new file mode 100644 index 0000000000000..878cb84de106f --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala @@ -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.sql.columnar.compression + +import java.nio.ByteBuffer + +import org.apache.spark.sql.catalyst.types.NativeType +import org.apache.spark.sql.columnar.{ColumnAccessor, NativeColumnAccessor} + +private[sql] trait CompressibleColumnAccessor[T <: NativeType] extends ColumnAccessor { + this: NativeColumnAccessor[T] => + + private var decoder: Decoder[T] = _ + + abstract override protected def initialize() = { + super.initialize() + decoder = CompressionScheme(underlyingBuffer.getInt()).decoder(buffer, columnType) + } + + abstract override def extractSingle(buffer: ByteBuffer): T#JvmType = decoder.next() +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala new file mode 100644 index 0000000000000..3ac4b358ddf83 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.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.sql.columnar.compression + +import java.nio.{ByteBuffer, ByteOrder} + +import org.apache.spark.sql.{Logging, Row} +import org.apache.spark.sql.catalyst.types.NativeType +import org.apache.spark.sql.columnar.{ColumnBuilder, NativeColumnBuilder} + +/** + * A stackable trait that builds optionally compressed byte buffer for a column. Memory layout of + * the final byte buffer is: + * {{{ + * .--------------------------- Column type ID (4 bytes) + * | .----------------------- Null count N (4 bytes) + * | | .------------------- Null positions (4 x N bytes, empty if null count is zero) + * | | | .------------- Compression scheme ID (4 bytes) + * | | | | .--------- Compressed non-null elements + * V V V V V + * +---+---+-----+---+---------+ + * | | | ... | | ... ... | + * +---+---+-----+---+---------+ + * \-----------/ \-----------/ + * header body + * }}} + */ +private[sql] trait CompressibleColumnBuilder[T <: NativeType] + extends ColumnBuilder with Logging { + + this: NativeColumnBuilder[T] with WithCompressionSchemes => + + import CompressionScheme._ + + val compressionEncoders = schemes.filter(_.supports(columnType)).map(_.encoder) + + protected def isWorthCompressing(encoder: Encoder) = { + encoder.compressionRatio < 0.8 + } + + private def gatherCompressibilityStats(row: Row, ordinal: Int) { + val field = columnType.getField(row, ordinal) + + var i = 0 + while (i < compressionEncoders.length) { + compressionEncoders(i).gatherCompressibilityStats(field, columnType) + i += 1 + } + } + + abstract override def appendFrom(row: Row, ordinal: Int) { + super.appendFrom(row, ordinal) + gatherCompressibilityStats(row, ordinal) + } + + abstract override def build() = { + val rawBuffer = super.build() + val encoder = { + val candidate = compressionEncoders.minBy(_.compressionRatio) + if (isWorthCompressing(candidate)) candidate else PassThrough.encoder + } + + val headerSize = columnHeaderSize(rawBuffer) + val compressedSize = if (encoder.compressedSize == 0) { + rawBuffer.limit - headerSize + } else { + encoder.compressedSize + } + + // Reserves 4 bytes for compression scheme ID + val compressedBuffer = ByteBuffer + .allocate(headerSize + 4 + compressedSize) + .order(ByteOrder.nativeOrder) + + copyColumnHeader(rawBuffer, compressedBuffer) + + logger.info(s"Compressor for [$columnName]: $encoder, ratio: ${encoder.compressionRatio}") + encoder.compress(rawBuffer, compressedBuffer, columnType) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala new file mode 100644 index 0000000000000..d3a4ac8df926b --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala @@ -0,0 +1,94 @@ +/* + * 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.columnar.compression + +import java.nio.ByteBuffer + +import org.apache.spark.sql.catalyst.types.NativeType +import org.apache.spark.sql.columnar.{ColumnType, NativeColumnType} + +private[sql] trait Encoder { + def gatherCompressibilityStats[T <: NativeType]( + value: T#JvmType, + columnType: ColumnType[T, T#JvmType]) {} + + def compressedSize: Int + + def uncompressedSize: Int + + def compressionRatio: Double = { + if (uncompressedSize > 0) compressedSize.toDouble / uncompressedSize else 1.0 + } + + def compress[T <: NativeType]( + from: ByteBuffer, + to: ByteBuffer, + columnType: ColumnType[T, T#JvmType]): ByteBuffer +} + +private[sql] trait Decoder[T <: NativeType] extends Iterator[T#JvmType] + +private[sql] trait CompressionScheme { + def typeId: Int + + def supports(columnType: ColumnType[_, _]): Boolean + + def encoder: Encoder + + def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]): Decoder[T] +} + +private[sql] trait WithCompressionSchemes { + def schemes: Seq[CompressionScheme] +} + +private[sql] trait AllCompressionSchemes extends WithCompressionSchemes { + override val schemes: Seq[CompressionScheme] = { + Seq(PassThrough, RunLengthEncoding, DictionaryEncoding) + } +} + +private[sql] object CompressionScheme { + def apply(typeId: Int): CompressionScheme = typeId match { + case PassThrough.typeId => PassThrough + case _ => throw new UnsupportedOperationException() + } + + def copyColumnHeader(from: ByteBuffer, to: ByteBuffer) { + // Writes column type ID + to.putInt(from.getInt()) + + // Writes null count + val nullCount = from.getInt() + to.putInt(nullCount) + + // Writes null positions + var i = 0 + while (i < nullCount) { + to.putInt(from.getInt()) + i += 1 + } + } + + def columnHeaderSize(columnBuffer: ByteBuffer): Int = { + val header = columnBuffer.duplicate() + val nullCount = header.getInt(4) + // Column type ID + null count + null positions + 4 + 4 + 4 * nullCount + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala new file mode 100644 index 0000000000000..dc2c153faf8ad --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala @@ -0,0 +1,288 @@ +/* + * 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.columnar.compression + +import java.nio.ByteBuffer + +import scala.collection.mutable +import scala.reflect.ClassTag +import scala.reflect.runtime.universe.runtimeMirror + +import org.apache.spark.sql.catalyst.expressions.GenericMutableRow +import org.apache.spark.sql.catalyst.types.NativeType +import org.apache.spark.sql.columnar._ + +private[sql] case object PassThrough extends CompressionScheme { + override val typeId = 0 + + override def supports(columnType: ColumnType[_, _]) = true + + override def encoder = new this.Encoder + + override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + new this.Decoder(buffer, columnType) + } + + class Encoder extends compression.Encoder { + override def uncompressedSize = 0 + + override def compressedSize = 0 + + override def compress[T <: NativeType]( + from: ByteBuffer, + to: ByteBuffer, + columnType: ColumnType[T, T#JvmType]) = { + + // Writes compression type ID and copies raw contents + to.putInt(PassThrough.typeId).put(from).rewind() + to + } + } + + class Decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + extends compression.Decoder[T] { + + override def next() = columnType.extract(buffer) + + override def hasNext = buffer.hasRemaining + } +} + +private[sql] case object RunLengthEncoding extends CompressionScheme { + override def typeId = 1 + + override def encoder = new this.Encoder + + override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + new this.Decoder(buffer, columnType) + } + + override def supports(columnType: ColumnType[_, _]) = columnType match { + case INT | LONG | SHORT | BYTE | STRING | BOOLEAN => true + case _ => false + } + + class Encoder extends compression.Encoder { + private var _uncompressedSize = 0 + private var _compressedSize = 0 + + // Using `MutableRow` to store the last value to avoid boxing/unboxing cost. + private val lastValue = new GenericMutableRow(1) + private var lastRun = 0 + + override def uncompressedSize = _uncompressedSize + + override def compressedSize = _compressedSize + + override def gatherCompressibilityStats[T <: NativeType]( + value: T#JvmType, + columnType: ColumnType[T, T#JvmType]) { + + val actualSize = columnType.actualSize(value) + _uncompressedSize += actualSize + + if (lastValue.isNullAt(0)) { + columnType.setField(lastValue, 0, value) + lastRun = 1 + _compressedSize += actualSize + 4 + } else { + if (columnType.getField(lastValue, 0) == value) { + lastRun += 1 + } else { + _compressedSize += actualSize + 4 + columnType.setField(lastValue, 0, value) + lastRun = 1 + } + } + } + + override def compress[T <: NativeType]( + from: ByteBuffer, + to: ByteBuffer, + columnType: ColumnType[T, T#JvmType]) = { + + to.putInt(RunLengthEncoding.typeId) + + if (from.hasRemaining) { + var currentValue = columnType.extract(from) + var currentRun = 1 + + while (from.hasRemaining) { + val value = columnType.extract(from) + + if (value == currentValue) { + currentRun += 1 + } else { + // Writes current run + columnType.append(currentValue, to) + to.putInt(currentRun) + + // Resets current run + currentValue = value + currentRun = 1 + } + } + + // Writes the last run + columnType.append(currentValue, to) + to.putInt(currentRun) + } + + to.rewind() + to + } + } + + class Decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + extends compression.Decoder[T] { + + private var run = 0 + private var valueCount = 0 + private var currentValue: T#JvmType = _ + + override def next() = { + if (valueCount == run) { + currentValue = columnType.extract(buffer) + run = buffer.getInt() + valueCount = 1 + } else { + valueCount += 1 + } + + currentValue + } + + override def hasNext = buffer.hasRemaining + } +} + +private[sql] case object DictionaryEncoding extends CompressionScheme { + override def typeId: Int = 2 + + // 32K unique values allowed + private val MAX_DICT_SIZE = Short.MaxValue - 1 + + override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + new this.Decoder[T](buffer, columnType) + } + + override def encoder = new this.Encoder + + override def supports(columnType: ColumnType[_, _]) = columnType match { + case INT | LONG | STRING => true + case _ => false + } + + class Encoder extends compression.Encoder{ + // Size of the input, uncompressed, in bytes. Note that we only count until the dictionary + // overflows. + private var _uncompressedSize = 0 + + // If the number of distinct elements is too large, we discard the use of dictionary encoding + // and set the overflow flag to true. + private var overflow = false + + // Total number of elements. + private var count = 0 + + // The reverse mapping of _dictionary, i.e. mapping encoded integer to the value itself. + private var values = new mutable.ArrayBuffer[Any](1024) + + // The dictionary that maps a value to the encoded short integer. + private val dictionary = mutable.HashMap.empty[Any, Short] + + // Size of the serialized dictionary in bytes. Initialized to 4 since we need at least an `Int` + // to store dictionary element count. + private var dictionarySize = 4 + + override def gatherCompressibilityStats[T <: NativeType]( + value: T#JvmType, + columnType: ColumnType[T, T#JvmType]) { + + if (!overflow) { + val actualSize = columnType.actualSize(value) + count += 1 + _uncompressedSize += actualSize + + if (!dictionary.contains(value)) { + if (dictionary.size < MAX_DICT_SIZE) { + val clone = columnType.clone(value) + values += clone + dictionarySize += actualSize + dictionary(clone) = dictionary.size.toShort + } else { + overflow = true + values.clear() + dictionary.clear() + } + } + } + } + + override def compress[T <: NativeType]( + from: ByteBuffer, + to: ByteBuffer, + columnType: ColumnType[T, T#JvmType]) = { + + if (overflow) { + throw new IllegalStateException( + "Dictionary encoding should not be used because of dictionary overflow.") + } + + to.putInt(DictionaryEncoding.typeId) + .putInt(dictionary.size) + + var i = 0 + while (i < values.length) { + columnType.append(values(i).asInstanceOf[T#JvmType], to) + i += 1 + } + + while (from.hasRemaining) { + to.putShort(dictionary(columnType.extract(from))) + } + + to.rewind() + to + } + + override def uncompressedSize = _uncompressedSize + + override def compressedSize = if (overflow) Int.MaxValue else dictionarySize + count * 2 + } + + class Decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + extends compression.Decoder[T] { + + private val dictionary = { + // TODO Can we clean up this mess? Maybe move this to `DataType`? + implicit val classTag = { + val mirror = runtimeMirror(getClass.getClassLoader) + ClassTag[T#JvmType](mirror.runtimeClass(columnType.scalaTag.tpe)) + } + + Array.fill(buffer.getInt()) { + columnType.extract(buffer) + } + } + + override def next() = dictionary(buffer.getShort()) + + override def hasNext = buffer.hasRemaining + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala new file mode 100644 index 0000000000000..78640b876d4aa --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala @@ -0,0 +1,61 @@ +/* + * 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.columnar + +import org.scalatest.FunSuite + +import org.apache.spark.sql.catalyst.types._ + +class ColumnStatsSuite extends FunSuite { + testColumnStats(classOf[BooleanColumnStats], BOOLEAN) + testColumnStats(classOf[ByteColumnStats], BYTE) + testColumnStats(classOf[ShortColumnStats], SHORT) + testColumnStats(classOf[IntColumnStats], INT) + testColumnStats(classOf[LongColumnStats], LONG) + testColumnStats(classOf[FloatColumnStats], FLOAT) + testColumnStats(classOf[DoubleColumnStats], DOUBLE) + testColumnStats(classOf[StringColumnStats], STRING) + + def testColumnStats[T <: NativeType, U <: NativeColumnStats[T]]( + columnStatsClass: Class[U], + columnType: NativeColumnType[T]) { + + val columnStatsName = columnStatsClass.getSimpleName + + test(s"$columnStatsName: empty") { + val columnStats = columnStatsClass.newInstance() + expectResult(columnStats.initialBounds, "Wrong initial bounds") { + (columnStats.lowerBound, columnStats.upperBound) + } + } + + test(s"$columnStatsName: non-empty") { + import ColumnarTestUtils._ + + val columnStats = columnStatsClass.newInstance() + val rows = Seq.fill(10)(makeRandomRow(columnType)) + rows.foreach(columnStats.gatherStats(_, 0)) + + val values = rows.map(_.head.asInstanceOf[T#JvmType]) + val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#JvmType]] + + expectResult(values.min(ordering), "Wrong lower bound")(columnStats.lowerBound) + expectResult(values.max(ordering), "Wrong upper bound")(columnStats.upperBound) + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala index 2d431affbcfcc..1d3608ed2d9ab 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala @@ -19,46 +19,56 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer -import scala.util.Random - import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.types._ +import org.apache.spark.sql.columnar.ColumnarTestUtils._ import org.apache.spark.sql.execution.SparkSqlSerializer class ColumnTypeSuite extends FunSuite { - val columnTypes = Seq(INT, SHORT, LONG, BYTE, DOUBLE, FLOAT, STRING, BINARY, GENERIC) + val DEFAULT_BUFFER_SIZE = 512 test("defaultSize") { - val defaultSize = Seq(4, 2, 8, 1, 8, 4, 8, 16, 16) + val checks = Map( + INT -> 4, SHORT -> 2, LONG -> 8, BYTE -> 1, DOUBLE -> 8, FLOAT -> 4, + BOOLEAN -> 1, STRING -> 8, BINARY -> 16, GENERIC -> 16) - columnTypes.zip(defaultSize).foreach { case (columnType, size) => - assert(columnType.defaultSize === size) + checks.foreach { case (columnType, expectedSize) => + expectResult(expectedSize, s"Wrong defaultSize for $columnType") { + columnType.defaultSize + } } } test("actualSize") { - val expectedSizes = Seq(4, 2, 8, 1, 8, 4, 4 + 5, 4 + 4, 4 + 11) - val actualSizes = Seq( - INT.actualSize(Int.MaxValue), - SHORT.actualSize(Short.MaxValue), - LONG.actualSize(Long.MaxValue), - BYTE.actualSize(Byte.MaxValue), - DOUBLE.actualSize(Double.MaxValue), - FLOAT.actualSize(Float.MaxValue), - STRING.actualSize("hello"), - BINARY.actualSize(new Array[Byte](4)), - GENERIC.actualSize(SparkSqlSerializer.serialize(Map(1 -> "a")))) - - expectedSizes.zip(actualSizes).foreach { case (expected, actual) => - assert(expected === actual) + def checkActualSize[T <: DataType, JvmType]( + columnType: ColumnType[T, JvmType], + value: JvmType, + expected: Int) { + + expectResult(expected, s"Wrong actualSize for $columnType") { + columnType.actualSize(value) + } } + + checkActualSize(INT, Int.MaxValue, 4) + checkActualSize(SHORT, Short.MaxValue, 2) + checkActualSize(LONG, Long.MaxValue, 8) + checkActualSize(BYTE, Byte.MaxValue, 1) + checkActualSize(DOUBLE, Double.MaxValue, 8) + checkActualSize(FLOAT, Float.MaxValue, 4) + checkActualSize(BOOLEAN, true, 1) + checkActualSize(STRING, "hello", 4 + 5) + + val binary = Array.fill[Byte](4)(0: Byte) + checkActualSize(BINARY, binary, 4 + 4) + + val generic = Map(1 -> "a") + checkActualSize(GENERIC, SparkSqlSerializer.serialize(generic), 4 + 11) } - testNumericColumnType[BooleanType.type, Boolean]( + testNativeColumnType[BooleanType.type]( BOOLEAN, - Array.fill(4)(Random.nextBoolean()), - ByteBuffer.allocate(32), (buffer: ByteBuffer, v: Boolean) => { buffer.put((if (v) 1 else 0).toByte) }, @@ -66,105 +76,42 @@ class ColumnTypeSuite extends FunSuite { buffer.get() == 1 }) - testNumericColumnType[IntegerType.type, Int]( - INT, - Array.fill(4)(Random.nextInt()), - ByteBuffer.allocate(32), - (_: ByteBuffer).putInt(_), - (_: ByteBuffer).getInt) - - testNumericColumnType[ShortType.type, Short]( - SHORT, - Array.fill(4)(Random.nextInt(Short.MaxValue).asInstanceOf[Short]), - ByteBuffer.allocate(32), - (_: ByteBuffer).putShort(_), - (_: ByteBuffer).getShort) - - testNumericColumnType[LongType.type, Long]( - LONG, - Array.fill(4)(Random.nextLong()), - ByteBuffer.allocate(64), - (_: ByteBuffer).putLong(_), - (_: ByteBuffer).getLong) - - testNumericColumnType[ByteType.type, Byte]( - BYTE, - Array.fill(4)(Random.nextInt(Byte.MaxValue).asInstanceOf[Byte]), - ByteBuffer.allocate(64), - (_: ByteBuffer).put(_), - (_: ByteBuffer).get) - - testNumericColumnType[DoubleType.type, Double]( - DOUBLE, - Array.fill(4)(Random.nextDouble()), - ByteBuffer.allocate(64), - (_: ByteBuffer).putDouble(_), - (_: ByteBuffer).getDouble) - - testNumericColumnType[FloatType.type, Float]( - FLOAT, - Array.fill(4)(Random.nextFloat()), - ByteBuffer.allocate(64), - (_: ByteBuffer).putFloat(_), - (_: ByteBuffer).getFloat) - - test("STRING") { - val buffer = ByteBuffer.allocate(128) - val seq = Array("hello", "world", "spark", "sql") - - seq.map(_.getBytes).foreach { bytes: Array[Byte] => - buffer.putInt(bytes.length).put(bytes) - } + testNativeColumnType[IntegerType.type](INT, _.putInt(_), _.getInt) - buffer.rewind() - seq.foreach { s => - assert(s === STRING.extract(buffer)) - } + testNativeColumnType[ShortType.type](SHORT, _.putShort(_), _.getShort) - buffer.rewind() - seq.foreach(STRING.append(_, buffer)) + testNativeColumnType[LongType.type](LONG, _.putLong(_), _.getLong) - buffer.rewind() - seq.foreach { s => - val length = buffer.getInt - assert(length === s.getBytes.length) + testNativeColumnType[ByteType.type](BYTE, _.put(_), _.get) + + testNativeColumnType[DoubleType.type](DOUBLE, _.putDouble(_), _.getDouble) + + testNativeColumnType[FloatType.type](FLOAT, _.putFloat(_), _.getFloat) + testNativeColumnType[StringType.type]( + STRING, + (buffer: ByteBuffer, string: String) => { + val bytes = string.getBytes() + buffer.putInt(bytes.length).put(string.getBytes) + }, + (buffer: ByteBuffer) => { + val length = buffer.getInt() val bytes = new Array[Byte](length) buffer.get(bytes, 0, length) - assert(s === new String(bytes)) - } - } - - test("BINARY") { - val buffer = ByteBuffer.allocate(128) - val seq = Array.fill(4) { - val bytes = new Array[Byte](4) - Random.nextBytes(bytes) - bytes - } + new String(bytes) + }) - seq.foreach { bytes => + testColumnType[BinaryType.type, Array[Byte]]( + BINARY, + (buffer: ByteBuffer, bytes: Array[Byte]) => { buffer.putInt(bytes.length).put(bytes) - } - - buffer.rewind() - seq.foreach { b => - assert(b === BINARY.extract(buffer)) - } - - buffer.rewind() - seq.foreach(BINARY.append(_, buffer)) - - buffer.rewind() - seq.foreach { b => - val length = buffer.getInt - assert(length === b.length) - + }, + (buffer: ByteBuffer) => { + val length = buffer.getInt() val bytes = new Array[Byte](length) buffer.get(bytes, 0, length) - assert(b === bytes) - } - } + bytes + }) test("GENERIC") { val buffer = ByteBuffer.allocate(512) @@ -177,43 +124,58 @@ class ColumnTypeSuite extends FunSuite { val length = buffer.getInt() assert(length === serializedObj.length) - val bytes = new Array[Byte](length) - buffer.get(bytes, 0, length) - assert(obj === SparkSqlSerializer.deserialize(bytes)) + expectResult(obj, "Deserialized object didn't equal to the original object") { + val bytes = new Array[Byte](length) + buffer.get(bytes, 0, length) + SparkSqlSerializer.deserialize(bytes) + } buffer.rewind() buffer.putInt(serializedObj.length).put(serializedObj) - buffer.rewind() - assert(obj === SparkSqlSerializer.deserialize(GENERIC.extract(buffer))) + expectResult(obj, "Deserialized object didn't equal to the original object") { + buffer.rewind() + SparkSqlSerializer.deserialize(GENERIC.extract(buffer)) + } + } + + def testNativeColumnType[T <: NativeType]( + columnType: NativeColumnType[T], + putter: (ByteBuffer, T#JvmType) => Unit, + getter: (ByteBuffer) => T#JvmType) { + + testColumnType[T, T#JvmType](columnType, putter, getter) } - def testNumericColumnType[T <: DataType, JvmType]( + def testColumnType[T <: DataType, JvmType]( columnType: ColumnType[T, JvmType], - seq: Seq[JvmType], - buffer: ByteBuffer, putter: (ByteBuffer, JvmType) => Unit, getter: (ByteBuffer) => JvmType) { - val columnTypeName = columnType.getClass.getSimpleName.stripSuffix("$") + val buffer = ByteBuffer.allocate(DEFAULT_BUFFER_SIZE) + val seq = (0 until 4).map(_ => makeRandomValue(columnType)) - test(s"$columnTypeName.extract") { + test(s"$columnType.extract") { buffer.rewind() seq.foreach(putter(buffer, _)) buffer.rewind() - seq.foreach { i => - assert(i === columnType.extract(buffer)) + seq.foreach { expected => + assert( + expected === columnType.extract(buffer), + "Extracted value didn't equal to the original one") } } - test(s"$columnTypeName.append") { + test(s"$columnType.append") { buffer.rewind() seq.foreach(columnType.append(_, buffer)) buffer.rewind() - seq.foreach { i => - assert(i === getter(buffer)) + seq.foreach { expected => + assert( + expected === getter(buffer), + "Extracted value didn't equal to the original one") } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarQuerySuite.scala index 928851a385d41..70b2e851737f8 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarQuerySuite.scala @@ -17,11 +17,11 @@ package org.apache.spark.sql.columnar +import org.apache.spark.sql.{QueryTest, TestData} import org.apache.spark.sql.execution.SparkLogicalPlan import org.apache.spark.sql.test.TestSQLContext -import org.apache.spark.sql.{TestData, DslQuerySuite} -class ColumnarQuerySuite extends DslQuerySuite { +class ColumnarQuerySuite extends QueryTest { import TestData._ import TestSQLContext._ diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestData.scala deleted file mode 100644 index ddcdede8d1a4a..0000000000000 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestData.scala +++ /dev/null @@ -1,55 +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.columnar - -import scala.util.Random - -import org.apache.spark.sql.catalyst.expressions.GenericMutableRow - -// TODO Enrich test data -object ColumnarTestData { - object GenericMutableRow { - def apply(values: Any*) = { - val row = new GenericMutableRow(values.length) - row.indices.foreach { i => - row(i) = values(i) - } - row - } - } - - def randomBytes(length: Int) = { - val bytes = new Array[Byte](length) - Random.nextBytes(bytes) - bytes - } - - val nonNullRandomRow = GenericMutableRow( - Random.nextInt(), - Random.nextLong(), - Random.nextFloat(), - Random.nextDouble(), - Random.nextBoolean(), - Random.nextInt(Byte.MaxValue).asInstanceOf[Byte], - Random.nextInt(Short.MaxValue).asInstanceOf[Short], - Random.nextString(Random.nextInt(64)), - randomBytes(Random.nextInt(64)), - Map(Random.nextInt() -> Random.nextString(4))) - - val nullRow = GenericMutableRow(Seq.fill(10)(null): _*) -} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala new file mode 100644 index 0000000000000..04bdc43d95328 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala @@ -0,0 +1,100 @@ +/* + * 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.columnar + +import scala.collection.immutable.HashSet +import scala.util.Random + +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.expressions.GenericMutableRow +import org.apache.spark.sql.catalyst.types.{DataType, NativeType} + +object ColumnarTestUtils { + def makeNullRow(length: Int) = { + val row = new GenericMutableRow(length) + (0 until length).foreach(row.setNullAt) + row + } + + def makeRandomValue[T <: DataType, JvmType](columnType: ColumnType[T, JvmType]): JvmType = { + def randomBytes(length: Int) = { + val bytes = new Array[Byte](length) + Random.nextBytes(bytes) + bytes + } + + (columnType match { + case BYTE => (Random.nextInt(Byte.MaxValue * 2) - Byte.MaxValue).toByte + case SHORT => (Random.nextInt(Short.MaxValue * 2) - Short.MaxValue).toShort + case INT => Random.nextInt() + case LONG => Random.nextLong() + case FLOAT => Random.nextFloat() + case DOUBLE => Random.nextDouble() + case STRING => Random.nextString(Random.nextInt(32)) + case BOOLEAN => Random.nextBoolean() + case BINARY => randomBytes(Random.nextInt(32)) + case _ => + // Using a random one-element map instead of an arbitrary object + Map(Random.nextInt() -> Random.nextString(Random.nextInt(32))) + }).asInstanceOf[JvmType] + } + + def makeRandomValues( + head: ColumnType[_ <: DataType, _], + tail: ColumnType[_ <: DataType, _]*): Seq[Any] = makeRandomValues(Seq(head) ++ tail) + + def makeRandomValues(columnTypes: Seq[ColumnType[_ <: DataType, _]]): Seq[Any] = { + columnTypes.map(makeRandomValue(_)) + } + + def makeUniqueRandomValues[T <: DataType, JvmType]( + columnType: ColumnType[T, JvmType], + count: Int): Seq[JvmType] = { + + Iterator.iterate(HashSet.empty[JvmType]) { set => + set + Iterator.continually(makeRandomValue(columnType)).filterNot(set.contains).next() + }.drop(count).next().toSeq + } + + def makeRandomRow( + head: ColumnType[_ <: DataType, _], + tail: ColumnType[_ <: DataType, _]*): Row = makeRandomRow(Seq(head) ++ tail) + + def makeRandomRow(columnTypes: Seq[ColumnType[_ <: DataType, _]]): Row = { + val row = new GenericMutableRow(columnTypes.length) + makeRandomValues(columnTypes).zipWithIndex.foreach { case (value, index) => + row(index) = value + } + row + } + + def makeUniqueValuesAndSingleValueRows[T <: NativeType]( + columnType: NativeColumnType[T], + count: Int) = { + + val values = makeUniqueRandomValues(columnType, count) + val rows = values.map { value => + val row = new GenericMutableRow(1) + row(0) = value + row + } + + (values, rows) + } + +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala index d413d483f4e7e..4a21eb6201a69 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala @@ -17,12 +17,29 @@ package org.apache.spark.sql.columnar +import java.nio.ByteBuffer + import org.scalatest.FunSuite -import org.apache.spark.sql.catalyst.types.DataType + import org.apache.spark.sql.catalyst.expressions.GenericMutableRow +import org.apache.spark.sql.catalyst.types.DataType + +class TestNullableColumnAccessor[T <: DataType, JvmType]( + buffer: ByteBuffer, + columnType: ColumnType[T, JvmType]) + extends BasicColumnAccessor(buffer, columnType) + with NullableColumnAccessor + +object TestNullableColumnAccessor { + def apply[T <: DataType, JvmType](buffer: ByteBuffer, columnType: ColumnType[T, JvmType]) = { + // Skips the column type ID + buffer.getInt() + new TestNullableColumnAccessor(buffer, columnType) + } +} class NullableColumnAccessorSuite extends FunSuite { - import ColumnarTestData._ + import ColumnarTestUtils._ Seq(INT, LONG, SHORT, BOOLEAN, BYTE, STRING, DOUBLE, FLOAT, BINARY, GENERIC).foreach { testNullableColumnAccessor(_) @@ -30,30 +47,32 @@ class NullableColumnAccessorSuite extends FunSuite { def testNullableColumnAccessor[T <: DataType, JvmType](columnType: ColumnType[T, JvmType]) { val typeName = columnType.getClass.getSimpleName.stripSuffix("$") + val nullRow = makeNullRow(1) - test(s"$typeName accessor: empty column") { - val builder = ColumnBuilder(columnType.typeId, 4) - val accessor = ColumnAccessor(builder.build()) + test(s"Nullable $typeName column accessor: empty column") { + val builder = TestNullableColumnBuilder(columnType) + val accessor = TestNullableColumnAccessor(builder.build(), columnType) assert(!accessor.hasNext) } - test(s"$typeName accessor: access null values") { - val builder = ColumnBuilder(columnType.typeId, 4) + test(s"Nullable $typeName column accessor: access null values") { + val builder = TestNullableColumnBuilder(columnType) + val randomRow = makeRandomRow(columnType) (0 until 4).foreach { _ => - builder.appendFrom(nonNullRandomRow, columnType.typeId) - builder.appendFrom(nullRow, columnType.typeId) + builder.appendFrom(randomRow, 0) + builder.appendFrom(nullRow, 0) } - val accessor = ColumnAccessor(builder.build()) + val accessor = TestNullableColumnAccessor(builder.build(), columnType) val row = new GenericMutableRow(1) (0 until 4).foreach { _ => accessor.extractTo(row, 0) - assert(row(0) === nonNullRandomRow(columnType.typeId)) + assert(row(0) === randomRow(0)) accessor.extractTo(row, 0) - assert(row(0) === null) + assert(row.isNullAt(0)) } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala index 5222a47e1ab87..d9d1e1bfddb75 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala @@ -19,63 +19,71 @@ package org.apache.spark.sql.columnar import org.scalatest.FunSuite -import org.apache.spark.sql.catalyst.types.DataType +import org.apache.spark.sql.catalyst.types._ import org.apache.spark.sql.execution.SparkSqlSerializer +class TestNullableColumnBuilder[T <: DataType, JvmType](columnType: ColumnType[T, JvmType]) + extends BasicColumnBuilder[T, JvmType](new NoopColumnStats[T, JvmType], columnType) + with NullableColumnBuilder + +object TestNullableColumnBuilder { + def apply[T <: DataType, JvmType](columnType: ColumnType[T, JvmType], initialSize: Int = 0) = { + val builder = new TestNullableColumnBuilder(columnType) + builder.initialize(initialSize) + builder + } +} + class NullableColumnBuilderSuite extends FunSuite { - import ColumnarTestData._ + import ColumnarTestUtils._ Seq(INT, LONG, SHORT, BOOLEAN, BYTE, STRING, DOUBLE, FLOAT, BINARY, GENERIC).foreach { testNullableColumnBuilder(_) } def testNullableColumnBuilder[T <: DataType, JvmType](columnType: ColumnType[T, JvmType]) { - val columnBuilder = ColumnBuilder(columnType.typeId) val typeName = columnType.getClass.getSimpleName.stripSuffix("$") test(s"$typeName column builder: empty column") { - columnBuilder.initialize(4) - + val columnBuilder = TestNullableColumnBuilder(columnType) val buffer = columnBuilder.build() - // For column type ID - assert(buffer.getInt() === columnType.typeId) - // For null count - assert(buffer.getInt === 0) + expectResult(columnType.typeId, "Wrong column type ID")(buffer.getInt()) + expectResult(0, "Wrong null count")(buffer.getInt()) assert(!buffer.hasRemaining) } test(s"$typeName column builder: buffer size auto growth") { - columnBuilder.initialize(4) + val columnBuilder = TestNullableColumnBuilder(columnType) + val randomRow = makeRandomRow(columnType) - (0 until 4) foreach { _ => - columnBuilder.appendFrom(nonNullRandomRow, columnType.typeId) + (0 until 4).foreach { _ => + columnBuilder.appendFrom(randomRow, 0) } val buffer = columnBuilder.build() - // For column type ID - assert(buffer.getInt() === columnType.typeId) - // For null count - assert(buffer.getInt() === 0) + expectResult(columnType.typeId, "Wrong column type ID")(buffer.getInt()) + expectResult(0, "Wrong null count")(buffer.getInt()) } test(s"$typeName column builder: null values") { - columnBuilder.initialize(4) + val columnBuilder = TestNullableColumnBuilder(columnType) + val randomRow = makeRandomRow(columnType) + val nullRow = makeNullRow(1) - (0 until 4) foreach { _ => - columnBuilder.appendFrom(nonNullRandomRow, columnType.typeId) - columnBuilder.appendFrom(nullRow, columnType.typeId) + (0 until 4).foreach { _ => + columnBuilder.appendFrom(randomRow, 0) + columnBuilder.appendFrom(nullRow, 0) } val buffer = columnBuilder.build() - // For column type ID - assert(buffer.getInt() === columnType.typeId) - // For null count - assert(buffer.getInt() === 4) + expectResult(columnType.typeId, "Wrong column type ID")(buffer.getInt()) + expectResult(4, "Wrong null count")(buffer.getInt()) + // For null positions - (1 to 7 by 2).foreach(i => assert(buffer.getInt() === i)) + (1 to 7 by 2).foreach(expectResult(_, "Wrong null position")(buffer.getInt())) // For non-null values (0 until 4).foreach { _ => @@ -84,7 +92,8 @@ class NullableColumnBuilderSuite extends FunSuite { } else { columnType.extract(buffer) } - assert(actual === nonNullRandomRow(columnType.typeId)) + + assert(actual === randomRow(0), "Extracted value didn't equal to the original one") } assert(!buffer.hasRemaining) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala new file mode 100644 index 0000000000000..184691ab5b46a --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.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.sql.columnar.compression + +import java.nio.ByteBuffer + +import org.scalatest.FunSuite + +import org.apache.spark.sql.catalyst.types.NativeType +import org.apache.spark.sql.columnar._ +import org.apache.spark.sql.columnar.ColumnarTestUtils._ +import org.apache.spark.sql.catalyst.expressions.GenericMutableRow + +class DictionaryEncodingSuite extends FunSuite { + testDictionaryEncoding(new IntColumnStats, INT) + testDictionaryEncoding(new LongColumnStats, LONG) + testDictionaryEncoding(new StringColumnStats, STRING) + + def testDictionaryEncoding[T <: NativeType]( + columnStats: NativeColumnStats[T], + columnType: NativeColumnType[T]) { + + val typeName = columnType.getClass.getSimpleName.stripSuffix("$") + + def buildDictionary(buffer: ByteBuffer) = { + (0 until buffer.getInt()).map(columnType.extract(buffer) -> _.toShort).toMap + } + + test(s"$DictionaryEncoding with $typeName: simple case") { + // ------------- + // Tests encoder + // ------------- + + val builder = TestCompressibleColumnBuilder(columnStats, columnType, DictionaryEncoding) + val (values, rows) = makeUniqueValuesAndSingleValueRows(columnType, 2) + + builder.initialize(0) + builder.appendFrom(rows(0), 0) + builder.appendFrom(rows(1), 0) + builder.appendFrom(rows(0), 0) + builder.appendFrom(rows(1), 0) + + val buffer = builder.build() + val headerSize = CompressionScheme.columnHeaderSize(buffer) + // 4 extra bytes for dictionary size + val dictionarySize = 4 + values.map(columnType.actualSize).sum + // 4 `Short`s, 2 bytes each + val compressedSize = dictionarySize + 2 * 4 + // 4 extra bytes for compression scheme type ID + expectResult(headerSize + 4 + compressedSize, "Wrong buffer capacity")(buffer.capacity) + + // Skips column header + buffer.position(headerSize) + expectResult(DictionaryEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt()) + + val dictionary = buildDictionary(buffer) + Array[Short](0, 1).foreach { i => + expectResult(i, "Wrong dictionary entry")(dictionary(values(i))) + } + + Array[Short](0, 1, 0, 1).foreach { + expectResult(_, "Wrong column element value")(buffer.getShort()) + } + + // ------------- + // Tests decoder + // ------------- + + // Rewinds, skips column header and 4 more bytes for compression scheme ID + buffer.rewind().position(headerSize + 4) + + val decoder = new DictionaryEncoding.Decoder[T](buffer, columnType) + + Array[Short](0, 1, 0, 1).foreach { i => + expectResult(values(i), "Wrong decoded value")(decoder.next()) + } + + assert(!decoder.hasNext) + } + } + + test(s"$DictionaryEncoding: overflow") { + val builder = TestCompressibleColumnBuilder(new IntColumnStats, INT, DictionaryEncoding) + builder.initialize(0) + + (0 to Short.MaxValue).foreach { n => + val row = new GenericMutableRow(1) + row.setInt(0, n) + builder.appendFrom(row, 0) + } + + withClue("Dictionary overflowed, encoding should fail") { + intercept[Throwable] { + builder.build() + } + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala new file mode 100644 index 0000000000000..2089ad120d4f2 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala @@ -0,0 +1,130 @@ +/* + * 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.columnar.compression + +import org.scalatest.FunSuite + +import org.apache.spark.sql.catalyst.types.NativeType +import org.apache.spark.sql.columnar._ +import org.apache.spark.sql.columnar.ColumnarTestUtils._ + +class RunLengthEncodingSuite extends FunSuite { + testRunLengthEncoding(new BooleanColumnStats, BOOLEAN) + testRunLengthEncoding(new ByteColumnStats, BYTE) + testRunLengthEncoding(new ShortColumnStats, SHORT) + testRunLengthEncoding(new IntColumnStats, INT) + testRunLengthEncoding(new LongColumnStats, LONG) + testRunLengthEncoding(new StringColumnStats, STRING) + + def testRunLengthEncoding[T <: NativeType]( + columnStats: NativeColumnStats[T], + columnType: NativeColumnType[T]) { + + val typeName = columnType.getClass.getSimpleName.stripSuffix("$") + + test(s"$RunLengthEncoding with $typeName: simple case") { + // ------------- + // Tests encoder + // ------------- + + val builder = TestCompressibleColumnBuilder(columnStats, columnType, RunLengthEncoding) + val (values, rows) = makeUniqueValuesAndSingleValueRows(columnType, 2) + + builder.initialize(0) + builder.appendFrom(rows(0), 0) + builder.appendFrom(rows(0), 0) + builder.appendFrom(rows(1), 0) + builder.appendFrom(rows(1), 0) + + val buffer = builder.build() + val headerSize = CompressionScheme.columnHeaderSize(buffer) + // 4 extra bytes each run for run length + val compressedSize = values.map(columnType.actualSize(_) + 4).sum + // 4 extra bytes for compression scheme type ID + expectResult(headerSize + 4 + compressedSize, "Wrong buffer capacity")(buffer.capacity) + + // Skips column header + buffer.position(headerSize) + expectResult(RunLengthEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt()) + + Array(0, 1).foreach { i => + expectResult(values(i), "Wrong column element value")(columnType.extract(buffer)) + expectResult(2, "Wrong run length")(buffer.getInt()) + } + + // ------------- + // Tests decoder + // ------------- + + // Rewinds, skips column header and 4 more bytes for compression scheme ID + buffer.rewind().position(headerSize + 4) + + val decoder = new RunLengthEncoding.Decoder[T](buffer, columnType) + + Array(0, 0, 1, 1).foreach { i => + expectResult(values(i), "Wrong decoded value")(decoder.next()) + } + + assert(!decoder.hasNext) + } + + test(s"$RunLengthEncoding with $typeName: run length == 1") { + // ------------- + // Tests encoder + // ------------- + + val builder = TestCompressibleColumnBuilder(columnStats, columnType, RunLengthEncoding) + val (values, rows) = makeUniqueValuesAndSingleValueRows(columnType, 2) + + builder.initialize(0) + builder.appendFrom(rows(0), 0) + builder.appendFrom(rows(1), 0) + + val buffer = builder.build() + val headerSize = CompressionScheme.columnHeaderSize(buffer) + // 4 bytes each run for run length + val compressedSize = values.map(columnType.actualSize(_) + 4).sum + // 4 bytes for compression scheme type ID + expectResult(headerSize + 4 + compressedSize, "Wrong buffer capacity")(buffer.capacity) + + // Skips column header + buffer.position(headerSize) + expectResult(RunLengthEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt()) + + Array(0, 1).foreach { i => + expectResult(values(i), "Wrong column element value")(columnType.extract(buffer)) + expectResult(1, "Wrong run length")(buffer.getInt()) + } + + // ------------- + // Tests decoder + // ------------- + + // Rewinds, skips column header and 4 more bytes for compression scheme ID + buffer.rewind().position(headerSize + 4) + + val decoder = new RunLengthEncoding.Decoder[T](buffer, columnType) + + Array(0, 1).foreach { i => + expectResult(values(i), "Wrong decoded value")(decoder.next()) + } + + assert(!decoder.hasNext) + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala new file mode 100644 index 0000000000000..e0ec812863dcf --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala @@ -0,0 +1,43 @@ +/* + * 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.columnar.compression + +import org.apache.spark.sql.catalyst.types.NativeType +import org.apache.spark.sql.columnar._ + +class TestCompressibleColumnBuilder[T <: NativeType]( + override val columnStats: NativeColumnStats[T], + override val columnType: NativeColumnType[T], + override val schemes: Seq[CompressionScheme]) + extends NativeColumnBuilder(columnStats, columnType) + with NullableColumnBuilder + with CompressibleColumnBuilder[T] { + + override protected def isWorthCompressing(encoder: Encoder) = true +} + +object TestCompressibleColumnBuilder { + def apply[T <: NativeType]( + columnStats: NativeColumnStats[T], + columnType: NativeColumnType[T], + scheme: CompressionScheme) = { + + new TestCompressibleColumnBuilder(columnStats, columnType, Seq(scheme)) + } +} + From ed730c95026d322f4b24d3d9fe92050ffa74cf4a Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 2 Apr 2014 12:48:04 -0700 Subject: [PATCH 17/78] StopAfter / TopK related changes 1. Renamed StopAfter to Limit to be more consistent with naming in other relational databases. 2. Renamed TopK to TakeOrdered to be more consistent with Spark RDD API. 3. Avoid breaking lineage in Limit. 4. Added a bunch of override's to execution/basicOperators.scala. @marmbrus @liancheng Author: Reynold Xin Author: Michael Armbrust Closes #233 from rxin/limit and squashes the following commits: 13eb12a [Reynold Xin] Merge pull request #1 from marmbrus/limit 92b9727 [Michael Armbrust] More hacks to make Maps serialize with Kryo. 4fc8b4e [Reynold Xin] Merge branch 'master' of github.com:apache/spark into limit 87b7d37 [Reynold Xin] Use the proper serializer in limit. 9b79246 [Reynold Xin] Updated doc for Limit. 47d3327 [Reynold Xin] Copy tuples in Limit before shuffle. 231af3a [Reynold Xin] Limit/TakeOrdered: 1. Renamed StopAfter to Limit to be more consistent with naming in other relational databases. 2. Renamed TopK to TakeOrdered to be more consistent with Spark RDD API. 3. Avoid breaking lineage in Limit. 4. Added a bunch of override's to execution/basicOperators.scala. --- .../apache/spark/sql/catalyst/SqlParser.scala | 2 +- .../plans/logical/basicOperators.scala | 2 +- .../org/apache/spark/sql/SQLContext.scala | 2 +- .../sql/execution/SparkSqlSerializer.scala | 6 ++ .../spark/sql/execution/SparkStrategies.scala | 10 +-- .../spark/sql/execution/basicOperators.scala | 71 ++++++++++++------- .../apache/spark/sql/hive/HiveContext.scala | 2 +- .../org/apache/spark/sql/hive/HiveQl.scala | 4 +- 8 files changed, 64 insertions(+), 35 deletions(-) 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 0c851c2ee2183..8de87594c8ab9 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 @@ -181,7 +181,7 @@ class SqlParser extends StandardTokenParsers { val withDistinct = d.map(_ => Distinct(withProjection)).getOrElse(withProjection) val withHaving = h.map(h => Filter(h, withDistinct)).getOrElse(withDistinct) val withOrder = o.map(o => Sort(o, withHaving)).getOrElse(withHaving) - val withLimit = l.map { l => StopAfter(l, withOrder) }.getOrElse(withOrder) + val withLimit = l.map { l => Limit(l, withOrder) }.getOrElse(withOrder) withLimit } 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 9d16189deedfe..b39c2b32cc42c 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 @@ -130,7 +130,7 @@ case class Aggregate( def references = child.references } -case class StopAfter(limit: Expression, child: LogicalPlan) extends UnaryNode { +case class Limit(limit: Expression, child: LogicalPlan) extends UnaryNode { def output = child.output def references = limit.references } 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 69bbbdc8943fa..f4bf00f4cffa6 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 @@ -145,7 +145,7 @@ class SQLContext(@transient val sparkContext: SparkContext) val sparkContext = self.sparkContext val strategies: Seq[Strategy] = - TopK :: + TakeOrdered :: PartialAggregation :: HashJoin :: ParquetOperations :: diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala index 915f551fb2f01..d8e1b970c1d88 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala @@ -32,7 +32,13 @@ class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(conf) { kryo.setRegistrationRequired(false) kryo.register(classOf[MutablePair[_, _]]) kryo.register(classOf[Array[Any]]) + // This is kinda hacky... kryo.register(classOf[scala.collection.immutable.Map$Map1], new MapSerializer) + kryo.register(classOf[scala.collection.immutable.Map$Map2], new MapSerializer) + kryo.register(classOf[scala.collection.immutable.Map$Map3], new MapSerializer) + kryo.register(classOf[scala.collection.immutable.Map$Map4], new MapSerializer) + kryo.register(classOf[scala.collection.immutable.Map[_,_]], new MapSerializer) + kryo.register(classOf[scala.collection.Map[_,_]], new MapSerializer) kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericRow]) kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericMutableRow]) kryo.register(classOf[scala.collection.mutable.ArrayBuffer[_]]) 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 e35ac0b6ca95a..b3e51fdf75270 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 @@ -158,10 +158,10 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case other => other } - object TopK extends Strategy { + object TakeOrdered extends Strategy { def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match { - case logical.StopAfter(IntegerLiteral(limit), logical.Sort(order, child)) => - execution.TopK(limit, order, planLater(child))(sparkContext) :: Nil + case logical.Limit(IntegerLiteral(limit), logical.Sort(order, child)) => + execution.TakeOrdered(limit, order, planLater(child))(sparkContext) :: Nil case _ => Nil } } @@ -213,8 +213,8 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { sparkContext.parallelize(data.map(r => new GenericRow(r.productIterator.map(convertToCatalyst).toArray): Row)) execution.ExistingRdd(output, dataAsRdd) :: Nil - case logical.StopAfter(IntegerLiteral(limit), child) => - execution.StopAfter(limit, planLater(child))(sparkContext) :: Nil + case logical.Limit(IntegerLiteral(limit), child) => + execution.Limit(limit, planLater(child))(sparkContext) :: Nil case Unions(unionChildren) => execution.Union(unionChildren.map(planLater))(sparkContext) :: Nil case logical.Generate(generator, join, outer, _, child) => 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 65cb8f8becefa..524e5022ee14b 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 @@ -19,27 +19,28 @@ package org.apache.spark.sql.execution import scala.reflect.runtime.universe.TypeTag -import org.apache.spark.rdd.RDD -import org.apache.spark.SparkContext - +import org.apache.spark.{HashPartitioner, SparkConf, SparkContext} +import org.apache.spark.rdd.{RDD, ShuffledRDD} +import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical.{OrderedDistribution, UnspecifiedDistribution} -import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.util.MutablePair + case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode { - def output = projectList.map(_.toAttribute) + override def output = projectList.map(_.toAttribute) - def execute() = child.execute().mapPartitions { iter => + override def execute() = child.execute().mapPartitions { iter => @transient val reusableProjection = new MutableProjection(projectList) iter.map(reusableProjection) } } case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { - def output = child.output + override def output = child.output - def execute() = child.execute().mapPartitions { iter => + override def execute() = child.execute().mapPartitions { iter => iter.filter(condition.apply(_).asInstanceOf[Boolean]) } } @@ -47,37 +48,59 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { case class Sample(fraction: Double, withReplacement: Boolean, seed: Int, child: SparkPlan) extends UnaryNode { - def output = child.output + override def output = child.output // TODO: How to pick seed? - def execute() = child.execute().sample(withReplacement, fraction, seed) + override def execute() = child.execute().sample(withReplacement, fraction, seed) } case class Union(children: Seq[SparkPlan])(@transient sc: SparkContext) extends SparkPlan { // TODO: attributes output by union should be distinct for nullability purposes - def output = children.head.output - def execute() = sc.union(children.map(_.execute())) + override def output = children.head.output + override def execute() = sc.union(children.map(_.execute())) override def otherCopyArgs = sc :: Nil } -case class StopAfter(limit: Int, child: SparkPlan)(@transient sc: SparkContext) extends UnaryNode { +/** + * Take the first limit elements. Note that the implementation is different depending on whether + * this is a terminal operator or not. If it is terminal and is invoked using executeCollect, + * this operator uses Spark's take method on the Spark driver. If it is not terminal or is + * invoked using execute, we first take the limit on each partition, and then repartition all the + * data to a single partition to compute the global limit. + */ +case class Limit(limit: Int, child: SparkPlan)(@transient sc: SparkContext) extends UnaryNode { + // TODO: Implement a partition local limit, and use a strategy to generate the proper limit plan: + // partition local limit -> exchange into one partition -> partition local limit again + override def otherCopyArgs = sc :: Nil - def output = child.output + override def output = child.output override def executeCollect() = child.execute().map(_.copy()).take(limit) - // TODO: Terminal split should be implemented differently from non-terminal split. - // TODO: Pick num splits based on |limit|. - def execute() = sc.makeRDD(executeCollect(), 1) + override def execute() = { + val rdd = child.execute().mapPartitions { iter => + val mutablePair = new MutablePair[Boolean, Row]() + iter.take(limit).map(row => mutablePair.update(false, row)) + } + val part = new HashPartitioner(1) + val shuffled = new ShuffledRDD[Boolean, Row, MutablePair[Boolean, Row]](rdd, part) + shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) + shuffled.mapPartitions(_.take(limit).map(_._2)) + } } -case class TopK(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) - (@transient sc: SparkContext) extends UnaryNode { +/** + * Take the first limit elements as defined by the sortOrder. This is logically equivalent to + * having a [[Limit]] operator after a [[Sort]] operator. This could have been named TopK, but + * Spark's top operator does the opposite in ordering so we name it TakeOrdered to avoid confusion. + */ +case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) + (@transient sc: SparkContext) extends UnaryNode { override def otherCopyArgs = sc :: Nil - def output = child.output + override def output = child.output @transient lazy val ordering = new RowOrdering(sortOrder) @@ -86,7 +109,7 @@ case class TopK(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) // TODO: Terminal split should be implemented differently from non-terminal split. // TODO: Pick num splits based on |limit|. - def execute() = sc.makeRDD(executeCollect(), 1) + override def execute() = sc.makeRDD(executeCollect(), 1) } @@ -101,7 +124,7 @@ case class Sort( @transient lazy val ordering = new RowOrdering(sortOrder) - def execute() = attachTree(this, "sort") { + override def execute() = attachTree(this, "sort") { // TODO: Optimize sorting operation? child.execute() .mapPartitions( @@ -109,7 +132,7 @@ case class Sort( preservesPartitioning = true) } - def output = child.output + override def output = child.output } object ExistingRdd { @@ -130,6 +153,6 @@ object ExistingRdd { } case class ExistingRdd(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode { - def execute() = rdd + override def execute() = rdd } 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 197b557cba5f4..46febbfad037d 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 @@ -188,7 +188,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { val hiveContext = self override val strategies: Seq[Strategy] = Seq( - TopK, + TakeOrdered, ParquetOperations, HiveTableScans, DataSinks, 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 490a592a588d0..b2b03bc790fcc 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 @@ -529,7 +529,7 @@ object HiveQl { val withLimit = limitClause.map(l => nodeToExpr(l.getChildren.head)) - .map(StopAfter(_, withSort)) + .map(Limit(_, withSort)) .getOrElse(withSort) // TOK_INSERT_INTO means to add files to the table. @@ -602,7 +602,7 @@ object HiveQl { case Token("TOK_TABLESPLITSAMPLE", Token("TOK_ROWCOUNT", Nil) :: Token(count, Nil) :: Nil) => - StopAfter(Literal(count.toInt), relation) + Limit(Literal(count.toInt), relation) case Token("TOK_TABLESPLITSAMPLE", Token("TOK_PERCENT", Nil) :: Token(fraction, Nil) :: Nil) => From 9c65fa76f9d413e311a80f29d35d3ff7722e9476 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Wed, 2 Apr 2014 14:01:12 -0700 Subject: [PATCH 18/78] [SPARK-1212, Part II] Support sparse data in MLlib In PR https://github.com/apache/spark/pull/117, we added dense/sparse vector data model and updated KMeans to support sparse input. This PR is to replace all other `Array[Double]` usage by `Vector` in generalized linear models (GLMs) and Naive Bayes. Major changes: 1. `LabeledPoint` becomes `LabeledPoint(Double, Vector)`. 2. Methods that accept `RDD[Array[Double]]` now accept `RDD[Vector]`. We cannot support both in an elegant way because of type erasure. 3. Mark 'createModel' and 'predictPoint' protected because they are not for end users. 4. Add libSVMFile to MLContext. 5. NaiveBayes can accept arbitrary labels (introducing a breaking change to Python's `NaiveBayesModel`). 6. Gradient computation no longer creates temp vectors. 7. Column normalization and centering are removed from Lasso and Ridge because the operation will densify the data. Simple feature transformation can be done before training. TODO: 1. ~~Use axpy when possible.~~ 2. ~~Optimize Naive Bayes.~~ Author: Xiangrui Meng Closes #245 from mengxr/vector and squashes the following commits: eb6e793 [Xiangrui Meng] move libSVMFile to MLUtils and rename to loadLibSVMData c26c4fc [Xiangrui Meng] update DecisionTree to use RDD[Vector] 11999c7 [Xiangrui Meng] Merge branch 'master' into vector f7da54b [Xiangrui Meng] add minSplits to libSVMFile da25e24 [Xiangrui Meng] revert the change to default addIntercept because it might change the behavior of existing code without warning 493f26f [Xiangrui Meng] Merge branch 'master' into vector 7c1bc01 [Xiangrui Meng] add a TODO to NB b9b7ef7 [Xiangrui Meng] change default value of addIntercept to false b01df54 [Xiangrui Meng] allow to change or clear threshold in LR and SVM 4addc50 [Xiangrui Meng] merge master 4ca5b1b [Xiangrui Meng] remove normalization from Lasso and update tests f04fe8a [Xiangrui Meng] remove normalization from RidgeRegression and update tests d088552 [Xiangrui Meng] use static constructor for MLContext 6f59eed [Xiangrui Meng] update libSVMFile to determine number of features automatically 3432e84 [Xiangrui Meng] update NaiveBayes to support sparse data 0f8759b [Xiangrui Meng] minor updates to NB b11659c [Xiangrui Meng] style update 78c4671 [Xiangrui Meng] add libSVMFile to MLContext f0fe616 [Xiangrui Meng] add a test for sparse linear regression 44733e1 [Xiangrui Meng] use in-place gradient computation e981396 [Xiangrui Meng] use axpy in Updater db808a1 [Xiangrui Meng] update JavaLR example befa592 [Xiangrui Meng] passed scala/java tests 75c83a4 [Xiangrui Meng] passed test compile 1859701 [Xiangrui Meng] passed compile 834ada2 [Xiangrui Meng] optimized MLUtils.computeStats update some ml algorithms to use Vector (cont.) 135ab72 [Xiangrui Meng] merge glm 0e57aa4 [Xiangrui Meng] update Lasso and RidgeRegression to parse the weights correctly from GLM mark createModel protected mark predictPoint protected d7f629f [Xiangrui Meng] fix a bug in GLM when intercept is not used 3f346ba [Xiangrui Meng] update some ml algorithms to use Vector --- .../apache/spark/mllib/examples/JavaLR.java | 14 +- .../mllib/api/python/PythonMLLibAPI.scala | 154 ++++++++++---- .../classification/ClassificationModel.scala | 13 +- .../classification/LogisticRegression.scala | 84 ++++---- .../mllib/classification/NaiveBayes.scala | 116 ++++++----- .../spark/mllib/classification/SVM.scala | 69 ++++--- .../spark/mllib/clustering/KMeans.scala | 3 +- .../apache/spark/mllib/linalg/Vectors.scala | 8 + .../spark/mllib/optimization/Gradient.scala | 117 ++++++++--- .../mllib/optimization/GradientDescent.scala | 58 +++--- .../spark/mllib/optimization/Optimizer.scala | 7 +- .../spark/mllib/optimization/Updater.scala | 69 +++++-- .../GeneralizedLinearAlgorithm.scala | 69 ++++--- .../spark/mllib/regression/LabeledPoint.scala | 6 +- .../apache/spark/mllib/regression/Lasso.scala | 79 ++----- .../mllib/regression/LinearRegression.scala | 37 ++-- .../mllib/regression/RegressionModel.scala | 5 +- .../mllib/regression/RidgeRegression.scala | 75 ++----- .../spark/mllib/tree/DecisionTree.scala | 9 +- .../mllib/tree/model/DecisionTreeModel.scala | 5 +- .../apache/spark/mllib/tree/model/Node.scala | 7 +- .../mllib/util/LinearDataGenerator.scala | 3 +- .../LogisticRegressionDataGenerator.scala | 3 +- .../org/apache/spark/mllib/util/MLUtils.scala | 193 ++++++++++++++---- .../spark/mllib/util/SVMDataGenerator.scala | 3 +- .../classification/JavaNaiveBayesSuite.java | 13 +- .../mllib/classification/JavaSVMSuite.java | 3 - .../spark/mllib/linalg/JavaVectorsSuite.java | 6 +- .../mllib/regression/JavaLassoSuite.java | 4 +- .../regression/JavaRidgeRegressionSuite.java | 38 ++-- .../LogisticRegressionSuite.scala | 7 +- .../classification/NaiveBayesSuite.scala | 4 +- .../spark/mllib/classification/SVMSuite.scala | 10 +- .../optimization/GradientDescentSuite.scala | 14 +- .../spark/mllib/regression/LassoSuite.scala | 51 +++-- .../regression/LinearRegressionSuite.scala | 54 ++++- .../regression/RidgeRegressionSuite.scala | 27 +-- .../spark/mllib/tree/DecisionTreeSuite.scala | 9 +- .../spark/mllib/util/MLUtilsSuite.scala | 59 +++++- python/pyspark/mllib/classification.py | 12 +- 40 files changed, 926 insertions(+), 591 deletions(-) diff --git a/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java b/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java index 667c72f379e71..cd8879ff886e2 100644 --- a/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java +++ b/examples/src/main/java/org/apache/spark/mllib/examples/JavaLR.java @@ -17,6 +17,7 @@ package org.apache.spark.mllib.examples; +import java.util.regex.Pattern; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; @@ -24,11 +25,9 @@ import org.apache.spark.mllib.classification.LogisticRegressionWithSGD; import org.apache.spark.mllib.classification.LogisticRegressionModel; +import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; -import java.util.Arrays; -import java.util.regex.Pattern; - /** * Logistic regression based classification using ML Lib. */ @@ -47,14 +46,10 @@ public LabeledPoint call(String line) { for (int i = 0; i < tok.length; ++i) { x[i] = Double.parseDouble(tok[i]); } - return new LabeledPoint(y, x); + return new LabeledPoint(y, Vectors.dense(x)); } } - public static void printWeights(double[] a) { - System.out.println(Arrays.toString(a)); - } - public static void main(String[] args) { if (args.length != 4) { System.err.println("Usage: JavaLR "); @@ -80,8 +75,7 @@ public static void main(String[] args) { LogisticRegressionModel model = LogisticRegressionWithSGD.train(points.rdd(), iterations, stepSize); - System.out.print("Final w: "); - printWeights(model.weights()); + System.out.print("Final w: " + model.weights()); System.exit(0); } 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 3449c698da60b..2df5b0d02b699 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 @@ -110,16 +110,16 @@ class PythonMLLibAPI extends Serializable { private def trainRegressionModel( trainFunc: (RDD[LabeledPoint], Array[Double]) => GeneralizedLinearModel, - dataBytesJRDD: JavaRDD[Array[Byte]], initialWeightsBA: Array[Byte]): - java.util.LinkedList[java.lang.Object] = { + dataBytesJRDD: JavaRDD[Array[Byte]], + initialWeightsBA: Array[Byte]): java.util.LinkedList[java.lang.Object] = { val data = dataBytesJRDD.rdd.map(xBytes => { val x = deserializeDoubleVector(xBytes) - LabeledPoint(x(0), x.slice(1, x.length)) + LabeledPoint(x(0), Vectors.dense(x.slice(1, x.length))) }) val initialWeights = deserializeDoubleVector(initialWeightsBA) val model = trainFunc(data, initialWeights) val ret = new java.util.LinkedList[java.lang.Object]() - ret.add(serializeDoubleVector(model.weights)) + ret.add(serializeDoubleVector(model.weights.toArray)) ret.add(model.intercept: java.lang.Double) ret } @@ -127,75 +127,127 @@ class PythonMLLibAPI extends Serializable { /** * Java stub for Python mllib LinearRegressionWithSGD.train() */ - def trainLinearRegressionModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], - numIterations: Int, stepSize: Double, miniBatchFraction: Double, + def trainLinearRegressionModelWithSGD( + dataBytesJRDD: JavaRDD[Array[Byte]], + numIterations: Int, + stepSize: Double, + miniBatchFraction: Double, initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { - trainRegressionModel((data, initialWeights) => - LinearRegressionWithSGD.train(data, numIterations, stepSize, - miniBatchFraction, initialWeights), - dataBytesJRDD, initialWeightsBA) + trainRegressionModel( + (data, initialWeights) => + LinearRegressionWithSGD.train( + data, + numIterations, + stepSize, + miniBatchFraction, + Vectors.dense(initialWeights)), + dataBytesJRDD, + initialWeightsBA) } /** * Java stub for Python mllib LassoWithSGD.train() */ - def trainLassoModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, - stepSize: Double, regParam: Double, miniBatchFraction: Double, + def trainLassoModelWithSGD( + dataBytesJRDD: JavaRDD[Array[Byte]], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double, initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { - trainRegressionModel((data, initialWeights) => - LassoWithSGD.train(data, numIterations, stepSize, regParam, - miniBatchFraction, initialWeights), - dataBytesJRDD, initialWeightsBA) + trainRegressionModel( + (data, initialWeights) => + LassoWithSGD.train( + data, + numIterations, + stepSize, + regParam, + miniBatchFraction, + Vectors.dense(initialWeights)), + dataBytesJRDD, + initialWeightsBA) } /** * Java stub for Python mllib RidgeRegressionWithSGD.train() */ - def trainRidgeModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, - stepSize: Double, regParam: Double, miniBatchFraction: Double, + def trainRidgeModelWithSGD( + dataBytesJRDD: JavaRDD[Array[Byte]], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double, initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { - trainRegressionModel((data, initialWeights) => - RidgeRegressionWithSGD.train(data, numIterations, stepSize, regParam, - miniBatchFraction, initialWeights), - dataBytesJRDD, initialWeightsBA) + trainRegressionModel( + (data, initialWeights) => + RidgeRegressionWithSGD.train( + data, + numIterations, + stepSize, + regParam, + miniBatchFraction, + Vectors.dense(initialWeights)), + dataBytesJRDD, + initialWeightsBA) } /** * Java stub for Python mllib SVMWithSGD.train() */ - def trainSVMModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], numIterations: Int, - stepSize: Double, regParam: Double, miniBatchFraction: Double, + def trainSVMModelWithSGD( + dataBytesJRDD: JavaRDD[Array[Byte]], + numIterations: Int, + stepSize: Double, + regParam: Double, + miniBatchFraction: Double, initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { - trainRegressionModel((data, initialWeights) => - SVMWithSGD.train(data, numIterations, stepSize, regParam, - miniBatchFraction, initialWeights), - dataBytesJRDD, initialWeightsBA) + trainRegressionModel( + (data, initialWeights) => + SVMWithSGD.train( + data, + numIterations, + stepSize, + regParam, + miniBatchFraction, + Vectors.dense(initialWeights)), + dataBytesJRDD, + initialWeightsBA) } /** * Java stub for Python mllib LogisticRegressionWithSGD.train() */ - def trainLogisticRegressionModelWithSGD(dataBytesJRDD: JavaRDD[Array[Byte]], - numIterations: Int, stepSize: Double, miniBatchFraction: Double, + def trainLogisticRegressionModelWithSGD( + dataBytesJRDD: JavaRDD[Array[Byte]], + numIterations: Int, + stepSize: Double, + miniBatchFraction: Double, initialWeightsBA: Array[Byte]): java.util.List[java.lang.Object] = { - trainRegressionModel((data, initialWeights) => - LogisticRegressionWithSGD.train(data, numIterations, stepSize, - miniBatchFraction, initialWeights), - dataBytesJRDD, initialWeightsBA) + trainRegressionModel( + (data, initialWeights) => + LogisticRegressionWithSGD.train( + data, + numIterations, + stepSize, + miniBatchFraction, + Vectors.dense(initialWeights)), + dataBytesJRDD, + initialWeightsBA) } /** * Java stub for NaiveBayes.train() */ - def trainNaiveBayes(dataBytesJRDD: JavaRDD[Array[Byte]], lambda: Double) - : java.util.List[java.lang.Object] = - { + def trainNaiveBayes( + dataBytesJRDD: JavaRDD[Array[Byte]], + lambda: Double): java.util.List[java.lang.Object] = { val data = dataBytesJRDD.rdd.map(xBytes => { val x = deserializeDoubleVector(xBytes) - LabeledPoint(x(0), x.slice(1, x.length)) + LabeledPoint(x(0), Vectors.dense(x.slice(1, x.length))) }) val model = NaiveBayes.train(data, lambda) val ret = new java.util.LinkedList[java.lang.Object]() + ret.add(serializeDoubleVector(model.labels)) ret.add(serializeDoubleVector(model.pi)) ret.add(serializeDoubleMatrix(model.theta)) ret @@ -204,9 +256,12 @@ class PythonMLLibAPI extends Serializable { /** * Java stub for Python mllib KMeans.train() */ - def trainKMeansModel(dataBytesJRDD: JavaRDD[Array[Byte]], k: Int, - maxIterations: Int, runs: Int, initializationMode: String): - java.util.List[java.lang.Object] = { + def trainKMeansModel( + dataBytesJRDD: JavaRDD[Array[Byte]], + k: Int, + maxIterations: Int, + runs: Int, + initializationMode: String): java.util.List[java.lang.Object] = { val data = dataBytesJRDD.rdd.map(xBytes => Vectors.dense(deserializeDoubleVector(xBytes))) val model = KMeans.train(data, k, maxIterations, runs, initializationMode) val ret = new java.util.LinkedList[java.lang.Object]() @@ -259,8 +314,12 @@ class PythonMLLibAPI extends Serializable { * needs to be taken in the Python code to ensure it gets freed on exit; see * the Py4J documentation. */ - def trainALSModel(ratingsBytesJRDD: JavaRDD[Array[Byte]], rank: Int, - iterations: Int, lambda: Double, blocks: Int): MatrixFactorizationModel = { + def trainALSModel( + ratingsBytesJRDD: JavaRDD[Array[Byte]], + rank: Int, + iterations: Int, + lambda: Double, + blocks: Int): MatrixFactorizationModel = { val ratings = ratingsBytesJRDD.rdd.map(unpackRating) ALS.train(ratings, rank, iterations, lambda, blocks) } @@ -271,8 +330,13 @@ class PythonMLLibAPI extends Serializable { * Extra care needs to be taken in the Python code to ensure it gets freed on * exit; see the Py4J documentation. */ - def trainImplicitALSModel(ratingsBytesJRDD: JavaRDD[Array[Byte]], rank: Int, - iterations: Int, lambda: Double, blocks: Int, alpha: Double): MatrixFactorizationModel = { + def trainImplicitALSModel( + ratingsBytesJRDD: JavaRDD[Array[Byte]], + rank: Int, + iterations: Int, + lambda: Double, + blocks: Int, + alpha: Double): MatrixFactorizationModel = { val ratings = ratingsBytesJRDD.rdd.map(unpackRating) ALS.trainImplicit(ratings, rank, iterations, lambda, blocks, alpha) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala index 391f5b9b7a7de..bd10e2e9e10e2 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala @@ -17,22 +17,27 @@ package org.apache.spark.mllib.classification +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.rdd.RDD +/** + * Represents a classification model that predicts to which of a set of categories an example + * belongs. The categories are represented by double values: 0.0, 1.0, 2.0, etc. + */ trait ClassificationModel extends Serializable { /** * Predict values for the given data set using the model trained. * * @param testData RDD representing data points to be predicted - * @return RDD[Int] where each entry contains the corresponding prediction + * @return an RDD[Double] where each entry contains the corresponding prediction */ - def predict(testData: RDD[Array[Double]]): RDD[Double] + def predict(testData: RDD[Vector]): RDD[Double] /** * Predict values for a single data point using the model trained. * * @param testData array representing a single data point - * @return Int prediction from the trained model + * @return predicted category from the trained model */ - def predict(testData: Array[Double]): Double + def predict(testData: Vector): Double } 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 a481f522761e2..798f3a5c94740 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 @@ -17,16 +17,12 @@ package org.apache.spark.mllib.classification -import scala.math.round - import org.apache.spark.SparkContext -import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.regression._ -import org.apache.spark.mllib.util.MLUtils -import org.apache.spark.mllib.util.DataValidators - -import org.jblas.DoubleMatrix +import org.apache.spark.mllib.util.{DataValidators, MLUtils} +import org.apache.spark.rdd.RDD /** * Classification model trained using Logistic Regression. @@ -35,15 +31,38 @@ import org.jblas.DoubleMatrix * @param intercept Intercept computed for this model. */ class LogisticRegressionModel( - override val weights: Array[Double], + override val weights: Vector, override val intercept: Double) - extends GeneralizedLinearModel(weights, intercept) - with ClassificationModel with Serializable { + extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable { + + private var threshold: Option[Double] = Some(0.5) + + /** + * Sets the threshold that separates positive predictions from negative predictions. An example + * with prediction score greater than or equal to this threshold is identified as an positive, + * and negative otherwise. The default value is 0.5. + */ + def setThreshold(threshold: Double): this.type = { + this.threshold = Some(threshold) + this + } - override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix, + /** + * Clears the threshold so that `predict` will output raw prediction scores. + */ + def clearThreshold(): this.type = { + threshold = None + this + } + + override def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double) = { - val margin = dataMatrix.mmul(weightMatrix).get(0) + intercept - round(1.0/ (1.0 + math.exp(margin * -1))) + val margin = weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept + val score = 1.0/ (1.0 + math.exp(-margin)) + threshold match { + case Some(t) => if (score < t) 0.0 else 1.0 + case None => score + } } } @@ -56,16 +75,15 @@ class LogisticRegressionWithSGD private ( var numIterations: Int, var regParam: Double, var miniBatchFraction: Double) - extends GeneralizedLinearAlgorithm[LogisticRegressionModel] - with Serializable { + extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable { val gradient = new LogisticGradient() val updater = new SimpleUpdater() override val optimizer = new GradientDescent(gradient, updater) - .setStepSize(stepSize) - .setNumIterations(numIterations) - .setRegParam(regParam) - .setMiniBatchFraction(miniBatchFraction) + .setStepSize(stepSize) + .setNumIterations(numIterations) + .setRegParam(regParam) + .setMiniBatchFraction(miniBatchFraction) override val validators = List(DataValidators.classificationLabels) /** @@ -73,7 +91,7 @@ class LogisticRegressionWithSGD private ( */ def this() = this(1.0, 100, 0.0, 1.0) - def createModel(weights: Array[Double], intercept: Double) = { + def createModel(weights: Vector, intercept: Double) = { new LogisticRegressionModel(weights, intercept) } } @@ -105,11 +123,9 @@ object LogisticRegressionWithSGD { numIterations: Int, stepSize: Double, miniBatchFraction: Double, - initialWeights: Array[Double]) - : LogisticRegressionModel = - { - new LogisticRegressionWithSGD(stepSize, numIterations, 0.0, miniBatchFraction).run( - input, initialWeights) + initialWeights: Vector): LogisticRegressionModel = { + new LogisticRegressionWithSGD(stepSize, numIterations, 0.0, miniBatchFraction) + .run(input, initialWeights) } /** @@ -128,11 +144,9 @@ object LogisticRegressionWithSGD { input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, - miniBatchFraction: Double) - : LogisticRegressionModel = - { - new LogisticRegressionWithSGD(stepSize, numIterations, 0.0, miniBatchFraction).run( - input) + miniBatchFraction: Double): LogisticRegressionModel = { + new LogisticRegressionWithSGD(stepSize, numIterations, 0.0, miniBatchFraction) + .run(input) } /** @@ -150,9 +164,7 @@ object LogisticRegressionWithSGD { def train( input: RDD[LabeledPoint], numIterations: Int, - stepSize: Double) - : LogisticRegressionModel = - { + stepSize: Double): LogisticRegressionModel = { train(input, numIterations, stepSize, 1.0) } @@ -168,9 +180,7 @@ object LogisticRegressionWithSGD { */ def train( input: RDD[LabeledPoint], - numIterations: Int) - : LogisticRegressionModel = - { + numIterations: Int): LogisticRegressionModel = { train(input, numIterations, 1.0, 1.0) } @@ -183,7 +193,7 @@ object LogisticRegressionWithSGD { val sc = new SparkContext(args(0), "LogisticRegression") val data = MLUtils.loadLabeledData(sc, args(1)) val model = LogisticRegressionWithSGD.train(data, args(3).toInt, args(2).toDouble) - println("Weights: " + model.weights.mkString("[", ", ", "]")) + println("Weights: " + model.weights) println("Intercept: " + model.intercept) sc.stop() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala index 6539b2f339465..e956185319a69 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala @@ -17,14 +17,14 @@ package org.apache.spark.mllib.classification -import scala.collection.mutable +import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum} -import org.jblas.DoubleMatrix - -import org.apache.spark.{SparkContext, Logging} +import org.apache.spark.{Logging, SparkContext} +import org.apache.spark.SparkContext._ +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint -import org.apache.spark.rdd.RDD import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.rdd.RDD /** * Model for Naive Bayes Classifiers. @@ -32,19 +32,28 @@ import org.apache.spark.mllib.util.MLUtils * @param pi Log of class priors, whose dimension is C. * @param theta Log of class conditional probabilities, whose dimension is CxD. */ -class NaiveBayesModel(val pi: Array[Double], val theta: Array[Array[Double]]) - extends ClassificationModel with Serializable { - - // Create a column vector that can be used for predictions - private val _pi = new DoubleMatrix(pi.length, 1, pi: _*) - private val _theta = new DoubleMatrix(theta) +class NaiveBayesModel( + val labels: Array[Double], + val pi: Array[Double], + val theta: Array[Array[Double]]) extends ClassificationModel with Serializable { + + private val brzPi = new BDV[Double](pi) + private val brzTheta = new BDM[Double](theta.length, theta(0).length) + + var i = 0 + while (i < theta.length) { + var j = 0 + while (j < theta(i).length) { + brzTheta(i, j) = theta(i)(j) + j += 1 + } + i += 1 + } - def predict(testData: RDD[Array[Double]]): RDD[Double] = testData.map(predict) + override def predict(testData: RDD[Vector]): RDD[Double] = testData.map(predict) - def predict(testData: Array[Double]): Double = { - val dataMatrix = new DoubleMatrix(testData.length, 1, testData: _*) - val result = _pi.add(_theta.mmul(dataMatrix)) - result.argmax() + override def predict(testData: Vector): Double = { + labels(brzArgmax(brzPi + brzTheta * testData.toBreeze)) } } @@ -56,9 +65,8 @@ class NaiveBayesModel(val pi: Array[Double], val theta: Array[Array[Double]]) * document classification. By making every vector a 0-1 vector, it can also be used as * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). */ -class NaiveBayes private (var lambda: Double) - extends Serializable with Logging -{ +class NaiveBayes private (var lambda: Double) extends Serializable with Logging { + def this() = this(1.0) /** Set the smoothing parameter. Default: 1.0. */ @@ -70,45 +78,42 @@ class NaiveBayes private (var lambda: Double) /** * Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries. * - * @param data RDD of (label, array of features) pairs. + * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]. */ def run(data: RDD[LabeledPoint]) = { - // Aggregates all sample points to driver side to get sample count and summed feature vector - // for each label. The shape of `zeroCombiner` & `aggregated` is: - // - // label: Int -> (count: Int, featuresSum: DoubleMatrix) - val zeroCombiner = mutable.Map.empty[Int, (Int, DoubleMatrix)] - val aggregated = data.aggregate(zeroCombiner)({ (combiner, point) => - point match { - case LabeledPoint(label, features) => - val (count, featuresSum) = combiner.getOrElse(label.toInt, (0, DoubleMatrix.zeros(1))) - val fs = new DoubleMatrix(features.length, 1, features: _*) - combiner += label.toInt -> (count + 1, featuresSum.addi(fs)) - } - }, { (lhs, rhs) => - for ((label, (c, fs)) <- rhs) { - val (count, featuresSum) = lhs.getOrElse(label, (0, DoubleMatrix.zeros(1))) - lhs(label) = (count + c, featuresSum.addi(fs)) + // Aggregates term frequencies per label. + // TODO: Calling combineByKey and collect creates two stages, we can implement something + // TODO: similar to reduceByKeyLocally to save one stage. + val aggregated = data.map(p => (p.label, p.features)).combineByKey[(Long, BDV[Double])]( + createCombiner = (v: Vector) => (1L, v.toBreeze.toDenseVector), + mergeValue = (c: (Long, BDV[Double]), v: Vector) => (c._1 + 1L, c._2 += v.toBreeze), + mergeCombiners = (c1: (Long, BDV[Double]), c2: (Long, BDV[Double])) => + (c1._1 + c2._1, c1._2 += c2._2) + ).collect() + val numLabels = aggregated.length + var numDocuments = 0L + aggregated.foreach { case (_, (n, _)) => + numDocuments += n + } + val numFeatures = aggregated.head match { case (_, (_, v)) => v.size } + val labels = new Array[Double](numLabels) + val pi = new Array[Double](numLabels) + val theta = Array.fill(numLabels)(new Array[Double](numFeatures)) + val piLogDenom = math.log(numDocuments + numLabels * lambda) + var i = 0 + aggregated.foreach { case (label, (n, sumTermFreqs)) => + labels(i) = label + val thetaLogDenom = math.log(brzSum(sumTermFreqs) + numFeatures * lambda) + pi(i) = math.log(n + lambda) - piLogDenom + var j = 0 + while (j < numFeatures) { + theta(i)(j) = math.log(sumTermFreqs(j) + lambda) - thetaLogDenom + j += 1 } - lhs - }) - - // Kinds of label - val C = aggregated.size - // Total sample count - val N = aggregated.values.map(_._1).sum - - val pi = new Array[Double](C) - val theta = new Array[Array[Double]](C) - val piLogDenom = math.log(N + C * lambda) - - for ((label, (count, fs)) <- aggregated) { - val thetaLogDenom = math.log(fs.sum() + fs.length * lambda) - pi(label) = math.log(count + lambda) - piLogDenom - theta(label) = fs.toArray.map(f => math.log(f + lambda) - thetaLogDenom) + i += 1 } - new NaiveBayesModel(pi, theta) + new NaiveBayesModel(labels, pi, theta) } } @@ -158,8 +163,9 @@ object NaiveBayes { } else { NaiveBayes.train(data, args(2).toDouble) } - println("Pi: " + model.pi.mkString("[", ", ", "]")) - println("Theta:\n" + model.theta.map(_.mkString("[", ", ", "]")).mkString("[", "\n ", "]")) + + println("Pi\n: " + model.pi) + println("Theta:\n" + model.theta) sc.stop() } 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 6dff29dfb45cc..e31a08899f8bc 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 @@ -18,13 +18,11 @@ package org.apache.spark.mllib.classification import org.apache.spark.SparkContext -import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.regression._ -import org.apache.spark.mllib.util.MLUtils -import org.apache.spark.mllib.util.DataValidators - -import org.jblas.DoubleMatrix +import org.apache.spark.mllib.util.{DataValidators, MLUtils} +import org.apache.spark.rdd.RDD /** * Model for Support Vector Machines (SVMs). @@ -33,15 +31,37 @@ import org.jblas.DoubleMatrix * @param intercept Intercept computed for this model. */ class SVMModel( - override val weights: Array[Double], + override val weights: Vector, override val intercept: Double) - extends GeneralizedLinearModel(weights, intercept) - with ClassificationModel with Serializable { + extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable { + + private var threshold: Option[Double] = Some(0.0) + + /** + * Sets the threshold that separates positive predictions from negative predictions. An example + * with prediction score greater than or equal to this threshold is identified as an positive, + * and negative otherwise. The default value is 0.0. + */ + def setThreshold(threshold: Double): this.type = { + this.threshold = Some(threshold) + this + } - override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix, + /** + * Clears the threshold so that `predict` will output raw prediction scores. + */ + def clearThreshold(): this.type = { + threshold = None + this + } + + override def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double) = { - val margin = dataMatrix.dot(weightMatrix) + intercept - if (margin < 0) 0.0 else 1.0 + val margin = weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept + threshold match { + case Some(t) => if (margin < 0) 0.0 else 1.0 + case None => margin + } } } @@ -71,7 +91,7 @@ class SVMWithSGD private ( */ def this() = this(1.0, 100, 1.0, 1.0) - def createModel(weights: Array[Double], intercept: Double) = { + def createModel(weights: Vector, intercept: Double) = { new SVMModel(weights, intercept) } } @@ -103,11 +123,9 @@ object SVMWithSGD { stepSize: Double, regParam: Double, miniBatchFraction: Double, - initialWeights: Array[Double]) - : SVMModel = - { - new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction).run(input, - initialWeights) + initialWeights: Vector): SVMModel = { + new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction) + .run(input, initialWeights) } /** @@ -127,9 +145,7 @@ object SVMWithSGD { numIterations: Int, stepSize: Double, regParam: Double, - miniBatchFraction: Double) - : SVMModel = - { + miniBatchFraction: Double): SVMModel = { new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction).run(input) } @@ -149,9 +165,7 @@ object SVMWithSGD { input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, - regParam: Double) - : SVMModel = - { + regParam: Double): SVMModel = { train(input, numIterations, stepSize, regParam, 1.0) } @@ -165,11 +179,7 @@ object SVMWithSGD { * @param numIterations Number of iterations of gradient descent to run. * @return a SVMModel which has the weights and offset from training. */ - def train( - input: RDD[LabeledPoint], - numIterations: Int) - : SVMModel = - { + def train(input: RDD[LabeledPoint], numIterations: Int): SVMModel = { train(input, numIterations, 1.0, 1.0, 1.0) } @@ -181,7 +191,8 @@ object SVMWithSGD { val sc = new SparkContext(args(0), "SVM") val data = MLUtils.loadLabeledData(sc, args(1)) val model = SVMWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble) - println("Weights: " + model.weights.mkString("[", ", ", "]")) + + println("Weights: " + model.weights) println("Intercept: " + model.intercept) sc.stop() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala index b412738e3f00a..a78503df3134d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala @@ -42,8 +42,7 @@ class KMeans private ( var runs: Int, var initializationMode: String, var initializationSteps: Int, - var epsilon: Double) - extends Serializable with Logging { + var epsilon: Double) extends Serializable with Logging { def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4) /** Set the number of clusters to create (k). Default: 2. */ 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 01c1501548f87..2cea58cd3fd22 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 @@ -54,6 +54,12 @@ trait Vector extends Serializable { * Converts the instance to a breeze vector. */ private[mllib] def toBreeze: BV[Double] + + /** + * Gets the value of the ith element. + * @param i index + */ + private[mllib] def apply(i: Int): Double = toBreeze(i) } /** @@ -145,6 +151,8 @@ class DenseVector(val values: Array[Double]) extends Vector { override def toArray: Array[Double] = values private[mllib] override def toBreeze: BV[Double] = new BDV[Double](values) + + override def apply(i: Int) = values(i) } /** diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala index 82124703da6cd..20654284965ed 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala @@ -17,7 +17,9 @@ package org.apache.spark.mllib.optimization -import org.jblas.DoubleMatrix +import breeze.linalg.{axpy => brzAxpy} + +import org.apache.spark.mllib.linalg.{Vectors, Vector} /** * Class used to compute the gradient for a loss function, given a single data point. @@ -26,17 +28,26 @@ abstract class Gradient extends Serializable { /** * Compute the gradient and loss given the features of a single data point. * - * @param data - Feature values for one data point. Column matrix of size dx1 - * where d is the number of features. - * @param label - Label for this data item. - * @param weights - Column matrix containing weights for every feature. + * @param data features for one data point + * @param label label for this data point + * @param weights weights/coefficients corresponding to features * - * @return A tuple of 2 elements. The first element is a column matrix containing the computed - * gradient and the second element is the loss computed at this data point. + * @return (gradient: Vector, loss: Double) + */ + def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) + + /** + * Compute the gradient and loss given the features of a single data point, + * add the gradient to a provided vector to avoid creating new objects, and return loss. * + * @param data features for one data point + * @param label label for this data point + * @param weights weights/coefficients corresponding to features + * @param cumGradient the computed gradient will be added to this vector + * + * @return loss */ - def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): - (DoubleMatrix, Double) + def compute(data: Vector, label: Double, weights: Vector, cumGradient: Vector): Double } /** @@ -44,12 +55,12 @@ abstract class Gradient extends Serializable { * See also the documentation for the precise formulation. */ class LogisticGradient extends Gradient { - override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): - (DoubleMatrix, Double) = { - val margin: Double = -1.0 * data.dot(weights) + override def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = { + val brzData = data.toBreeze + val brzWeights = weights.toBreeze + val margin: Double = -1.0 * brzWeights.dot(brzData) val gradientMultiplier = (1.0 / (1.0 + math.exp(margin))) - label - - val gradient = data.mul(gradientMultiplier) + val gradient = brzData * gradientMultiplier val loss = if (label > 0) { math.log(1 + math.exp(margin)) @@ -57,7 +68,26 @@ class LogisticGradient extends Gradient { math.log(1 + math.exp(margin)) - margin } - (gradient, loss) + (Vectors.fromBreeze(gradient), loss) + } + + override def compute( + data: Vector, + label: Double, + weights: Vector, + cumGradient: Vector): Double = { + val brzData = data.toBreeze + val brzWeights = weights.toBreeze + val margin: Double = -1.0 * brzWeights.dot(brzData) + val gradientMultiplier = (1.0 / (1.0 + math.exp(margin))) - label + + brzAxpy(gradientMultiplier, brzData, cumGradient.toBreeze) + + if (label > 0) { + math.log(1 + math.exp(margin)) + } else { + math.log(1 + math.exp(margin)) - margin + } } } @@ -68,14 +98,28 @@ class LogisticGradient extends Gradient { * See also the documentation for the precise formulation. */ class LeastSquaresGradient extends Gradient { - override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): - (DoubleMatrix, Double) = { - val diff: Double = data.dot(weights) - label - + override def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = { + val brzData = data.toBreeze + val brzWeights = weights.toBreeze + val diff = brzWeights.dot(brzData) - label val loss = diff * diff - val gradient = data.mul(2.0 * diff) + val gradient = brzData * (2.0 * diff) - (gradient, loss) + (Vectors.fromBreeze(gradient), loss) + } + + override def compute( + data: Vector, + label: Double, + weights: Vector, + cumGradient: Vector): Double = { + val brzData = data.toBreeze + val brzWeights = weights.toBreeze + val diff = brzWeights.dot(brzData) - label + + brzAxpy(2.0 * diff, brzData, cumGradient.toBreeze) + + diff * diff } } @@ -85,19 +129,40 @@ class LeastSquaresGradient extends Gradient { * NOTE: This assumes that the labels are {0,1} */ class HingeGradient extends Gradient { - override def compute(data: DoubleMatrix, label: Double, weights: DoubleMatrix): - (DoubleMatrix, Double) = { + override def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = { + val brzData = data.toBreeze + val brzWeights = weights.toBreeze + val dotProduct = brzWeights.dot(brzData) + + // Our loss function with {0, 1} labels is max(0, 1 - (2y – 1) (f_w(x))) + // Therefore the gradient is -(2y - 1)*x + val labelScaled = 2 * label - 1.0 + + if (1.0 > labelScaled * dotProduct) { + (Vectors.fromBreeze(brzData * (-labelScaled)), 1.0 - labelScaled * dotProduct) + } else { + (Vectors.dense(new Array[Double](weights.size)), 0.0) + } + } - val dotProduct = data.dot(weights) + override def compute( + data: Vector, + label: Double, + weights: Vector, + cumGradient: Vector): Double = { + val brzData = data.toBreeze + val brzWeights = weights.toBreeze + val dotProduct = brzWeights.dot(brzData) // Our loss function with {0, 1} labels is max(0, 1 - (2y – 1) (f_w(x))) // Therefore the gradient is -(2y - 1)*x val labelScaled = 2 * label - 1.0 if (1.0 > labelScaled * dotProduct) { - (data.mul(-labelScaled), 1.0 - labelScaled * dotProduct) + brzAxpy(-labelScaled, brzData, cumGradient.toBreeze) + 1.0 - labelScaled * dotProduct } else { - (DoubleMatrix.zeros(1, weights.length), 0.0) + 0.0 } } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala index b967b22e818d3..d0777ffd63ff8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala @@ -17,12 +17,13 @@ package org.apache.spark.mllib.optimization -import org.apache.spark.Logging -import org.apache.spark.rdd.RDD +import scala.collection.mutable.ArrayBuffer -import org.jblas.DoubleMatrix +import breeze.linalg.{Vector => BV, DenseVector => BDV} -import scala.collection.mutable.ArrayBuffer +import org.apache.spark.Logging +import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.{Vectors, Vector} /** * Class used to solve an optimization problem using Gradient Descent. @@ -91,18 +92,16 @@ class GradientDescent(var gradient: Gradient, var updater: Updater) this } - def optimize(data: RDD[(Double, Array[Double])], initialWeights: Array[Double]) - : Array[Double] = { - - val (weights, stochasticLossHistory) = GradientDescent.runMiniBatchSGD( - data, - gradient, - updater, - stepSize, - numIterations, - regParam, - miniBatchFraction, - initialWeights) + def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector = { + val (weights, _) = GradientDescent.runMiniBatchSGD( + data, + gradient, + updater, + stepSize, + numIterations, + regParam, + miniBatchFraction, + initialWeights) weights } @@ -133,14 +132,14 @@ object GradientDescent extends Logging { * stochastic loss computed for every iteration. */ def runMiniBatchSGD( - data: RDD[(Double, Array[Double])], + data: RDD[(Double, Vector)], gradient: Gradient, updater: Updater, stepSize: Double, numIterations: Int, regParam: Double, miniBatchFraction: Double, - initialWeights: Array[Double]) : (Array[Double], Array[Double]) = { + initialWeights: Vector): (Vector, Array[Double]) = { val stochasticLossHistory = new ArrayBuffer[Double](numIterations) @@ -148,24 +147,27 @@ object GradientDescent extends Logging { val miniBatchSize = nexamples * miniBatchFraction // Initialize weights as a column vector - var weights = new DoubleMatrix(initialWeights.length, 1, initialWeights:_*) + var weights = Vectors.dense(initialWeights.toArray) /** * For the first iteration, the regVal will be initialized as sum of sqrt of * weights if it's L2 update; for L1 update; the same logic is followed. */ var regVal = updater.compute( - weights, new DoubleMatrix(initialWeights.length, 1), 0, 1, regParam)._2 + weights, Vectors.dense(new Array[Double](weights.size)), 0, 1, regParam)._2 for (i <- 1 to numIterations) { // Sample a subset (fraction miniBatchFraction) of the total data // compute and sum up the subgradients on this subset (this is one map-reduce) - val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42 + i).map { - case (y, features) => - val featuresCol = new DoubleMatrix(features.length, 1, features:_*) - val (grad, loss) = gradient.compute(featuresCol, y, weights) - (grad, loss) - }.reduce((a, b) => (a._1.addi(b._1), a._2 + b._2)) + val (gradientSum, lossSum) = data.sample(false, miniBatchFraction, 42 + i) + .aggregate((BDV.zeros[Double](weights.size), 0.0))( + seqOp = (c, v) => (c, v) match { case ((grad, loss), (label, features)) => + val l = gradient.compute(features, label, weights, Vectors.fromBreeze(grad)) + (grad, loss + l) + }, + combOp = (c1, c2) => (c1, c2) match { case ((grad1, loss1), (grad2, loss2)) => + (grad1 += grad2, loss1 + loss2) + }) /** * NOTE(Xinghao): lossSum is computed using the weights from the previous iteration @@ -173,7 +175,7 @@ object GradientDescent extends Logging { */ stochasticLossHistory.append(lossSum / miniBatchSize + regVal) val update = updater.compute( - weights, gradientSum.div(miniBatchSize), stepSize, i, regParam) + weights, Vectors.fromBreeze(gradientSum / miniBatchSize), stepSize, i, regParam) weights = update._1 regVal = update._2 } @@ -181,6 +183,6 @@ object GradientDescent extends Logging { logInfo("GradientDescent.runMiniBatchSGD finished. Last 10 stochastic losses %s".format( stochasticLossHistory.takeRight(10).mkString(", "))) - (weights.toArray, stochasticLossHistory.toArray) + (weights, stochasticLossHistory.toArray) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Optimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Optimizer.scala index 94d30b56f212b..f9ce908a5f3b0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Optimizer.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Optimizer.scala @@ -19,11 +19,12 @@ package org.apache.spark.mllib.optimization import org.apache.spark.rdd.RDD -trait Optimizer { +import org.apache.spark.mllib.linalg.Vector + +trait Optimizer extends Serializable { /** * Solve the provided convex optimization problem. */ - def optimize(data: RDD[(Double, Array[Double])], initialWeights: Array[Double]): Array[Double] - + def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala index bf8f731459e99..3b7754cd7ac28 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Updater.scala @@ -18,7 +18,10 @@ package org.apache.spark.mllib.optimization import scala.math._ -import org.jblas.DoubleMatrix + +import breeze.linalg.{norm => brzNorm, axpy => brzAxpy, Vector => BV} + +import org.apache.spark.mllib.linalg.{Vectors, Vector} /** * Class used to perform steps (weight update) using Gradient Descent methods. @@ -47,8 +50,12 @@ abstract class Updater extends Serializable { * @return A tuple of 2 elements. The first element is a column matrix containing updated weights, * and the second element is the regularization value computed using updated weights. */ - def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, stepSize: Double, iter: Int, - regParam: Double): (DoubleMatrix, Double) + def compute( + weightsOld: Vector, + gradient: Vector, + stepSize: Double, + iter: Int, + regParam: Double): (Vector, Double) } /** @@ -56,11 +63,17 @@ abstract class Updater extends Serializable { * Uses a step-size decreasing with the square root of the number of iterations. */ class SimpleUpdater extends Updater { - override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, - stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = { + override def compute( + weightsOld: Vector, + gradient: Vector, + stepSize: Double, + iter: Int, + regParam: Double): (Vector, Double) = { val thisIterStepSize = stepSize / math.sqrt(iter) - val step = gradient.mul(thisIterStepSize) - (weightsOld.sub(step), 0) + val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector + brzAxpy(-thisIterStepSize, gradient.toBreeze, brzWeights) + + (Vectors.fromBreeze(brzWeights), 0) } } @@ -83,19 +96,26 @@ class SimpleUpdater extends Updater { * Equivalently, set weight component to signum(w) * max(0.0, abs(w) - shrinkageVal) */ class L1Updater extends Updater { - override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, - stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = { + override def compute( + weightsOld: Vector, + gradient: Vector, + stepSize: Double, + iter: Int, + regParam: Double): (Vector, Double) = { val thisIterStepSize = stepSize / math.sqrt(iter) - val step = gradient.mul(thisIterStepSize) // Take gradient step - val newWeights = weightsOld.sub(step) + val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector + brzAxpy(-thisIterStepSize, gradient.toBreeze, brzWeights) // Apply proximal operator (soft thresholding) val shrinkageVal = regParam * thisIterStepSize - (0 until newWeights.length).foreach { i => - val wi = newWeights.get(i) - newWeights.put(i, signum(wi) * max(0.0, abs(wi) - shrinkageVal)) + var i = 0 + while (i < brzWeights.length) { + val wi = brzWeights(i) + brzWeights(i) = signum(wi) * max(0.0, abs(wi) - shrinkageVal) + i += 1 } - (newWeights, newWeights.norm1 * regParam) + + (Vectors.fromBreeze(brzWeights), brzNorm(brzWeights, 1.0) * regParam) } } @@ -105,16 +125,23 @@ class L1Updater extends Updater { * Uses a step-size decreasing with the square root of the number of iterations. */ class SquaredL2Updater extends Updater { - override def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, - stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double) = { - val thisIterStepSize = stepSize / math.sqrt(iter) - val step = gradient.mul(thisIterStepSize) + override def compute( + weightsOld: Vector, + gradient: Vector, + stepSize: Double, + iter: Int, + regParam: Double): (Vector, Double) = { // add up both updates from the gradient of the loss (= step) as well as // the gradient of the regularizer (= regParam * weightsOld) // w' = w - thisIterStepSize * (gradient + regParam * w) // w' = (1 - thisIterStepSize * regParam) * w - thisIterStepSize * gradient - val newWeights = weightsOld.mul(1.0 - thisIterStepSize * regParam).sub(step) - (newWeights, 0.5 * pow(newWeights.norm2, 2.0) * regParam) + val thisIterStepSize = stepSize / math.sqrt(iter) + val brzWeights: BV[Double] = weightsOld.toBreeze.toDenseVector + brzWeights :*= (1.0 - thisIterStepSize * regParam) + brzAxpy(-thisIterStepSize, gradient.toBreeze, brzWeights) + val norm = brzNorm(brzWeights, 2.0) + + (Vectors.fromBreeze(brzWeights), 0.5 * regParam * norm * norm) } } 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 3e1ed91bf6729..80dc0f12ff84f 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 @@ -17,11 +17,12 @@ package org.apache.spark.mllib.regression +import breeze.linalg.{DenseVector => BDV, SparseVector => BSV} + import org.apache.spark.{Logging, SparkException} import org.apache.spark.rdd.RDD import org.apache.spark.mllib.optimization._ - -import org.jblas.DoubleMatrix +import org.apache.spark.mllib.linalg.{Vectors, Vector} /** * GeneralizedLinearModel (GLM) represents a model trained using @@ -31,12 +32,9 @@ import org.jblas.DoubleMatrix * @param weights Weights computed for every feature. * @param intercept Intercept computed for this model. */ -abstract class GeneralizedLinearModel(val weights: Array[Double], val intercept: Double) +abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double) extends Serializable { - // Create a column vector that can be used for predictions - private val weightsMatrix = new DoubleMatrix(weights.length, 1, weights:_*) - /** * Predict the result given a data point and the weights learned. * @@ -44,8 +42,7 @@ abstract class GeneralizedLinearModel(val weights: Array[Double], val intercept: * @param weightMatrix Column vector containing the weights of the model * @param intercept Intercept of the model. */ - def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix, - intercept: Double): Double + protected def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double /** * Predict values for the given data set using the model trained. @@ -53,16 +50,13 @@ abstract class GeneralizedLinearModel(val weights: Array[Double], val intercept: * @param testData RDD representing data points to be predicted * @return RDD[Double] where each entry contains the corresponding prediction */ - def predict(testData: RDD[Array[Double]]): RDD[Double] = { + def predict(testData: RDD[Vector]): RDD[Double] = { // A small optimization to avoid serializing the entire model. Only the weightsMatrix // and intercept is needed. - val localWeights = weightsMatrix + val localWeights = weights val localIntercept = intercept - testData.map { x => - val dataMatrix = new DoubleMatrix(1, x.length, x:_*) - predictPoint(dataMatrix, localWeights, localIntercept) - } + testData.map(v => predictPoint(v, localWeights, localIntercept)) } /** @@ -71,14 +65,13 @@ abstract class GeneralizedLinearModel(val weights: Array[Double], val intercept: * @param testData array representing a single data point * @return Double prediction from the trained model */ - def predict(testData: Array[Double]): Double = { - val dataMat = new DoubleMatrix(1, testData.length, testData:_*) - predictPoint(dataMat, weightsMatrix, intercept) + def predict(testData: Vector): Double = { + predictPoint(testData, weights, intercept) } } /** - * GeneralizedLinearAlgorithm implements methods to train a Genearalized Linear Model (GLM). + * GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM). * This class should be extended with an Optimizer to create a new GLM. */ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] @@ -88,6 +81,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] val optimizer: Optimizer + /** Whether to add intercept (default: true). */ protected var addIntercept: Boolean = true protected var validateData: Boolean = true @@ -95,7 +89,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] /** * Create a model given the weights and intercept */ - protected def createModel(weights: Array[Double], intercept: Double): M + protected def createModel(weights: Vector, intercept: Double): M /** * Set if the algorithm should add an intercept. Default true. @@ -117,17 +111,27 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] * Run the algorithm with the configured parameters on an input * RDD of LabeledPoint entries. */ - def run(input: RDD[LabeledPoint]) : M = { - val nfeatures: Int = input.first().features.length - val initialWeights = new Array[Double](nfeatures) + def run(input: RDD[LabeledPoint]): M = { + val numFeatures: Int = input.first().features.size + val initialWeights = Vectors.dense(new Array[Double](numFeatures)) run(input, initialWeights) } + /** Prepends one to the input vector. */ + private def prependOne(vector: Vector): Vector = { + val vector1 = vector.toBreeze match { + case dv: BDV[Double] => BDV.vertcat(BDV.ones[Double](1), dv) + case sv: BSV[Double] => BSV.vertcat(new BSV[Double](Array(0), Array(1.0), 1), sv) + case v: Any => throw new IllegalArgumentException("Do not support vector type " + v.getClass) + } + Vectors.fromBreeze(vector1) + } + /** * Run the algorithm with the configured parameters on an input RDD * of LabeledPoint entries starting from the initial weights provided. */ - def run(input: RDD[LabeledPoint], initialWeights: Array[Double]) : M = { + def run(input: RDD[LabeledPoint], initialWeights: Vector): M = { // Check the data properties before running the optimizer if (validateData && !validators.forall(func => func(input))) { @@ -136,27 +140,26 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] // Prepend an extra variable consisting of all 1.0's for the intercept. val data = if (addIntercept) { - input.map(labeledPoint => (labeledPoint.label, 1.0 +: labeledPoint.features)) + input.map(labeledPoint => (labeledPoint.label, prependOne(labeledPoint.features))) } else { input.map(labeledPoint => (labeledPoint.label, labeledPoint.features)) } val initialWeightsWithIntercept = if (addIntercept) { - 0.0 +: initialWeights + prependOne(initialWeights) } else { initialWeights } val weightsWithIntercept = optimizer.optimize(data, initialWeightsWithIntercept) - val (intercept, weights) = if (addIntercept) { - (weightsWithIntercept(0), weightsWithIntercept.tail) - } else { - (0.0, weightsWithIntercept) - } - - logInfo("Final weights " + weights.mkString(",")) - logInfo("Final intercept " + intercept) + val intercept = if (addIntercept) weightsWithIntercept(0) else 0.0 + val weights = + if (addIntercept) { + Vectors.dense(weightsWithIntercept.toArray.slice(1, weightsWithIntercept.size)) + } else { + weightsWithIntercept + } createModel(weights, intercept) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala index 1a18292fe3f3b..3deab1ab785b9 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala @@ -17,14 +17,16 @@ package org.apache.spark.mllib.regression +import org.apache.spark.mllib.linalg.Vector + /** * Class that represents the features and labels of a data point. * * @param label Label for this data point. * @param features List of features for this data point. */ -case class LabeledPoint(label: Double, features: Array[Double]) { +case class LabeledPoint(label: Double, features: Vector) { override def toString: String = { - "LabeledPoint(%s, %s)".format(label, features.mkString("[", ", ", "]")) + "LabeledPoint(%s, %s)".format(label, features) } } 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 be63ce8538fef..25920d0dc976e 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 @@ -17,12 +17,11 @@ package org.apache.spark.mllib.regression -import org.apache.spark.{Logging, SparkContext} -import org.apache.spark.rdd.RDD +import org.apache.spark.SparkContext +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.util.MLUtils - -import org.jblas.DoubleMatrix +import org.apache.spark.rdd.RDD /** * Regression model trained using Lasso. @@ -31,16 +30,16 @@ import org.jblas.DoubleMatrix * @param intercept Intercept computed for this model. */ class LassoModel( - override val weights: Array[Double], + override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable { - override def predictPoint( - dataMatrix: DoubleMatrix, - weightMatrix: DoubleMatrix, + override protected def predictPoint( + dataMatrix: Vector, + weightMatrix: Vector, intercept: Double): Double = { - dataMatrix.dot(weightMatrix) + intercept + weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept } } @@ -57,8 +56,7 @@ class LassoWithSGD private ( var numIterations: Int, var regParam: Double, var miniBatchFraction: Double) - extends GeneralizedLinearAlgorithm[LassoModel] - with Serializable { + extends GeneralizedLinearAlgorithm[LassoModel] with Serializable { val gradient = new LeastSquaresGradient() val updater = new L1Updater() @@ -70,10 +68,6 @@ class LassoWithSGD private ( // We don't want to penalize the intercept, so set this to false. super.setIntercept(false) - var yMean = 0.0 - var xColMean: DoubleMatrix = _ - var xColSd: DoubleMatrix = _ - /** * Construct a Lasso object with default parameters */ @@ -85,36 +79,8 @@ class LassoWithSGD private ( this } - override def createModel(weights: Array[Double], intercept: Double) = { - val weightsMat = new DoubleMatrix(weights.length, 1, weights: _*) - val weightsScaled = weightsMat.div(xColSd) - val interceptScaled = yMean - weightsMat.transpose().mmul(xColMean.div(xColSd)).get(0) - - new LassoModel(weightsScaled.data, interceptScaled) - } - - override def run( - input: RDD[LabeledPoint], - initialWeights: Array[Double]) - : LassoModel = - { - val nfeatures: Int = input.first.features.length - val nexamples: Long = input.count() - - // To avoid penalizing the intercept, we center and scale the data. - val stats = MLUtils.computeStats(input, nfeatures, nexamples) - yMean = stats._1 - xColMean = stats._2 - xColSd = stats._3 - - val normalizedData = input.map { point => - val yNormalized = point.label - yMean - val featuresMat = new DoubleMatrix(nfeatures, 1, point.features:_*) - val featuresNormalized = featuresMat.sub(xColMean).divi(xColSd) - LabeledPoint(yNormalized, featuresNormalized.toArray) - } - - super.run(normalizedData, initialWeights) + override protected def createModel(weights: Vector, intercept: Double) = { + new LassoModel(weights, intercept) } } @@ -144,11 +110,9 @@ object LassoWithSGD { stepSize: Double, regParam: Double, miniBatchFraction: Double, - initialWeights: Array[Double]) - : LassoModel = - { - new LassoWithSGD(stepSize, numIterations, regParam, miniBatchFraction).run(input, - initialWeights) + initialWeights: Vector): LassoModel = { + new LassoWithSGD(stepSize, numIterations, regParam, miniBatchFraction) + .run(input, initialWeights) } /** @@ -168,9 +132,7 @@ object LassoWithSGD { numIterations: Int, stepSize: Double, regParam: Double, - miniBatchFraction: Double) - : LassoModel = - { + miniBatchFraction: Double): LassoModel = { new LassoWithSGD(stepSize, numIterations, regParam, miniBatchFraction).run(input) } @@ -190,9 +152,7 @@ object LassoWithSGD { input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, - regParam: Double) - : LassoModel = - { + regParam: Double): LassoModel = { train(input, numIterations, stepSize, regParam, 1.0) } @@ -208,9 +168,7 @@ object LassoWithSGD { */ def train( input: RDD[LabeledPoint], - numIterations: Int) - : LassoModel = - { + numIterations: Int): LassoModel = { train(input, numIterations, 1.0, 1.0, 1.0) } @@ -222,7 +180,8 @@ object LassoWithSGD { val sc = new SparkContext(args(0), "Lasso") val data = MLUtils.loadLabeledData(sc, args(1)) val model = LassoWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble) - println("Weights: " + model.weights.mkString("[", ", ", "]")) + + println("Weights: " + model.weights) println("Intercept: " + model.intercept) sc.stop() 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 f5f15d1a33f4d..9ed927994e795 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 @@ -19,11 +19,10 @@ package org.apache.spark.mllib.regression import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.util.MLUtils -import org.jblas.DoubleMatrix - /** * Regression model trained using LinearRegression. * @@ -31,15 +30,15 @@ import org.jblas.DoubleMatrix * @param intercept Intercept computed for this model. */ class LinearRegressionModel( - override val weights: Array[Double], + override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable { - override def predictPoint( - dataMatrix: DoubleMatrix, - weightMatrix: DoubleMatrix, + override protected def predictPoint( + dataMatrix: Vector, + weightMatrix: Vector, intercept: Double): Double = { - dataMatrix.dot(weightMatrix) + intercept + weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept } } @@ -69,7 +68,7 @@ class LinearRegressionWithSGD private ( */ def this() = this(1.0, 100, 1.0) - override def createModel(weights: Array[Double], intercept: Double) = { + override protected def createModel(weights: Vector, intercept: Double) = { new LinearRegressionModel(weights, intercept) } } @@ -98,11 +97,9 @@ object LinearRegressionWithSGD { numIterations: Int, stepSize: Double, miniBatchFraction: Double, - initialWeights: Array[Double]) - : LinearRegressionModel = - { - new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction).run(input, - initialWeights) + initialWeights: Vector): LinearRegressionModel = { + new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction) + .run(input, initialWeights) } /** @@ -120,9 +117,7 @@ object LinearRegressionWithSGD { input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, - miniBatchFraction: Double) - : LinearRegressionModel = - { + miniBatchFraction: Double): LinearRegressionModel = { new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction).run(input) } @@ -140,9 +135,7 @@ object LinearRegressionWithSGD { def train( input: RDD[LabeledPoint], numIterations: Int, - stepSize: Double) - : LinearRegressionModel = - { + stepSize: Double): LinearRegressionModel = { train(input, numIterations, stepSize, 1.0) } @@ -158,9 +151,7 @@ object LinearRegressionWithSGD { */ def train( input: RDD[LabeledPoint], - numIterations: Int) - : LinearRegressionModel = - { + numIterations: Int): LinearRegressionModel = { train(input, numIterations, 1.0, 1.0) } @@ -172,7 +163,7 @@ object LinearRegressionWithSGD { val sc = new SparkContext(args(0), "LinearRegression") val data = MLUtils.loadLabeledData(sc, args(1)) val model = LinearRegressionWithSGD.train(data, args(3).toInt, args(2).toDouble) - println("Weights: " + model.weights.mkString("[", ", ", "]")) + println("Weights: " + model.weights) println("Intercept: " + model.intercept) sc.stop() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala index 423afc32d665c..5e4b8a345b1c5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala @@ -18,6 +18,7 @@ package org.apache.spark.mllib.regression import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.Vector trait RegressionModel extends Serializable { /** @@ -26,7 +27,7 @@ trait RegressionModel extends Serializable { * @param testData RDD representing data points to be predicted * @return RDD[Double] where each entry contains the corresponding prediction */ - def predict(testData: RDD[Array[Double]]): RDD[Double] + def predict(testData: RDD[Vector]): RDD[Double] /** * Predict values for a single data point using the model trained. @@ -34,5 +35,5 @@ trait RegressionModel extends Serializable { * @param testData array representing a single data point * @return Double prediction from the trained model */ - def predict(testData: Array[Double]): Double + def predict(testData: Vector): Double } 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 feb100f21888f..1f17d2107f940 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 @@ -21,8 +21,7 @@ import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.util.MLUtils - -import org.jblas.DoubleMatrix +import org.apache.spark.mllib.linalg.Vector /** * Regression model trained using RidgeRegression. @@ -31,16 +30,16 @@ import org.jblas.DoubleMatrix * @param intercept Intercept computed for this model. */ class RidgeRegressionModel( - override val weights: Array[Double], + override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable { - override def predictPoint( - dataMatrix: DoubleMatrix, - weightMatrix: DoubleMatrix, + override protected def predictPoint( + dataMatrix: Vector, + weightMatrix: Vector, intercept: Double): Double = { - dataMatrix.dot(weightMatrix) + intercept + weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept } } @@ -57,8 +56,7 @@ class RidgeRegressionWithSGD private ( var numIterations: Int, var regParam: Double, var miniBatchFraction: Double) - extends GeneralizedLinearAlgorithm[RidgeRegressionModel] - with Serializable { + extends GeneralizedLinearAlgorithm[RidgeRegressionModel] with Serializable { val gradient = new LeastSquaresGradient() val updater = new SquaredL2Updater() @@ -71,10 +69,6 @@ class RidgeRegressionWithSGD private ( // We don't want to penalize the intercept in RidgeRegression, so set this to false. super.setIntercept(false) - var yMean = 0.0 - var xColMean: DoubleMatrix = _ - var xColSd: DoubleMatrix = _ - /** * Construct a RidgeRegression object with default parameters */ @@ -86,36 +80,8 @@ class RidgeRegressionWithSGD private ( this } - override def createModel(weights: Array[Double], intercept: Double) = { - val weightsMat = new DoubleMatrix(weights.length, 1, weights: _*) - val weightsScaled = weightsMat.div(xColSd) - val interceptScaled = yMean - weightsMat.transpose().mmul(xColMean.div(xColSd)).get(0) - - new RidgeRegressionModel(weightsScaled.data, interceptScaled) - } - - override def run( - input: RDD[LabeledPoint], - initialWeights: Array[Double]) - : RidgeRegressionModel = - { - val nfeatures: Int = input.first().features.length - val nexamples: Long = input.count() - - // To avoid penalizing the intercept, we center and scale the data. - val stats = MLUtils.computeStats(input, nfeatures, nexamples) - yMean = stats._1 - xColMean = stats._2 - xColSd = stats._3 - - val normalizedData = input.map { point => - val yNormalized = point.label - yMean - val featuresMat = new DoubleMatrix(nfeatures, 1, point.features:_*) - val featuresNormalized = featuresMat.sub(xColMean).divi(xColSd) - LabeledPoint(yNormalized, featuresNormalized.toArray) - } - - super.run(normalizedData, initialWeights) + override protected def createModel(weights: Vector, intercept: Double) = { + new RidgeRegressionModel(weights, intercept) } } @@ -144,9 +110,7 @@ object RidgeRegressionWithSGD { stepSize: Double, regParam: Double, miniBatchFraction: Double, - initialWeights: Array[Double]) - : RidgeRegressionModel = - { + initialWeights: Vector): RidgeRegressionModel = { new RidgeRegressionWithSGD(stepSize, numIterations, regParam, miniBatchFraction).run( input, initialWeights) } @@ -167,9 +131,7 @@ object RidgeRegressionWithSGD { numIterations: Int, stepSize: Double, regParam: Double, - miniBatchFraction: Double) - : RidgeRegressionModel = - { + miniBatchFraction: Double): RidgeRegressionModel = { new RidgeRegressionWithSGD(stepSize, numIterations, regParam, miniBatchFraction).run(input) } @@ -188,9 +150,7 @@ object RidgeRegressionWithSGD { input: RDD[LabeledPoint], numIterations: Int, stepSize: Double, - regParam: Double) - : RidgeRegressionModel = - { + regParam: Double): RidgeRegressionModel = { train(input, numIterations, stepSize, regParam, 1.0) } @@ -205,23 +165,22 @@ object RidgeRegressionWithSGD { */ def train( input: RDD[LabeledPoint], - numIterations: Int) - : RidgeRegressionModel = - { + numIterations: Int): RidgeRegressionModel = { train(input, numIterations, 1.0, 1.0, 1.0) } def main(args: Array[String]) { if (args.length != 5) { - println("Usage: RidgeRegression " + - " ") + println("Usage: RidgeRegression " + + " ") System.exit(1) } val sc = new SparkContext(args(0), "RidgeRegression") val data = MLUtils.loadLabeledData(sc, args(1)) val model = RidgeRegressionWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble) - println("Weights: " + model.weights.mkString("[", ", ", "]")) + + println("Weights: " + model.weights) println("Intercept: " + model.intercept) sc.stop() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala index 33205b919db8f..dee9594a9dd79 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala @@ -30,6 +30,7 @@ import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Impurity, Variance} import org.apache.spark.mllib.tree.model._ import org.apache.spark.rdd.RDD import org.apache.spark.util.random.XORShiftRandom +import org.apache.spark.mllib.linalg.{Vector, Vectors} /** * A class that implements a decision tree algorithm for classification and regression. It @@ -295,7 +296,7 @@ object DecisionTree extends Serializable with Logging { val numNodes = scala.math.pow(2, level).toInt logDebug("numNodes = " + numNodes) // Find the number of features by looking at the first sample. - val numFeatures = input.first().features.length + val numFeatures = input.first().features.size logDebug("numFeatures = " + numFeatures) val numBins = bins(0).length logDebug("numBins = " + numBins) @@ -902,7 +903,7 @@ object DecisionTree extends Serializable with Logging { val count = input.count() // Find the number of features by looking at the first sample - val numFeatures = input.take(1)(0).features.length + val numFeatures = input.take(1)(0).features.size val maxBins = strategy.maxBins val numBins = if (maxBins <= count) maxBins else count.toInt @@ -1116,7 +1117,7 @@ object DecisionTree extends Serializable with Logging { sc.textFile(dir).map { line => val parts = line.trim().split(",") val label = parts(0).toDouble - val features = parts.slice(1,parts.length).map(_.toDouble) + val features = Vectors.dense(parts.slice(1,parts.length).map(_.toDouble)) LabeledPoint(label, features) } } @@ -1127,7 +1128,7 @@ object DecisionTree extends Serializable with Logging { */ private def accuracyScore(model: DecisionTreeModel, data: RDD[LabeledPoint], threshold: Double = 0.5): Double = { - def predictedValue(features: Array[Double]) = { + def predictedValue(features: Vector) = { if (model.predict(features) < threshold) 0.0 else 1.0 } val correctCount = data.filter(y => predictedValue(y.features) == y.label).count() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala index a8bbf21daec01..a6dca84a2ce09 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala @@ -19,6 +19,7 @@ package org.apache.spark.mllib.tree.model import org.apache.spark.mllib.tree.configuration.Algo._ import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.Vector /** * Model to store the decision tree parameters @@ -33,7 +34,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable * @param features array representing a single data point * @return Double prediction from the trained model */ - def predict(features: Array[Double]): Double = { + def predict(features: Vector): Double = { topNode.predictIfLeaf(features) } @@ -43,7 +44,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable * @param features RDD representing data points to be predicted * @return RDD[Int] where each entry contains the corresponding prediction */ - def predict(features: RDD[Array[Double]]): RDD[Double] = { + def predict(features: RDD[Vector]): RDD[Double] = { features.map(x => predict(x)) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala index ea4693c5c2f4e..aac3f9ce308f7 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala @@ -19,6 +19,7 @@ package org.apache.spark.mllib.tree.model import org.apache.spark.Logging import org.apache.spark.mllib.tree.configuration.FeatureType._ +import org.apache.spark.mllib.linalg.Vector /** * Node in a decision tree @@ -54,8 +55,8 @@ class Node ( logDebug("stats = " + stats) logDebug("predict = " + predict) if (!isLeaf) { - val leftNodeIndex = id*2 + 1 - val rightNodeIndex = id*2 + 2 + val leftNodeIndex = id * 2 + 1 + val rightNodeIndex = id * 2 + 2 leftNode = Some(nodes(leftNodeIndex)) rightNode = Some(nodes(rightNodeIndex)) leftNode.get.build(nodes) @@ -68,7 +69,7 @@ class Node ( * @param feature feature value * @return predicted value */ - def predictIfLeaf(feature: Array[Double]) : Double = { + def predictIfLeaf(feature: Vector) : Double = { if (isLeaf) { predict } else{ 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 2e03684e62861..81e4eda2a68c4 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 @@ -24,6 +24,7 @@ import org.jblas.DoubleMatrix import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint /** @@ -74,7 +75,7 @@ object LinearDataGenerator { val y = x.map { xi => new DoubleMatrix(1, xi.length, xi: _*).dot(weightsMat) + intercept + eps * rnd.nextGaussian() } - y.zip(x).map(p => LabeledPoint(p._1, p._2)) + y.zip(x).map(p => LabeledPoint(p._1, Vectors.dense(p._2))) } /** diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala index 52c4a71d621a1..61498dcc2be00 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala @@ -22,6 +22,7 @@ import scala.util.Random import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.linalg.Vectors /** * Generate test data for LogisticRegression. This class chooses positive labels @@ -54,7 +55,7 @@ object LogisticRegressionDataGenerator { val x = Array.fill[Double](nfeatures) { rnd.nextGaussian() + (y * eps) } - LabeledPoint(y, x) + LabeledPoint(y, Vectors.dense(x)) } data } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala index 08cd9ab05547b..cb85e433bfc73 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala @@ -17,15 +17,13 @@ package org.apache.spark.mllib.util +import breeze.linalg.{Vector => BV, DenseVector => BDV, SparseVector => BSV, + squaredDistance => breezeSquaredDistance} + import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD -import org.apache.spark.SparkContext._ - -import org.jblas.DoubleMatrix - import org.apache.spark.mllib.regression.LabeledPoint - -import breeze.linalg.{Vector => BV, SparseVector => BSV, squaredDistance => breezeSquaredDistance} +import org.apache.spark.mllib.linalg.{Vector, Vectors} /** * Helper methods to load, save and pre-process data used in ML Lib. @@ -40,6 +38,107 @@ object MLUtils { eps } + /** + * Multiclass label parser, which parses a string into double. + */ + val multiclassLabelParser: String => Double = _.toDouble + + /** + * Binary label parser, which outputs 1.0 (positive) if the value is greater than 0.5, + * or 0.0 (negative) otherwise. + */ + val binaryLabelParser: String => Double = label => if (label.toDouble > 0.5) 1.0 else 0.0 + + /** + * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint]. + * The LIBSVM format is a text-based format used by LIBSVM and LIBLINEAR. + * Each line represents a labeled sparse feature vector using the following format: + * {{{label index1:value1 index2:value2 ...}}} + * where the indices are one-based and in ascending order. + * This method parses each line into a [[org.apache.spark.mllib.regression.LabeledPoint]], + * where the feature indices are converted to zero-based. + * + * @param sc Spark context + * @param path file or directory path in any Hadoop-supported file system URI + * @param labelParser parser for labels, default: 1.0 if label > 0.5 or 0.0 otherwise + * @param numFeatures number of features, which will be determined from the input data if a + * negative value is given. The default value is -1. + * @param minSplits min number of partitions, default: sc.defaultMinSplits + * @return labeled data stored as an RDD[LabeledPoint] + */ + def loadLibSVMData( + sc: SparkContext, + path: String, + labelParser: String => Double, + numFeatures: Int, + minSplits: Int): RDD[LabeledPoint] = { + val parsed = sc.textFile(path, minSplits) + .map(_.trim) + .filter(!_.isEmpty) + .map(_.split(' ')) + // Determine number of features. + val d = if (numFeatures >= 0) { + numFeatures + } else { + parsed.map { items => + if (items.length > 1) { + items.last.split(':')(0).toInt + } else { + 0 + } + }.reduce(math.max) + } + parsed.map { items => + val label = labelParser(items.head) + val (indices, values) = items.tail.map { item => + val indexAndValue = item.split(':') + val index = indexAndValue(0).toInt - 1 + val value = indexAndValue(1).toDouble + (index, value) + }.unzip + LabeledPoint(label, Vectors.sparse(d, indices.toArray, values.toArray)) + } + } + + // Convenient methods for calling from Java. + + /** + * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], + * with number of features determined automatically and the default number of partitions. + */ + def loadLibSVMData(sc: SparkContext, path: String): RDD[LabeledPoint] = + loadLibSVMData(sc, path, binaryLabelParser, -1, sc.defaultMinSplits) + + /** + * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], + * with number of features specified explicitly and the default number of partitions. + */ + def loadLibSVMData(sc: SparkContext, path: String, numFeatures: Int): RDD[LabeledPoint] = + loadLibSVMData(sc, path, binaryLabelParser, numFeatures, sc.defaultMinSplits) + + /** + * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], + * with the given label parser, number of features determined automatically, + * and the default number of partitions. + */ + def loadLibSVMData( + sc: SparkContext, + path: String, + labelParser: String => Double): RDD[LabeledPoint] = + loadLibSVMData(sc, path, labelParser, -1, sc.defaultMinSplits) + + /** + * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], + * with the given label parser, number of features specified explicitly, + * and the default number of partitions. + */ + def loadLibSVMData( + sc: SparkContext, + path: String, + labelParser: String => Double, + numFeatures: Int): RDD[LabeledPoint] = + loadLibSVMData(sc, path, labelParser, numFeatures, sc.defaultMinSplits) + /** * Load labeled data from a file. The data format used here is * , ... @@ -54,7 +153,7 @@ object MLUtils { sc.textFile(dir).map { line => val parts = line.split(',') val label = parts(0).toDouble - val features = parts(1).trim().split(' ').map(_.toDouble) + val features = Vectors.dense(parts(1).trim().split(' ').map(_.toDouble)) LabeledPoint(label, features) } } @@ -68,7 +167,7 @@ object MLUtils { * @param dir Directory to save the data. */ def saveLabeledData(data: RDD[LabeledPoint], dir: String) { - val dataStr = data.map(x => x.label + "," + x.features.mkString(" ")) + val dataStr = data.map(x => x.label + "," + x.features.toArray.mkString(" ")) dataStr.saveAsTextFile(dir) } @@ -76,44 +175,52 @@ object MLUtils { * Utility function to compute mean and standard deviation on a given dataset. * * @param data - input data set whose statistics are computed - * @param nfeatures - number of features - * @param nexamples - number of examples in input dataset + * @param numFeatures - number of features + * @param numExamples - number of examples in input dataset * * @return (yMean, xColMean, xColSd) - Tuple consisting of * yMean - mean of the labels * xColMean - Row vector with mean for every column (or feature) of the input data * xColSd - Row vector standard deviation for every column (or feature) of the input data. */ - def computeStats(data: RDD[LabeledPoint], nfeatures: Int, nexamples: Long): - (Double, DoubleMatrix, DoubleMatrix) = { - val yMean: Double = data.map { labeledPoint => labeledPoint.label }.reduce(_ + _) / nexamples - - // NOTE: We shuffle X by column here to compute column sum and sum of squares. - val xColSumSq: RDD[(Int, (Double, Double))] = data.flatMap { labeledPoint => - val nCols = labeledPoint.features.length - // Traverse over every column and emit (col, value, value^2) - Iterator.tabulate(nCols) { i => - (i, (labeledPoint.features(i), labeledPoint.features(i)*labeledPoint.features(i))) - } - }.reduceByKey { case(x1, x2) => - (x1._1 + x2._1, x1._2 + x2._2) + def computeStats( + data: RDD[LabeledPoint], + numFeatures: Int, + numExamples: Long): (Double, Vector, Vector) = { + val brzData = data.map { case LabeledPoint(label, features) => + (label, features.toBreeze) } - val xColSumsMap = xColSumSq.collectAsMap() - - val xColMean = DoubleMatrix.zeros(nfeatures, 1) - val xColSd = DoubleMatrix.zeros(nfeatures, 1) - - // Compute mean and unbiased variance using column sums - var col = 0 - while (col < nfeatures) { - xColMean.put(col, xColSumsMap(col)._1 / nexamples) - val variance = - (xColSumsMap(col)._2 - (math.pow(xColSumsMap(col)._1, 2) / nexamples)) / nexamples - xColSd.put(col, math.sqrt(variance)) - col += 1 + val aggStats = brzData.aggregate( + (0L, 0.0, BDV.zeros[Double](numFeatures), BDV.zeros[Double](numFeatures)) + )( + seqOp = (c, v) => (c, v) match { + case ((n, sumLabel, sum, sumSq), (label, features)) => + features.activeIterator.foreach { case (i, x) => + sumSq(i) += x * x + } + (n + 1L, sumLabel + label, sum += features, sumSq) + }, + combOp = (c1, c2) => (c1, c2) match { + case ((n1, sumLabel1, sum1, sumSq1), (n2, sumLabel2, sum2, sumSq2)) => + (n1 + n2, sumLabel1 + sumLabel2, sum1 += sum2, sumSq1 += sumSq2) + } + ) + val (nl, sumLabel, sum, sumSq) = aggStats + + require(nl > 0, "Input data is empty.") + require(nl == numExamples) + + val n = nl.toDouble + val yMean = sumLabel / n + val mean = sum / n + val std = new Array[Double](sum.length) + var i = 0 + while (i < numFeatures) { + std(i) = sumSq(i) / n - mean(i) * mean(i) + i += 1 } - (yMean, xColMean, xColSd) + (yMean, Vectors.fromBreeze(mean), Vectors.dense(std)) } /** @@ -144,6 +251,18 @@ object MLUtils { val sumSquaredNorm = norm1 * norm1 + norm2 * norm2 val normDiff = norm1 - norm2 var sqDist = 0.0 + /* + * The relative error is + * 
+     * EPSILON * ( \|a\|_2^2 + \|b\\_2^2 + 2 |a^T b|) / ( \|a - b\|_2^2 ),
+     *
+ * which is bounded by + * 
+     * 2.0 * EPSILON * ( \|a\|_2^2 + \|b\|_2^2 ) / ( (\|a\|_2 - \|b\|_2)^2 ).
+     *
+ * The bound doesn't need the inner product, so we can use it as a sufficient condition to + * check quickly whether the inner product approach is accurate. + */ val precisionBound1 = 2.0 * EPSILON * sumSquaredNorm / (normDiff * normDiff + EPSILON) if (precisionBound1 < precision) { sqDist = sumSquaredNorm - 2.0 * v1.dot(v2) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala index c96c94f70eef7..e300c3dbe1fe0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala @@ -23,6 +23,7 @@ import org.jblas.DoubleMatrix import org.apache.spark.SparkContext import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint /** @@ -58,7 +59,7 @@ object SVMDataGenerator { } val yD = new DoubleMatrix(1, x.length, x: _*).dot(trueWeights) + rnd.nextGaussian() * 0.1 val y = if (yD < 0) 0.0 else 1.0 - LabeledPoint(y, x) + LabeledPoint(y, Vectors.dense(x)) } MLUtils.saveLabeledData(data, outputPath) diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java index 073ded6f36933..c80b1134ed1b2 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java @@ -19,6 +19,7 @@ import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; import org.junit.After; import org.junit.Assert; @@ -45,12 +46,12 @@ public void tearDown() { } private static final List POINTS = Arrays.asList( - new LabeledPoint(0, new double[] {1.0, 0.0, 0.0}), - new LabeledPoint(0, new double[] {2.0, 0.0, 0.0}), - new LabeledPoint(1, new double[] {0.0, 1.0, 0.0}), - new LabeledPoint(1, new double[] {0.0, 2.0, 0.0}), - new LabeledPoint(2, new double[] {0.0, 0.0, 1.0}), - new LabeledPoint(2, new double[] {0.0, 0.0, 2.0}) + new LabeledPoint(0, Vectors.dense(1.0, 0.0, 0.0)), + new LabeledPoint(0, Vectors.dense(2.0, 0.0, 0.0)), + new LabeledPoint(1, Vectors.dense(0.0, 1.0, 0.0)), + new LabeledPoint(1, Vectors.dense(0.0, 2.0, 0.0)), + new LabeledPoint(2, Vectors.dense(0.0, 0.0, 1.0)), + new LabeledPoint(2, Vectors.dense(0.0, 0.0, 2.0)) ); private int validatePrediction(List points, NaiveBayesModel model) { diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java index 117e5eaa8b78e..4701a5e545020 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaSVMSuite.java @@ -17,7 +17,6 @@ package org.apache.spark.mllib.classification; - import java.io.Serializable; import java.util.List; @@ -28,7 +27,6 @@ import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; - import org.apache.spark.mllib.regression.LabeledPoint; public class JavaSVMSuite implements Serializable { @@ -94,5 +92,4 @@ public void runSVMUsingStaticMethods() { int numAccurate = validatePrediction(validationData, model); Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0); } - } diff --git a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java index 2c4d795f96e4e..c6d8425ffc38d 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java @@ -19,10 +19,10 @@ import java.io.Serializable; -import com.google.common.collect.Lists; - import scala.Tuple2; +import com.google.common.collect.Lists; + import org.junit.Test; import static org.junit.Assert.*; @@ -36,7 +36,7 @@ public void denseArrayConstruction() { @Test public void sparseArrayConstruction() { - Vector v = Vectors.sparse(3, Lists.newArrayList( + Vector v = Vectors.sparse(3, Lists.>newArrayList( new Tuple2(0, 2.0), new Tuple2(2, 3.0))); assertArrayEquals(new double[]{2.0, 0.0, 3.0}, v.toArray(), 0.0); diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLassoSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLassoSuite.java index f44b25cd44d19..f725924a2d971 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLassoSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLassoSuite.java @@ -59,7 +59,7 @@ int validatePrediction(List validationData, LassoModel model) { @Test public void runLassoUsingConstructor() { int nPoints = 10000; - double A = 2.0; + double A = 0.0; double[] weights = {-1.5, 1.0e-2}; JavaRDD testRDD = sc.parallelize(LinearDataGenerator.generateLinearInputAsList(A, @@ -80,7 +80,7 @@ public void runLassoUsingConstructor() { @Test public void runLassoUsingStaticMethods() { int nPoints = 10000; - double A = 2.0; + double A = 0.0; double[] weights = {-1.5, 1.0e-2}; JavaRDD testRDD = sc.parallelize(LinearDataGenerator.generateLinearInputAsList(A, diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaRidgeRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaRidgeRegressionSuite.java index 2fdd5fc8fdca6..03714ae7e4d00 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaRidgeRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaRidgeRegressionSuite.java @@ -55,30 +55,27 @@ public void tearDown() { return errorSum / validationData.size(); } - List generateRidgeData(int numPoints, int nfeatures, double eps) { + List generateRidgeData(int numPoints, int numFeatures, double std) { org.jblas.util.Random.seed(42); // Pick weights as random values distributed uniformly in [-0.5, 0.5] - DoubleMatrix w = DoubleMatrix.rand(nfeatures, 1).subi(0.5); - // Set first two weights to eps - w.put(0, 0, eps); - w.put(1, 0, eps); - return LinearDataGenerator.generateLinearInputAsList(0.0, w.data, numPoints, 42, eps); + DoubleMatrix w = DoubleMatrix.rand(numFeatures, 1).subi(0.5); + return LinearDataGenerator.generateLinearInputAsList(0.0, w.data, numPoints, 42, std); } @Test public void runRidgeRegressionUsingConstructor() { - int nexamples = 200; - int nfeatures = 20; - double eps = 10.0; - List data = generateRidgeData(2*nexamples, nfeatures, eps); + int numExamples = 50; + int numFeatures = 20; + List data = generateRidgeData(2*numExamples, numFeatures, 10.0); - JavaRDD testRDD = sc.parallelize(data.subList(0, nexamples)); - List validationData = data.subList(nexamples, 2*nexamples); + JavaRDD testRDD = sc.parallelize(data.subList(0, numExamples)); + List validationData = data.subList(numExamples, 2 * numExamples); RidgeRegressionWithSGD ridgeSGDImpl = new RidgeRegressionWithSGD(); - ridgeSGDImpl.optimizer().setStepSize(1.0) - .setRegParam(0.0) - .setNumIterations(200); + ridgeSGDImpl.optimizer() + .setStepSize(1.0) + .setRegParam(0.0) + .setNumIterations(200); RidgeRegressionModel model = ridgeSGDImpl.run(testRDD.rdd()); double unRegularizedErr = predictionError(validationData, model); @@ -91,13 +88,12 @@ public void runRidgeRegressionUsingConstructor() { @Test public void runRidgeRegressionUsingStaticMethods() { - int nexamples = 200; - int nfeatures = 20; - double eps = 10.0; - List data = generateRidgeData(2*nexamples, nfeatures, eps); + int numExamples = 50; + int numFeatures = 20; + List data = generateRidgeData(2 * numExamples, numFeatures, 10.0); - JavaRDD testRDD = sc.parallelize(data.subList(0, nexamples)); - List validationData = data.subList(nexamples, 2*nexamples); + JavaRDD testRDD = sc.parallelize(data.subList(0, numExamples)); + List validationData = data.subList(numExamples, 2 * numExamples); RidgeRegressionModel model = RidgeRegressionWithSGD.train(testRDD.rdd(), 200, 1.0, 0.0); double unRegularizedErr = predictionError(validationData, model); diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala index 05322b024d5f6..1e03c9df820b0 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala @@ -20,11 +20,10 @@ package org.apache.spark.mllib.classification import scala.util.Random import scala.collection.JavaConversions._ -import org.scalatest.BeforeAndAfterAll import org.scalatest.FunSuite import org.scalatest.matchers.ShouldMatchers -import org.apache.spark.SparkContext +import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.LocalSparkContext @@ -61,7 +60,7 @@ object LogisticRegressionSuite { if (yVal > 0) 1 else 0 } - val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Array(x1(i)))) + val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Vectors.dense(Array(x1(i))))) testData } @@ -113,7 +112,7 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Shoul val testData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 42) val initialB = -1.0 - val initialWeights = Array(initialB) + val initialWeights = Vectors.dense(initialB) val testRDD = sc.parallelize(testData, 2) testRDD.cache() diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala index 9dd6c79ee6ad8..516895d04222d 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala @@ -19,9 +19,9 @@ package org.apache.spark.mllib.classification import scala.util.Random -import org.scalatest.BeforeAndAfterAll import org.scalatest.FunSuite +import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.LocalSparkContext @@ -54,7 +54,7 @@ object NaiveBayesSuite { if (rnd.nextDouble() < _theta(y)(j)) 1 else 0 } - LabeledPoint(y, xi) + LabeledPoint(y, Vectors.dense(xi)) } } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala index bc7abb568a172..dfacbfeee6fb4 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala @@ -20,7 +20,6 @@ package org.apache.spark.mllib.classification import scala.util.Random import scala.collection.JavaConversions._ -import org.scalatest.BeforeAndAfterAll import org.scalatest.FunSuite import org.jblas.DoubleMatrix @@ -28,6 +27,7 @@ import org.jblas.DoubleMatrix import org.apache.spark.SparkException import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.LocalSparkContext +import org.apache.spark.mllib.linalg.Vectors object SVMSuite { @@ -54,7 +54,7 @@ object SVMSuite { intercept + 0.01 * rnd.nextGaussian() if (yD < 0) 0.0 else 1.0 } - y.zip(x).map(p => LabeledPoint(p._1, p._2)) + y.zip(x).map(p => LabeledPoint(p._1, Vectors.dense(p._2))) } } @@ -110,7 +110,7 @@ class SVMSuite extends FunSuite with LocalSparkContext { val initialB = -1.0 val initialC = -1.0 - val initialWeights = Array(initialB,initialC) + val initialWeights = Vectors.dense(initialB, initialC) val testRDD = sc.parallelize(testData, 2) testRDD.cache() @@ -150,10 +150,10 @@ class SVMSuite extends FunSuite with LocalSparkContext { } intercept[SparkException] { - val model = SVMWithSGD.train(testRDDInvalid, 100) + SVMWithSGD.train(testRDDInvalid, 100) } // Turning off data validation should not throw an exception - val noValidationModel = new SVMWithSGD().setValidateData(false).run(testRDDInvalid) + new SVMWithSGD().setValidateData(false).run(testRDDInvalid) } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala index 631d0e2ad9cdb..c4b433499a091 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala @@ -20,13 +20,12 @@ package org.apache.spark.mllib.optimization import scala.util.Random import scala.collection.JavaConversions._ -import org.scalatest.BeforeAndAfterAll import org.scalatest.FunSuite import org.scalatest.matchers.ShouldMatchers -import org.apache.spark.SparkContext import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.LocalSparkContext +import org.apache.spark.mllib.linalg.Vectors object GradientDescentSuite { @@ -58,8 +57,7 @@ object GradientDescentSuite { if (yVal > 0) 1 else 0 } - val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Array(x1(i)))) - testData + (0 until nPoints).map(i => LabeledPoint(y(i), Vectors.dense(x1(i)))) } } @@ -83,11 +81,11 @@ class GradientDescentSuite extends FunSuite with LocalSparkContext with ShouldMa // Add a extra variable consisting of all 1.0's for the intercept. val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42) val data = testData.map { case LabeledPoint(label, features) => - label -> Array(1.0, features: _*) + label -> Vectors.dense(1.0, features.toArray: _*) } val dataRDD = sc.parallelize(data, 2).cache() - val initialWeightsWithIntercept = Array(1.0, initialWeights: _*) + val initialWeightsWithIntercept = Vectors.dense(1.0, initialWeights: _*) val (_, loss) = GradientDescent.runMiniBatchSGD( dataRDD, @@ -113,13 +111,13 @@ class GradientDescentSuite extends FunSuite with LocalSparkContext with ShouldMa // Add a extra variable consisting of all 1.0's for the intercept. val testData = GradientDescentSuite.generateGDInput(2.0, -1.5, 10000, 42) val data = testData.map { case LabeledPoint(label, features) => - label -> Array(1.0, features: _*) + label -> Vectors.dense(1.0, features.toArray: _*) } val dataRDD = sc.parallelize(data, 2).cache() // Prepare non-zero weights - val initialWeightsWithIntercept = Array(1.0, 0.5) + val initialWeightsWithIntercept = Vectors.dense(1.0, 0.5) val regParam0 = 0 val (newWeights0, loss0) = GradientDescent.runMiniBatchSGD( diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala index 2cebac943e15f..6aad9eb84e13c 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.mllib.regression import org.scalatest.FunSuite +import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext} class LassoSuite extends FunSuite with LocalSparkContext { @@ -33,29 +34,33 @@ class LassoSuite extends FunSuite with LocalSparkContext { } test("Lasso local random SGD") { - val nPoints = 10000 + val nPoints = 1000 val A = 2.0 val B = -1.5 val C = 1.0e-2 - val testData = LinearDataGenerator.generateLinearInput(A, Array[Double](B,C), nPoints, 42) - - val testRDD = sc.parallelize(testData, 2) - testRDD.cache() + val testData = LinearDataGenerator.generateLinearInput(A, Array[Double](B, C), nPoints, 42) + .map { case LabeledPoint(label, features) => + LabeledPoint(label, Vectors.dense(1.0 +: features.toArray)) + } + val testRDD = sc.parallelize(testData, 2).cache() val ls = new LassoWithSGD() - ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(20) + ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(40) val model = ls.run(testRDD) - val weight0 = model.weights(0) val weight1 = model.weights(1) - assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]") - assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]") - assert(weight1 >= -1.0e-3 && weight1 <= 1.0e-3, weight1 + " not in [-0.001, 0.001]") + val weight2 = model.weights(2) + assert(weight0 >= 1.9 && weight0 <= 2.1, weight0 + " not in [1.9, 2.1]") + assert(weight1 >= -1.60 && weight1 <= -1.40, weight1 + " not in [-1.6, -1.4]") + assert(weight2 >= -1.0e-3 && weight2 <= 1.0e-3, weight2 + " not in [-0.001, 0.001]") val validationData = LinearDataGenerator.generateLinearInput(A, Array[Double](B,C), nPoints, 17) + .map { case LabeledPoint(label, features) => + LabeledPoint(label, Vectors.dense(1.0 +: features.toArray)) + } val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. @@ -66,33 +71,39 @@ class LassoSuite extends FunSuite with LocalSparkContext { } test("Lasso local random SGD with initial weights") { - val nPoints = 10000 + val nPoints = 1000 val A = 2.0 val B = -1.5 val C = 1.0e-2 - val testData = LinearDataGenerator.generateLinearInput(A, Array[Double](B,C), nPoints, 42) + val testData = LinearDataGenerator.generateLinearInput(A, Array[Double](B, C), nPoints, 42) + .map { case LabeledPoint(label, features) => + LabeledPoint(label, Vectors.dense(1.0 +: features.toArray)) + } + val initialA = -1.0 val initialB = -1.0 val initialC = -1.0 - val initialWeights = Array(initialB,initialC) + val initialWeights = Vectors.dense(initialA, initialB, initialC) - val testRDD = sc.parallelize(testData, 2) - testRDD.cache() + val testRDD = sc.parallelize(testData, 2).cache() val ls = new LassoWithSGD() - ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(20) + ls.optimizer.setStepSize(1.0).setRegParam(0.01).setNumIterations(40) val model = ls.run(testRDD, initialWeights) - val weight0 = model.weights(0) val weight1 = model.weights(1) - assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]") - assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]") - assert(weight1 >= -1.0e-3 && weight1 <= 1.0e-3, weight1 + " not in [-0.001, 0.001]") + val weight2 = model.weights(2) + assert(weight0 >= 1.9 && weight0 <= 2.1, weight0 + " not in [1.9, 2.1]") + assert(weight1 >= -1.60 && weight1 <= -1.40, weight1 + " not in [-1.6, -1.4]") + assert(weight2 >= -1.0e-3 && weight2 <= 1.0e-3, weight2 + " not in [-0.001, 0.001]") val validationData = LinearDataGenerator.generateLinearInput(A, Array[Double](B,C), nPoints, 17) + .map { case LabeledPoint(label, features) => + LabeledPoint(label, Vectors.dense(1.0 +: features.toArray)) + } val validationRDD = sc.parallelize(validationData,2) // Test prediction on RDD. diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala index 5d251bcbf35db..2f7d30708ce17 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.mllib.regression import org.scalatest.FunSuite +import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext} class LinearRegressionSuite extends FunSuite with LocalSparkContext { @@ -40,11 +41,12 @@ class LinearRegressionSuite extends FunSuite with LocalSparkContext { linReg.optimizer.setNumIterations(1000).setStepSize(1.0) val model = linReg.run(testRDD) - assert(model.intercept >= 2.5 && model.intercept <= 3.5) - assert(model.weights.length === 2) - assert(model.weights(0) >= 9.0 && model.weights(0) <= 11.0) - assert(model.weights(1) >= 9.0 && model.weights(1) <= 11.0) + + val weights = model.weights + assert(weights.size === 2) + assert(weights(0) >= 9.0 && weights(0) <= 11.0) + assert(weights(1) >= 9.0 && weights(1) <= 11.0) val validationData = LinearDataGenerator.generateLinearInput( 3.0, Array(10.0, 10.0), 100, 17) @@ -67,9 +69,11 @@ class LinearRegressionSuite extends FunSuite with LocalSparkContext { val model = linReg.run(testRDD) assert(model.intercept === 0.0) - assert(model.weights.length === 2) - assert(model.weights(0) >= 9.0 && model.weights(0) <= 11.0) - assert(model.weights(1) >= 9.0 && model.weights(1) <= 11.0) + + val weights = model.weights + assert(weights.size === 2) + assert(weights(0) >= 9.0 && weights(0) <= 11.0) + assert(weights(1) >= 9.0 && weights(1) <= 11.0) val validationData = LinearDataGenerator.generateLinearInput( 0.0, Array(10.0, 10.0), 100, 17) @@ -81,4 +85,40 @@ class LinearRegressionSuite extends FunSuite with LocalSparkContext { // Test prediction on Array. validatePrediction(validationData.map(row => model.predict(row.features)), validationData) } + + // Test if we can correctly learn Y = 10*X1 + 10*X10000 + test("sparse linear regression without intercept") { + val denseRDD = sc.parallelize( + LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 100, 42), 2) + val sparseRDD = denseRDD.map { case LabeledPoint(label, v) => + val sv = Vectors.sparse(10000, Seq((0, v(0)), (9999, v(1)))) + LabeledPoint(label, sv) + }.cache() + val linReg = new LinearRegressionWithSGD().setIntercept(false) + linReg.optimizer.setNumIterations(1000).setStepSize(1.0) + + val model = linReg.run(sparseRDD) + + assert(model.intercept === 0.0) + + val weights = model.weights + assert(weights.size === 10000) + assert(weights(0) >= 9.0 && weights(0) <= 11.0) + assert(weights(9999) >= 9.0 && weights(9999) <= 11.0) + + val validationData = LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 100, 17) + val sparseValidationData = validationData.map { case LabeledPoint(label, v) => + val sv = Vectors.sparse(10000, Seq((0, v(0)), (9999, v(1)))) + LabeledPoint(label, sv) + } + val sparseValidationRDD = sc.parallelize(sparseValidationData, 2) + + // Test prediction on RDD. + validatePrediction( + model.predict(sparseValidationRDD.map(_.features)).collect(), sparseValidationData) + + // Test prediction on Array. + validatePrediction( + sparseValidationData.map(row => model.predict(row.features)), sparseValidationData) + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala index b2044ed0d8066..f66fc6ea6c1ec 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala @@ -17,9 +17,10 @@ package org.apache.spark.mllib.regression -import org.jblas.DoubleMatrix import org.scalatest.FunSuite +import org.jblas.DoubleMatrix + import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext} class RidgeRegressionSuite extends FunSuite with LocalSparkContext { @@ -30,22 +31,22 @@ class RidgeRegressionSuite extends FunSuite with LocalSparkContext { }.reduceLeft(_ + _) / predictions.size } - test("regularization with skewed weights") { - val nexamples = 200 - val nfeatures = 20 - val eps = 10 + test("ridge regression can help avoid overfitting") { + + // For small number of examples and large variance of error distribution, + // ridge regression should give smaller generalization error that linear regression. + + val numExamples = 50 + val numFeatures = 20 org.jblas.util.Random.seed(42) // Pick weights as random values distributed uniformly in [-0.5, 0.5] - val w = DoubleMatrix.rand(nfeatures, 1).subi(0.5) - // Set first two weights to eps - w.put(0, 0, eps) - w.put(1, 0, eps) + val w = DoubleMatrix.rand(numFeatures, 1).subi(0.5) // Use half of data for training and other half for validation - val data = LinearDataGenerator.generateLinearInput(3.0, w.toArray, 2*nexamples, 42, eps) - val testData = data.take(nexamples) - val validationData = data.takeRight(nexamples) + val data = LinearDataGenerator.generateLinearInput(3.0, w.toArray, 2 * numExamples, 42, 10.0) + val testData = data.take(numExamples) + val validationData = data.takeRight(numExamples) val testRDD = sc.parallelize(testData, 2).cache() val validationRDD = sc.parallelize(validationData, 2).cache() @@ -67,7 +68,7 @@ class RidgeRegressionSuite extends FunSuite with LocalSparkContext { val ridgeErr = predictionError( ridgeModel.predict(validationRDD.map(_.features)).collect(), validationData) - // Ridge CV-error should be lower than linear regression + // Ridge validation error should be lower than linear regression. assert(ridgeErr < linearErr, "ridgeError (" + ridgeErr + ") was not less than linearError(" + linearErr + ")") } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala index 4349c7000a0ae..350130c914f26 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala @@ -27,6 +27,7 @@ import org.apache.spark.mllib.tree.model.Filter import org.apache.spark.mllib.tree.configuration.Strategy import org.apache.spark.mllib.tree.configuration.Algo._ import org.apache.spark.mllib.tree.configuration.FeatureType._ +import org.apache.spark.mllib.linalg.Vectors class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll { @@ -396,7 +397,7 @@ object DecisionTreeSuite { def generateOrderedLabeledPointsWithLabel0(): Array[LabeledPoint] = { val arr = new Array[LabeledPoint](1000) for (i <- 0 until 1000){ - val lp = new LabeledPoint(0.0,Array(i.toDouble,1000.0-i)) + val lp = new LabeledPoint(0.0, Vectors.dense(i.toDouble, 1000.0 - i)) arr(i) = lp } arr @@ -405,7 +406,7 @@ object DecisionTreeSuite { def generateOrderedLabeledPointsWithLabel1(): Array[LabeledPoint] = { val arr = new Array[LabeledPoint](1000) for (i <- 0 until 1000){ - val lp = new LabeledPoint(1.0,Array(i.toDouble,999.0-i)) + val lp = new LabeledPoint(1.0, Vectors.dense(i.toDouble, 999.0 - i)) arr(i) = lp } arr @@ -415,9 +416,9 @@ object DecisionTreeSuite { val arr = new Array[LabeledPoint](1000) for (i <- 0 until 1000){ if (i < 600){ - arr(i) = new LabeledPoint(1.0,Array(0.0,1.0)) + arr(i) = new LabeledPoint(1.0, Vectors.dense(0.0, 1.0)) } else { - arr(i) = new LabeledPoint(0.0,Array(1.0,0.0)) + arr(i) = new LabeledPoint(0.0, Vectors.dense(1.0, 0.0)) } } arr diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala index 60f053b381305..27d41c7869aa0 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala @@ -17,14 +17,20 @@ package org.apache.spark.mllib.util +import java.io.File + import org.scalatest.FunSuite import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, norm => breezeNorm, squaredDistance => breezeSquaredDistance} +import com.google.common.base.Charsets +import com.google.common.io.Files +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLUtils._ -class MLUtilsSuite extends FunSuite { +class MLUtilsSuite extends FunSuite with LocalSparkContext { test("epsilon computation") { assert(1.0 + EPSILON > 1.0, s"EPSILON is too small: $EPSILON.") @@ -49,4 +55,55 @@ class MLUtilsSuite extends FunSuite { assert((fastSquaredDist2 - squaredDist) <= precision * squaredDist, s"failed with m = $m") } } + + test("compute stats") { + val data = Seq.fill(3)(Seq( + LabeledPoint(1.0, Vectors.dense(1.0, 2.0, 3.0)), + LabeledPoint(0.0, Vectors.dense(3.0, 4.0, 5.0)) + )).flatten + val rdd = sc.parallelize(data, 2) + val (meanLabel, mean, std) = MLUtils.computeStats(rdd, 3, 6) + assert(meanLabel === 0.5) + assert(mean === Vectors.dense(2.0, 3.0, 4.0)) + assert(std === Vectors.dense(1.0, 1.0, 1.0)) + } + + test("loadLibSVMData") { + val lines = + """ + |+1 1:1.0 3:2.0 5:3.0 + |-1 + |-1 2:4.0 4:5.0 6:6.0 + """.stripMargin + val tempDir = Files.createTempDir() + val file = new File(tempDir.getPath, "part-00000") + Files.write(lines, file, Charsets.US_ASCII) + val path = tempDir.toURI.toString + + val pointsWithNumFeatures = MLUtils.loadLibSVMData(sc, path, 6).collect() + val pointsWithoutNumFeatures = MLUtils.loadLibSVMData(sc, path).collect() + + for (points <- Seq(pointsWithNumFeatures, pointsWithoutNumFeatures)) { + assert(points.length === 3) + assert(points(0).label === 1.0) + assert(points(0).features === Vectors.sparse(6, Seq((0, 1.0), (2, 2.0), (4, 3.0)))) + assert(points(1).label == 0.0) + assert(points(1).features == Vectors.sparse(6, Seq())) + assert(points(2).label === 0.0) + assert(points(2).features === Vectors.sparse(6, Seq((1, 4.0), (3, 5.0), (5, 6.0)))) + } + + val multiclassPoints = MLUtils.loadLibSVMData(sc, path, MLUtils.multiclassLabelParser).collect() + assert(multiclassPoints.length === 3) + assert(multiclassPoints(0).label === 1.0) + assert(multiclassPoints(1).label === -1.0) + assert(multiclassPoints(2).label === -1.0) + + try { + file.delete() + tempDir.delete() + } catch { + case t: Throwable => + } + } } diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index 19b90dfd6e167..d2f9cdb3f4298 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -87,18 +87,19 @@ class NaiveBayesModel(object): >>> data = array([0.0, 0.0, 1.0, 0.0, 0.0, 2.0, 1.0, 1.0, 0.0]).reshape(3,3) >>> model = NaiveBayes.train(sc.parallelize(data)) >>> model.predict(array([0.0, 1.0])) - 0 + 0.0 >>> model.predict(array([1.0, 0.0])) - 1 + 1.0 """ - def __init__(self, pi, theta): + def __init__(self, labels, pi, theta): + self.labels = labels self.pi = pi self.theta = theta def predict(self, x): """Return the most likely class for a data vector x""" - return numpy.argmax(self.pi + dot(x, self.theta)) + return self.labels[numpy.argmax(self.pi + dot(x, self.theta))] class NaiveBayes(object): @classmethod @@ -122,7 +123,8 @@ def train(cls, data, lambda_=1.0): ans = sc._jvm.PythonMLLibAPI().trainNaiveBayes(dataBytes._jrdd, lambda_) return NaiveBayesModel( _deserialize_double_vector(ans[0]), - _deserialize_double_matrix(ans[1])) + _deserialize_double_vector(ans[1]), + _deserialize_double_matrix(ans[2])) def _test(): From 47ebea5468df2e4f94ef493c5403fcdcda8c5eb2 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Wed, 2 Apr 2014 18:14:31 -0700 Subject: [PATCH 19/78] [SQL] SPARK-1364 Improve datatype and test coverage for ScalaReflection schema inference. Author: Michael Armbrust Closes #293 from marmbrus/reflectTypes and squashes the following commits: f54e8e8 [Michael Armbrust] Improve datatype and test coverage for ScalaReflection schema inference. --- .../spark/sql/catalyst/ScalaReflection.scala | 10 ++++ .../sql/ScalaReflectionRelationSuite.scala | 56 +++++++++++++++++++ 2 files changed, 66 insertions(+) create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index 976dda8d7e59a..5aaa63bf3b4b4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -43,15 +43,25 @@ object ScalaReflection { val params = t.member("": TermName).asMethod.paramss StructType( params.head.map(p => StructField(p.name.toString, schemaFor(p.typeSignature), true))) + // Need to decide if we actually need a special type here. + case t if t <:< typeOf[Array[Byte]] => BinaryType + case t if t <:< typeOf[Array[_]] => + sys.error(s"Only Array[Byte] supported now, use Seq instead of $t") case t if t <:< typeOf[Seq[_]] => val TypeRef(_, _, Seq(elementType)) = t ArrayType(schemaFor(elementType)) + case t if t <:< typeOf[Map[_,_]] => + val TypeRef(_, _, Seq(keyType, valueType)) = t + MapType(schemaFor(keyType), schemaFor(valueType)) case t if t <:< typeOf[String] => StringType case t if t <:< definitions.IntTpe => IntegerType case t if t <:< definitions.LongTpe => LongType + case t if t <:< definitions.FloatTpe => FloatType case t if t <:< definitions.DoubleTpe => DoubleType case t if t <:< definitions.ShortTpe => ShortType case t if t <:< definitions.ByteTpe => ByteType + case t if t <:< definitions.BooleanTpe => BooleanType + case t if t <:< typeOf[BigDecimal] => DecimalType } implicit class CaseClassRelation[A <: Product : TypeTag](data: Seq[A]) { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala new file mode 100644 index 0000000000000..70033a050c78c --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.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 + +import org.scalatest.FunSuite + +import org.apache.spark.sql.test.TestSQLContext._ + +case class ReflectData( + stringField: String, + intField: Int, + longField: Long, + floatField: Float, + doubleField: Double, + shortField: Short, + byteField: Byte, + booleanField: Boolean, + decimalField: BigDecimal, + seqInt: Seq[Int]) + +case class ReflectBinary(data: Array[Byte]) + +class ScalaReflectionRelationSuite extends FunSuite { + test("query case class RDD") { + val data = ReflectData("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true, + BigDecimal(1), Seq(1,2,3)) + val rdd = sparkContext.parallelize(data :: Nil) + rdd.registerAsTable("reflectData") + + assert(sql("SELECT * FROM reflectData").collect().head === data.productIterator.toSeq) + } + + // Equality is broken for Arrays, so we test that separately. + test("query binary data") { + val rdd = sparkContext.parallelize(ReflectBinary(Array[Byte](1)) :: Nil) + rdd.registerAsTable("reflectBinary") + + val result = sql("SELECT data FROM reflectBinary").collect().head(0).asInstanceOf[Array[Byte]] + assert(result.toSeq === Seq[Byte](1)) + } +} \ No newline at end of file From 92a86b285f8a4af1bdf577dd4c4ea0fd5ca8d682 Mon Sep 17 00:00:00 2001 From: Mark Hamstra Date: Thu, 3 Apr 2014 14:08:47 -0700 Subject: [PATCH 20/78] [SPARK-1398] Removed findbugs jsr305 dependency Should be a painless upgrade, and does offer some significant advantages should we want to leverage FindBugs more during the 1.0 lifecycle. http://findbugs.sourceforge.net/findbugs2.html Author: Mark Hamstra Closes #307 from markhamstra/findbugs and squashes the following commits: 99f2d09 [Mark Hamstra] Removed unnecessary findbugs jsr305 dependency --- core/pom.xml | 4 ---- pom.xml | 5 ----- project/SparkBuild.scala | 1 - 3 files changed, 10 deletions(-) diff --git a/core/pom.xml b/core/pom.xml index e4c32eff0cd77..273aa69659336 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -82,10 +82,6 @@ com.google.guava guava - - com.google.code.findbugs - jsr305 - org.slf4j slf4j-api diff --git a/pom.xml b/pom.xml index 7d58060cba606..b91b14d2f84d0 100644 --- a/pom.xml +++ b/pom.xml @@ -214,11 +214,6 @@ guava 14.0.1 - - com.google.code.findbugs - jsr305 - 1.3.9 - org.slf4j slf4j-api diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index c5c697e8e2427..a2a21d9763548 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -296,7 +296,6 @@ object SparkBuild extends Build { name := "spark-core", libraryDependencies ++= Seq( "com.google.guava" % "guava" % "14.0.1", - "com.google.code.findbugs" % "jsr305" % "1.3.9", "log4j" % "log4j" % "1.2.17", "org.slf4j" % "slf4j-api" % slf4jVersion, "org.slf4j" % "slf4j-log4j12" % slf4jVersion, From fbebaedf26286ee8a75065822a3af1148351f828 Mon Sep 17 00:00:00 2001 From: Andre Schumacher Date: Thu, 3 Apr 2014 15:31:47 -0700 Subject: [PATCH 21/78] Spark parquet improvements A few improvements to the Parquet support for SQL queries: - Instead of files a ParquetRelation is now backed by a directory, which simplifies importing data from other sources - InsertIntoParquetTable operation now supports switching between overwriting or appending (at least in HiveQL) - tests now use the new API - Parquet logging can be set to WARNING level (Default) - Default compression for Parquet files (GZIP, as in parquet-mr) Author: Andre Schumacher Closes #195 from AndreSchumacher/spark_parquet_improvements and squashes the following commits: 54df314 [Andre Schumacher] SPARK-1383 [SQL] Improvements to ParquetRelation --- .../apache/spark/sql/catalyst/SqlParser.scala | 14 +- .../spark/sql/catalyst/analysis/Catalog.scala | 26 ++- .../org/apache/spark/sql/SQLContext.scala | 4 +- .../spark/sql/execution/SparkStrategies.scala | 6 +- .../spark/sql/parquet/ParquetRelation.scala | 129 ++++++++----- .../sql/parquet/ParquetTableOperations.scala | 139 +++++++++++--- .../sql/parquet/ParquetTableSupport.scala | 35 ++-- .../spark/sql/parquet/ParquetTestData.scala | 10 +- sql/core/src/test/resources/log4j.properties | 8 +- .../spark/sql/parquet/ParquetQuerySuite.scala | 118 ++++++++++-- .../spark/sql/hive/HiveMetastoreCatalog.scala | 2 + .../org/apache/spark/sql/hive/TestHive.scala | 2 + .../spark/sql/hive/CachedTableSuite.scala | 4 +- .../hive/execution/HiveComparisonTest.scala | 6 +- .../spark/sql/parquet/HiveParquetSuite.scala | 169 +++++++++--------- 15 files changed, 460 insertions(+), 212 deletions(-) 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 8de87594c8ab9..4ea80fee23e1e 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 @@ -106,6 +106,8 @@ class SqlParser extends StandardTokenParsers { protected val IF = Keyword("IF") protected val IN = Keyword("IN") protected val INNER = Keyword("INNER") + protected val INSERT = Keyword("INSERT") + protected val INTO = Keyword("INTO") protected val IS = Keyword("IS") protected val JOIN = Keyword("JOIN") protected val LEFT = Keyword("LEFT") @@ -114,6 +116,7 @@ class SqlParser extends StandardTokenParsers { protected val NULL = Keyword("NULL") protected val ON = Keyword("ON") protected val OR = Keyword("OR") + protected val OVERWRITE = Keyword("OVERWRITE") protected val LIKE = Keyword("LIKE") protected val RLIKE = Keyword("RLIKE") protected val REGEXP = Keyword("REGEXP") @@ -162,7 +165,7 @@ class SqlParser extends StandardTokenParsers { select * ( UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } | UNION ~ opt(DISTINCT) ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) } - ) + ) | insert protected lazy val select: Parser[LogicalPlan] = SELECT ~> opt(DISTINCT) ~ projections ~ @@ -185,6 +188,13 @@ class SqlParser extends StandardTokenParsers { withLimit } + protected lazy val insert: Parser[LogicalPlan] = + INSERT ~> opt(OVERWRITE) ~ inTo ~ select <~ opt(";") ^^ { + case o ~ r ~ s => + val overwrite: Boolean = o.getOrElse("") == "OVERWRITE" + InsertIntoTable(r, Map[String, Option[String]](), s, overwrite) + } + protected lazy val projections: Parser[Seq[Expression]] = repsep(projection, ",") protected lazy val projection: Parser[Expression] = @@ -195,6 +205,8 @@ class SqlParser extends StandardTokenParsers { protected lazy val from: Parser[LogicalPlan] = FROM ~> relations + protected lazy val inTo: Parser[LogicalPlan] = INTO ~> relation + // Based very loosely on the MySQL Grammar. // http://dev.mysql.com/doc/refman/5.0/en/join.html protected lazy val relations: Parser[LogicalPlan] = 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 6b58b9322c4bf..f30b5d816703a 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 @@ -31,19 +31,33 @@ trait Catalog { alias: Option[String] = None): LogicalPlan def registerTable(databaseName: Option[String], tableName: String, plan: LogicalPlan): Unit + def unregisterTable(databaseName: Option[String], tableName: String): Unit + + def unregisterAllTables(): Unit } class SimpleCatalog extends Catalog { val tables = new mutable.HashMap[String, LogicalPlan]() - def registerTable(databaseName: Option[String],tableName: String, plan: LogicalPlan): Unit = { + override def registerTable( + databaseName: Option[String], + tableName: String, + plan: LogicalPlan): Unit = { tables += ((tableName, plan)) } - def unregisterTable(databaseName: Option[String], tableName: String) = { tables -= tableName } + override def unregisterTable( + databaseName: Option[String], + tableName: String) = { + tables -= tableName + } + + override def unregisterAllTables() = { + tables.clear() + } - def lookupRelation( + override def lookupRelation( databaseName: Option[String], tableName: String, alias: Option[String] = None): LogicalPlan = { @@ -92,6 +106,10 @@ trait OverrideCatalog extends Catalog { override def unregisterTable(databaseName: Option[String], tableName: String): Unit = { overrides.remove((databaseName, tableName)) } + + override def unregisterAllTables(): Unit = { + overrides.clear() + } } /** @@ -113,4 +131,6 @@ object EmptyCatalog extends Catalog { def unregisterTable(databaseName: Option[String], tableName: String): Unit = { throw new UnsupportedOperationException } + + override def unregisterAllTables(): Unit = {} } 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 f4bf00f4cffa6..36059c6630aa4 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 @@ -80,12 +80,12 @@ class SQLContext(@transient val sparkContext: SparkContext) new SchemaRDD(this, SparkLogicalPlan(ExistingRdd.fromProductRdd(rdd))) /** - * Loads a parequet file, returning the result as a [[SchemaRDD]]. + * Loads a Parquet file, returning the result as a [[SchemaRDD]]. * * @group userf */ def parquetFile(path: String): SchemaRDD = - new SchemaRDD(this, parquet.ParquetRelation("ParquetFile", path)) + new SchemaRDD(this, parquet.ParquetRelation(path)) /** 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 b3e51fdf75270..fe8bd5a508820 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 @@ -171,10 +171,10 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] { // TODO: need to support writing to other types of files. Unify the below code paths. case logical.WriteToFile(path, child) => val relation = - ParquetRelation.create(path, child, sparkContext.hadoopConfiguration, None) - InsertIntoParquetTable(relation, planLater(child))(sparkContext) :: Nil + ParquetRelation.create(path, child, sparkContext.hadoopConfiguration) + InsertIntoParquetTable(relation, planLater(child), overwrite=true)(sparkContext) :: Nil case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) => - InsertIntoParquetTable(table, planLater(child))(sparkContext) :: Nil + InsertIntoParquetTable(table, planLater(child), overwrite)(sparkContext) :: Nil case PhysicalOperation(projectList, filters, relation: ParquetRelation) => // TODO: Should be pushing down filters as well. pruneFilterProject( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala index 4ab755c096bd8..114bfbb719ee9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala @@ -17,30 +17,29 @@ package org.apache.spark.sql.parquet -import java.io.{IOException, FileNotFoundException} - -import scala.collection.JavaConversions._ +import java.io.IOException import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.permission.FsAction import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.fs.permission.FsAction import org.apache.hadoop.mapreduce.Job -import parquet.hadoop.metadata.{FileMetaData, ParquetMetadata} import parquet.hadoop.util.ContextUtil -import parquet.hadoop.{Footer, ParquetFileReader, ParquetFileWriter} +import parquet.hadoop.{ParquetOutputFormat, Footer, ParquetFileWriter, ParquetFileReader} +import parquet.hadoop.metadata.{CompressionCodecName, FileMetaData, ParquetMetadata} import parquet.io.api.{Binary, RecordConsumer} +import parquet.schema.{Type => ParquetType, PrimitiveType => ParquetPrimitiveType, MessageType, MessageTypeParser} import parquet.schema.PrimitiveType.{PrimitiveTypeName => ParquetPrimitiveTypeName} import parquet.schema.Type.Repetition -import parquet.schema.{MessageType, MessageTypeParser} -import parquet.schema.{PrimitiveType => ParquetPrimitiveType} -import parquet.schema.{Type => ParquetType} import org.apache.spark.sql.catalyst.analysis.{MultiInstanceRelation, UnresolvedException} import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Row} -import org.apache.spark.sql.catalyst.plans.logical.{BaseRelation, LogicalPlan} +import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LeafNode} import org.apache.spark.sql.catalyst.types._ +// Implicits +import scala.collection.JavaConversions._ + /** * Relation that consists of data stored in a Parquet columnar format. * @@ -48,14 +47,14 @@ import org.apache.spark.sql.catalyst.types._ * of using this class directly. * * {{{ - * val parquetRDD = sqlContext.parquetFile("path/to/parequet.file") + * val parquetRDD = sqlContext.parquetFile("path/to/parquet.file") * }}} * - * @param tableName The name of the relation that can be used in queries. * @param path The path to the Parquet file. */ -case class ParquetRelation(tableName: String, path: String) - extends BaseRelation with MultiInstanceRelation { +private[sql] case class ParquetRelation(val path: String) + extends LeafNode with MultiInstanceRelation { + self: Product => /** Schema derived from ParquetFile */ def parquetSchema: MessageType = @@ -65,33 +64,59 @@ case class ParquetRelation(tableName: String, path: String) .getSchema /** Attributes */ - val attributes = + override val output = ParquetTypesConverter - .convertToAttributes(parquetSchema) + .convertToAttributes(parquetSchema) - /** Output */ - override val output = attributes - - // Parquet files have no concepts of keys, therefore no Partitioner - // Note: we could allow Block level access; needs to be thought through - override def isPartitioned = false - - override def newInstance = ParquetRelation(tableName, path).asInstanceOf[this.type] + override def newInstance = ParquetRelation(path).asInstanceOf[this.type] // Equals must also take into account the output attributes so that we can distinguish between // different instances of the same relation, override def equals(other: Any) = other match { case p: ParquetRelation => - p.tableName == tableName && p.path == path && p.output == output + p.path == path && p.output == output case _ => false } } -object ParquetRelation { +private[sql] object ParquetRelation { + + def enableLogForwarding() { + // Note: Parquet does not use forwarding to parent loggers which + // is required for the JUL-SLF4J bridge to work. Also there is + // a default logger that appends to Console which needs to be + // reset. + import org.slf4j.bridge.SLF4JBridgeHandler + import java.util.logging.Logger + import java.util.logging.LogManager + + val loggerNames = Seq( + "parquet.hadoop.ColumnChunkPageWriteStore", + "parquet.hadoop.InternalParquetRecordWriter", + "parquet.hadoop.ParquetRecordReader", + "parquet.hadoop.ParquetInputFormat", + "parquet.hadoop.ParquetOutputFormat", + "parquet.hadoop.ParquetFileReader", + "parquet.hadoop.InternalParquetRecordReader", + "parquet.hadoop.codec.CodecConfig") + LogManager.getLogManager.reset() + SLF4JBridgeHandler.install() + for(name <- loggerNames) { + val logger = Logger.getLogger(name) + logger.setParent(Logger.getGlobal) + logger.setUseParentHandlers(true) + } + } // The element type for the RDDs that this relation maps to. type RowType = org.apache.spark.sql.catalyst.expressions.GenericMutableRow + // The compression type + type CompressionType = parquet.hadoop.metadata.CompressionCodecName + + // The default compression + val defaultCompression = CompressionCodecName.GZIP + /** * Creates a new ParquetRelation and underlying Parquetfile for the given LogicalPlan. Note that * this is used inside [[org.apache.spark.sql.execution.SparkStrategies SparkStrategies]] to @@ -100,24 +125,39 @@ object ParquetRelation { * * @param pathString The directory the Parquetfile will be stored in. * @param child The child node that will be used for extracting the schema. - * @param conf A configuration configuration to be used. - * @param tableName The name of the resulting relation. - * @return An empty ParquetRelation inferred metadata. + * @param conf A configuration to be used. + * @return An empty ParquetRelation with inferred metadata. */ def create(pathString: String, child: LogicalPlan, - conf: Configuration, - tableName: Option[String]): ParquetRelation = { + conf: Configuration): ParquetRelation = { if (!child.resolved) { throw new UnresolvedException[LogicalPlan]( child, "Attempt to create Parquet table from unresolved child (when schema is not available)") } + createEmpty(pathString, child.output, conf) + } - val name = s"${tableName.getOrElse(child.nodeName)}_parquet" + /** + * Creates an empty ParquetRelation and underlying Parquetfile that only + * consists of the Metadata for the given schema. + * + * @param pathString The directory the Parquetfile will be stored in. + * @param attributes The schema of the relation. + * @param conf A configuration to be used. + * @return An empty ParquetRelation. + */ + def createEmpty(pathString: String, + attributes: Seq[Attribute], + conf: Configuration): ParquetRelation = { val path = checkPath(pathString, conf) - ParquetTypesConverter.writeMetaData(child.output, path, conf) - new ParquetRelation(name, path.toString) + if (conf.get(ParquetOutputFormat.COMPRESSION) == null) { + conf.set(ParquetOutputFormat.COMPRESSION, ParquetRelation.defaultCompression.name()) + } + ParquetRelation.enableLogForwarding() + ParquetTypesConverter.writeMetaData(attributes, path, conf) + new ParquetRelation(path.toString) } private def checkPath(pathStr: String, conf: Configuration): Path = { @@ -143,7 +183,7 @@ object ParquetRelation { } } -object ParquetTypesConverter { +private[parquet] object ParquetTypesConverter { def toDataType(parquetType : ParquetPrimitiveTypeName): DataType = parquetType match { // for now map binary to string type // TODO: figure out how Parquet uses strings or why we can't use them in a MessageType schema @@ -242,6 +282,7 @@ object ParquetTypesConverter { extraMetadata, "Spark") + ParquetRelation.enableLogForwarding() ParquetFileWriter.writeMetadataFile( conf, path, @@ -268,16 +309,24 @@ object ParquetTypesConverter { throw new IllegalArgumentException(s"Incorrectly formatted Parquet metadata path $origPath") } val path = origPath.makeQualified(fs) + if (!fs.getFileStatus(path).isDir) { + throw new IllegalArgumentException( + s"Expected $path for be a directory with Parquet files/metadata") + } + ParquetRelation.enableLogForwarding() val metadataPath = new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE) + // if this is a new table that was just created we will find only the metadata file if (fs.exists(metadataPath) && fs.isFile(metadataPath)) { - // TODO: improve exception handling, etc. ParquetFileReader.readFooter(conf, metadataPath) } else { - if (!fs.exists(path) || !fs.isFile(path)) { - throw new FileNotFoundException( - s"Could not find file ${path.toString} when trying to read metadata") + // there may be one or more Parquet files in the given directory + val footers = ParquetFileReader.readFooters(conf, fs.getFileStatus(path)) + // TODO: for now we assume that all footers (if there is more than one) have identical + // metadata; we may want to add a check here at some point + if (footers.size() == 0) { + throw new IllegalArgumentException(s"Could not find Parquet metadata at path $path") } - ParquetFileReader.readFooter(conf, path) + footers(0).getParquetMetadata } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala index 7285f5b88b9bf..d5846baa72ada 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala @@ -24,26 +24,29 @@ import java.util.Date import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.mapreduce._ -import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat} +import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} +import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat, FileOutputCommitter} -import parquet.hadoop.util.ContextUtil import parquet.hadoop.{ParquetInputFormat, ParquetOutputFormat} +import parquet.hadoop.util.ContextUtil import parquet.io.InvalidRecordException import parquet.schema.MessageType +import org.apache.spark.{SerializableWritable, SparkContext, TaskContext} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row} import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode} -import org.apache.spark.{SerializableWritable, SparkContext, TaskContext} /** * Parquet table scan operator. Imports the file that backs the given * [[ParquetRelation]] as a RDD[Row]. */ case class ParquetTableScan( - @transient output: Seq[Attribute], - @transient relation: ParquetRelation, - @transient columnPruningPred: Option[Expression])( + // note: output cannot be transient, see + // https://issues.apache.org/jira/browse/SPARK-1367 + output: Seq[Attribute], + relation: ParquetRelation, + columnPruningPred: Option[Expression])( @transient val sc: SparkContext) extends LeafNode { @@ -53,6 +56,12 @@ case class ParquetTableScan( job, classOf[org.apache.spark.sql.parquet.RowReadSupport]) val conf: Configuration = ContextUtil.getConfiguration(job) + val fileList = FileSystemHelper.listFiles(relation.path, conf) + // add all paths in the directory but skip "hidden" ones such + // as "_SUCCESS" and "_metadata" + for (path <- fileList if !path.getName.startsWith("_")) { + NewFileInputFormat.addInputPath(job, path) + } conf.set( RowReadSupport.PARQUET_ROW_REQUESTED_SCHEMA, ParquetTypesConverter.convertFromAttributes(output).toString) @@ -63,14 +72,12 @@ case class ParquetTableScan( ``FilteredRecordReader`` (via Configuration, for example). Simple filter-rows-by-column-values however should be supported. */ - sc.newAPIHadoopFile( - relation.path, - classOf[ParquetInputFormat[Row]], - classOf[Void], classOf[Row], - conf) + sc.newAPIHadoopRDD(conf, classOf[ParquetInputFormat[Row]], classOf[Void], classOf[Row]) .map(_._2) } + override def otherCopyArgs = sc :: Nil + /** * Applies a (candidate) projection. * @@ -108,15 +115,31 @@ case class ParquetTableScan( } } +/** + * Operator that acts as a sink for queries on RDDs and can be used to + * store the output inside a directory of Parquet files. This operator + * is similar to Hive's INSERT INTO TABLE operation in the sense that + * one can choose to either overwrite or append to a directory. Note + * that consecutive insertions to the same table must have compatible + * (source) schemas. + * + * WARNING: EXPERIMENTAL! InsertIntoParquetTable with overwrite=false may + * cause data corruption in the case that multiple users try to append to + * the same table simultaneously. Inserting into a table that was + * previously generated by other means (e.g., by creating an HDFS + * directory and importing Parquet files generated by other tools) may + * cause unpredicted behaviour and therefore results in a RuntimeException + * (only detected via filename pattern so will not catch all cases). + */ case class InsertIntoParquetTable( - @transient relation: ParquetRelation, - @transient child: SparkPlan)( + relation: ParquetRelation, + child: SparkPlan, + overwrite: Boolean = false)( @transient val sc: SparkContext) extends UnaryNode with SparkHadoopMapReduceUtil { /** - * Inserts all the rows in the Parquet file. Note that OVERWRITE is implicit, since - * Parquet files are write-once. + * Inserts all rows into the Parquet file. */ override def execute() = { // TODO: currently we do not check whether the "schema"s are compatible @@ -135,19 +158,21 @@ case class InsertIntoParquetTable( classOf[org.apache.spark.sql.parquet.RowWriteSupport]) // TODO: move that to function in object - val conf = job.getConfiguration + val conf = ContextUtil.getConfiguration(job) conf.set(RowWriteSupport.PARQUET_ROW_SCHEMA, relation.parquetSchema.toString) val fspath = new Path(relation.path) val fs = fspath.getFileSystem(conf) - try { - fs.delete(fspath, true) - } catch { - case e: IOException => - throw new IOException( - s"Unable to clear output directory ${fspath.toString} prior" - + s" to InsertIntoParquetTable:\n${e.toString}") + if (overwrite) { + try { + fs.delete(fspath, true) + } catch { + case e: IOException => + throw new IOException( + s"Unable to clear output directory ${fspath.toString} prior" + + s" to InsertIntoParquetTable:\n${e.toString}") + } } saveAsHadoopFile(childRdd, relation.path.toString, conf) @@ -157,6 +182,8 @@ case class InsertIntoParquetTable( override def output = child.output + override def otherCopyArgs = sc :: Nil + // based on ``saveAsNewAPIHadoopFile`` in [[PairRDDFunctions]] // TODO: Maybe PairRDDFunctions should use Product2 instead of Tuple2? // .. then we could use the default one and could use [[MutablePair]] @@ -167,15 +194,21 @@ case class InsertIntoParquetTable( conf: Configuration) { val job = new Job(conf) val keyType = classOf[Void] - val outputFormatType = classOf[parquet.hadoop.ParquetOutputFormat[Row]] job.setOutputKeyClass(keyType) job.setOutputValueClass(classOf[Row]) - val wrappedConf = new SerializableWritable(job.getConfiguration) NewFileOutputFormat.setOutputPath(job, new Path(path)) + val wrappedConf = new SerializableWritable(job.getConfiguration) val formatter = new SimpleDateFormat("yyyyMMddHHmm") val jobtrackerID = formatter.format(new Date()) val stageId = sc.newRddId() + val taskIdOffset = + if (overwrite) 1 + else { + FileSystemHelper + .findMaxTaskId(NewFileOutputFormat.getOutputPath(job).toString, job.getConfiguration) + 1 + } + def writeShard(context: TaskContext, iter: Iterator[Row]): Int = { // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it // around by taking a mod. We expect that no task will be attempted 2 billion times. @@ -184,7 +217,7 @@ case class InsertIntoParquetTable( val attemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = false, context.partitionId, attemptNumber) val hadoopContext = newTaskAttemptContext(wrappedConf.value, attemptId) - val format = outputFormatType.newInstance + val format = new AppendingParquetOutputFormat(taskIdOffset) val committer = format.getOutputCommitter(hadoopContext) committer.setupTask(hadoopContext) val writer = format.getRecordWriter(hadoopContext) @@ -196,7 +229,7 @@ case class InsertIntoParquetTable( committer.commitTask(hadoopContext) return 1 } - val jobFormat = outputFormatType.newInstance + val jobFormat = new AppendingParquetOutputFormat(taskIdOffset) /* apparently we need a TaskAttemptID to construct an OutputCommitter; * however we're only going to use this local OutputCommitter for * setupJob/commitJob, so we just use a dummy "map" task. @@ -210,3 +243,55 @@ case class InsertIntoParquetTable( } } +// TODO: this will be able to append to directories it created itself, not necessarily +// to imported ones +private[parquet] class AppendingParquetOutputFormat(offset: Int) + extends parquet.hadoop.ParquetOutputFormat[Row] { + // override to accept existing directories as valid output directory + override def checkOutputSpecs(job: JobContext): Unit = {} + + // override to choose output filename so not overwrite existing ones + override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = { + val taskId: TaskID = context.getTaskAttemptID.getTaskID + val partition: Int = taskId.getId + val filename = s"part-r-${partition + offset}.parquet" + val committer: FileOutputCommitter = + getOutputCommitter(context).asInstanceOf[FileOutputCommitter] + new Path(committer.getWorkPath, filename) + } +} + +private[parquet] object FileSystemHelper { + def listFiles(pathStr: String, conf: Configuration): Seq[Path] = { + val origPath = new Path(pathStr) + val fs = origPath.getFileSystem(conf) + if (fs == null) { + throw new IllegalArgumentException( + s"ParquetTableOperations: Path $origPath is incorrectly formatted") + } + val path = origPath.makeQualified(fs) + if (!fs.exists(path) || !fs.getFileStatus(path).isDir) { + throw new IllegalArgumentException( + s"ParquetTableOperations: path $path does not exist or is not a directory") + } + fs.listStatus(path).map(_.getPath) + } + + // finds the maximum taskid in the output file names at the given path + def findMaxTaskId(pathStr: String, conf: Configuration): Int = { + val files = FileSystemHelper.listFiles(pathStr, conf) + // filename pattern is part-r-.parquet + val nameP = new scala.util.matching.Regex("""part-r-(\d{1,}).parquet""", "taskid") + val hiddenFileP = new scala.util.matching.Regex("_.*") + files.map(_.getName).map { + case nameP(taskid) => taskid.toInt + case hiddenFileP() => 0 + case other: String => { + sys.error("ERROR: attempting to append to set of Parquet files and found file" + + s"that does not match name pattern: $other") + 0 + } + case _ => 0 + }.reduceLeft((a, b) => if (a < b) b else a) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala index c21e400282004..84b1b4609458b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala @@ -35,7 +35,8 @@ import org.apache.spark.sql.catalyst.types._ * *@param root The root group converter for the record. */ -class RowRecordMaterializer(root: CatalystGroupConverter) extends RecordMaterializer[Row] { +private[parquet] class RowRecordMaterializer(root: CatalystGroupConverter) + extends RecordMaterializer[Row] { def this(parquetSchema: MessageType) = this(new CatalystGroupConverter(ParquetTypesConverter.convertToAttributes(parquetSchema))) @@ -48,14 +49,14 @@ class RowRecordMaterializer(root: CatalystGroupConverter) extends RecordMaterial /** * A `parquet.hadoop.api.ReadSupport` for Row objects. */ -class RowReadSupport extends ReadSupport[Row] with Logging { +private[parquet] class RowReadSupport extends ReadSupport[Row] with Logging { override def prepareForRead( conf: Configuration, stringMap: java.util.Map[String, String], fileSchema: MessageType, readContext: ReadContext): RecordMaterializer[Row] = { - log.debug(s"preparing for read with schema ${fileSchema.toString}") + log.debug(s"preparing for read with file schema $fileSchema") new RowRecordMaterializer(readContext.getRequestedSchema) } @@ -67,20 +68,20 @@ class RowReadSupport extends ReadSupport[Row] with Logging { configuration.get(RowReadSupport.PARQUET_ROW_REQUESTED_SCHEMA, fileSchema.toString) val requested_schema = MessageTypeParser.parseMessageType(requested_schema_string) - - log.debug(s"read support initialized for original schema ${requested_schema.toString}") + log.debug(s"read support initialized for requested schema $requested_schema") + ParquetRelation.enableLogForwarding() new ReadContext(requested_schema, keyValueMetaData) } } -object RowReadSupport { +private[parquet] object RowReadSupport { val PARQUET_ROW_REQUESTED_SCHEMA = "org.apache.spark.sql.parquet.row.requested_schema" } /** * A `parquet.hadoop.api.WriteSupport` for Row ojects. */ -class RowWriteSupport extends WriteSupport[Row] with Logging { +private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { def setSchema(schema: MessageType, configuration: Configuration) { // for testing this.schema = schema @@ -104,6 +105,8 @@ class RowWriteSupport extends WriteSupport[Row] with Logging { override def init(configuration: Configuration): WriteSupport.WriteContext = { schema = if (schema == null) getSchema(configuration) else schema attributes = ParquetTypesConverter.convertToAttributes(schema) + log.debug(s"write support initialized for requested schema $schema") + ParquetRelation.enableLogForwarding() new WriteSupport.WriteContext( schema, new java.util.HashMap[java.lang.String, java.lang.String]()) @@ -111,10 +114,16 @@ class RowWriteSupport extends WriteSupport[Row] with Logging { override def prepareForWrite(recordConsumer: RecordConsumer): Unit = { writer = recordConsumer + log.debug(s"preparing for write with schema $schema") } // TODO: add groups (nested fields) override def write(record: Row): Unit = { + if (attributes.size > record.size) { + throw new IndexOutOfBoundsException( + s"Trying to write more fields than contained in row (${attributes.size}>${record.size})") + } + var index = 0 writer.startMessage() while(index < attributes.size) { @@ -130,7 +139,7 @@ class RowWriteSupport extends WriteSupport[Row] with Logging { } } -object RowWriteSupport { +private[parquet] object RowWriteSupport { val PARQUET_ROW_SCHEMA: String = "org.apache.spark.sql.parquet.row.schema" } @@ -139,7 +148,7 @@ object RowWriteSupport { * * @param schema The corresponding Catalyst schema in the form of a list of attributes. */ -class CatalystGroupConverter( +private[parquet] class CatalystGroupConverter( schema: Seq[Attribute], protected[parquet] val current: ParquetRelation.RowType) extends GroupConverter { @@ -177,13 +186,12 @@ class CatalystGroupConverter( * @param parent The parent group converter. * @param fieldIndex The index inside the record. */ -class CatalystPrimitiveConverter( +private[parquet] class CatalystPrimitiveConverter( parent: CatalystGroupConverter, fieldIndex: Int) extends PrimitiveConverter { // TODO: consider refactoring these together with ParquetTypesConverter override def addBinary(value: Binary): Unit = - // TODO: fix this once a setBinary will become available in MutableRow - parent.getCurrentRecord.setByte(fieldIndex, value.getBytes.apply(0)) + parent.getCurrentRecord.update(fieldIndex, value.getBytes) override def addBoolean(value: Boolean): Unit = parent.getCurrentRecord.setBoolean(fieldIndex, value) @@ -208,10 +216,9 @@ class CatalystPrimitiveConverter( * @param parent The parent group converter. * @param fieldIndex The index inside the record. */ -class CatalystPrimitiveStringConverter( +private[parquet] class CatalystPrimitiveStringConverter( parent: CatalystGroupConverter, fieldIndex: Int) extends CatalystPrimitiveConverter(parent, fieldIndex) { override def addBinary(value: Binary): Unit = parent.getCurrentRecord.setString(fieldIndex, value.toStringUsingUTF8) } - diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala index 3340c3ff81f0a..728e3dd1dc02b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala @@ -26,7 +26,7 @@ import parquet.hadoop.util.ContextUtil import parquet.schema.{MessageType, MessageTypeParser} import org.apache.spark.sql.catalyst.expressions.GenericRow -import org.apache.spark.sql.catalyst.util.getTempFilePath +import org.apache.spark.util.Utils object ParquetTestData { @@ -64,13 +64,13 @@ object ParquetTestData { "mylong:Long" ) - val testFile = getTempFilePath("testParquetFile").getCanonicalFile + val testDir = Utils.createTempDir() - lazy val testData = new ParquetRelation("testData", testFile.toURI.toString) + lazy val testData = new ParquetRelation(testDir.toURI.toString) def writeFile() = { - testFile.delete - val path: Path = new Path(testFile.toURI) + testDir.delete + val path: Path = new Path(new Path(testDir.toURI), new Path("part-r-0.parquet")) val job = new Job() val configuration: Configuration = ContextUtil.getConfiguration(job) val schema: MessageType = MessageTypeParser.parseMessageType(testSchema) diff --git a/sql/core/src/test/resources/log4j.properties b/sql/core/src/test/resources/log4j.properties index 7bb6789bd33a5..dffd15a61838b 100644 --- a/sql/core/src/test/resources/log4j.properties +++ b/sql/core/src/test/resources/log4j.properties @@ -45,8 +45,6 @@ log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=OFF log4j.additivity.hive.ql.metadata.Hive=false log4j.logger.hive.ql.metadata.Hive=OFF -# Parquet logging -parquet.hadoop.InternalParquetRecordReader=WARN -log4j.logger.parquet.hadoop.InternalParquetRecordReader=WARN -parquet.hadoop.ParquetInputFormat=WARN -log4j.logger.parquet.hadoop.ParquetInputFormat=WARN +# Parquet related logging +log4j.logger.parquet.hadoop=WARN +log4j.logger.org.apache.spark.sql.parquet=INFO diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala index ea1733b3614e5..a62a3c4d02354 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala @@ -19,27 +19,40 @@ package org.apache.spark.sql.parquet import org.scalatest.{BeforeAndAfterAll, FunSuite} -import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.fs.{Path, FileSystem} import org.apache.hadoop.mapreduce.Job + import parquet.hadoop.ParquetFileWriter -import parquet.hadoop.util.ContextUtil import parquet.schema.MessageTypeParser +import parquet.hadoop.util.ContextUtil -import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.catalyst.util.getTempFilePath +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Row} import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.util.Utils +import org.apache.spark.sql.catalyst.types.{StringType, IntegerType, DataType} +import org.apache.spark.sql.{parquet, SchemaRDD} +import org.apache.spark.sql.catalyst.expressions.AttributeReference +import scala.Tuple2 // Implicits import org.apache.spark.sql.test.TestSQLContext._ +case class TestRDDEntry(key: Int, value: String) + class ParquetQuerySuite extends FunSuite with BeforeAndAfterAll { + + var testRDD: SchemaRDD = null + override def beforeAll() { ParquetTestData.writeFile() + testRDD = parquetFile(ParquetTestData.testDir.toString) + testRDD.registerAsTable("testsource") } override def afterAll() { - ParquetTestData.testFile.delete() + Utils.deleteRecursively(ParquetTestData.testDir) + // here we should also unregister the table?? } test("self-join parquet files") { @@ -55,11 +68,18 @@ class ParquetQuerySuite extends FunSuite with BeforeAndAfterAll { case Seq(_, _) => // All good } - // TODO: We can't run this query as it NPEs + val result = query.collect() + assert(result.size === 9, "self-join result has incorrect size") + assert(result(0).size === 12, "result row has incorrect size") + result.zipWithIndex.foreach { + case (row, index) => row.zipWithIndex.foreach { + case (field, column) => assert(field != null, s"self-join contains null value in row $index field $column") + } + } } test("Import of simple Parquet file") { - val result = getRDD(ParquetTestData.testData).collect() + val result = parquetFile(ParquetTestData.testDir.toString).collect() assert(result.size === 15) result.zipWithIndex.foreach { case (row, index) => { @@ -125,20 +145,82 @@ class ParquetQuerySuite extends FunSuite with BeforeAndAfterAll { fs.delete(path, true) } + test("Creating case class RDD table") { + TestSQLContext.sparkContext.parallelize((1 to 100)) + .map(i => TestRDDEntry(i, s"val_$i")) + .registerAsTable("tmp") + val rdd = sql("SELECT * FROM tmp").collect().sortBy(_.getInt(0)) + var counter = 1 + rdd.foreach { + // '===' does not like string comparison? + row: Row => { + assert(row.getString(1).equals(s"val_$counter"), s"row $counter value ${row.getString(1)} does not match val_$counter") + counter = counter + 1 + } + } + } + + test("Saving case class RDD table to file and reading it back in") { + val file = getTempFilePath("parquet") + val path = file.toString + val rdd = TestSQLContext.sparkContext.parallelize((1 to 100)) + .map(i => TestRDDEntry(i, s"val_$i")) + rdd.saveAsParquetFile(path) + val readFile = parquetFile(path) + readFile.registerAsTable("tmpx") + val rdd_copy = sql("SELECT * FROM tmpx").collect() + val rdd_orig = rdd.collect() + for(i <- 0 to 99) { + assert(rdd_copy(i).apply(0) === rdd_orig(i).key, s"key error in line $i") + assert(rdd_copy(i).apply(1) === rdd_orig(i).value, s"value in line $i") + } + Utils.deleteRecursively(file) + assert(true) + } + + test("insert (overwrite) via Scala API (new SchemaRDD)") { + val dirname = Utils.createTempDir() + val source_rdd = TestSQLContext.sparkContext.parallelize((1 to 100)) + .map(i => TestRDDEntry(i, s"val_$i")) + source_rdd.registerAsTable("source") + val dest_rdd = createParquetFile(dirname.toString, ("key", IntegerType), ("value", StringType)) + dest_rdd.registerAsTable("dest") + sql("INSERT OVERWRITE INTO dest SELECT * FROM source").collect() + val rdd_copy1 = sql("SELECT * FROM dest").collect() + assert(rdd_copy1.size === 100) + assert(rdd_copy1(0).apply(0) === 1) + assert(rdd_copy1(0).apply(1) === "val_1") + sql("INSERT INTO dest SELECT * FROM source").collect() + val rdd_copy2 = sql("SELECT * FROM dest").collect() + assert(rdd_copy2.size === 200) + Utils.deleteRecursively(dirname) + } + + test("insert (appending) to same table via Scala API") { + sql("INSERT INTO testsource SELECT * FROM testsource").collect() + val double_rdd = sql("SELECT * FROM testsource").collect() + assert(double_rdd != null) + assert(double_rdd.size === 30) + for(i <- (0 to 14)) { + assert(double_rdd(i) === double_rdd(i+15), s"error: lines $i and ${i+15} to not match") + } + // let's restore the original test data + Utils.deleteRecursively(ParquetTestData.testDir) + ParquetTestData.writeFile() + } + /** - * Computes the given [[ParquetRelation]] and returns its RDD. + * Creates an empty SchemaRDD backed by a ParquetRelation. * - * @param parquetRelation The Parquet relation. - * @return An RDD of Rows. + * TODO: since this is so experimental it is better to have it here and not + * in SQLContext. Also note that when creating new AttributeReferences + * one needs to take care not to create duplicate Attribute ID's. */ - private def getRDD(parquetRelation: ParquetRelation): RDD[Row] = { - val scanner = new ParquetTableScan( - parquetRelation.output, - parquetRelation, - None)(TestSQLContext.sparkContext) - scanner - .execute - .map(_.copy()) + private def createParquetFile(path: String, schema: (Tuple2[String, DataType])*): SchemaRDD = { + val attributes = schema.map(t => new AttributeReference(t._1, t._2)()) + new SchemaRDD( + TestSQLContext, + parquet.ParquetRelation.createEmpty(path, attributes, sparkContext.hadoopConfiguration)) } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 29834a11f41dc..fc053c56c052d 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -148,6 +148,8 @@ class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with Logging { */ override def unregisterTable( databaseName: Option[String], tableName: String): Unit = ??? + + override def unregisterAllTables() = {} } object HiveMetastoreTypes extends RegexParsers { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala index bc3447b9d802d..0a6bea0162430 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala @@ -313,6 +313,8 @@ class TestHiveContext(sc: SparkContext) extends LocalHiveContext(sc) { catalog.client.dropDatabase(db, true, false, true) } + catalog.unregisterAllTables() + FunctionRegistry.getFunctionNames.filterNot(originalUdfs.contains(_)).foreach { udfName => FunctionRegistry.unregisterTemporaryUDF(udfName) } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala index 68d45e53cdf26..79ec1f1cde019 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala @@ -29,7 +29,7 @@ class CachedTableSuite extends HiveComparisonTest { } createQueryTest("read from cached table", - "SELECT * FROM src LIMIT 1") + "SELECT * FROM src LIMIT 1", reset = false) test("check that table is cached and uncache") { TestHive.table("src").queryExecution.analyzed match { @@ -40,7 +40,7 @@ class CachedTableSuite extends HiveComparisonTest { } createQueryTest("read from uncached table", - "SELECT * FROM src LIMIT 1") + "SELECT * FROM src LIMIT 1", reset = false) test("make sure table is uncached") { TestHive.table("src").queryExecution.analyzed match { 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 c7a350ef94edd..18654b308d234 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 @@ -170,7 +170,7 @@ abstract class HiveComparisonTest } val installHooksCommand = "(?i)SET.*hooks".r - def createQueryTest(testCaseName: String, sql: String) { + def createQueryTest(testCaseName: String, sql: String, reset: Boolean = true) { // If test sharding is enable, skip tests that are not in the correct shard. shardInfo.foreach { case (shardId, numShards) if testCaseName.hashCode % numShards != shardId => return @@ -228,7 +228,7 @@ abstract class HiveComparisonTest try { // MINOR HACK: You must run a query before calling reset the first time. TestHive.sql("SHOW TABLES") - TestHive.reset() + if (reset) { TestHive.reset() } val hiveCacheFiles = queryList.zipWithIndex.map { case (queryString, i) => @@ -295,7 +295,7 @@ abstract class HiveComparisonTest fail(errorMessage) } }.toSeq - TestHive.reset() + if (reset) { TestHive.reset() } computedResults } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala index 05ad85b622ac8..314ca48ad8f6a 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala @@ -17,147 +17,138 @@ package org.apache.spark.sql.parquet -import java.io.File - import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite} -import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation -import org.apache.spark.sql.catalyst.expressions.Row -import org.apache.spark.sql.catalyst.plans.logical._ -import org.apache.spark.sql.catalyst.util.getTempFilePath +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Row} +import org.apache.spark.sql.catalyst.types.{DataType, StringType, IntegerType} +import org.apache.spark.sql.{parquet, SchemaRDD} import org.apache.spark.sql.hive.TestHive +import org.apache.spark.util.Utils + +// Implicits +import org.apache.spark.sql.hive.TestHive._ class HiveParquetSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach { - val filename = getTempFilePath("parquettest").getCanonicalFile.toURI.toString - - // runs a SQL and optionally resolves one Parquet table - def runQuery( - querystr: String, - tableName: Option[String] = None, - filename: Option[String] = None): Array[Row] = { - - // call to resolve references in order to get CREATE TABLE AS to work - val query = TestHive - .parseSql(querystr) - val finalQuery = - if (tableName.nonEmpty && filename.nonEmpty) - resolveParquetTable(tableName.get, filename.get, query) - else - query - TestHive.executePlan(finalQuery) - .toRdd - .collect() - } - // stores a query output to a Parquet file - def storeQuery(querystr: String, filename: String): Unit = { - val query = WriteToFile( - filename, - TestHive.parseSql(querystr)) - TestHive - .executePlan(query) - .stringResult() - } + val dirname = Utils.createTempDir() - /** - * TODO: This function is necessary as long as there is no notion of a Catalog for - * Parquet tables. Once such a thing exists this functionality should be moved there. - */ - def resolveParquetTable(tableName: String, filename: String, plan: LogicalPlan): LogicalPlan = { - TestHive.loadTestTable("src") // may not be loaded now - plan.transform { - case relation @ UnresolvedRelation(databaseName, name, alias) => - if (name == tableName) - ParquetRelation(tableName, filename) - else - relation - case op @ InsertIntoCreatedTable(databaseName, name, child) => - if (name == tableName) { - // note: at this stage the plan is not yet analyzed but Parquet needs to know the schema - // and for that we need the child to be resolved - val relation = ParquetRelation.create( - filename, - TestHive.analyzer(child), - TestHive.sparkContext.hadoopConfiguration, - Some(tableName)) - InsertIntoTable( - relation.asInstanceOf[BaseRelation], - Map.empty, - child, - overwrite = false) - } else - op - } - } + var testRDD: SchemaRDD = null override def beforeAll() { // write test data - ParquetTestData.writeFile() - // Override initial Parquet test table - TestHive.catalog.registerTable(Some[String]("parquet"), "testsource", ParquetTestData.testData) + ParquetTestData.writeFile + testRDD = parquetFile(ParquetTestData.testDir.toString) + testRDD.registerAsTable("testsource") } override def afterAll() { - ParquetTestData.testFile.delete() + Utils.deleteRecursively(ParquetTestData.testDir) + Utils.deleteRecursively(dirname) + reset() // drop all tables that were registered as part of the tests } + // in case tests are failing we delete before and after each test override def beforeEach() { - new File(filename).getAbsoluteFile.delete() + Utils.deleteRecursively(dirname) } override def afterEach() { - new File(filename).getAbsoluteFile.delete() + Utils.deleteRecursively(dirname) } test("SELECT on Parquet table") { - val rdd = runQuery("SELECT * FROM parquet.testsource") + val rdd = sql("SELECT * FROM testsource").collect() assert(rdd != null) assert(rdd.forall(_.size == 6)) } test("Simple column projection + filter on Parquet table") { - val rdd = runQuery("SELECT myboolean, mylong FROM parquet.testsource WHERE myboolean=true") + val rdd = sql("SELECT myboolean, mylong FROM testsource WHERE myboolean=true").collect() assert(rdd.size === 5, "Filter returned incorrect number of rows") assert(rdd.forall(_.getBoolean(0)), "Filter returned incorrect Boolean field value") } - test("Converting Hive to Parquet Table via WriteToFile") { - storeQuery("SELECT * FROM src", filename) - val rddOne = runQuery("SELECT * FROM src").sortBy(_.getInt(0)) - val rddTwo = runQuery("SELECT * from ptable", Some("ptable"), Some(filename)).sortBy(_.getInt(0)) + test("Converting Hive to Parquet Table via saveAsParquetFile") { + sql("SELECT * FROM src").saveAsParquetFile(dirname.getAbsolutePath) + parquetFile(dirname.getAbsolutePath).registerAsTable("ptable") + val rddOne = sql("SELECT * FROM src").collect().sortBy(_.getInt(0)) + val rddTwo = sql("SELECT * from ptable").collect().sortBy(_.getInt(0)) compareRDDs(rddOne, rddTwo, "src (Hive)", Seq("key:Int", "value:String")) } test("INSERT OVERWRITE TABLE Parquet table") { - storeQuery("SELECT * FROM parquet.testsource", filename) - runQuery("INSERT OVERWRITE TABLE ptable SELECT * FROM parquet.testsource", Some("ptable"), Some(filename)) - runQuery("INSERT OVERWRITE TABLE ptable SELECT * FROM parquet.testsource", Some("ptable"), Some(filename)) - val rddCopy = runQuery("SELECT * FROM ptable", Some("ptable"), Some(filename)) - val rddOrig = runQuery("SELECT * FROM parquet.testsource") - compareRDDs(rddOrig, rddCopy, "parquet.testsource", ParquetTestData.testSchemaFieldNames) + sql("SELECT * FROM testsource").saveAsParquetFile(dirname.getAbsolutePath) + parquetFile(dirname.getAbsolutePath).registerAsTable("ptable") + // let's do three overwrites for good measure + sql("INSERT OVERWRITE TABLE ptable SELECT * FROM testsource").collect() + sql("INSERT OVERWRITE TABLE ptable SELECT * FROM testsource").collect() + sql("INSERT OVERWRITE TABLE ptable SELECT * FROM testsource").collect() + val rddCopy = sql("SELECT * FROM ptable").collect() + val rddOrig = sql("SELECT * FROM testsource").collect() + assert(rddCopy.size === rddOrig.size, "INSERT OVERWRITE changed size of table??") + compareRDDs(rddOrig, rddCopy, "testsource", ParquetTestData.testSchemaFieldNames) } - test("CREATE TABLE AS Parquet table") { - runQuery("CREATE TABLE ptable AS SELECT * FROM src", Some("ptable"), Some(filename)) - val rddCopy = runQuery("SELECT * FROM ptable", Some("ptable"), Some(filename)) + test("CREATE TABLE of Parquet table") { + createParquetFile(dirname.getAbsolutePath, ("key", IntegerType), ("value", StringType)) + .registerAsTable("tmp") + val rddCopy = + sql("INSERT INTO TABLE tmp SELECT * FROM src") + .collect() .sortBy[Int](_.apply(0) match { case x: Int => x case _ => 0 }) - val rddOrig = runQuery("SELECT * FROM src").sortBy(_.getInt(0)) + val rddOrig = sql("SELECT * FROM src") + .collect() + .sortBy(_.getInt(0)) compareRDDs(rddOrig, rddCopy, "src (Hive)", Seq("key:Int", "value:String")) } + test("Appending to Parquet table") { + createParquetFile(dirname.getAbsolutePath, ("key", IntegerType), ("value", StringType)) + .registerAsTable("tmpnew") + sql("INSERT INTO TABLE tmpnew SELECT * FROM src").collect() + sql("INSERT INTO TABLE tmpnew SELECT * FROM src").collect() + sql("INSERT INTO TABLE tmpnew SELECT * FROM src").collect() + val rddCopies = sql("SELECT * FROM tmpnew").collect() + val rddOrig = sql("SELECT * FROM src").collect() + assert(rddCopies.size === 3 * rddOrig.size, "number of copied rows via INSERT INTO did not match correct number") + } + + test("Appending to and then overwriting Parquet table") { + createParquetFile(dirname.getAbsolutePath, ("key", IntegerType), ("value", StringType)) + .registerAsTable("tmp") + sql("INSERT INTO TABLE tmp SELECT * FROM src").collect() + sql("INSERT INTO TABLE tmp SELECT * FROM src").collect() + sql("INSERT OVERWRITE TABLE tmp SELECT * FROM src").collect() + val rddCopies = sql("SELECT * FROM tmp").collect() + val rddOrig = sql("SELECT * FROM src").collect() + assert(rddCopies.size === rddOrig.size, "INSERT OVERWRITE did not actually overwrite") + } + private def compareRDDs(rddOne: Array[Row], rddTwo: Array[Row], tableName: String, fieldNames: Seq[String]) { var counter = 0 (rddOne, rddTwo).zipped.foreach { (a,b) => (a,b).zipped.toArray.zipWithIndex.foreach { - case ((value_1:Array[Byte], value_2:Array[Byte]), index) => - assert(new String(value_1) === new String(value_2), s"table $tableName row $counter field ${fieldNames(index)} don't match") case ((value_1, value_2), index) => assert(value_1 === value_2, s"table $tableName row $counter field ${fieldNames(index)} don't match") } counter = counter + 1 } } + + /** + * Creates an empty SchemaRDD backed by a ParquetRelation. + * + * TODO: since this is so experimental it is better to have it here and not + * in SQLContext. Also note that when creating new AttributeReferences + * one needs to take care not to create duplicate Attribute ID's. + */ + private def createParquetFile(path: String, schema: (Tuple2[String, DataType])*): SchemaRDD = { + val attributes = schema.map(t => new AttributeReference(t._1, t._2)()) + new SchemaRDD( + TestHive, + parquet.ParquetRelation.createEmpty(path, attributes, sparkContext.hadoopConfiguration)) + } } From 5d1feda217d25616d190f9bb369664e57417cd45 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Thu, 3 Apr 2014 15:33:17 -0700 Subject: [PATCH 22/78] [SPARK-1360] Add Timestamp Support for SQL This PR includes: 1) Add new data type Timestamp 2) Add more data type casting base on Hive's Rule 3) Fix bug missing data type in both parsers (HiveQl & SQLParser). Author: Cheng Hao Closes #275 from chenghao-intel/timestamp and squashes the following commits: df709e5 [Cheng Hao] Move orc_ends_with_nulls to blacklist 24b04b0 [Cheng Hao] Put 3 cases into the black lists(describe_pretty,describe_syntax,lateral_view_outer) fc512c2 [Cheng Hao] remove the unnecessary data type equality check in data casting d0d1919 [Cheng Hao] Add more data type for scala reflection 3259808 [Cheng Hao] Add the new Golden files 3823b97 [Cheng Hao] Update the UnitTest cases & add timestamp type for HiveQL 54a0489 [Cheng Hao] fix bug mapping to 0 (which is supposed to be null) when NumberFormatException occurs 9cb505c [Cheng Hao] Fix issues according to PR comments e529168 [Cheng Hao] Fix bug of converting from String 6fc8100 [Cheng Hao] Update Unit Test & CodeStyle 8a1d4d6 [Cheng Hao] Add DataType for SqlParser ce4385e [Cheng Hao] Add TimestampType Support --- .../spark/sql/catalyst/ScalaReflection.scala | 7 +- .../spark/sql/catalyst/dsl/package.scala | 41 +++- 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sql/hive/src/test/resources/golden/udf_to_boolean-18-fcd7af0e71d3e2d934239ba606e3ed87 create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-19-dcdb12fe551aa68a56921822f5d1a343 create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-20-131900d39d9a20b431731a32fb9715f8 create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-21-a5e28f4eb819e5a5e292e279f2990a7a create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-22-93278c10d642fa242f303d89b3b1961d create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-23-828558020ce907ffa7e847762a5e2358 create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-24-e8ca597d87932af16c0cf29d662e92da create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-25-86245727f90de9ce65a12c97a03a5635 create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-26-552d7ec5a4e0c93dc59a61973e2d63a2 create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-27-b61509b01b2fe3e7e4b72fedc74ff4f9 create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-8-37229f303635a030f6cab20e0381f51f create mode 100644 sql/hive/src/test/resources/golden/udf_to_boolean-9-be623247e4dbf119b43458b72d1be017 diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index 5aaa63bf3b4b4..446d0e0bd7f54 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql.catalyst +import java.sql.Timestamp + import org.apache.spark.sql.catalyst.expressions.Attribute import org.apache.spark.sql.catalyst.expressions.AttributeReference import org.apache.spark.sql.catalyst.plans.logical.LocalRelation @@ -54,14 +56,15 @@ object ScalaReflection { val TypeRef(_, _, Seq(keyType, valueType)) = t MapType(schemaFor(keyType), schemaFor(valueType)) case t if t <:< typeOf[String] => StringType + case t if t <:< typeOf[Timestamp] => TimestampType + case t if t <:< typeOf[BigDecimal] => DecimalType case t if t <:< definitions.IntTpe => IntegerType case t if t <:< definitions.LongTpe => LongType - case t if t <:< definitions.FloatTpe => FloatType case t if t <:< definitions.DoubleTpe => DoubleType + case t if t <:< definitions.FloatTpe => FloatType case t if t <:< definitions.ShortTpe => ShortType case t if t <:< definitions.ByteTpe => ByteType case t if t <:< definitions.BooleanTpe => BooleanType - case t if t <:< typeOf[BigDecimal] => DecimalType } implicit class CaseClassRelation[A <: Product : TypeTag](data: Seq[A]) { 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 44abe671c07a4..2c4bf1715b646 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 @@ -17,6 +17,8 @@ package org.apache.spark.sql.catalyst +import java.sql.Timestamp + import scala.language.implicitConversions import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute @@ -72,6 +74,7 @@ package object dsl { def like(other: Expression) = Like(expr, other) def rlike(other: Expression) = RLike(expr, other) + def cast(to: DataType) = Cast(expr, to) def asc = SortOrder(expr, Ascending) def desc = SortOrder(expr, Descending) @@ -84,15 +87,22 @@ package object dsl { def expr = e } + implicit def booleanToLiteral(b: Boolean) = Literal(b) + implicit def byteToLiteral(b: Byte) = Literal(b) + implicit def shortToLiteral(s: Short) = Literal(s) implicit def intToLiteral(i: Int) = Literal(i) implicit def longToLiteral(l: Long) = Literal(l) implicit def floatToLiteral(f: Float) = Literal(f) implicit def doubleToLiteral(d: Double) = Literal(d) implicit def stringToLiteral(s: String) = Literal(s) + implicit def decimalToLiteral(d: BigDecimal) = Literal(d) + implicit def timestampToLiteral(t: Timestamp) = Literal(t) + implicit def binaryToLiteral(a: Array[Byte]) = Literal(a) implicit def symbolToUnresolvedAttribute(s: Symbol) = analysis.UnresolvedAttribute(s.name) implicit class DslSymbol(sym: Symbol) extends ImplicitAttribute { def s = sym.name } + // TODO more implicit class for literal? implicit class DslString(val s: String) extends ImplicitOperators { def expr: Expression = Literal(s) def attr = analysis.UnresolvedAttribute(s) @@ -103,11 +113,38 @@ package object dsl { def expr = attr def attr = analysis.UnresolvedAttribute(s) - /** Creates a new typed attributes of type int */ + /** Creates a new AttributeReference of type boolean */ + def boolean = AttributeReference(s, BooleanType, nullable = false)() + + /** Creates a new AttributeReference of type byte */ + def byte = AttributeReference(s, ByteType, nullable = false)() + + /** Creates a new AttributeReference of type short */ + def short = AttributeReference(s, ShortType, nullable = false)() + + /** Creates a new AttributeReference of type int */ def int = AttributeReference(s, IntegerType, nullable = false)() - /** Creates a new typed attributes of type string */ + /** Creates a new AttributeReference of type long */ + def long = AttributeReference(s, LongType, nullable = false)() + + /** Creates a new AttributeReference of type float */ + def float = AttributeReference(s, FloatType, nullable = false)() + + /** Creates a new AttributeReference of type double */ + def double = AttributeReference(s, DoubleType, nullable = false)() + + /** Creates a new AttributeReference of type string */ def string = AttributeReference(s, StringType, nullable = false)() + + /** Creates a new AttributeReference of type decimal */ + def decimal = AttributeReference(s, DecimalType, nullable = false)() + + /** Creates a new AttributeReference of type timestamp */ + def timestamp = AttributeReference(s, TimestampType, nullable = false)() + + /** Creates a new AttributeReference of type binary */ + def binary = AttributeReference(s, BinaryType, nullable = false)() } implicit class DslAttribute(a: AttributeReference) { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala index c26fc3d0f305f..941b53fe70d23 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql.catalyst.expressions +import java.sql.Timestamp + import org.apache.spark.sql.catalyst.types._ /** Cast the child expression to the target data type. */ @@ -26,52 +28,169 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression { override def toString = s"CAST($child, $dataType)" type EvaluatedType = Any + + def nullOrCast[T](a: Any, func: T => Any): Any = if(a == null) { + null + } else { + func(a.asInstanceOf[T]) + } - lazy val castingFunction: Any => Any = (child.dataType, dataType) match { - case (BinaryType, StringType) => a: Any => new String(a.asInstanceOf[Array[Byte]]) - case (StringType, BinaryType) => a: Any => a.asInstanceOf[String].getBytes - case (_, StringType) => a: Any => a.toString - case (StringType, IntegerType) => a: Any => castOrNull(a, _.toInt) - case (StringType, DoubleType) => a: Any => castOrNull(a, _.toDouble) - case (StringType, FloatType) => a: Any => castOrNull(a, _.toFloat) - case (StringType, LongType) => a: Any => castOrNull(a, _.toLong) - case (StringType, ShortType) => a: Any => castOrNull(a, _.toShort) - case (StringType, ByteType) => a: Any => castOrNull(a, _.toByte) - case (StringType, DecimalType) => a: Any => castOrNull(a, BigDecimal(_)) - case (BooleanType, ByteType) => { - case null => null - case true => 1.toByte - case false => 0.toByte - } - case (dt, IntegerType) => - a: Any => dt.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].toInt(a) - case (dt, DoubleType) => - a: Any => dt.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].toDouble(a) - case (dt, FloatType) => - a: Any => dt.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].toFloat(a) - case (dt, LongType) => - a: Any => dt.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].toLong(a) - case (dt, ShortType) => - a: Any => dt.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].toInt(a).toShort - case (dt, ByteType) => - a: Any => dt.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].toInt(a).toByte - case (dt, DecimalType) => - a: Any => - BigDecimal(dt.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].toDouble(a)) + // UDFToString + def castToString: Any => Any = child.dataType match { + case BinaryType => nullOrCast[Array[Byte]](_, new String(_, "UTF-8")) + case _ => nullOrCast[Any](_, _.toString) + } + + // BinaryConverter + def castToBinary: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, _.getBytes("UTF-8")) } - @inline - protected def castOrNull[A](a: Any, f: String => A) = - try f(a.asInstanceOf[String]) catch { - case _: java.lang.NumberFormatException => null - } + // UDFToBoolean + def castToBoolean: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, _.length() != 0) + case TimestampType => nullOrCast[Timestamp](_, b => {(b.getTime() != 0 || b.getNanos() != 0)}) + case LongType => nullOrCast[Long](_, _ != 0) + case IntegerType => nullOrCast[Int](_, _ != 0) + case ShortType => nullOrCast[Short](_, _ != 0) + case ByteType => nullOrCast[Byte](_, _ != 0) + case DecimalType => nullOrCast[BigDecimal](_, _ != 0) + case DoubleType => nullOrCast[Double](_, _ != 0) + case FloatType => nullOrCast[Float](_, _ != 0) + } + + // TimestampConverter + def castToTimestamp: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, s => { + // Throw away extra if more than 9 decimal places + val periodIdx = s.indexOf("."); + var n = s + if (periodIdx != -1) { + if (n.length() - periodIdx > 9) { + n = n.substring(0, periodIdx + 10) + } + } + try Timestamp.valueOf(n) catch { case _: java.lang.IllegalArgumentException => null} + }) + case BooleanType => nullOrCast[Boolean](_, b => new Timestamp((if(b) 1 else 0) * 1000)) + case LongType => nullOrCast[Long](_, l => new Timestamp(l * 1000)) + case IntegerType => nullOrCast[Int](_, i => new Timestamp(i * 1000)) + case ShortType => nullOrCast[Short](_, s => new Timestamp(s * 1000)) + case ByteType => nullOrCast[Byte](_, b => new Timestamp(b * 1000)) + // TimestampWritable.decimalToTimestamp + case DecimalType => nullOrCast[BigDecimal](_, d => decimalToTimestamp(d)) + // TimestampWritable.doubleToTimestamp + case DoubleType => nullOrCast[Double](_, d => decimalToTimestamp(d)) + // TimestampWritable.floatToTimestamp + case FloatType => nullOrCast[Float](_, f => decimalToTimestamp(f)) + } + + private def decimalToTimestamp(d: BigDecimal) = { + val seconds = d.longValue() + val bd = (d - seconds) * (1000000000) + val nanos = bd.intValue() + + // Convert to millis + val millis = seconds * 1000 + val t = new Timestamp(millis) + + // remaining fractional portion as nanos + t.setNanos(nanos) + + t + } + + private def timestampToDouble(t: Timestamp) = (t.getSeconds() + t.getNanos().toDouble / 1000) + + def castToLong: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, s => try s.toLong catch { + case _: NumberFormatException => null + }) + case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toLong) + case DecimalType => nullOrCast[BigDecimal](_, _.toLong) + case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b) + } + + def castToInt: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, s => try s.toInt catch { + case _: NumberFormatException => null + }) + case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toInt) + case DecimalType => nullOrCast[BigDecimal](_, _.toInt) + case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b) + } + + def castToShort: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, s => try s.toShort catch { + case _: NumberFormatException => null + }) + case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toShort) + case DecimalType => nullOrCast[BigDecimal](_, _.toShort) + case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toShort + } + + def castToByte: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, s => try s.toByte catch { + case _: NumberFormatException => null + }) + case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toByte) + case DecimalType => nullOrCast[BigDecimal](_, _.toByte) + case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toByte + } + + def castToDecimal: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, s => try BigDecimal(s.toDouble) catch { + case _: NumberFormatException => null + }) + case BooleanType => nullOrCast[Boolean](_, b => if(b) BigDecimal(1) else BigDecimal(0)) + case TimestampType => nullOrCast[Timestamp](_, t => BigDecimal(timestampToDouble(t))) + case x: NumericType => b => BigDecimal(x.numeric.asInstanceOf[Numeric[Any]].toDouble(b)) + } + + def castToDouble: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, s => try s.toDouble catch { + case _: NumberFormatException => null + }) + case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t)) + case DecimalType => nullOrCast[BigDecimal](_, _.toDouble) + case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toDouble(b) + } + + def castToFloat: Any => Any = child.dataType match { + case StringType => nullOrCast[String](_, s => try s.toFloat catch { + case _: NumberFormatException => null + }) + case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toFloat) + case DecimalType => nullOrCast[BigDecimal](_, _.toFloat) + case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toFloat(b) + } + + def cast: Any => Any = dataType match { + case StringType => castToString + case BinaryType => castToBinary + case DecimalType => castToDecimal + case TimestampType => castToTimestamp + case BooleanType => castToBoolean + case ByteType => castToByte + case ShortType => castToShort + case IntegerType => castToInt + case FloatType => castToFloat + case LongType => castToLong + case DoubleType => castToDouble + } override def apply(input: Row): Any = { val evaluated = child.apply(input) if (evaluated == null) { null } else { - castingFunction(evaluated) + cast(evaluated) } } } 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 81fd160e00ca1..a3d19525503ba 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 @@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.trees.TreeNode -import org.apache.spark.sql.catalyst.types.{DataType, FractionalType, IntegralType, NumericType} +import org.apache.spark.sql.catalyst.types.{DataType, FractionalType, IntegralType, NumericType, NativeType} abstract class Expression extends TreeNode[Expression] { self: Product => @@ -86,6 +86,11 @@ abstract class Expression extends TreeNode[Expression] { } } + /** + * Evaluation helper function for 2 Numeric children expressions. Those expressions are supposed + * to be in the same data type, and also the return type. + * Either one of the expressions result is null, the evaluation result should be null. + */ @inline protected final def n2( i: Row, @@ -115,6 +120,11 @@ abstract class Expression extends TreeNode[Expression] { } } + /** + * Evaluation helper function for 2 Fractional children expressions. Those expressions are + * supposed to be in the same data type, and also the return type. + * Either one of the expressions result is null, the evaluation result should be null. + */ @inline protected final def f2( i: Row, @@ -143,6 +153,11 @@ abstract class Expression extends TreeNode[Expression] { } } + /** + * Evaluation helper function for 2 Integral children expressions. Those expressions are + * supposed to be in the same data type, and also the return type. + * Either one of the expressions result is null, the evaluation result should be null. + */ @inline protected final def i2( i: Row, @@ -170,6 +185,43 @@ abstract class Expression extends TreeNode[Expression] { } } } + + /** + * Evaluation helper function for 2 Comparable children expressions. Those expressions are + * supposed to be in the same data type, and the return type should be Integer: + * Negative value: 1st argument less than 2nd argument + * Zero: 1st argument equals 2nd argument + * Positive value: 1st argument greater than 2nd argument + * + * Either one of the expressions result is null, the evaluation result should be null. + */ + @inline + protected final def c2( + i: Row, + e1: Expression, + e2: Expression, + f: ((Ordering[Any], Any, Any) => Any)): Any = { + if (e1.dataType != e2.dataType) { + throw new TreeNodeException(this, s"Types do not match ${e1.dataType} != ${e2.dataType}") + } + + val evalE1 = e1.apply(i) + if(evalE1 == null) { + null + } else { + val evalE2 = e2.apply(i) + if (evalE2 == null) { + null + } else { + e1.dataType match { + case i: NativeType => + f.asInstanceOf[(Ordering[i.JvmType], i.JvmType, i.JvmType) => Boolean]( + i.ordering, evalE1.asInstanceOf[i.JvmType], evalE2.asInstanceOf[i.JvmType]) + case other => sys.error(s"Type $other does not support ordered operations") + } + } + } + } } abstract class BinaryExpression extends Expression with trees.BinaryNode[Expression] { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala index b82a12e0f754e..d879b2b5e8ba1 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql.catalyst.expressions +import java.sql.Timestamp + import org.apache.spark.sql.catalyst.types._ object Literal { @@ -29,6 +31,9 @@ object Literal { case s: Short => Literal(s, ShortType) case s: String => Literal(s, StringType) case b: Boolean => Literal(b, BooleanType) + case d: BigDecimal => Literal(d, DecimalType) + case t: Timestamp => Literal(t, TimestampType) + case a: Array[Byte] => Literal(a, BinaryType) case null => Literal(null, NullType) } } 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 02fedd16b8d4b..b74809e5ca67d 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 @@ -18,8 +18,9 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.analysis.UnresolvedException -import org.apache.spark.sql.catalyst.types.{BooleanType, StringType} +import org.apache.spark.sql.catalyst.types.{BooleanType, StringType, TimestampType} object InterpretedPredicate { def apply(expression: Expression): (Row => Boolean) = { @@ -123,70 +124,22 @@ case class Equals(left: Expression, right: Expression) extends BinaryComparison case class LessThan(left: Expression, right: Expression) extends BinaryComparison { def symbol = "<" - override def apply(input: Row): Any = { - if (left.dataType == StringType && right.dataType == StringType) { - val l = left.apply(input) - val r = right.apply(input) - if(l == null || r == null) { - null - } else { - l.asInstanceOf[String] < r.asInstanceOf[String] - } - } else { - n2(input, left, right, _.lt(_, _)) - } - } + override def apply(input: Row): Any = c2(input, left, right, _.lt(_, _)) } case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { def symbol = "<=" - override def apply(input: Row): Any = { - if (left.dataType == StringType && right.dataType == StringType) { - val l = left.apply(input) - val r = right.apply(input) - if(l == null || r == null) { - null - } else { - l.asInstanceOf[String] <= r.asInstanceOf[String] - } - } else { - n2(input, left, right, _.lteq(_, _)) - } - } + override def apply(input: Row): Any = c2(input, left, right, _.lteq(_, _)) } case class GreaterThan(left: Expression, right: Expression) extends BinaryComparison { def symbol = ">" - override def apply(input: Row): Any = { - if (left.dataType == StringType && right.dataType == StringType) { - val l = left.apply(input) - val r = right.apply(input) - if(l == null || r == null) { - null - } else { - l.asInstanceOf[String] > r.asInstanceOf[String] - } - } else { - n2(input, left, right, _.gt(_, _)) - } - } + override def apply(input: Row): Any = c2(input, left, right, _.gt(_, _)) } case class GreaterThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { def symbol = ">=" - override def apply(input: Row): Any = { - if (left.dataType == StringType && right.dataType == StringType) { - val l = left.apply(input) - val r = right.apply(input) - if(l == null || r == null) { - null - } else { - l.asInstanceOf[String] >= r.asInstanceOf[String] - } - } else { - n2(input, left, right, _.gteq(_, _)) - } - } + override def apply(input: Row): Any = c2(input, left, right, _.gteq(_, _)) } case class If(predicate: Expression, trueValue: Expression, falseValue: Expression) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala index 7a45d1a1b8195..cdeb01a9656f4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql.catalyst.types +import java.sql.Timestamp + import scala.reflect.runtime.universe.{typeTag, TypeTag} import org.apache.spark.sql.catalyst.expressions.Expression @@ -51,6 +53,16 @@ case object BooleanType extends NativeType { val ordering = implicitly[Ordering[JvmType]] } +case object TimestampType extends NativeType { + type JvmType = Timestamp + + @transient lazy val tag = typeTag[JvmType] + + val ordering = new Ordering[JvmType] { + def compare(x: Timestamp, y: Timestamp) = x.compareTo(y) + } +} + abstract class NumericType extends NativeType { // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a 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 52a205be3e9f4..43876033d327b 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 @@ -17,6 +17,8 @@ package org.apache.spark.sql.catalyst.expressions +import java.sql.Timestamp + import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.types._ @@ -191,5 +193,56 @@ class ExpressionEvaluationSuite extends FunSuite { evaluate("abbbbc" rlike regEx, new GenericRow(Array[Any]("**"))) } } + + test("data type casting") { + + val sts = "1970-01-01 00:00:01.0" + val ts = Timestamp.valueOf(sts) + + checkEvaluation("abdef" cast StringType, "abdef") + checkEvaluation("abdef" cast DecimalType, null) + checkEvaluation("abdef" cast TimestampType, null) + checkEvaluation("12.65" cast DecimalType, BigDecimal(12.65)) + + checkEvaluation(Literal(1) cast LongType, 1) + checkEvaluation(Cast(Literal(1) cast TimestampType, LongType), 1) + checkEvaluation(Cast(Literal(BigDecimal(1)) cast TimestampType, DecimalType), 1) + checkEvaluation(Cast(Literal(1.toDouble) cast TimestampType, DoubleType), 1.toDouble) + + checkEvaluation(Cast(Literal(sts) cast TimestampType, StringType), sts) + checkEvaluation(Cast(Literal(ts) cast StringType, TimestampType), ts) + + checkEvaluation(Cast("abdef" cast BinaryType, StringType), "abdef") + + checkEvaluation(Cast(Cast(Cast(Cast( + Cast("5" cast ByteType, ShortType), IntegerType), FloatType), DoubleType), LongType), 5) + checkEvaluation(Cast(Cast(Cast(Cast( + Cast("5" cast ByteType, TimestampType), DecimalType), LongType), StringType), ShortType), 5) + checkEvaluation(Cast(Cast(Cast(Cast( + Cast("5" cast TimestampType, ByteType), DecimalType), LongType), StringType), ShortType), null) + checkEvaluation(Cast(Cast(Cast(Cast( + Cast("5" cast DecimalType, ByteType), TimestampType), LongType), StringType), ShortType), 5) + checkEvaluation(Literal(true) cast IntegerType, 1) + checkEvaluation(Literal(false) cast IntegerType, 0) + checkEvaluation(Cast(Literal(1) cast BooleanType, IntegerType), 1) + checkEvaluation(Cast(Literal(0) cast BooleanType, IntegerType), 0) + checkEvaluation("23" cast DoubleType, 23) + checkEvaluation("23" cast IntegerType, 23) + checkEvaluation("23" cast FloatType, 23) + checkEvaluation("23" cast DecimalType, 23) + checkEvaluation("23" cast ByteType, 23) + checkEvaluation("23" cast ShortType, 23) + checkEvaluation("2012-12-11" cast DoubleType, null) + checkEvaluation(Literal(123) cast IntegerType, 123) + + intercept[Exception] {evaluate(Literal(1) cast BinaryType, null)} + } + + test("timestamp") { + val ts1 = new Timestamp(12) + val ts2 = new Timestamp(123) + checkEvaluation(Literal("ab") < Literal("abc"), true) + checkEvaluation(Literal(ts1) < Literal(ts2), true) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala index 70033a050c78c..65eae3357a21e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql +import java.sql.Timestamp + import org.scalatest.FunSuite import org.apache.spark.sql.test.TestSQLContext._ @@ -31,6 +33,7 @@ case class ReflectData( byteField: Byte, booleanField: Boolean, decimalField: BigDecimal, + timestampField: Timestamp, seqInt: Seq[Int]) case class ReflectBinary(data: Array[Byte]) @@ -38,7 +41,7 @@ case class ReflectBinary(data: Array[Byte]) class ScalaReflectionRelationSuite extends FunSuite { test("query case class RDD") { val data = ReflectData("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true, - BigDecimal(1), Seq(1,2,3)) + BigDecimal(1), new Timestamp(12345), Seq(1,2,3)) val rdd = sparkContext.parallelize(data :: Nil) rdd.registerAsTable("reflectData") 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 b2b03bc790fcc..4dac25b3f60e4 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 @@ -300,14 +300,17 @@ object HiveQl { } protected def nodeToDataType(node: Node): DataType = node match { - case Token("TOK_BIGINT", Nil) => IntegerType + case Token("TOK_DECIMAL", Nil) => DecimalType + case Token("TOK_BIGINT", Nil) => LongType case Token("TOK_INT", Nil) => IntegerType - case Token("TOK_TINYINT", Nil) => IntegerType - case Token("TOK_SMALLINT", Nil) => IntegerType + case Token("TOK_TINYINT", Nil) => ByteType + case Token("TOK_SMALLINT", Nil) => ShortType case Token("TOK_BOOLEAN", Nil) => BooleanType case Token("TOK_STRING", Nil) => StringType case Token("TOK_FLOAT", Nil) => FloatType - case Token("TOK_DOUBLE", Nil) => FloatType + case Token("TOK_DOUBLE", Nil) => DoubleType + case Token("TOK_TIMESTAMP", Nil) => TimestampType + case Token("TOK_BINARY", Nil) => BinaryType case Token("TOK_LIST", elementType :: Nil) => ArrayType(nodeToDataType(elementType)) case Token("TOK_STRUCT", Token("TOK_TABCOLLIST", fields) :: Nil) => @@ -829,6 +832,8 @@ object HiveQl { Cast(nodeToExpr(arg), BooleanType) case Token("TOK_FUNCTION", Token("TOK_DECIMAL", Nil) :: arg :: Nil) => Cast(nodeToExpr(arg), DecimalType) + case Token("TOK_FUNCTION", Token("TOK_TIMESTAMP", Nil) :: arg :: Nil) => + Cast(nodeToExpr(arg), TimestampType) /* Arithmetic */ case Token("-", child :: Nil) => UnaryMinus(nodeToExpr(child)) diff --git a/sql/hive/src/test/resources/golden/insert1-0-7faa9807151781e4207103aa568e321c b/sql/hive/src/test/resources/golden/insert1-0-7faa9807151781e4207103aa568e321c new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-1-91d7b05c9024bff60b55f415cbeacc8b b/sql/hive/src/test/resources/golden/insert1-1-91d7b05c9024bff60b55f415cbeacc8b new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-10-64f83491a8fe675ef3a4a9a474ac0439 b/sql/hive/src/test/resources/golden/insert1-10-64f83491a8fe675ef3a4a9a474ac0439 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-11-6f2797b6f81943d3b53b8d247ae8512b b/sql/hive/src/test/resources/golden/insert1-11-6f2797b6f81943d3b53b8d247ae8512b new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-12-7a3c0a3f06484c912b9e951d8a2d8ac6 b/sql/hive/src/test/resources/golden/insert1-12-7a3c0a3f06484c912b9e951d8a2d8ac6 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-13-42b03f938894fdafc7fff640711a9b2f b/sql/hive/src/test/resources/golden/insert1-13-42b03f938894fdafc7fff640711a9b2f new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-14-e021dfb28597811870c03b3242972927 b/sql/hive/src/test/resources/golden/insert1-14-e021dfb28597811870c03b3242972927 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-15-c7fca497a4580b54a0a13b3b72da5d7c b/sql/hive/src/test/resources/golden/insert1-15-c7fca497a4580b54a0a13b3b72da5d7c new file mode 100644 index 0000000000000..5be49cad9a8ba --- /dev/null +++ b/sql/hive/src/test/resources/golden/insert1-15-c7fca497a4580b54a0a13b3b72da5d7c @@ -0,0 +1,2 @@ +db2_insert1 +db2_insert2 diff --git a/sql/hive/src/test/resources/golden/insert1-16-7a9e67189d3d4151f23b12c22bde06b5 b/sql/hive/src/test/resources/golden/insert1-16-7a9e67189d3d4151f23b12c22bde06b5 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-17-5528e36b3b0f5b14313898cc45f9c23a b/sql/hive/src/test/resources/golden/insert1-17-5528e36b3b0f5b14313898cc45f9c23a new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-18-16d78fba2d86277bc2f804037cc0a8b4 b/sql/hive/src/test/resources/golden/insert1-18-16d78fba2d86277bc2f804037cc0a8b4 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-19-62518ff6810db9cdd8926702192a206b b/sql/hive/src/test/resources/golden/insert1-19-62518ff6810db9cdd8926702192a206b new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-2-3f1de4475930285c3fdbe3a5ccd4e868 b/sql/hive/src/test/resources/golden/insert1-2-3f1de4475930285c3fdbe3a5ccd4e868 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-20-f4dc51ad64bb8662d066a8b9003da3d4 b/sql/hive/src/test/resources/golden/insert1-20-f4dc51ad64bb8662d066a8b9003da3d4 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-21-bb7624250ab556f2d40bfb8d419be487 b/sql/hive/src/test/resources/golden/insert1-21-bb7624250ab556f2d40bfb8d419be487 new file mode 100644 index 0000000000000..1e3637ebc6af2 --- /dev/null +++ b/sql/hive/src/test/resources/golden/insert1-21-bb7624250ab556f2d40bfb8d419be487 @@ -0,0 +1,2 @@ +db1_insert1 +db1_insert2 diff --git a/sql/hive/src/test/resources/golden/insert1-3-89f8a028e32fae213b575b4df4e26e9c b/sql/hive/src/test/resources/golden/insert1-3-89f8a028e32fae213b575b4df4e26e9c new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-4-c7a68c0884785d0f5e62b287eb305d64 b/sql/hive/src/test/resources/golden/insert1-4-c7a68c0884785d0f5e62b287eb305d64 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-5-cb87ee12092fdf05daed82485c32a285 b/sql/hive/src/test/resources/golden/insert1-5-cb87ee12092fdf05daed82485c32a285 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-6-b97ba93a2c9ae671ecfc4fa95c024dda b/sql/hive/src/test/resources/golden/insert1-6-b97ba93a2c9ae671ecfc4fa95c024dda new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-7-a2cd0615b9e79befd9c1842516150a61 b/sql/hive/src/test/resources/golden/insert1-7-a2cd0615b9e79befd9c1842516150a61 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-8-5942e331621fe522fc297844046d2370 b/sql/hive/src/test/resources/golden/insert1-8-5942e331621fe522fc297844046d2370 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1-9-5c5132707d7a4fb6e6a3de1a6719721a b/sql/hive/src/test/resources/golden/insert1-9-5c5132707d7a4fb6e6a3de1a6719721a new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-0-5528e36b3b0f5b14313898cc45f9c23a b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-0-5528e36b3b0f5b14313898cc45f9c23a new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-1-deb504f4f70fd7db975950c3c47959ee b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-1-deb504f4f70fd7db975950c3c47959ee new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-10-fda2e4be738186c0938f92d5072df55a b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-10-fda2e4be738186c0938f92d5072df55a new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-11-9fb177236623d1b62acff28507033436 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-11-9fb177236623d1b62acff28507033436 new file mode 100644 index 0000000000000..01f2b7063f91b --- /dev/null +++ b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-11-9fb177236623d1b62acff28507033436 @@ -0,0 +1,5 @@ +98 val_98 +98 val_98 +98 val_98 +97 val_97 +97 val_97 diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-12-99d5ad32bb81640cb284312841b60000 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-12-99d5ad32bb81640cb284312841b60000 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-13-9dda06e1aae1860bd19eee97703a8217 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-13-9dda06e1aae1860bd19eee97703a8217 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-14-19daabdd4c0d403c8781967248d09c53 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-14-19daabdd4c0d403c8781967248d09c53 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-15-812006e1f11e005e5029866d1cf004f6 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-15-812006e1f11e005e5029866d1cf004f6 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-2-bd042746328158822a25d711ffed18dd b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-2-bd042746328158822a25d711ffed18dd new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-3-b7aaedd7d624af4e48637ff1acabe485 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-3-b7aaedd7d624af4e48637ff1acabe485 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-4-dece2650bf0615e566cd6c84181ce026 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-4-dece2650bf0615e566cd6c84181ce026 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-5-1eb5c694e5a02aa292e24a0849350108 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-5-1eb5c694e5a02aa292e24a0849350108 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-6-ab49e0665a80a6b34dadc96f1d18ce26 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-6-ab49e0665a80a6b34dadc96f1d18ce26 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-7-fda2e4be738186c0938f92d5072df55a b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-7-fda2e4be738186c0938f92d5072df55a new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-8-9fb177236623d1b62acff28507033436 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-8-9fb177236623d1b62acff28507033436 new file mode 100644 index 0000000000000..01f2b7063f91b --- /dev/null +++ b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-8-9fb177236623d1b62acff28507033436 @@ -0,0 +1,5 @@ +98 val_98 +98 val_98 +98 val_98 +97 val_97 +97 val_97 diff --git a/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-9-ab49e0665a80a6b34dadc96f1d18ce26 b/sql/hive/src/test/resources/golden/insert2_overwrite_partitions-9-ab49e0665a80a6b34dadc96f1d18ce26 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/load_binary_data-0-491edd0c42ceb79e799ba50555bc8c15 b/sql/hive/src/test/resources/golden/load_binary_data-0-491edd0c42ceb79e799ba50555bc8c15 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/load_binary_data-1-5d72f8449b69df3c08e3f444f09428bc b/sql/hive/src/test/resources/golden/load_binary_data-1-5d72f8449b69df3c08e3f444f09428bc new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/load_binary_data-2-242b1655c7e7325ee9f26552ea8fc25 b/sql/hive/src/test/resources/golden/load_binary_data-2-242b1655c7e7325ee9f26552ea8fc25 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/load_binary_data-3-2a72df8d3e398d0963ef91162ce7d268 b/sql/hive/src/test/resources/golden/load_binary_data-3-2a72df8d3e398d0963ef91162ce7d268 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-0-86a409d8b868dc5f1a3bd1e04c2bc28c b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-0-86a409d8b868dc5f1a3bd1e04c2bc28c new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-0-86a409d8b868dc5f1a3bd1e04c2bc28c @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-1-2b1df88619e34f221d39598b5cd73283 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-1-2b1df88619e34f221d39598b5cd73283 new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-1-2b1df88619e34f221d39598b5cd73283 @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-10-60eadbb52f8857830a3034952c631ace b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-10-60eadbb52f8857830a3034952c631ace new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-11-dbe79f90862dc5c6cc4a4fa4b4b6c655 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-11-dbe79f90862dc5c6cc4a4fa4b4b6c655 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-12-60018cae9a0476dc6a0ab4264310edb5 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-12-60018cae9a0476dc6a0ab4264310edb5 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-2-7562d4fee13f3ba935a2e824f86a4224 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-2-7562d4fee13f3ba935a2e824f86a4224 new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-2-7562d4fee13f3ba935a2e824f86a4224 @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-3-bdb30a5d6887ee4fb089f8676313eafd b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-3-bdb30a5d6887ee4fb089f8676313eafd new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-3-bdb30a5d6887ee4fb089f8676313eafd @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-4-10713b30ecb3c88acdd775bf9628c38c b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-4-10713b30ecb3c88acdd775bf9628c38c new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-4-10713b30ecb3c88acdd775bf9628c38c @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-5-bab89dfffa77258e34a595e0e79986e3 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-5-bab89dfffa77258e34a595e0e79986e3 new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-5-bab89dfffa77258e34a595e0e79986e3 @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-6-6f53d5613262d393d82d159ec5dc16dc b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-6-6f53d5613262d393d82d159ec5dc16dc new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-6-6f53d5613262d393d82d159ec5dc16dc @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-7-ad4ddb5c5d6b994f4dba35f6162b6a9f b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-7-ad4ddb5c5d6b994f4dba35f6162b6a9f new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-8-f9dd797f1c90e2108cfee585f443c132 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-8-f9dd797f1c90e2108cfee585f443c132 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook-9-22fdd8380f2652de2492b34a425d46d7 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook-9-22fdd8380f2652de2492b34a425d46d7 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-0-7a9e67189d3d4151f23b12c22bde06b5 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-0-7a9e67189d3d4151f23b12c22bde06b5 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-1-86a409d8b868dc5f1a3bd1e04c2bc28c b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-1-86a409d8b868dc5f1a3bd1e04c2bc28c new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-1-86a409d8b868dc5f1a3bd1e04c2bc28c @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-10-22fdd8380f2652de2492b34a425d46d7 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-10-22fdd8380f2652de2492b34a425d46d7 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-11-60eadbb52f8857830a3034952c631ace b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-11-60eadbb52f8857830a3034952c631ace new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-12-dbe79f90862dc5c6cc4a4fa4b4b6c655 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-12-dbe79f90862dc5c6cc4a4fa4b4b6c655 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-13-60018cae9a0476dc6a0ab4264310edb5 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-13-60018cae9a0476dc6a0ab4264310edb5 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-2-2b1df88619e34f221d39598b5cd73283 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-2-2b1df88619e34f221d39598b5cd73283 new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-2-2b1df88619e34f221d39598b5cd73283 @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-3-7562d4fee13f3ba935a2e824f86a4224 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-3-7562d4fee13f3ba935a2e824f86a4224 new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-3-7562d4fee13f3ba935a2e824f86a4224 @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-4-bdb30a5d6887ee4fb089f8676313eafd b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-4-bdb30a5d6887ee4fb089f8676313eafd new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-4-bdb30a5d6887ee4fb089f8676313eafd @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-5-10713b30ecb3c88acdd775bf9628c38c b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-5-10713b30ecb3c88acdd775bf9628c38c new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-5-10713b30ecb3c88acdd775bf9628c38c @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-6-bab89dfffa77258e34a595e0e79986e3 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-6-bab89dfffa77258e34a595e0e79986e3 new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-6-bab89dfffa77258e34a595e0e79986e3 @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-7-6f53d5613262d393d82d159ec5dc16dc b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-7-6f53d5613262d393d82d159ec5dc16dc new file mode 100644 index 0000000000000..573541ac9702d --- /dev/null +++ b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-7-6f53d5613262d393d82d159ec5dc16dc @@ -0,0 +1 @@ +0 diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-8-7a45282169e5a15d70ae0afb9e67ec9a b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-8-7a45282169e5a15d70ae0afb9e67ec9a new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-9-f9dd797f1c90e2108cfee585f443c132 b/sql/hive/src/test/resources/golden/sample_islocalmode_hook_hadoop20-9-f9dd797f1c90e2108cfee585f443c132 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/timestamp_comparison-0-48751533b44ea9e8ac3131767c2fed05 b/sql/hive/src/test/resources/golden/timestamp_comparison-0-48751533b44ea9e8ac3131767c2fed05 new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/timestamp_comparison-0-48751533b44ea9e8ac3131767c2fed05 @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/timestamp_comparison-1-60557e7bd2822c89fa8b076a9d0520fc b/sql/hive/src/test/resources/golden/timestamp_comparison-1-60557e7bd2822c89fa8b076a9d0520fc new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/timestamp_comparison-1-60557e7bd2822c89fa8b076a9d0520fc @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/timestamp_comparison-2-f96a9d88327951bd93f672dc2463ecd4 b/sql/hive/src/test/resources/golden/timestamp_comparison-2-f96a9d88327951bd93f672dc2463ecd4 new file mode 100644 index 0000000000000..27ba77ddaf615 --- /dev/null +++ b/sql/hive/src/test/resources/golden/timestamp_comparison-2-f96a9d88327951bd93f672dc2463ecd4 @@ -0,0 +1 @@ +true diff --git a/sql/hive/src/test/resources/golden/timestamp_comparison-3-13e17ed811165196416f777cbc162592 b/sql/hive/src/test/resources/golden/timestamp_comparison-3-13e17ed811165196416f777cbc162592 new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/timestamp_comparison-3-13e17ed811165196416f777cbc162592 @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/timestamp_comparison-4-4fa8a36edbefde4427c2ab2cf30e6399 b/sql/hive/src/test/resources/golden/timestamp_comparison-4-4fa8a36edbefde4427c2ab2cf30e6399 new file mode 100644 index 0000000000000..27ba77ddaf615 --- /dev/null +++ b/sql/hive/src/test/resources/golden/timestamp_comparison-4-4fa8a36edbefde4427c2ab2cf30e6399 @@ -0,0 +1 @@ +true diff --git a/sql/hive/src/test/resources/golden/timestamp_comparison-5-7e4fb6e8ba01df422e4c67e06a0c8453 b/sql/hive/src/test/resources/golden/timestamp_comparison-5-7e4fb6e8ba01df422e4c67e06a0c8453 new file mode 100644 index 0000000000000..27ba77ddaf615 --- /dev/null +++ b/sql/hive/src/test/resources/golden/timestamp_comparison-5-7e4fb6e8ba01df422e4c67e06a0c8453 @@ -0,0 +1 @@ +true diff --git a/sql/hive/src/test/resources/golden/timestamp_comparison-6-8c8e73673a950f6b3d960b08fcea076f b/sql/hive/src/test/resources/golden/timestamp_comparison-6-8c8e73673a950f6b3d960b08fcea076f new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/timestamp_comparison-6-8c8e73673a950f6b3d960b08fcea076f @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/timestamp_comparison-7-510c0a2a57dc5df8588bd13c4152f8bc b/sql/hive/src/test/resources/golden/timestamp_comparison-7-510c0a2a57dc5df8588bd13c4152f8bc new file mode 100644 index 0000000000000..27ba77ddaf615 --- /dev/null +++ b/sql/hive/src/test/resources/golden/timestamp_comparison-7-510c0a2a57dc5df8588bd13c4152f8bc @@ -0,0 +1 @@ +true diff --git a/sql/hive/src/test/resources/golden/timestamp_comparison-8-659d5b1ae8200f13f265270e52a3dd65 b/sql/hive/src/test/resources/golden/timestamp_comparison-8-659d5b1ae8200f13f265270e52a3dd65 new file mode 100644 index 0000000000000..27ba77ddaf615 --- /dev/null +++ b/sql/hive/src/test/resources/golden/timestamp_comparison-8-659d5b1ae8200f13f265270e52a3dd65 @@ -0,0 +1 @@ +true diff --git a/sql/hive/src/test/resources/golden/type_cast_1-0-60ea21e6e7d054a65f959fc89acf1b3d b/sql/hive/src/test/resources/golden/type_cast_1-0-60ea21e6e7d054a65f959fc89acf1b3d new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/type_cast_1-1-53a667981ad567b2ab977f67d65c5825 b/sql/hive/src/test/resources/golden/type_cast_1-1-53a667981ad567b2ab977f67d65c5825 new file mode 100644 index 0000000000000..7ed6ff82de6bc --- /dev/null +++ b/sql/hive/src/test/resources/golden/type_cast_1-1-53a667981ad567b2ab977f67d65c5825 @@ -0,0 +1 @@ +5 diff --git a/sql/hive/src/test/resources/golden/udf_printf-0-e86d559aeb84a4cc017a103182c22bfb b/sql/hive/src/test/resources/golden/udf_printf-0-e86d559aeb84a4cc017a103182c22bfb new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/udf_printf-1-19c61fce27310ab2590062d643f7b26e b/sql/hive/src/test/resources/golden/udf_printf-1-19c61fce27310ab2590062d643f7b26e new file mode 100644 index 0000000000000..1635ff88dd768 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_printf-1-19c61fce27310ab2590062d643f7b26e @@ -0,0 +1 @@ +printf(String format, Obj... args) - function that can format strings according to printf-style format strings diff --git a/sql/hive/src/test/resources/golden/udf_printf-2-25aa6950cae2bb781c336378f63ceaee b/sql/hive/src/test/resources/golden/udf_printf-2-25aa6950cae2bb781c336378f63ceaee new file mode 100644 index 0000000000000..62440ee68e145 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_printf-2-25aa6950cae2bb781c336378f63ceaee @@ -0,0 +1,4 @@ +printf(String format, Obj... args) - function that can format strings according to printf-style format strings +Example: + > SELECT printf("Hello World %d %s", 100, "days")FROM src LIMIT 1; + "Hello World 100 days" diff --git a/sql/hive/src/test/resources/golden/udf_printf-3-9c568a0473888396bd46507e8b330c36 b/sql/hive/src/test/resources/golden/udf_printf-3-9c568a0473888396bd46507e8b330c36 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/udf_printf-4-91728e546b450bdcbb05ef30f13be475 b/sql/hive/src/test/resources/golden/udf_printf-4-91728e546b450bdcbb05ef30f13be475 new file mode 100644 index 0000000000000..39cb945991403 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_printf-4-91728e546b450bdcbb05ef30f13be475 @@ -0,0 +1 @@ +Hello World 100 days diff --git a/sql/hive/src/test/resources/golden/udf_printf-5-3141a0421605b091ee5a9e99d7d605fb b/sql/hive/src/test/resources/golden/udf_printf-5-3141a0421605b091ee5a9e99d7d605fb new file mode 100644 index 0000000000000..04bf5e552a576 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_printf-5-3141a0421605b091ee5a9e99d7d605fb @@ -0,0 +1 @@ +All Type Test: false, A, 15000, 1.234000e+01, +27183.2401, 2300.41, 32, corret, 0x1.002p8 diff --git a/sql/hive/src/test/resources/golden/udf_printf-6-ec37b73012f3cbbbc0422744b0db8294 b/sql/hive/src/test/resources/golden/udf_printf-6-ec37b73012f3cbbbc0422744b0db8294 new file mode 100644 index 0000000000000..2e9f7509968a3 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_printf-6-ec37b73012f3cbbbc0422744b0db8294 @@ -0,0 +1 @@ +Color red, String Null: null, number1 123456, number2 00089, Integer Null: null, hex 0xff, float 3.14 Double Null: null diff --git a/sql/hive/src/test/resources/golden/udf_printf-7-5769f3a5b3300ca1d8b861229e976126 b/sql/hive/src/test/resources/golden/udf_printf-7-5769f3a5b3300ca1d8b861229e976126 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-10-51822ac740629bebd81d2abda6e1144 b/sql/hive/src/test/resources/golden/udf_to_boolean-10-51822ac740629bebd81d2abda6e1144 new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-10-51822ac740629bebd81d2abda6e1144 @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-11-441306cae24618c49ec63445a31bf16b b/sql/hive/src/test/resources/golden/udf_to_boolean-11-441306cae24618c49ec63445a31bf16b new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-11-441306cae24618c49ec63445a31bf16b @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-12-bfcc534e73e320a1cfad9c584678d870 b/sql/hive/src/test/resources/golden/udf_to_boolean-12-bfcc534e73e320a1cfad9c584678d870 new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-12-bfcc534e73e320a1cfad9c584678d870 @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-13-a2bddaa5db1841bb4617239b9f17a06d b/sql/hive/src/test/resources/golden/udf_to_boolean-13-a2bddaa5db1841bb4617239b9f17a06d new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-13-a2bddaa5db1841bb4617239b9f17a06d @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-14-773801b833cf72d35016916b786275b5 b/sql/hive/src/test/resources/golden/udf_to_boolean-14-773801b833cf72d35016916b786275b5 new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-14-773801b833cf72d35016916b786275b5 @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-15-4071ed0ff57b53963d5ee662fa9db0b0 b/sql/hive/src/test/resources/golden/udf_to_boolean-15-4071ed0ff57b53963d5ee662fa9db0b0 new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-15-4071ed0ff57b53963d5ee662fa9db0b0 @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-16-6b441df08afdc0c6c4a82670997dabb5 b/sql/hive/src/test/resources/golden/udf_to_boolean-16-6b441df08afdc0c6c4a82670997dabb5 new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-16-6b441df08afdc0c6c4a82670997dabb5 @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-17-85342c694d7f35e7eedb24e850d0c7df b/sql/hive/src/test/resources/golden/udf_to_boolean-17-85342c694d7f35e7eedb24e850d0c7df new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-17-85342c694d7f35e7eedb24e850d0c7df @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-18-fcd7af0e71d3e2d934239ba606e3ed87 b/sql/hive/src/test/resources/golden/udf_to_boolean-18-fcd7af0e71d3e2d934239ba606e3ed87 new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-18-fcd7af0e71d3e2d934239ba606e3ed87 @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-19-dcdb12fe551aa68a56921822f5d1a343 b/sql/hive/src/test/resources/golden/udf_to_boolean-19-dcdb12fe551aa68a56921822f5d1a343 new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-19-dcdb12fe551aa68a56921822f5d1a343 @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-20-131900d39d9a20b431731a32fb9715f8 b/sql/hive/src/test/resources/golden/udf_to_boolean-20-131900d39d9a20b431731a32fb9715f8 new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-20-131900d39d9a20b431731a32fb9715f8 @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-21-a5e28f4eb819e5a5e292e279f2990a7a b/sql/hive/src/test/resources/golden/udf_to_boolean-21-a5e28f4eb819e5a5e292e279f2990a7a new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-21-a5e28f4eb819e5a5e292e279f2990a7a @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-22-93278c10d642fa242f303d89b3b1961d b/sql/hive/src/test/resources/golden/udf_to_boolean-22-93278c10d642fa242f303d89b3b1961d new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-22-93278c10d642fa242f303d89b3b1961d @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-23-828558020ce907ffa7e847762a5e2358 b/sql/hive/src/test/resources/golden/udf_to_boolean-23-828558020ce907ffa7e847762a5e2358 new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-23-828558020ce907ffa7e847762a5e2358 @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-24-e8ca597d87932af16c0cf29d662e92da b/sql/hive/src/test/resources/golden/udf_to_boolean-24-e8ca597d87932af16c0cf29d662e92da new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-24-e8ca597d87932af16c0cf29d662e92da @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-25-86245727f90de9ce65a12c97a03a5635 b/sql/hive/src/test/resources/golden/udf_to_boolean-25-86245727f90de9ce65a12c97a03a5635 new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-25-86245727f90de9ce65a12c97a03a5635 @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-26-552d7ec5a4e0c93dc59a61973e2d63a2 b/sql/hive/src/test/resources/golden/udf_to_boolean-26-552d7ec5a4e0c93dc59a61973e2d63a2 new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-26-552d7ec5a4e0c93dc59a61973e2d63a2 @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-27-b61509b01b2fe3e7e4b72fedc74ff4f9 b/sql/hive/src/test/resources/golden/udf_to_boolean-27-b61509b01b2fe3e7e4b72fedc74ff4f9 new file mode 100644 index 0000000000000..7951defec192a --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-27-b61509b01b2fe3e7e4b72fedc74ff4f9 @@ -0,0 +1 @@ +NULL diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-8-37229f303635a030f6cab20e0381f51f b/sql/hive/src/test/resources/golden/udf_to_boolean-8-37229f303635a030f6cab20e0381f51f new file mode 100644 index 0000000000000..27ba77ddaf615 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-8-37229f303635a030f6cab20e0381f51f @@ -0,0 +1 @@ +true diff --git a/sql/hive/src/test/resources/golden/udf_to_boolean-9-be623247e4dbf119b43458b72d1be017 b/sql/hive/src/test/resources/golden/udf_to_boolean-9-be623247e4dbf119b43458b72d1be017 new file mode 100644 index 0000000000000..c508d5366f70b --- /dev/null +++ b/sql/hive/src/test/resources/golden/udf_to_boolean-9-be623247e4dbf119b43458b72d1be017 @@ -0,0 +1 @@ +false diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala index f74b0fbb97c83..f76e16bc1afc5 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala @@ -42,6 +42,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "bucket_num_reducers", "column_access_stats", "concatenate_inherit_table_location", + "describe_pretty", + "describe_syntax", + "orc_ends_with_nulls", // Setting a default property does not seem to get reset and thus changes the answer for many // subsequent tests. @@ -80,7 +83,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "index_auto_update", "index_auto_self_join", "index_stale.*", - "type_cast_1", "index_compression", "index_bitmap_compression", "index_auto_multiple", @@ -237,9 +239,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "compute_stats_binary", "compute_stats_boolean", "compute_stats_double", - "compute_stats_table", + "compute_stats_empty_table", "compute_stats_long", "compute_stats_string", + "compute_stats_table", "convert_enum_to_string", "correlationoptimizer11", "correlationoptimizer15", @@ -266,8 +269,8 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "desc_non_existent_tbl", "describe_comment_indent", "describe_database_json", - "describe_pretty", - "describe_syntax", + "describe_formatted_view_partitioned", + "describe_formatted_view_partitioned_json", "describe_table_json", "diff_part_input_formats", "disable_file_format_check", @@ -339,8 +342,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "input11_limit", "input12", "input12_hadoop20", + "input14", "input19", "input1_limit", + "input21", "input22", "input23", "input24", @@ -355,6 +360,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "input7", "input8", "input9", + "inputddl4", + "inputddl7", + "inputddl8", "input_limit", "input_part0", "input_part1", @@ -368,9 +376,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "input_part7", "input_part8", "input_part9", - "inputddl4", - "inputddl7", - "inputddl8", + "input_testsequencefile", + "insert1", + "insert2_overwrite_partitions", "insert_compressed", "join0", "join1", @@ -385,6 +393,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "join17", "join18", "join19", + "join_1to1", "join2", "join20", "join21", @@ -400,6 +409,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "join30", "join31", "join32", + "join32_lessSize", "join33", "join34", "join35", @@ -415,13 +425,14 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "join7", "join8", "join9", - "join_1to1", "join_array", "join_casesensitive", "join_empty", "join_filters", "join_hive_626", + "join_map_ppr", "join_nulls", + "join_rc", "join_reorder2", "join_reorder3", "join_reorder4", @@ -435,22 +446,32 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "literal_string", "load_dyn_part7", "load_file_with_space_in_the_name", + "loadpart1", "louter_join_ppr", "mapjoin_distinct", "mapjoin_mapjoin", "mapjoin_subquery", "mapjoin_subquery2", "mapjoin_test_outer", + "mapreduce1", + "mapreduce2", "mapreduce3", + "mapreduce4", + "mapreduce5", + "mapreduce6", "mapreduce7", + "mapreduce8", "merge1", "merge2", "mergejoins", "mergejoins_mixed", + "multigroupby_singlemr", + "multi_insert_gby", + "multi_insert_gby3", + "multi_insert_lateral_view", + "multi_join_union", "multiMapJoin1", "multiMapJoin2", - "multi_join_union", - "multigroupby_singlemr", "noalias_subq1", "nomore_ambiguous_table_col", "nonblock_op_deduplicate", @@ -466,16 +487,30 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "nullinput2", "nullscript", "optional_outer", + "orc_dictionary_threshold", + "orc_empty_files", "order", "order2", "outer_join_ppr", + "parallel", + "parenthesis_star_by", + "partcols1", "part_inherit_tbl_props", "part_inherit_tbl_props_empty", "part_inherit_tbl_props_with_star", "partition_schema1", + "partition_serde_format", "partition_varchar1", + "partition_wise_fileformat4", + "partition_wise_fileformat5", + "partition_wise_fileformat6", + "partition_wise_fileformat7", + "partition_wise_fileformat9", "plan_json", "ppd1", + "ppd2", + "ppd_clusterby", + "ppd_constant_expr", "ppd_constant_where", "ppd_gby", "ppd_gby2", @@ -491,6 +526,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "ppd_outer_join5", "ppd_random", "ppd_repeated_alias", + "ppd_transform", "ppd_udf_col", "ppd_union", "ppr_allchildsarenull", @@ -503,7 +539,15 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "query_with_semi", "quote1", "quote2", + "rcfile_columnar", + "rcfile_lazydecompress", + "rcfile_null_value", + "rcfile_toleratecorruptions", + "rcfile_union", + "reduce_deduplicate", + "reduce_deduplicate_exclude_gby", "reduce_deduplicate_exclude_join", + "reducesink_dedup", "rename_column", "router_join_ppr", "select_as_omitted", @@ -531,6 +575,8 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "smb_mapjoin_3", "smb_mapjoin_4", "smb_mapjoin_5", + "smb_mapjoin_6", + "smb_mapjoin_7", "smb_mapjoin_8", "sort", "sort_merge_join_desc_1", @@ -541,21 +587,27 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "sort_merge_join_desc_6", "sort_merge_join_desc_7", "stats0", + "stats_aggregator_error_1", "stats_empty_partition", + "stats_publisher_error_1", "subq2", "tablename_with_select", + "timestamp_comparison", "touch", + "transform_ppr1", + "transform_ppr2", + "type_cast_1", "type_widening", "udaf_collect_set", "udaf_corr", "udaf_covar_pop", "udaf_covar_samp", + "udaf_histogram_numeric", + "udf_10_trims", "udf2", "udf6", + "udf8", "udf9", - "udf_10_trims", - "udf_E", - "udf_PI", "udf_abs", "udf_acos", "udf_add", @@ -585,13 +637,14 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "udf_cos", "udf_count", "udf_date_add", - "udf_date_sub", "udf_datediff", + "udf_date_sub", "udf_day", "udf_dayofmonth", "udf_degrees", "udf_div", "udf_double", + "udf_E", "udf_exp", "udf_field", "udf_find_in_set", @@ -631,6 +684,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "udf_nvl", "udf_or", "udf_parse_url", + "udf_PI", "udf_positive", "udf_pow", "udf_power", @@ -671,9 +725,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "udf_trim", "udf_ucase", "udf_upper", + "udf_variance", "udf_var_pop", "udf_var_samp", - "udf_variance", "udf_weekofyear", "udf_when", "udf_xpath", @@ -703,8 +757,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "union27", "union28", "union29", + "union3", "union30", "union31", + "union33", "union34", "union4", "union5", @@ -714,6 +770,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest { "union9", "union_lateralview", "union_ppr", + "union_remove_11", "union_remove_3", "union_remove_6", "union_script", From c1ea3afb516c204925259f0928dfb17d0fa89621 Mon Sep 17 00:00:00 2001 From: Prashant Sharma Date: Thu, 3 Apr 2014 15:42:17 -0700 Subject: [PATCH 23/78] Spark 1162 Implemented takeOrdered in pyspark. Since python does not have a library for max heap and usual tricks like inverting values etc.. does not work for all cases. We have our own implementation of max heap. Author: Prashant Sharma Closes #97 from ScrapCodes/SPARK-1162/pyspark-top-takeOrdered2 and squashes the following commits: 35f86ba [Prashant Sharma] code review 2b1124d [Prashant Sharma] fixed tests e8a08e2 [Prashant Sharma] Code review comments. 49e6ba7 [Prashant Sharma] SPARK-1162 added takeOrdered to pyspark --- python/pyspark/rdd.py | 107 ++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 102 insertions(+), 5 deletions(-) diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 019c249699c2d..9943296b927dc 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -29,7 +29,7 @@ from tempfile import NamedTemporaryFile from threading import Thread import warnings -from heapq import heappush, heappop, heappushpop +import heapq from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \ BatchedSerializer, CloudPickleSerializer, PairDeserializer, pack_long @@ -41,9 +41,9 @@ from py4j.java_collections import ListConverter, MapConverter - __all__ = ["RDD"] + def _extract_concise_traceback(): """ This function returns the traceback info for a callsite, returns a dict @@ -91,6 +91,73 @@ def __exit__(self, type, value, tb): if _spark_stack_depth == 0: self._context._jsc.setCallSite(None) +class MaxHeapQ(object): + """ + An implementation of MaxHeap. + >>> import pyspark.rdd + >>> heap = pyspark.rdd.MaxHeapQ(5) + >>> [heap.insert(i) for i in range(10)] + [None, None, None, None, None, None, None, None, None, None] + >>> sorted(heap.getElements()) + [0, 1, 2, 3, 4] + >>> heap = pyspark.rdd.MaxHeapQ(5) + >>> [heap.insert(i) for i in range(9, -1, -1)] + [None, None, None, None, None, None, None, None, None, None] + >>> sorted(heap.getElements()) + [0, 1, 2, 3, 4] + >>> heap = pyspark.rdd.MaxHeapQ(1) + >>> [heap.insert(i) for i in range(9, -1, -1)] + [None, None, None, None, None, None, None, None, None, None] + >>> heap.getElements() + [0] + """ + + def __init__(self, maxsize): + # we start from q[1], this makes calculating children as trivial as 2 * k + self.q = [0] + self.maxsize = maxsize + + def _swim(self, k): + while (k > 1) and (self.q[k/2] < self.q[k]): + self._swap(k, k/2) + k = k/2 + + def _swap(self, i, j): + t = self.q[i] + self.q[i] = self.q[j] + self.q[j] = t + + def _sink(self, k): + N = self.size() + while 2 * k <= N: + j = 2 * k + # Here we test if both children are greater than parent + # if not swap with larger one. + if j < N and self.q[j] < self.q[j + 1]: + j = j + 1 + if(self.q[k] > self.q[j]): + break + self._swap(k, j) + k = j + + def size(self): + return len(self.q) - 1 + + def insert(self, value): + if (self.size()) < self.maxsize: + self.q.append(value) + self._swim(self.size()) + else: + self._replaceRoot(value) + + def getElements(self): + return self.q[1:] + + def _replaceRoot(self, value): + if(self.q[1] > value): + self.q[1] = value + self._sink(1) + class RDD(object): """ A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. @@ -696,16 +763,16 @@ def top(self, num): Note: It returns the list sorted in descending order. >>> sc.parallelize([10, 4, 2, 12, 3]).top(1) [12] - >>> sc.parallelize([2, 3, 4, 5, 6]).cache().top(2) + >>> sc.parallelize([2, 3, 4, 5, 6], 2).cache().top(2) [6, 5] """ def topIterator(iterator): q = [] for k in iterator: if len(q) < num: - heappush(q, k) + heapq.heappush(q, k) else: - heappushpop(q, k) + heapq.heappushpop(q, k) yield q def merge(a, b): @@ -713,6 +780,36 @@ def merge(a, b): return sorted(self.mapPartitions(topIterator).reduce(merge), reverse=True) + def takeOrdered(self, num, key=None): + """ + Get the N elements from a RDD ordered in ascending order or as specified + by the optional key function. + + >>> sc.parallelize([10, 1, 2, 9, 3, 4, 5, 6, 7]).takeOrdered(6) + [1, 2, 3, 4, 5, 6] + >>> sc.parallelize([10, 1, 2, 9, 3, 4, 5, 6, 7], 2).takeOrdered(6, key=lambda x: -x) + [10, 9, 7, 6, 5, 4] + """ + + def topNKeyedElems(iterator, key_=None): + q = MaxHeapQ(num) + for k in iterator: + if key_ != None: + k = (key_(k), k) + q.insert(k) + yield q.getElements() + + def unKey(x, key_=None): + if key_ != None: + x = [i[1] for i in x] + return x + + def merge(a, b): + return next(topNKeyedElems(a + b)) + result = self.mapPartitions(lambda i: topNKeyedElems(i, key)).reduce(merge) + return sorted(unKey(result, key), key=key) + + def take(self, num): """ Take the first num elements of the RDD. From b8f534196f9a8c99f75728a06e62282d139dee28 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Thu, 3 Apr 2014 15:45:34 -0700 Subject: [PATCH 24/78] [SQL] SPARK-1333 First draft of java API WIP: Some work remains... * [x] Hive support * [x] Tests * [x] Update docs Feedback welcome! Author: Michael Armbrust Closes #248 from marmbrus/javaSchemaRDD and squashes the following commits: b393913 [Michael Armbrust] @srowen 's java style suggestions. f531eb1 [Michael Armbrust] Address matei's comments. 33a1b1a [Michael Armbrust] Ignore JavaHiveSuite. 822f626 [Michael Armbrust] improve docs. ab91750 [Michael Armbrust] Improve Java SQL API: * Change JavaRow => Row * Add support for querying RDDs of JavaBeans * Docs * Tests * Hive support 0b859c8 [Michael Armbrust] First draft of java API. --- docs/sql-programming-guide.md | 204 ++++++++++++++++-- .../spark/examples/sql/JavaSparkSQL.java | 99 +++++++++ .../org/apache/spark/sql/SchemaRDD.scala | 42 +--- .../org/apache/spark/sql/SchemaRDDLike.scala | 66 ++++++ .../spark/sql/api/java/JavaSQLContext.scala | 100 +++++++++ .../spark/sql/api/java/JavaSchemaRDD.scala | 48 +++++ .../org/apache/spark/sql/api/java/Row.scala | 93 ++++++++ .../spark/sql/api/java/JavaSQLSuite.scala | 53 +++++ .../apache/spark/sql/hive/HiveContext.scala | 12 ++ .../sql/hive/api/java/JavaHiveContext.scala | 42 ++++ .../sql/hive/api/java/JavaHiveSuite.scala | 41 ++++ 11 files changed, 750 insertions(+), 50 deletions(-) create mode 100644 examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index b6f21a5dc62c3..f849716f7a48f 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -8,6 +8,10 @@ title: Spark SQL Programming Guide {:toc} # Overview + +
+
+ Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark. At the core of this component is a new type of RDD, [SchemaRDD](api/sql/core/index.html#org.apache.spark.sql.SchemaRDD). SchemaRDDs are composed @@ -18,11 +22,27 @@ file, or by running HiveQL against data stored in [Apache Hive](http://hive.apac **All of the examples on this page use sample data included in the Spark distribution and can be run in the spark-shell.** +
+ +
+Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using +Spark. At the core of this component is a new type of RDD, +[JavaSchemaRDD](api/sql/core/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD). JavaSchemaRDDs are composed +[Row](api/sql/catalyst/index.html#org.apache.spark.sql.api.java.Row) objects along with +a schema that describes the data types of each column in the row. A JavaSchemaRDD is similar to a table +in a traditional relational database. A JavaSchemaRDD can be created from an existing RDD, parquet +file, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/). +
+
+ *************************************************************************************************** # Getting Started -The entry point into all relational functionallity in Spark is the +
+
+ +The entry point into all relational functionality in Spark is the [SQLContext](api/sql/core/index.html#org.apache.spark.sql.SQLContext) class, or one of its decendents. To create a basic SQLContext, all you need is a SparkContext. @@ -34,8 +54,30 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc) import sqlContext._ {% endhighlight %} +
+ +
+ +The entry point into all relational functionality in Spark is the +[JavaSQLContext](api/sql/core/index.html#org.apache.spark.sql.api.java.JavaSQLContext) class, or one +of its decendents. To create a basic JavaSQLContext, all you need is a JavaSparkContext. + +{% highlight java %} +JavaSparkContext ctx = ...; // An existing JavaSparkContext. +JavaSQLContext sqlCtx = new org.apache.spark.sql.api.java.JavaSQLContext(ctx); +{% endhighlight %} + +
+ +
+ ## Running SQL on RDDs -One type of table that is supported by Spark SQL is an RDD of Scala case classetees. The case class + +
+ +
+ +One type of table that is supported by Spark SQL is an RDD of Scala case classes. The case class defines the schema of the table. The names of the arguments to the case class are read using reflection and become the names of the columns. Case classes can also be nested or contain complex types such as Sequences or Arrays. This RDD can be implicitly converted to a SchemaRDD and then be @@ -60,7 +102,83 @@ val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") teenagers.map(t => "Name: " + t(0)).collect().foreach(println) {% endhighlight %} -**Note that Spark SQL currently uses a very basic SQL parser, and the keywords are case sensitive.** +
+ +
+ +One type of table that is supported by Spark SQL is an RDD of [JavaBeans](http://stackoverflow.com/questions/3295496/what-is-a-javabean-exactly). The BeanInfo +defines the schema of the table. Currently, Spark SQL does not support JavaBeans that contain +nested or contain complex types such as Lists or Arrays. You can create a JavaBean by creating a +class that implements Serializable and has getters and setters for all of its fields. + +{% highlight java %} + +public static class Person implements Serializable { + private String name; + private int age; + + String getName() { + return name; + } + + void setName(String name) { + this.name = name; + } + + int getAge() { + return age; + } + + void setAge(int age) { + this.age = age; + } +} + +{% endhighlight %} + + +A schema can be applied to an existing RDD by calling `applySchema` and providing the Class object +for the JavaBean. + +{% highlight java %} +JavaSQLContext ctx = new org.apache.spark.sql.api.java.JavaSQLContext(sc) + +// Load a text file and convert each line to a JavaBean. +JavaRDD people = ctx.textFile("examples/src/main/resources/people.txt").map( + new Function() { + public Person call(String line) throws Exception { + String[] parts = line.split(","); + + Person person = new Person(); + person.setName(parts[0]); + person.setAge(Integer.parseInt(parts[1].trim())); + + return person; + } + }); + +// Apply a schema to an RDD of JavaBeans and register it as a table. +JavaSchemaRDD schemaPeople = sqlCtx.applySchema(people, Person.class); +schemaPeople.registerAsTable("people"); + +// SQL can be run over RDDs that have been registered as tables. +JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") + +// The results of SQL queries are SchemaRDDs and support all the normal RDD operations. +// The columns of a row in the result can be accessed by ordinal. +List teenagerNames = teenagers.map(new Function() { + public String call(Row row) { + return "Name: " + row.getString(0); + } +}).collect(); + +{% endhighlight %} + +
+ +
+ +**Note that Spark SQL currently uses a very basic SQL parser.** Users that want a more complete dialect of SQL should look at the HiveQL support provided by `HiveContext`. @@ -70,17 +188,21 @@ Parquet is a columnar format that is supported by many other data processing sys provides support for both reading and writing parquet files that automatically preserves the schema of the original data. Using the data from the above example: +
+ +
+ {% highlight scala %} val sqlContext = new org.apache.spark.sql.SQLContext(sc) import sqlContext._ -val people: RDD[Person] // An RDD of case class objects, from the previous example. +val people: RDD[Person] = ... // An RDD of case class objects, from the previous example. // The RDD is implicitly converted to a SchemaRDD, allowing it to be stored using parquet. people.saveAsParquetFile("people.parquet") // Read in the parquet file created above. Parquet files are self-describing so the schema is preserved. -// The result of loading a parquet file is also a SchemaRDD. +// The result of loading a parquet file is also a JavaSchemaRDD. val parquetFile = sqlContext.parquetFile("people.parquet") //Parquet files can also be registered as tables and then used in SQL statements. @@ -89,15 +211,43 @@ val teenagers = sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19" teenagers.collect().foreach(println) {% endhighlight %} +
+ +
+ +{% highlight java %} + +JavaSchemaRDD schemaPeople = ... // The JavaSchemaRDD from the previous example. + +// JavaSchemaRDDs can be saved as parquet files, maintaining the schema information. +schemaPeople.saveAsParquetFile("people.parquet"); + +// Read in the parquet file created above. Parquet files are self-describing so the schema is preserved. +// The result of loading a parquet file is also a JavaSchemaRDD. +JavaSchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet"); + +//Parquet files can also be registered as tables and then used in SQL statements. +parquetFile.registerAsTable("parquetFile"); +JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); + + +{% endhighlight %} + +
+ +
+ ## Writing Language-Integrated Relational Queries +**Language-Integrated queries are currently only supported in Scala.** + Spark SQL also supports a domain specific language for writing queries. Once again, using the data from the above examples: {% highlight scala %} val sqlContext = new org.apache.spark.sql.SQLContext(sc) import sqlContext._ -val people: RDD[Person] // An RDD of case class objects, from the first example. +val people: RDD[Person] = ... // An RDD of case class objects, from the first example. // The following is the same as 'SELECT name FROM people WHERE age >= 10 AND age <= 19' val teenagers = people.where('age >= 10).where('age <= 19).select('name) @@ -114,14 +264,17 @@ evaluated by the SQL execution engine. A full list of the functions supported c Spark SQL also supports reading and writing data stored in [Apache Hive](http://hive.apache.org/). However, since Hive has a large number of dependencies, it is not included in the default Spark assembly. -In order to use Hive you must first run '`sbt/sbt hive/assembly`'. This command builds a new assembly -jar that includes Hive. When this jar is present, Spark will use the Hive -assembly instead of the normal Spark assembly. Note that this Hive assembly jar must also be present +In order to use Hive you must first run '`SPARK_HIVE=true sbt/sbt assembly/assembly`'. This command builds a new assembly +jar that includes Hive. Note that this Hive assembly jar must also be present on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries (SerDes) in order to acccess data stored in Hive. Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. +
+ +
+ When working with Hive one must construct a `HiveContext`, which inherits from `SQLContext`, and adds support for finding tables in in the MetaStore and writing queries using HiveQL. Users who do not have an existing Hive deployment can also experiment with the `LocalHiveContext`, @@ -135,9 +288,34 @@ val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc) // Importing the SQL context gives access to all the public SQL functions and implicit conversions. import hiveContext._ -sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") -sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src") +hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") +hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src") // Queries are expressed in HiveQL -sql("SELECT key, value FROM src").collect().foreach(println) -{% endhighlight %} \ No newline at end of file +hql("FROM src SELECT key, value").collect().foreach(println) +{% endhighlight %} + +
+ +
+ +When working with Hive one must construct a `JavaHiveContext`, which inherits from `JavaSQLContext`, and +adds support for finding tables in in the MetaStore and writing queries using HiveQL. In addition to +the `sql` method a `JavaHiveContext` also provides an `hql` methods, which allows queries to be +expressed in HiveQL. + +{% highlight java %} +JavaSparkContext ctx = ...; // An existing JavaSparkContext. +JavaHiveContext hiveCtx = new org.apache.spark.sql.hive.api.java.HiveContext(ctx); + +hiveCtx.hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)"); +hiveCtx.hql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src"); + +// Queries are expressed in HiveQL. +Row[] results = hiveCtx.hql("FROM src SELECT key, value").collect(); + +{% endhighlight %} + +
+ +
diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java new file mode 100644 index 0000000000000..e8e63d2745692 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java @@ -0,0 +1,99 @@ +/* + * 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.examples.sql; + +import java.io.Serializable; +import java.util.List; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.api.java.function.VoidFunction; + +import org.apache.spark.sql.api.java.JavaSQLContext; +import org.apache.spark.sql.api.java.JavaSchemaRDD; +import org.apache.spark.sql.api.java.Row; + +public class JavaSparkSQL { + public static class Person implements Serializable { + private String name; + private int age; + + String getName() { + return name; + } + + void setName(String name) { + this.name = name; + } + + int getAge() { + return age; + } + + void setAge(int age) { + this.age = age; + } + } + + public static void main(String[] args) throws Exception { + JavaSparkContext ctx = new JavaSparkContext("local", "JavaSparkSQL", + System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaSparkSQL.class)); + JavaSQLContext sqlCtx = new JavaSQLContext(ctx); + + // Load a text file and convert each line to a Java Bean. + JavaRDD people = ctx.textFile("examples/src/main/resources/people.txt").map( + new Function() { + public Person call(String line) throws Exception { + String[] parts = line.split(","); + + Person person = new Person(); + person.setName(parts[0]); + person.setAge(Integer.parseInt(parts[1].trim())); + + return person; + } + }); + + // Apply a schema to an RDD of Java Beans and register it as a table. + JavaSchemaRDD schemaPeople = sqlCtx.applySchema(people, Person.class); + schemaPeople.registerAsTable("people"); + + // SQL can be run over RDDs that have been registered as tables. + JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); + + // The results of SQL queries are SchemaRDDs and support all the normal RDD operations. + // The columns of a row in the result can be accessed by ordinal. + List teenagerNames = teenagers.map(new Function() { + public String call(Row row) { + return "Name: " + row.getString(0); + } + }).collect(); + + // JavaSchemaRDDs can be saved as parquet files, maintaining the schema information. + schemaPeople.saveAsParquetFile("people.parquet"); + + // Read in the parquet file created above. Parquet files are self-describing so the schema is preserved. + // The result of loading a parquet file is also a JavaSchemaRDD. + JavaSchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet"); + + //Parquet files can also be registered as tables and then used in SQL statements. + parquetFile.registerAsTable("parquetFile"); + JavaSchemaRDD teenagers2 = sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala index 770cabcb31d13..a62cb8aa1321f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala @@ -17,13 +17,13 @@ package org.apache.spark.sql +import org.apache.spark.{Dependency, OneToOneDependency, Partition, TaskContext} 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.plans.logical._ import org.apache.spark.sql.catalyst.plans.{Inner, JoinType} import org.apache.spark.sql.catalyst.types.BooleanType -import org.apache.spark.{Dependency, OneToOneDependency, Partition, TaskContext} /** * ALPHA COMPONENT @@ -92,23 +92,10 @@ import org.apache.spark.{Dependency, OneToOneDependency, Partition, TaskContext} */ class SchemaRDD( @transient val sqlContext: SQLContext, - @transient val logicalPlan: LogicalPlan) - extends RDD[Row](sqlContext.sparkContext, Nil) { + @transient protected[spark] val logicalPlan: LogicalPlan) + extends RDD[Row](sqlContext.sparkContext, Nil) with SchemaRDDLike { - /** - * A lazily computed query execution workflow. All other RDD operations are passed - * through to the RDD that is produced by this workflow. - * - * We want this to be lazy because invoking the whole query optimization pipeline can be - * expensive. - */ - @transient - protected[spark] lazy val queryExecution = sqlContext.executePlan(logicalPlan) - - override def toString = - s"""${super.toString} - |== Query Plan == - |${queryExecution.executedPlan}""".stripMargin.trim + def baseSchemaRDD = this // ========================================================================================= // RDD functions: Copy the interal row representation so we present immutable data to users. @@ -312,31 +299,12 @@ class SchemaRDD( sqlContext, InsertIntoTable(UnresolvedRelation(None, tableName), Map.empty, logicalPlan, overwrite)) - /** - * Saves the contents of this `SchemaRDD` as a parquet file, preserving the schema. Files that - * are written out using this method can be read back in as a SchemaRDD using the ``function - * - * @group schema - */ - def saveAsParquetFile(path: String): Unit = { - sqlContext.executePlan(WriteToFile(path, logicalPlan)).toRdd - } - - /** - * Registers this RDD as a temporary table using the given name. The lifetime of this temporary - * table is tied to the [[SQLContext]] that was used to create this SchemaRDD. - * - * @group schema - */ - def registerAsTable(tableName: String): Unit = { - sqlContext.registerRDDAsTable(this, tableName) - } - /** * Returns this RDD as a SchemaRDD. * @group schema */ def toSchemaRDD = this + /** FOR INTERNAL USE ONLY */ def analyze = sqlContext.analyzer(logicalPlan) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala new file mode 100644 index 0000000000000..840803a52c1cf --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala @@ -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.sql + +import org.apache.spark.sql.catalyst.plans.logical._ + +/** + * Contains functions that are shared between all SchemaRDD types (i.e., Scala, Java) + */ +trait SchemaRDDLike { + @transient val sqlContext: SQLContext + @transient protected[spark] val logicalPlan: LogicalPlan + + private[sql] def baseSchemaRDD: SchemaRDD + + /** + * A lazily computed query execution workflow. All other RDD operations are passed + * through to the RDD that is produced by this workflow. + * + * We want this to be lazy because invoking the whole query optimization pipeline can be + * expensive. + */ + @transient + protected[spark] lazy val queryExecution = sqlContext.executePlan(logicalPlan) + + override def toString = + s"""${super.toString} + |== Query Plan == + |${queryExecution.executedPlan}""".stripMargin.trim + + + /** + * Saves the contents of this `SchemaRDD` as a parquet file, preserving the schema. Files that + * are written out using this method can be read back in as a SchemaRDD using the ``function + * + * @group schema + */ + def saveAsParquetFile(path: String): Unit = { + sqlContext.executePlan(WriteToFile(path, logicalPlan)).toRdd + } + + /** + * Registers this RDD as a temporary table using the given name. The lifetime of this temporary + * table is tied to the [[SQLContext]] that was used to create this SchemaRDD. + * + * @group schema + */ + def registerAsTable(tableName: String): Unit = { + sqlContext.registerRDDAsTable(baseSchemaRDD, tableName) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala new file mode 100644 index 0000000000000..7b41aa1f1bbe6 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala @@ -0,0 +1,100 @@ +/* +* 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.api.java + +import java.beans.{Introspector, PropertyDescriptor} + +import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} +import org.apache.spark.sql.SQLContext +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, GenericRow, Row => ScalaRow} +import org.apache.spark.sql.catalyst.types._ +import org.apache.spark.sql.parquet.ParquetRelation +import org.apache.spark.sql.execution.{ExistingRdd, SparkLogicalPlan} + +/** + * The entry point for executing Spark SQL queries from a Java program. + */ +class JavaSQLContext(sparkContext: JavaSparkContext) { + + val sqlContext = new SQLContext(sparkContext.sc) + + /** + * Executes a query expressed in SQL, returning the result as a JavaSchemaRDD + */ + def sql(sqlQuery: String): JavaSchemaRDD = { + val result = new JavaSchemaRDD(sqlContext, sqlContext.parseSql(sqlQuery)) + // We force query optimization to happen right away instead of letting it happen lazily like + // when using the query DSL. This is so DDL commands behave as expected. This is only + // generates the RDD lineage for DML queries, but do not perform any execution. + result.queryExecution.toRdd + result + } + + /** + * Applies a schema to an RDD of Java Beans. + */ + def applySchema(rdd: JavaRDD[_], beanClass: Class[_]): JavaSchemaRDD = { + // TODO: All of this could probably be moved to Catalyst as it is mostly not Spark specific. + val beanInfo = Introspector.getBeanInfo(beanClass) + + val fields = beanInfo.getPropertyDescriptors.filterNot(_.getName == "class") + val schema = fields.map { property => + val dataType = property.getPropertyType match { + case c: Class[_] if c == classOf[java.lang.String] => StringType + case c: Class[_] if c == java.lang.Short.TYPE => ShortType + case c: Class[_] if c == java.lang.Integer.TYPE => IntegerType + case c: Class[_] if c == java.lang.Long.TYPE => LongType + case c: Class[_] if c == java.lang.Double.TYPE => DoubleType + case c: Class[_] if c == java.lang.Byte.TYPE => ByteType + case c: Class[_] if c == java.lang.Float.TYPE => FloatType + case c: Class[_] if c == java.lang.Boolean.TYPE => BooleanType + } + + AttributeReference(property.getName, dataType, true)() + } + + val className = beanClass.getCanonicalName + val rowRdd = rdd.rdd.mapPartitions { iter => + // BeanInfo is not serializable so we must rediscover it remotely for each partition. + val localBeanInfo = Introspector.getBeanInfo(Class.forName(className)) + val extractors = + localBeanInfo.getPropertyDescriptors.filterNot(_.getName == "class").map(_.getReadMethod) + + iter.map { row => + new GenericRow(extractors.map(e => e.invoke(row)).toArray[Any]): ScalaRow + } + } + new JavaSchemaRDD(sqlContext, SparkLogicalPlan(ExistingRdd(schema, rowRdd))) + } + + + /** + * Loads a parquet file, returning the result as a [[JavaSchemaRDD]]. + */ + def parquetFile(path: String): JavaSchemaRDD = + new JavaSchemaRDD(sqlContext, ParquetRelation("ParquetFile", path)) + + + /** + * Registers the given RDD as a temporary table in the catalog. Temporary tables exist only + * during the lifetime of this instance of SQLContext. + */ + def registerRDDAsTable(rdd: JavaSchemaRDD, tableName: String): Unit = { + sqlContext.registerRDDAsTable(rdd.baseSchemaRDD, tableName) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala new file mode 100644 index 0000000000000..d43d672938f51 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala @@ -0,0 +1,48 @@ +/* + * 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.api.java + +import org.apache.spark.api.java.{JavaRDDLike, JavaRDD} +import org.apache.spark.sql.{SQLContext, SchemaRDD, SchemaRDDLike} +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.rdd.RDD + +/** + * An RDD of [[Row]] objects that is returned as the result of a Spark SQL query. In addition to + * standard RDD operations, a JavaSchemaRDD can also be registered as a table in the JavaSQLContext + * that was used to create. Registering a JavaSchemaRDD allows its contents to be queried in + * future SQL statement. + * + * @groupname schema SchemaRDD Functions + * @groupprio schema -1 + * @groupname Ungrouped Base RDD Functions + */ +class JavaSchemaRDD( + @transient val sqlContext: SQLContext, + @transient protected[spark] val logicalPlan: LogicalPlan) + extends JavaRDDLike[Row, JavaRDD[Row]] + with SchemaRDDLike { + + private[sql] val baseSchemaRDD = new SchemaRDD(sqlContext, logicalPlan) + + override val classTag = scala.reflect.classTag[Row] + + override def wrapRDD(rdd: RDD[Row]): JavaRDD[Row] = JavaRDD.fromRDD(rdd) + + val rdd = baseSchemaRDD.map(new Row(_)) +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala new file mode 100644 index 0000000000000..362fe769581d7 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala @@ -0,0 +1,93 @@ +/* + * 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.api.java + +import org.apache.spark.sql.catalyst.expressions.{Row => ScalaRow} + +/** + * A result row from a SparkSQL query. + */ +class Row(row: ScalaRow) extends Serializable { + + /** Returns the number of columns present in this Row. */ + def length: Int = row.length + + /** Returns the value of column `i`. */ + def get(i: Int): Any = + row(i) + + /** Returns true if value at column `i` is NULL. */ + def isNullAt(i: Int) = get(i) == null + + /** + * Returns the value of column `i` as an int. This function will throw an exception if the value + * is at `i` is not an integer, or if it is null. + */ + def getInt(i: Int): Int = + row.getInt(i) + + /** + * Returns the value of column `i` as a long. This function will throw an exception if the value + * is at `i` is not a long, or if it is null. + */ + def getLong(i: Int): Long = + row.getLong(i) + + /** + * Returns the value of column `i` as a double. This function will throw an exception if the + * value is at `i` is not a double, or if it is null. + */ + def getDouble(i: Int): Double = + row.getDouble(i) + + /** + * Returns the value of column `i` as a bool. This function will throw an exception if the value + * is at `i` is not a boolean, or if it is null. + */ + def getBoolean(i: Int): Boolean = + row.getBoolean(i) + + /** + * Returns the value of column `i` as a short. This function will throw an exception if the value + * is at `i` is not a short, or if it is null. + */ + def getShort(i: Int): Short = + row.getShort(i) + + /** + * Returns the value of column `i` as a byte. This function will throw an exception if the value + * is at `i` is not a byte, or if it is null. + */ + def getByte(i: Int): Byte = + row.getByte(i) + + /** + * Returns the value of column `i` as a float. This function will throw an exception if the value + * is at `i` is not a float, or if it is null. + */ + def getFloat(i: Int): Float = + row.getFloat(i) + + /** + * Returns the value of column `i` as a String. This function will throw an exception if the + * value is at `i` is not a String. + */ + def getString(i: Int): String = + row.getString(i) +} + diff --git a/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala new file mode 100644 index 0000000000000..def0e046a3831 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/api/java/JavaSQLSuite.scala @@ -0,0 +1,53 @@ +/* + * 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.api.java + +import scala.beans.BeanProperty + +import org.scalatest.FunSuite + +import org.apache.spark.api.java.JavaSparkContext +import org.apache.spark.sql.test.TestSQLContext + +// Implicits +import scala.collection.JavaConversions._ + +class PersonBean extends Serializable { + @BeanProperty + var name: String = _ + + @BeanProperty + var age: Int = _ +} + +class JavaSQLSuite extends FunSuite { + val javaCtx = new JavaSparkContext(TestSQLContext.sparkContext) + val javaSqlCtx = new JavaSQLContext(javaCtx) + + test("schema from JavaBeans") { + val person = new PersonBean + person.setName("Michael") + person.setAge(29) + + val rdd = javaCtx.parallelize(person :: Nil) + val schemaRDD = javaSqlCtx.applySchema(rdd, classOf[PersonBean]) + + schemaRDD.registerAsTable("people") + javaSqlCtx.sql("SELECT * FROM people").collect() + } +} 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 46febbfad037d..ff8eaacded4c8 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 @@ -71,6 +71,18 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { override def executePlan(plan: LogicalPlan): this.QueryExecution = new this.QueryExecution { val logical = plan } + /** + * Executes a query expressed in HiveQL using Spark, returning the result as a SchemaRDD. + */ + def hql(hqlQuery: String): SchemaRDD = { + val result = new SchemaRDD(this, HiveQl.parseSql(hqlQuery)) + // We force query optimization to happen right away instead of letting it happen lazily like + // when using the query DSL. This is so DDL commands behave as expected. This is only + // generates the RDD lineage for DML queries, but do not perform any execution. + result.queryExecution.toRdd + result + } + // Circular buffer to hold what hive prints to STDOUT and ERR. Only printed when failures occur. @transient protected val outputBuffer = new java.io.OutputStream { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala new file mode 100644 index 0000000000000..6df76fa825101 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/api/java/JavaHiveContext.scala @@ -0,0 +1,42 @@ +/* + * 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.api.java + +import org.apache.spark.api.java.JavaSparkContext +import org.apache.spark.sql.api.java.{JavaSQLContext, JavaSchemaRDD} +import org.apache.spark.sql.hive.{HiveContext, HiveQl} + +/** + * The entry point for executing Spark SQL queries from a Java program. + */ +class JavaHiveContext(sparkContext: JavaSparkContext) extends JavaSQLContext(sparkContext) { + + override val sqlContext = new HiveContext(sparkContext) + + /** + * Executes a query expressed in HiveQL, returning the result as a JavaSchemaRDD. + */ + def hql(hqlQuery: String): JavaSchemaRDD = { + val result = new JavaSchemaRDD(sqlContext, HiveQl.parseSql(hqlQuery)) + // We force query optimization to happen right away instead of letting it happen lazily like + // when using the query DSL. This is so DDL commands behave as expected. This is only + // generates the RDD lineage for DML queries, but do not perform any execution. + result.queryExecution.toRdd + result + } +} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.scala new file mode 100644 index 0000000000000..8137f99b227f4 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/api/java/JavaHiveSuite.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.sql.hive.api.java + +import org.scalatest.FunSuite + +import org.apache.spark.api.java.JavaSparkContext +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.hive.TestHive + +// Implicits +import scala.collection.JavaConversions._ + +class JavaHiveSQLSuite extends FunSuite { + ignore("SELECT * FROM src") { + val javaCtx = new JavaSparkContext(TestSQLContext.sparkContext) + // There is a little trickery here to avoid instantiating two HiveContexts in the same JVM + val javaSqlCtx = new JavaHiveContext(javaCtx) { + override val sqlContext = TestHive + } + + assert( + javaSqlCtx.hql("SELECT * FROM src").collect().map(_.getInt(0)) === + TestHive.sql("SELECT * FROM src").collect().map(_.getInt(0)).toSeq) + } +} From a599e43d6e0950f6b6b32150ce264a8c2711470c Mon Sep 17 00:00:00 2001 From: Diana Carroll Date: Thu, 3 Apr 2014 15:48:42 -0700 Subject: [PATCH 25/78] [SPARK-1134] Fix and document passing of arguments to IPython This is based on @dianacarroll's previous pull request https://github.com/apache/spark/pull/227, and @joshrosen's comments on https://github.com/apache/spark/pull/38. Since we do want to allow passing arguments to IPython, this does the following: * It documents that IPython can't be used with standalone jobs for now. (Later versions of IPython will deal with PYTHONSTARTUP properly and enable this, see https://github.com/ipython/ipython/pull/5226, but no released version has that fix.) * If you run `pyspark` with `IPYTHON=1`, it passes your command-line arguments to it. This way you can do stuff like `IPYTHON=1 bin/pyspark notebook`. * The old `IPYTHON_OPTS` remains, but I've removed it from the documentation. This is in case people read an old tutorial that uses it. This is not a perfect solution and I'd also be okay with keeping things as they are today (ignoring `$@` for IPython and using IPYTHON_OPTS), and only doing the doc change. With this change though, when IPython fixes https://github.com/ipython/ipython/pull/5226, people will immediately be able to do `IPYTHON=1 bin/pyspark myscript.py` to run a standalone script and get all the benefits of running scripts in IPython (presumably better debugging and such). Without it, there will be no way to run scripts in IPython. @joshrosen you should probably take the final call on this. Author: Diana Carroll Closes #294 from mateiz/spark-1134 and squashes the following commits: 747bb13 [Diana Carroll] SPARK-1134 bug with ipython prevents non-interactive use with spark; only call ipython if no command line arguments were supplied --- bin/pyspark | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/bin/pyspark b/bin/pyspark index 67e1f61eeb1e5..cad982bc33477 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -55,7 +55,8 @@ if [ -n "$IPYTHON_OPTS" ]; then IPYTHON=1 fi -if [[ "$IPYTHON" = "1" ]] ; then +# Only use ipython if no command line arguments were provided [SPARK-1134] +if [[ "$IPYTHON" = "1" && $# = 0 ]] ; then exec ipython $IPYTHON_OPTS else exec "$PYSPARK_PYTHON" "$@" From d94826be6d46edf3bc6377d33787df23a6030a6c Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Thu, 3 Apr 2014 16:12:08 -0700 Subject: [PATCH 26/78] [BUILD FIX] Fix compilation of Spark SQL Java API. The JavaAPI and the Parquet improvements PRs didn't conflict, but broke the build. Author: Michael Armbrust Closes #316 from marmbrus/hotFixJavaApi and squashes the following commits: 0b84c2d [Michael Armbrust] Fix compilation of Spark SQL Java API. --- .../scala/org/apache/spark/sql/api/java/JavaSQLContext.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala index 7b41aa1f1bbe6..573345e42c43c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala @@ -87,7 +87,7 @@ class JavaSQLContext(sparkContext: JavaSparkContext) { * Loads a parquet file, returning the result as a [[JavaSchemaRDD]]. */ def parquetFile(path: String): JavaSchemaRDD = - new JavaSchemaRDD(sqlContext, ParquetRelation("ParquetFile", path)) + new JavaSchemaRDD(sqlContext, ParquetRelation(path)) /** From 9231b011a9ba5a2b25bd3d1a68be7d1a7cb735da Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Thu, 3 Apr 2014 16:53:35 -0700 Subject: [PATCH 27/78] Fix jenkins from giving the green light to builds that don't compile. Adding `| grep` swallows the non-zero return code from sbt failures. See [here](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/13735/consoleFull) for a Jenkins run that fails to compile, but still gets a green light. Note the [BUILD FIX] commit isn't actually part of this PR, but github is out of date. Author: Michael Armbrust Closes #317 from marmbrus/fixJenkins and squashes the following commits: 7c77ff9 [Michael Armbrust] Remove output filter that was swallowing non-zero exit codes for test failures. --- dev/run-tests | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dev/run-tests b/dev/run-tests index a6fcc40a5ba6e..fff949e04fcd7 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -49,7 +49,7 @@ dev/scalastyle echo "=========================================================================" echo "Running Spark unit tests" echo "=========================================================================" -sbt/sbt assembly test | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including" +sbt/sbt assembly test echo "=========================================================================" echo "Running PySpark tests" From 33e63618d061eeaae257a7350ea3287a702fc123 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Thu, 3 Apr 2014 17:00:06 -0700 Subject: [PATCH 28/78] Revert "[SPARK-1398] Removed findbugs jsr305 dependency" This reverts commit 92a86b285f8a4af1bdf577dd4c4ea0fd5ca8d682. --- core/pom.xml | 4 ++++ pom.xml | 5 +++++ project/SparkBuild.scala | 1 + 3 files changed, 10 insertions(+) diff --git a/core/pom.xml b/core/pom.xml index 273aa69659336..e4c32eff0cd77 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -82,6 +82,10 @@ com.google.guava guava + + com.google.code.findbugs + jsr305 + org.slf4j slf4j-api diff --git a/pom.xml b/pom.xml index b91b14d2f84d0..7d58060cba606 100644 --- a/pom.xml +++ b/pom.xml @@ -214,6 +214,11 @@ guava 14.0.1 + + com.google.code.findbugs + jsr305 + 1.3.9 + org.slf4j slf4j-api diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index a2a21d9763548..c5c697e8e2427 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -296,6 +296,7 @@ object SparkBuild extends Build { name := "spark-core", libraryDependencies ++= Seq( "com.google.guava" % "guava" % "14.0.1", + "com.google.code.findbugs" % "jsr305" % "1.3.9", "log4j" % "log4j" % "1.2.17", "org.slf4j" % "slf4j-api" % slf4jVersion, "org.slf4j" % "slf4j-log4j12" % slf4jVersion, From ee6e9e7d863022304ac9ced405b353b63accb6ab Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Thu, 3 Apr 2014 22:13:56 -0700 Subject: [PATCH 29/78] SPARK-1337: Application web UI garbage collects newest stages Simple fix... Author: Patrick Wendell Closes #320 from pwendell/stage-clean-up and squashes the following commits: 29be62e [Patrick Wendell] SPARK-1337: Application web UI garbage collects newest stages instead old ones --- .../spark/ui/jobs/JobProgressListener.scala | 8 ++--- .../ui/jobs/JobProgressListenerSuite.scala | 33 +++++++++++++++++-- 2 files changed, 35 insertions(+), 6 deletions(-) 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 d10aa12b9ebca..cd4be57227a16 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 @@ -81,8 +81,8 @@ private[ui] class JobProgressListener(conf: SparkConf) extends SparkListener { /** If stages is too large, remove and garbage collect old stages */ private def trimIfNecessary(stages: ListBuffer[StageInfo]) = synchronized { if (stages.size > retainedStages) { - val toRemove = retainedStages / 10 - stages.takeRight(toRemove).foreach( s => { + val toRemove = math.max(retainedStages / 10, 1) + stages.take(toRemove).foreach { s => stageIdToTaskData.remove(s.stageId) stageIdToTime.remove(s.stageId) stageIdToShuffleRead.remove(s.stageId) @@ -94,8 +94,8 @@ private[ui] class JobProgressListener(conf: SparkConf) extends SparkListener { stageIdToTasksFailed.remove(s.stageId) stageIdToPool.remove(s.stageId) if (stageIdToDescription.contains(s.stageId)) {stageIdToDescription.remove(s.stageId)} - }) - stages.trimEnd(toRemove) + } + stages.trimStart(toRemove) } } 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 d8a3e859f85cd..67ceee505db3c 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 @@ -18,13 +18,42 @@ package org.apache.spark.ui.jobs import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers -import org.apache.spark.{LocalSparkContext, SparkContext, Success} +import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, Success} import org.apache.spark.executor.{ShuffleReadMetrics, TaskMetrics} import org.apache.spark.scheduler._ import org.apache.spark.util.Utils -class JobProgressListenerSuite extends FunSuite with LocalSparkContext { +class JobProgressListenerSuite extends FunSuite with LocalSparkContext with ShouldMatchers { + test("test LRU eviction of stages") { + val conf = new SparkConf() + conf.set("spark.ui.retainedStages", 5.toString) + val listener = new JobProgressListener(conf) + + def createStageStartEvent(stageId: Int) = { + val stageInfo = new StageInfo(stageId, stageId.toString, 0, null) + SparkListenerStageSubmitted(stageInfo) + } + + def createStageEndEvent(stageId: Int) = { + val stageInfo = new StageInfo(stageId, stageId.toString, 0, null) + SparkListenerStageCompleted(stageInfo) + } + + for (i <- 1 to 50) { + listener.onStageSubmitted(createStageStartEvent(i)) + listener.onStageCompleted(createStageEndEvent(i)) + } + + listener.completedStages.size should be (5) + listener.completedStages.filter(_.stageId == 50).size should be (1) + listener.completedStages.filter(_.stageId == 49).size should be (1) + listener.completedStages.filter(_.stageId == 48).size should be (1) + listener.completedStages.filter(_.stageId == 47).size should be (1) + listener.completedStages.filter(_.stageId == 46).size should be (1) + } + test("test executor id to summary") { val sc = new SparkContext("local", "test") val listener = new JobProgressListener(sc.conf) From 7f32fd42aaadcf6626b4d8bcf6295203b43b2037 Mon Sep 17 00:00:00 2001 From: Sandy Ryza Date: Fri, 4 Apr 2014 08:54:04 -0500 Subject: [PATCH 30/78] SPARK-1350. Always use JAVA_HOME to run executor container JVMs. Author: Sandy Ryza Closes #313 from sryza/sandy-spark-1350 and squashes the following commits: bb6d187 [Sandy Ryza] SPARK-1350. Always use JAVA_HOME to run executor container JVMs. --- .../scala/org/apache/spark/deploy/yarn/ClientBase.scala | 8 +------- .../apache/spark/deploy/yarn/ExecutorRunnableUtil.scala | 9 ++------- 2 files changed, 3 insertions(+), 14 deletions(-) diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala index 6568003bf1008..bc267900fcf1d 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala @@ -345,14 +345,8 @@ trait ClientBase extends Logging { } // Command for the ApplicationMaster - var javaCommand = "java" - val javaHome = System.getenv("JAVA_HOME") - if ((javaHome != null && !javaHome.isEmpty()) || env.isDefinedAt("JAVA_HOME")) { - javaCommand = Environment.JAVA_HOME.$() + "/bin/java" - } - val commands = List[String]( - javaCommand + + Environment.JAVA_HOME.$() + "/bin/java" + " -server " + JAVA_OPTS + " " + args.amClass + diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala index da0a6f74efcd5..2079697d8160e 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala @@ -88,13 +88,8 @@ trait ExecutorRunnableUtil extends Logging { } */ - var javaCommand = "java" - val javaHome = System.getenv("JAVA_HOME") - if ((javaHome != null && !javaHome.isEmpty()) || env.isDefinedAt("JAVA_HOME")) { - javaCommand = Environment.JAVA_HOME.$() + "/bin/java" - } - - val commands = List[String](javaCommand + + val commands = List[String]( + Environment.JAVA_HOME.$() + "/bin/java" + " -server " + // Kill if OOM is raised - leverage yarn's failure handling to cause rescheduling. // Not killing the task leaves various aspects of the executor and (to some extent) the jvm in From 01cf4c402b9fda59680e56112bfaa2b748416d0e Mon Sep 17 00:00:00 2001 From: Aaron Davidson Date: Fri, 4 Apr 2014 09:50:24 -0700 Subject: [PATCH 31/78] SPARK-1404: Always upgrade spark-env.sh vars to environment vars This was broken when spark-env.sh was made idempotent, as the idempotence check is an environment variable, but the spark-env.sh variables may not have been. Tested in zsh, bash, and sh. Author: Aaron Davidson Closes #310 from aarondav/SPARK-1404 and squashes the following commits: c3406a5 [Aaron Davidson] Add extra export in spark-shell 6a0e340 [Aaron Davidson] SPARK-1404: Always upgrade spark-env.sh vars to environment vars --- bin/load-spark-env.sh | 3 +++ bin/spark-shell | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/bin/load-spark-env.sh b/bin/load-spark-env.sh index 476dd826551fd..d425f9feaac54 100644 --- a/bin/load-spark-env.sh +++ b/bin/load-spark-env.sh @@ -30,6 +30,9 @@ if [ -z "$SPARK_ENV_LOADED" ]; then use_conf_dir=${SPARK_CONF_DIR:-"$parent_dir/conf"} if [ -f "${use_conf_dir}/spark-env.sh" ]; then + # Promote all variable declarations to environment (exported) variables + set -a . "${use_conf_dir}/spark-env.sh" + set +a fi fi diff --git a/bin/spark-shell b/bin/spark-shell index fac006cf492ed..535ee3ccd8269 100755 --- a/bin/spark-shell +++ b/bin/spark-shell @@ -127,7 +127,7 @@ function set_spark_log_conf(){ function set_spark_master(){ if ! [[ "$1" =~ $ARG_FLAG_PATTERN ]]; then - MASTER="$1" + export MASTER="$1" else out_error "wrong format for $2" fi @@ -145,7 +145,7 @@ function resolve_spark_master(){ fi if [ -z "$MASTER" ]; then - MASTER="$DEFAULT_MASTER" + export MASTER="$DEFAULT_MASTER" fi } From f1fa617023d30d8cdc5acef0274bad8cc3e89cea Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Fri, 4 Apr 2014 11:12:47 -0700 Subject: [PATCH 32/78] [SPARK-1133] Add whole text files reader in MLlib Here is a pointer to the former [PR164](https://github.com/apache/spark/pull/164). I add the pull request for the JIRA issue [SPARK-1133](https://spark-project.atlassian.net/browse/SPARK-1133), which brings a new files reader API in MLlib. Author: Xusen Yin Closes #252 from yinxusen/whole-files-input and squashes the following commits: 7191be6 [Xusen Yin] refine comments 0af3faf [Xusen Yin] add JavaAPI test 01745ee [Xusen Yin] fix deletion error cc97dca [Xusen Yin] move whole text file API to Spark core d792cee [Xusen Yin] remove the typo character "+" 6bdf2c2 [Xusen Yin] test for small local file system block size a1f1e7e [Xusen Yin] add two extra spaces 28cb0fe [Xusen Yin] add whole text files reader --- .../scala/org/apache/spark/SparkContext.scala | 34 ++++++ .../spark/api/java/JavaSparkContext.scala | 28 +++++ .../input/WholeTextFileInputFormat.scala | 47 ++++++++ .../input/WholeTextFileRecordReader.scala | 72 ++++++++++++ .../java/org/apache/spark/JavaAPISuite.java | 30 ++++- .../WholeTextFileRecordReaderSuite.scala | 105 ++++++++++++++++++ 6 files changed, 313 insertions(+), 3 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala create mode 100644 core/src/main/scala/org/apache/spark/input/WholeTextFileRecordReader.scala create mode 100644 core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index b23accbbb9410..28a865c0ad3b5 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -37,6 +37,7 @@ import org.apache.mesos.MesosNativeLibrary import org.apache.spark.broadcast.Broadcast import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil} +import org.apache.spark.input.WholeTextFileInputFormat import org.apache.spark.partial.{ApproximateEvaluator, PartialResult} import org.apache.spark.rdd._ import org.apache.spark.scheduler._ @@ -371,6 +372,39 @@ class SparkContext( minSplits).map(pair => pair._2.toString) } + /** + * Read a directory of text files from HDFS, a local file system (available on all nodes), or any + * Hadoop-supported file system URI. Each file is read as a single record and returned in a + * key-value pair, where the key is the path of each file, the value is the content of each file. + * + *

For example, if you have the following files: + * {{{ + * hdfs://a-hdfs-path/part-00000 + * hdfs://a-hdfs-path/part-00001 + * ... + * hdfs://a-hdfs-path/part-nnnnn + * }}} + * + * Do `val rdd = sparkContext.wholeTextFile("hdfs://a-hdfs-path")`, + * + *

then `rdd` contains + * {{{ + * (a-hdfs-path/part-00000, its content) + * (a-hdfs-path/part-00001, its content) + * ... + * (a-hdfs-path/part-nnnnn, its content) + * }}} + * + * @note Small files are perferred, large file is also allowable, but may cause bad performance. + */ + def wholeTextFiles(path: String): RDD[(String, String)] = { + newAPIHadoopFile( + path, + classOf[WholeTextFileInputFormat], + classOf[String], + classOf[String]) + } + /** * Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and other * necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable), 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 e531a57aced31..6cbdeac58d5e2 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 @@ -154,6 +154,34 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork */ def textFile(path: String, minSplits: Int): JavaRDD[String] = sc.textFile(path, minSplits) + /** + * Read a directory of text files from HDFS, a local file system (available on all nodes), or any + * Hadoop-supported file system URI. Each file is read as a single record and returned in a + * key-value pair, where the key is the path of each file, the value is the content of each file. + * + *

For example, if you have the following files: + * {{{ + * hdfs://a-hdfs-path/part-00000 + * hdfs://a-hdfs-path/part-00001 + * ... + * hdfs://a-hdfs-path/part-nnnnn + * }}} + * + * Do `JavaPairRDD rdd = sparkContext.wholeTextFiles("hdfs://a-hdfs-path")`, + * + *

then `rdd` contains + * {{{ + * (a-hdfs-path/part-00000, its content) + * (a-hdfs-path/part-00001, its content) + * ... + * (a-hdfs-path/part-nnnnn, its content) + * }}} + * + * @note Small files are perferred, large file is also allowable, but may cause bad performance. + */ + def wholeTextFiles(path: String): JavaPairRDD[String, String] = + new JavaPairRDD(sc.wholeTextFiles(path)) + /** Get an RDD for a Hadoop SequenceFile with given key and value types. * * '''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each diff --git a/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala b/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala new file mode 100644 index 0000000000000..4887fb6b84eb2 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.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.input + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.mapreduce.InputSplit +import org.apache.hadoop.mapreduce.JobContext +import org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat +import org.apache.hadoop.mapreduce.RecordReader +import org.apache.hadoop.mapreduce.TaskAttemptContext +import org.apache.hadoop.mapreduce.lib.input.CombineFileRecordReader +import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit + +/** + * A [[org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat CombineFileInputFormat]] for + * reading whole text files. Each file is read as key-value pair, where the key is the file path and + * the value is the entire content of file. + */ + +private[spark] class WholeTextFileInputFormat extends CombineFileInputFormat[String, String] { + override protected def isSplitable(context: JobContext, file: Path): Boolean = false + + override def createRecordReader( + split: InputSplit, + context: TaskAttemptContext): RecordReader[String, String] = { + + new CombineFileRecordReader[String, String]( + split.asInstanceOf[CombineFileSplit], + context, + classOf[WholeTextFileRecordReader]) + } +} diff --git a/core/src/main/scala/org/apache/spark/input/WholeTextFileRecordReader.scala b/core/src/main/scala/org/apache/spark/input/WholeTextFileRecordReader.scala new file mode 100644 index 0000000000000..c3dabd2e79995 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/input/WholeTextFileRecordReader.scala @@ -0,0 +1,72 @@ +/* + * 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.input + +import com.google.common.io.{ByteStreams, Closeables} + +import org.apache.hadoop.io.Text +import org.apache.hadoop.mapreduce.InputSplit +import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit +import org.apache.hadoop.mapreduce.RecordReader +import org.apache.hadoop.mapreduce.TaskAttemptContext + +/** + * A [[org.apache.hadoop.mapreduce.RecordReader RecordReader]] for reading a single whole text file + * out in a key-value pair, where the key is the file path and the value is the entire content of + * the file. + */ +private[spark] class WholeTextFileRecordReader( + split: CombineFileSplit, + context: TaskAttemptContext, + index: Integer) + extends RecordReader[String, String] { + + private val path = split.getPath(index) + private val fs = path.getFileSystem(context.getConfiguration) + + // True means the current file has been processed, then skip it. + private var processed = false + + private val key = path.toString + private var value: String = null + + override def initialize(split: InputSplit, context: TaskAttemptContext) = {} + + override def close() = {} + + override def getProgress = if (processed) 1.0f else 0.0f + + override def getCurrentKey = key + + override def getCurrentValue = value + + override def nextKeyValue = { + if (!processed) { + val fileIn = fs.open(path) + val innerBuffer = ByteStreams.toByteArray(fileIn) + + value = new Text(innerBuffer).toString + Closeables.close(fileIn, false) + + processed = true + true + } else { + false + } + } +} diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java index c6b65c7348ae0..2372f2d9924a1 100644 --- a/core/src/test/java/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java @@ -17,9 +17,7 @@ package org.apache.spark; -import java.io.File; -import java.io.IOException; -import java.io.Serializable; +import java.io.*; import java.util.*; import scala.Tuple2; @@ -599,6 +597,32 @@ public void textFiles() throws IOException { Assert.assertEquals(expected, readRDD.collect()); } + @Test + public void wholeTextFiles() throws IOException { + byte[] content1 = "spark is easy to use.\n".getBytes(); + byte[] content2 = "spark is also easy to use.\n".getBytes(); + + File tempDir = Files.createTempDir(); + String tempDirName = tempDir.getAbsolutePath(); + DataOutputStream ds = new DataOutputStream(new FileOutputStream(tempDirName + "/part-00000")); + ds.write(content1); + ds.close(); + ds = new DataOutputStream(new FileOutputStream(tempDirName + "/part-00001")); + ds.write(content2); + ds.close(); + + HashMap container = new HashMap(); + container.put(tempDirName+"/part-00000", new Text(content1).toString()); + container.put(tempDirName+"/part-00001", new Text(content2).toString()); + + JavaPairRDD readRDD = sc.wholeTextFiles(tempDirName); + List> result = readRDD.collect(); + + for (Tuple2 res : result) { + Assert.assertEquals(res._2(), container.get(res._1())); + } + } + @Test public void textFilesCompressed() throws IOException { File tempDir = Files.createTempDir(); diff --git a/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala new file mode 100644 index 0000000000000..09e35bfc8f85f --- /dev/null +++ b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.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.input + +import java.io.DataOutputStream +import java.io.File +import java.io.FileOutputStream + +import scala.collection.immutable.IndexedSeq + +import com.google.common.io.Files + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import org.apache.hadoop.io.Text + +import org.apache.spark.SparkContext + +/** + * Tests the correctness of + * [[org.apache.spark.input.WholeTextFileRecordReader WholeTextFileRecordReader]]. A temporary + * directory is created as fake input. Temporal storage would be deleted in the end. + */ +class WholeTextFileRecordReaderSuite extends FunSuite with BeforeAndAfterAll { + private var sc: SparkContext = _ + + override def beforeAll() { + sc = new SparkContext("local", "test") + + // Set the block size of local file system to test whether files are split right or not. + sc.hadoopConfiguration.setLong("fs.local.block.size", 32) + } + + override def afterAll() { + sc.stop() + } + + private def createNativeFile(inputDir: File, fileName: String, contents: Array[Byte]) = { + val out = new DataOutputStream(new FileOutputStream(s"${inputDir.toString}/$fileName")) + out.write(contents, 0, contents.length) + out.close() + } + + /** + * This code will test the behaviors of WholeTextFileRecordReader based on local disk. There are + * three aspects to check: + * 1) Whether all files are read; + * 2) Whether paths are read correctly; + * 3) Does the contents be the same. + */ + test("Correctness of WholeTextFileRecordReader.") { + + val dir = Files.createTempDir() + println(s"Local disk address is ${dir.toString}.") + + WholeTextFileRecordReaderSuite.files.foreach { case (filename, contents) => + createNativeFile(dir, filename, contents) + } + + val res = sc.wholeTextFiles(dir.toString).collect() + + assert(res.size === WholeTextFileRecordReaderSuite.fileNames.size, + "Number of files read out does not fit with the actual value.") + + for ((filename, contents) <- res) { + val shortName = filename.split('/').last + assert(WholeTextFileRecordReaderSuite.fileNames.contains(shortName), + s"Missing file name $filename.") + assert(contents === new Text(WholeTextFileRecordReaderSuite.files(shortName)).toString, + s"file $filename contents can not match.") + } + + dir.delete() + } +} + +/** + * Files to be tested are defined here. + */ +object WholeTextFileRecordReaderSuite { + private val testWords: IndexedSeq[Byte] = "Spark is easy to use.\n".map(_.toByte) + + private val fileNames = Array("part-00000", "part-00001", "part-00002") + private val fileLengths = Array(10, 100, 1000) + + private val files = fileLengths.zip(fileNames).map { case (upperBound, filename) => + filename -> Stream.continually(testWords.toList.toStream).flatten.take(upperBound).toArray + }.toMap +} From 16b830888734de260f460506b766edab79d30ecd Mon Sep 17 00:00:00 2001 From: Sandy Ryza Date: Fri, 4 Apr 2014 13:28:42 -0700 Subject: [PATCH 33/78] SPARK-1375. Additional spark-submit cleanup Author: Sandy Ryza Closes #278 from sryza/sandy-spark-1375 and squashes the following commits: 5fbf1e9 [Sandy Ryza] SPARK-1375. Additional spark-submit cleanup --- .../scala/org/apache/spark/deploy/SparkSubmit.scala | 13 ++++++++----- .../apache/spark/deploy/SparkSubmitArguments.scala | 2 +- docs/cluster-overview.md | 2 +- 3 files changed, 10 insertions(+), 7 deletions(-) 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 1fa799190409f..e05fbfe321495 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -79,20 +79,23 @@ object SparkSubmit { printErrorAndExit("master must start with yarn, mesos, spark, or local") } - // Because "yarn-standalone" and "yarn-client" encapsulate both the master + // Because "yarn-cluster" and "yarn-client" encapsulate both the master // and deploy mode, we have some logic to infer the master and deploy mode // from each other if only one is specified, or exit early if they are at odds. - if (appArgs.deployMode == null && appArgs.master == "yarn-standalone") { + if (appArgs.deployMode == null && + (appArgs.master == "yarn-standalone" || appArgs.master == "yarn-cluster")) { appArgs.deployMode = "cluster" } if (appArgs.deployMode == "cluster" && appArgs.master == "yarn-client") { printErrorAndExit("Deploy mode \"cluster\" and master \"yarn-client\" are not compatible") } - if (appArgs.deployMode == "client" && appArgs.master == "yarn-standalone") { - printErrorAndExit("Deploy mode \"client\" and master \"yarn-standalone\" are not compatible") + if (appArgs.deployMode == "client" && + (appArgs.master == "yarn-standalone" || appArgs.master == "yarn-cluster")) { + printErrorAndExit("Deploy mode \"client\" and master \"" + appArgs.master + + "\" are not compatible") } if (appArgs.deployMode == "cluster" && appArgs.master.startsWith("yarn")) { - appArgs.master = "yarn-standalone" + appArgs.master = "yarn-cluster" } if (appArgs.deployMode != "cluster" && appArgs.master.startsWith("yarn")) { appArgs.master = "yarn-client" 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 9c8f54ea6f77a..834b3df2f164b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -171,7 +171,7 @@ private[spark] class SparkSubmitArguments(args: Array[String]) { outStream.println("Unknown/unsupported param " + unknownParam) } outStream.println( - """Usage: spark-submit [options] + """Usage: spark-submit [options] |Options: | --master MASTER_URL spark://host:port, mesos://host:port, yarn, or local. | --deploy-mode DEPLOY_MODE Mode to deploy the app in, either 'client' or 'cluster'. diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md index b69e3416fb322..7f75ea44e4cea 100644 --- a/docs/cluster-overview.md +++ b/docs/cluster-overview.md @@ -56,7 +56,7 @@ The recommended way to launch a compiled Spark application is through the spark- bin directory), which takes care of setting up the classpath with Spark and its dependencies, as well as provides a layer over the different cluster managers and deploy modes that Spark supports. It's usage is - spark-submit `` `` + spark-submit `` `` Where options are any of: From a02b535d5e18e987a4b9c4c352838d294f9e853b Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Fri, 4 Apr 2014 14:46:32 -0700 Subject: [PATCH 34/78] Don't create SparkContext in JobProgressListenerSuite. This reduces the time of the test from 11 seconds to 20 milliseconds. Author: Patrick Wendell Closes #324 from pwendell/job-test and squashes the following commits: 868d9eb [Patrick Wendell] Don't create SparkContext in JobProgressListenerSuite. --- .../org/apache/spark/ui/jobs/JobProgressListenerSuite.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) 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 67ceee505db3c..beac656f573b4 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 @@ -55,8 +55,8 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Shou } test("test executor id to summary") { - val sc = new SparkContext("local", "test") - val listener = new JobProgressListener(sc.conf) + val conf = new SparkConf() + val listener = new JobProgressListener(conf) val taskMetrics = new TaskMetrics() val shuffleReadMetrics = new ShuffleReadMetrics() From 198892fe8d39a2fad585fa2a7579d8b478456c33 Mon Sep 17 00:00:00 2001 From: Thomas Graves Date: Fri, 4 Apr 2014 17:16:31 -0700 Subject: [PATCH 35/78] [SPARK-1198] Allow pipes tasks to run in different sub-directories This works as is on Linux/Mac/etc but doesn't cover working on Windows. In here I use ln -sf for symlinks. Putting this up for comments on that. Do we want to create perhaps some classes for doing shell commands - Linux vs Windows. Is there some other way we want to do this? I assume we are still supporting jdk1.6? Also should I update the Java API for pipes to allow this parameter? Author: Thomas Graves Closes #128 from tgravescs/SPARK1198 and squashes the following commits: abc1289 [Thomas Graves] remove extra tag in pom file ba23fc0 [Thomas Graves] Add support for symlink on windows, remove commons-io usage da4b221 [Thomas Graves] Merge branch 'master' of https://github.com/tgravescs/spark into SPARK1198 61be271 [Thomas Graves] Fix file name filter 6b783bd [Thomas Graves] style fixes 1ab49ca [Thomas Graves] Add support for running pipe tasks is separate directories --- .../scala/org/apache/spark/rdd/PipedRDD.scala | 64 ++++++++++++++++++- .../main/scala/org/apache/spark/rdd/RDD.scala | 7 +- .../scala/org/apache/spark/util/Utils.scala | 45 ++++++++++++- .../org/apache/spark/PipedRDDSuite.scala | 28 +++++++- 4 files changed, 137 insertions(+), 7 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala index 4250a9d02f764..41ae0fec823e7 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala @@ -17,6 +17,9 @@ package org.apache.spark.rdd +import java.io.File +import java.io.FilenameFilter +import java.io.IOException import java.io.PrintWriter import java.util.StringTokenizer @@ -27,6 +30,7 @@ import scala.io.Source import scala.reflect.ClassTag import org.apache.spark.{Partition, SparkEnv, TaskContext} +import org.apache.spark.util.Utils /** @@ -38,7 +42,8 @@ class PipedRDD[T: ClassTag]( command: Seq[String], envVars: Map[String, String], printPipeContext: (String => Unit) => Unit, - printRDDElement: (T, String => Unit) => Unit) + printRDDElement: (T, String => Unit) => Unit, + separateWorkingDir: Boolean) extends RDD[String](prev) { // Similar to Runtime.exec(), if we are given a single string, split it into words @@ -48,12 +53,24 @@ class PipedRDD[T: ClassTag]( command: String, envVars: Map[String, String] = Map(), printPipeContext: (String => Unit) => Unit = null, - printRDDElement: (T, String => Unit) => Unit = null) = - this(prev, PipedRDD.tokenize(command), envVars, printPipeContext, printRDDElement) + printRDDElement: (T, String => Unit) => Unit = null, + separateWorkingDir: Boolean = false) = + this(prev, PipedRDD.tokenize(command), envVars, printPipeContext, printRDDElement, + separateWorkingDir) override def getPartitions: Array[Partition] = firstParent[T].partitions + /** + * A FilenameFilter that accepts anything that isn't equal to the name passed in. + * @param name of file or directory to leave out + */ + class NotEqualsFileNameFilter(filterName: String) extends FilenameFilter { + def accept(dir: File, name: String): Boolean = { + !name.equals(filterName) + } + } + override def compute(split: Partition, context: TaskContext): Iterator[String] = { val pb = new ProcessBuilder(command) // Add the environmental variables to the process. @@ -67,6 +84,38 @@ class PipedRDD[T: ClassTag]( currentEnvVars.putAll(hadoopSplit.getPipeEnvVars()) } + // When spark.worker.separated.working.directory option is turned on, each + // task will be run in separate directory. This should be resolve file + // access conflict issue + val taskDirectory = "./tasks/" + java.util.UUID.randomUUID.toString + var workInTaskDirectory = false + logDebug("taskDirectory = " + taskDirectory) + if (separateWorkingDir == true) { + val currentDir = new File(".") + logDebug("currentDir = " + currentDir.getAbsolutePath()) + val taskDirFile = new File(taskDirectory) + taskDirFile.mkdirs() + + try { + val tasksDirFilter = new NotEqualsFileNameFilter("tasks") + + // Need to add symlinks to jars, files, and directories. On Yarn we could have + // directories and other files not known to the SparkContext that were added via the + // Hadoop distributed cache. We also don't want to symlink to the /tasks directories we + // are creating here. + for (file <- currentDir.list(tasksDirFilter)) { + val fileWithDir = new File(currentDir, file) + Utils.symlink(new File(fileWithDir.getAbsolutePath()), + new File(taskDirectory + "/" + fileWithDir.getName())) + } + pb.directory(taskDirFile) + workInTaskDirectory = true + } catch { + case e: Exception => logError("Unable to setup task working directory: " + e.getMessage + + " (" + taskDirectory + ")") + } + } + val proc = pb.start() val env = SparkEnv.get @@ -112,6 +161,15 @@ class PipedRDD[T: ClassTag]( if (exitStatus != 0) { throw new Exception("Subprocess exited with status " + exitStatus) } + + // cleanup task working directory if used + if (workInTaskDirectory == true) { + scala.util.control.Exception.ignoring(classOf[IOException]) { + Utils.deleteRecursively(new File(taskDirectory)) + } + logDebug("Removed task working directory " + taskDirectory) + } + false } } 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 ce2b8ac27206b..08c42c5ee87b6 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -481,16 +481,19 @@ abstract class RDD[T: ClassTag]( * instead of constructing a huge String to concat all the elements: * def printRDDElement(record:(String, Seq[String]), f:String=>Unit) = * for (e <- record._2){f(e)} + * @param separateWorkingDir Use separate working directories for each task. * @return the result RDD */ def pipe( command: Seq[String], env: Map[String, String] = Map(), printPipeContext: (String => Unit) => Unit = null, - printRDDElement: (T, String => Unit) => Unit = null): RDD[String] = { + printRDDElement: (T, String => Unit) => Unit = null, + separateWorkingDir: Boolean = false): RDD[String] = { new PipedRDD(this, command, env, if (printPipeContext ne null) sc.clean(printPipeContext) else null, - if (printRDDElement ne null) sc.clean(printRDDElement) else null) + if (printRDDElement ne null) sc.clean(printRDDElement) else null, + separateWorkingDir) } /** 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 62ee704d580c2..737b765e2aed6 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -26,6 +26,7 @@ import java.util.concurrent.{ConcurrentHashMap, Executors, ThreadPoolExecutor} import scala.collection.JavaConversions._ import scala.collection.Map import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.SortedSet import scala.io.Source import scala.reflect.ClassTag @@ -43,6 +44,8 @@ import org.apache.spark.serializer.{DeserializationStream, SerializationStream, */ private[spark] object Utils extends Logging { + val osName = System.getProperty("os.name") + /** Serialize an object using Java serialization */ def serialize[T](o: T): Array[Byte] = { val bos = new ByteArrayOutputStream() @@ -521,9 +524,10 @@ private[spark] object Utils extends Logging { /** * Delete a file or directory and its contents recursively. + * Don't follow directories if they are symlinks. */ def deleteRecursively(file: File) { - if (file.isDirectory) { + if ((file.isDirectory) && !isSymlink(file)) { for (child <- listFilesSafely(file)) { deleteRecursively(child) } @@ -536,6 +540,25 @@ private[spark] object Utils extends Logging { } } + /** + * Check to see if file is a symbolic link. + */ + def isSymlink(file: File): Boolean = { + if (file == null) throw new NullPointerException("File must not be null") + if (osName.startsWith("Windows")) return false + val fileInCanonicalDir = if (file.getParent() == null) { + file + } else { + new File(file.getParentFile().getCanonicalFile(), file.getName()) + } + + if (fileInCanonicalDir.getCanonicalFile().equals(fileInCanonicalDir.getAbsoluteFile())) { + return false; + } else { + return true; + } + } + /** * Convert a Java memory parameter passed to -Xmx (such as 300m or 1g) to a number of megabytes. */ @@ -898,6 +921,26 @@ private[spark] object Utils extends Logging { count } + /** + * Creates a symlink. Note jdk1.7 has Files.createSymbolicLink but not used here + * for jdk1.6 support. Supports windows by doing copy, everything else uses "ln -sf". + * @param src absolute path to the source + * @param dst relative path for the destination + */ + def symlink(src: File, dst: File) { + if (!src.isAbsolute()) { + throw new IOException("Source must be absolute") + } + if (dst.isAbsolute()) { + throw new IOException("Destination must be relative") + } + val linkCmd = if (osName.startsWith("Windows")) "copy" else "ln -sf" + import scala.sys.process._ + (linkCmd + " " + src.getAbsolutePath() + " " + dst.getPath()) lines_! ProcessLogger(line => + (logInfo(line))) + } + + /** Return the class name of the given object, removing all dollar signs */ def getFormattedClassName(obj: AnyRef) = { obj.getClass.getSimpleName.replace("$", "") diff --git a/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala index 6e7fd55fa4bb1..627e9b5cd9060 100644 --- a/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala @@ -17,8 +17,11 @@ package org.apache.spark -import org.scalatest.FunSuite +import java.io.File + +import com.google.common.io.Files +import org.scalatest.FunSuite import org.apache.spark.rdd.{HadoopRDD, PipedRDD, HadoopPartition} import org.apache.hadoop.mapred.{JobConf, TextInputFormat, FileSplit} @@ -126,6 +129,29 @@ class PipedRDDSuite extends FunSuite with SharedSparkContext { } } + test("basic pipe with separate working directory") { + if (testCommandAvailable("cat")) { + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + val piped = nums.pipe(Seq("cat"), separateWorkingDir = true) + val c = piped.collect() + assert(c.size === 4) + assert(c(0) === "1") + assert(c(1) === "2") + assert(c(2) === "3") + assert(c(3) === "4") + val pipedPwd = nums.pipe(Seq("pwd"), separateWorkingDir = true) + val collectPwd = pipedPwd.collect() + assert(collectPwd(0).contains("tasks/")) + val pipedLs = nums.pipe(Seq("ls"), separateWorkingDir = true).collect() + // make sure symlinks were created + assert(pipedLs.length > 0) + // clean up top level tasks directory + new File("tasks").delete() + } else { + assert(true) + } + } + test("test pipe exports map_input_file") { testExportInputFile("map_input_file") } From d956cc251676d67d87bd6dbfa82be864933d8136 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Fri, 4 Apr 2014 17:23:17 -0700 Subject: [PATCH 36/78] [SQL] Minor fixes. Author: Michael Armbrust Closes #315 from marmbrus/minorFixes and squashes the following commits: b23a15d [Michael Armbrust] fix scaladoc 11062ac [Michael Armbrust] Fix registering "SELECT *" queries as tables and caching them. As some tests for this and self-joins. 3997dc9 [Michael Armbrust] Move Row extractor to catalyst. 208bf5e [Michael Armbrust] More idiomatic naming of DSL functions. * subquery => as * for join condition => on, i.e., `r.join(s, condition = 'a == 'b)` =>`r.join(s, on = 'a == 'b)` 87211ce [Michael Armbrust] Correctly handle self joins of in-memory cached tables. 69e195e [Michael Armbrust] Change != to !== in the DSL since != will always translate to != on Any. 01f2dd5 [Michael Armbrust] Correctly assign aliases to tables in SqlParser. --- .../apache/spark/sql/catalyst/SqlParser.scala | 2 +- .../apache/spark/sql/catalyst/dsl/package.scala | 2 +- .../spark/sql/catalyst/expressions/Row.scala | 15 +++++++++++++++ .../catalyst/plans/logical/basicOperators.scala | 1 + .../scala/org/apache/spark/sql/package.scala | 15 +-------------- .../scala/org/apache/spark/sql/SchemaRDD.scala | 16 ++++++++-------- .../apache/spark/sql/execution/SparkPlan.scala | 3 +++ .../org/apache/spark/sql/CachedTableSuite.scala | 13 +++++++++++++ .../org/apache/spark/sql/DslQuerySuite.scala | 16 ++++++++-------- .../spark/sql/parquet/ParquetQuerySuite.scala | 4 ++-- 10 files changed, 53 insertions(+), 34 deletions(-) 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 4ea80fee23e1e..5b6aea81cb7d1 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 @@ -219,7 +219,7 @@ class SqlParser extends StandardTokenParsers { protected lazy val relationFactor: Parser[LogicalPlan] = ident ~ (opt(AS) ~> opt(ident)) ^^ { - case ident ~ alias => UnresolvedRelation(alias, ident) + case tableName ~ alias => UnresolvedRelation(None, tableName, alias) } | "(" ~> query ~ ")" ~ opt(AS) ~ ident ^^ { case s ~ _ ~ _ ~ a => Subquery(a, s) } 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 2c4bf1715b646..2d62e4cbbce01 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 @@ -70,7 +70,7 @@ package object dsl { def > (other: Expression) = GreaterThan(expr, other) def >= (other: Expression) = GreaterThanOrEqual(expr, other) def === (other: Expression) = Equals(expr, other) - def != (other: Expression) = Not(Equals(expr, other)) + def !== (other: Expression) = Not(Equals(expr, other)) def like(other: Expression) = Like(expr, other) def rlike(other: Expression) = RLike(expr, other) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala index 6f939e6c41f6b..9f4d84466e6ac 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala @@ -19,6 +19,21 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.types.NativeType +object Row { + /** + * This method can be used to extract fields from a [[Row]] object in a pattern match. Example: + * {{{ + * import org.apache.spark.sql._ + * + * val pairs = sql("SELECT key, value FROM src").rdd.map { + * case Row(key: Int, value: String) => + * key -> value + * } + * }}} + */ + def unapplySeq(row: Row): Some[Seq[Any]] = Some(row) +} + /** * Represents one row of output from a relational operator. Allows both generic access by ordinal, * which will incur boxing overhead for primitives, as well as native primitive access. 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 b39c2b32cc42c..cfc0b0c3a8d98 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 @@ -162,6 +162,7 @@ case class LowerCaseSchema(child: LogicalPlan) extends UnaryNode { a.nullable)( a.exprId, a.qualifiers) + case other => other } def references = Set.empty diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/package.scala index 9ec31689b5098..4589129cd1c90 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/package.scala @@ -32,18 +32,5 @@ package object sql { type Row = catalyst.expressions.Row - object Row { - /** - * This method can be used to extract fields from a [[Row]] object in a pattern match. Example: - * {{{ - * import org.apache.spark.sql._ - * - * val pairs = sql("SELECT key, value FROM src").rdd.map { - * case Row(key: Int, value: String) => - * key -> value - * } - * }}} - */ - def unapplySeq(row: Row): Some[Seq[Any]] = Some(row) - } + val Row = catalyst.expressions.Row } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala index a62cb8aa1321f..fc95781448569 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala @@ -148,17 +148,17 @@ class SchemaRDD( * * @param otherPlan the [[SchemaRDD]] that should be joined with this one. * @param joinType One of `Inner`, `LeftOuter`, `RightOuter`, or `FullOuter`. Defaults to `Inner.` - * @param condition An optional condition for the join operation. This is equivilent to the `ON` - * clause in standard SQL. In the case of `Inner` joins, specifying a - * `condition` is equivilent to adding `where` clauses after the `join`. + * @param on An optional condition for the join operation. This is equivilent to the `ON` + * clause in standard SQL. In the case of `Inner` joins, specifying a + * `condition` is equivilent to adding `where` clauses after the `join`. * * @group Query */ def join( otherPlan: SchemaRDD, joinType: JoinType = Inner, - condition: Option[Expression] = None): SchemaRDD = - new SchemaRDD(sqlContext, Join(logicalPlan, otherPlan.logicalPlan, joinType, condition)) + on: Option[Expression] = None): SchemaRDD = + new SchemaRDD(sqlContext, Join(logicalPlan, otherPlan.logicalPlan, joinType, on)) /** * Sorts the results by the given expressions. @@ -195,14 +195,14 @@ class SchemaRDD( * with the same name, for example, when peforming self-joins. * * {{{ - * val x = schemaRDD.where('a === 1).subquery('x) - * val y = schemaRDD.where('a === 2).subquery('y) + * val x = schemaRDD.where('a === 1).as('x) + * val y = schemaRDD.where('a === 2).as('y) * x.join(y).where("x.a".attr === "y.a".attr), * }}} * * @group Query */ - def subquery(alias: Symbol) = + def as(alias: Symbol) = new SchemaRDD(sqlContext, Subquery(alias.name, logicalPlan)) /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala index acb1ee83a72f6..daa423cb8ea1a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala @@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation import org.apache.spark.sql.catalyst.expressions.GenericRow import org.apache.spark.sql.catalyst.plans.{QueryPlan, logical} import org.apache.spark.sql.catalyst.plans.physical._ +import org.apache.spark.sql.columnar.InMemoryColumnarTableScan abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging { self: Product => @@ -69,6 +70,8 @@ case class SparkLogicalPlan(alreadyPlanned: SparkPlan) SparkLogicalPlan( alreadyPlanned match { case ExistingRdd(output, rdd) => ExistingRdd(output.map(_.newInstance), rdd) + case InMemoryColumnarTableScan(output, child) => + InMemoryColumnarTableScan(output.map(_.newInstance), child) case _ => sys.error("Multiple instance of the same relation detected.") }).asInstanceOf[this.type] } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala index e5902c3cae381..7c6a642278226 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala @@ -58,4 +58,17 @@ class CachedTableSuite extends QueryTest { TestSQLContext.uncacheTable("testData") } } + + test("SELECT Star Cached Table") { + TestSQLContext.sql("SELECT * FROM testData").registerAsTable("selectStar") + TestSQLContext.cacheTable("selectStar") + TestSQLContext.sql("SELECT * FROM selectStar") + TestSQLContext.uncacheTable("selectStar") + } + + test("Self-join cached") { + TestSQLContext.cacheTable("testData") + TestSQLContext.sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key") + TestSQLContext.uncacheTable("testData") + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala index 2524a37cbac13..be0f4a4c73b36 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala @@ -119,8 +119,8 @@ class DslQuerySuite extends QueryTest { } test("inner join, where, multiple matches") { - val x = testData2.where('a === 1).subquery('x) - val y = testData2.where('a === 1).subquery('y) + val x = testData2.where('a === 1).as('x) + val y = testData2.where('a === 1).as('y) checkAnswer( x.join(y).where("x.a".attr === "y.a".attr), (1,1,1,1) :: @@ -131,8 +131,8 @@ class DslQuerySuite extends QueryTest { } test("inner join, no matches") { - val x = testData2.where('a === 1).subquery('x) - val y = testData2.where('a === 2).subquery('y) + val x = testData2.where('a === 1).as('x) + val y = testData2.where('a === 2).as('y) checkAnswer( x.join(y).where("x.a".attr === "y.a".attr), Nil) @@ -140,8 +140,8 @@ class DslQuerySuite extends QueryTest { test("big inner join, 4 matches per row") { val bigData = testData.unionAll(testData).unionAll(testData).unionAll(testData) - val bigDataX = bigData.subquery('x) - val bigDataY = bigData.subquery('y) + val bigDataX = bigData.as('x) + val bigDataY = bigData.as('y) checkAnswer( bigDataX.join(bigDataY).where("x.key".attr === "y.key".attr), @@ -181,8 +181,8 @@ class DslQuerySuite extends QueryTest { } test("full outer join") { - val left = upperCaseData.where('N <= 4).subquery('left) - val right = upperCaseData.where('N >= 3).subquery('right) + val left = upperCaseData.where('N <= 4).as('left) + val right = upperCaseData.where('N >= 3).as('right) checkAnswer( left.join(right, FullOuter, Some("left.N".attr === "right.N".attr)), diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala index a62a3c4d02354..fc68d6c5620d3 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala @@ -56,8 +56,8 @@ class ParquetQuerySuite extends FunSuite with BeforeAndAfterAll { } test("self-join parquet files") { - val x = ParquetTestData.testData.subquery('x) - val y = ParquetTestData.testData.subquery('y) + val x = ParquetTestData.testData.as('x) + val y = ParquetTestData.testData.as('y) val query = x.join(y).where("x.myint".attr === "y.myint".attr) // Check to make sure that the attributes from either side of the join have unique expression From 60e18ce7dd1016647b63586520b713efc45494a8 Mon Sep 17 00:00:00 2001 From: Matei Zaharia Date: Fri, 4 Apr 2014 17:29:29 -0700 Subject: [PATCH 37/78] SPARK-1414. Python API for SparkContext.wholeTextFiles Also clarified comment on each file having to fit in memory Author: Matei Zaharia Closes #327 from mateiz/py-whole-files and squashes the following commits: 9ad64a5 [Matei Zaharia] SPARK-1414. Python API for SparkContext.wholeTextFiles --- .../scala/org/apache/spark/SparkContext.scala | 2 +- .../spark/api/java/JavaSparkContext.scala | 2 +- .../apache/spark/api/python/PythonRDD.scala | 6 ++- python/pyspark/context.py | 44 ++++++++++++++++++- python/pyspark/serializers.py | 2 +- 5 files changed, 49 insertions(+), 7 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 28a865c0ad3b5..835cffe37a938 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -395,7 +395,7 @@ class SparkContext( * (a-hdfs-path/part-nnnnn, its content) * }}} * - * @note Small files are perferred, large file is also allowable, but may cause bad performance. + * @note Small files are preferred, as each file will be loaded fully in memory. */ def wholeTextFiles(path: String): RDD[(String, String)] = { newAPIHadoopFile( 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 6cbdeac58d5e2..a2855d4db1d2e 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 @@ -177,7 +177,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork * (a-hdfs-path/part-nnnnn, its content) * }}} * - * @note Small files are perferred, large file is also allowable, but may cause bad performance. + * @note Small files are preferred, as each file will be loaded fully in memory. */ def wholeTextFiles(path: String): JavaPairRDD[String, String] = new JavaPairRDD(sc.wholeTextFiles(path)) diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index b67286a4e3b75..32f1100406d74 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -19,6 +19,7 @@ package org.apache.spark.api.python import java.io._ import java.net._ +import java.nio.charset.Charset import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections} import scala.collection.JavaConversions._ @@ -206,6 +207,7 @@ private object SpecialLengths { } private[spark] object PythonRDD { + val UTF8 = Charset.forName("UTF-8") def readRDDFromFile(sc: JavaSparkContext, filename: String, parallelism: Int): JavaRDD[Array[Byte]] = { @@ -266,7 +268,7 @@ private[spark] object PythonRDD { } def writeUTF(str: String, dataOut: DataOutputStream) { - val bytes = str.getBytes("UTF-8") + val bytes = str.getBytes(UTF8) dataOut.writeInt(bytes.length) dataOut.write(bytes) } @@ -286,7 +288,7 @@ private[spark] object PythonRDD { private class BytesToString extends org.apache.spark.api.java.function.Function[Array[Byte], String] { - override def call(arr: Array[Byte]) : String = new String(arr, "UTF-8") + override def call(arr: Array[Byte]) : String = new String(arr, PythonRDD.UTF8) } /** diff --git a/python/pyspark/context.py b/python/pyspark/context.py index bf2454fd7e38e..ff1023bbfa539 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -28,7 +28,8 @@ from pyspark.conf import SparkConf from pyspark.files import SparkFiles from pyspark.java_gateway import launch_gateway -from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer +from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer, \ + PairDeserializer from pyspark.storagelevel import StorageLevel from pyspark import rdd from pyspark.rdd import RDD @@ -257,6 +258,45 @@ def textFile(self, name, minSplits=None): return RDD(self._jsc.textFile(name, minSplits), self, UTF8Deserializer()) + def wholeTextFiles(self, path): + """ + Read a directory of text files from HDFS, a local file system + (available on all nodes), or any Hadoop-supported file system + URI. Each file is read as a single record and returned in a + key-value pair, where the key is the path of each file, the + value is the content of each file. + + For example, if you have the following files:: + + hdfs://a-hdfs-path/part-00000 + hdfs://a-hdfs-path/part-00001 + ... + hdfs://a-hdfs-path/part-nnnnn + + Do C{rdd = sparkContext.wholeTextFiles("hdfs://a-hdfs-path")}, + then C{rdd} contains:: + + (a-hdfs-path/part-00000, its content) + (a-hdfs-path/part-00001, its content) + ... + (a-hdfs-path/part-nnnnn, its content) + + NOTE: Small files are preferred, as each file will be loaded + fully in memory. + + >>> dirPath = os.path.join(tempdir, "files") + >>> os.mkdir(dirPath) + >>> with open(os.path.join(dirPath, "1.txt"), "w") as file1: + ... file1.write("1") + >>> with open(os.path.join(dirPath, "2.txt"), "w") as file2: + ... file2.write("2") + >>> textFiles = sc.wholeTextFiles(dirPath) + >>> sorted(textFiles.collect()) + [(u'.../1.txt', u'1'), (u'.../2.txt', u'2')] + """ + return RDD(self._jsc.wholeTextFiles(path), self, + PairDeserializer(UTF8Deserializer(), UTF8Deserializer())) + def _checkpointFile(self, name, input_deserializer): jrdd = self._jsc.checkpointFile(name) return RDD(jrdd, self, input_deserializer) @@ -425,7 +465,7 @@ def _test(): globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) globs['tempdir'] = tempfile.mkdtemp() atexit.register(lambda: shutil.rmtree(globs['tempdir'])) - (failure_count, test_count) = doctest.testmod(globs=globs) + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index 12c63f186a2b7..4d802924df4a1 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -290,7 +290,7 @@ class MarshalSerializer(FramedSerializer): class UTF8Deserializer(Serializer): """ - Deserializes streams written by getBytes. + Deserializes streams written by String.getBytes. """ def loads(self, stream): From 5f3c1bb5136b3389bea3af4fb39a083d979efa4c Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Fri, 4 Apr 2014 19:15:15 -0700 Subject: [PATCH 38/78] Add test utility for generating Jar files with compiled classes. This was requested by a few different people and may be generally useful, so I'd like to contribute this and not block on a different PR for it to get in. Author: Patrick Wendell Closes #326 from pwendell/class-loader-test-utils and squashes the following commits: ff3e88e [Patrick Wendell] Add test utility for generating Jar files with compiled classes. --- .../scala/org/apache/spark/TestUtils.scala | 98 +++++++++++++++++++ 1 file changed, 98 insertions(+) create mode 100644 core/src/test/scala/org/apache/spark/TestUtils.scala diff --git a/core/src/test/scala/org/apache/spark/TestUtils.scala b/core/src/test/scala/org/apache/spark/TestUtils.scala new file mode 100644 index 0000000000000..1611d09652d40 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/TestUtils.scala @@ -0,0 +1,98 @@ +/* + * 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 java.io.{File, FileInputStream, FileOutputStream} +import java.net.{URI, URL} +import java.util.jar.{JarEntry, JarOutputStream} + +import scala.collection.JavaConversions._ + +import javax.tools.{JavaFileObject, SimpleJavaFileObject, ToolProvider} +import com.google.common.io.Files + +object TestUtils { + + /** + * Create a jar that defines classes with the given names. + * + * Note: if this is used during class loader tests, class names should be unique + * in order to avoid interference between tests. + */ + def createJarWithClasses(classNames: Seq[String]): URL = { + val tempDir = Files.createTempDir() + val files = for (name <- classNames) yield createCompiledClass(name, tempDir) + val jarFile = new File(tempDir, "testJar-%s.jar".format(System.currentTimeMillis())) + createJar(files, jarFile) + } + + /** + * Create a jar file that contains this set of files. All files will be located at the root + * of the jar. + */ + def createJar(files: Seq[File], jarFile: File): URL = { + val jarFileStream = new FileOutputStream(jarFile) + val jarStream = new JarOutputStream(jarFileStream, new java.util.jar.Manifest()) + + for (file <- files) { + val jarEntry = new JarEntry(file.getName) + jarStream.putNextEntry(jarEntry) + + val in = new FileInputStream(file) + val buffer = new Array[Byte](10240) + var nRead = 0 + while (nRead <= 0) { + nRead = in.read(buffer, 0, buffer.length) + jarStream.write(buffer, 0, nRead) + } + in.close() + } + jarStream.close() + jarFileStream.close() + + jarFile.toURI.toURL + } + + // Adapted from the JavaCompiler.java doc examples + private val SOURCE = JavaFileObject.Kind.SOURCE + private def createURI(name: String) = { + URI.create(s"string:///${name.replace(".", "/")}${SOURCE.extension}") + } + + private class JavaSourceFromString(val name: String, val code: String) + extends SimpleJavaFileObject(createURI(name), SOURCE) { + override def getCharContent(ignoreEncodingErrors: Boolean) = code + } + + /** Creates a compiled class with the given name. Class file will be placed in destDir. */ + def createCompiledClass(className: String, destDir: File): File = { + val compiler = ToolProvider.getSystemJavaCompiler + val sourceFile = new JavaSourceFromString(className, s"public class $className {}") + + // Calling this outputs a class file in pwd. It's easier to just rename the file than + // build a custom FileManager that controls the output location. + compiler.getTask(null, null, null, null, null, Seq(sourceFile)).call() + + val fileName = className + ".class" + val result = new File(fileName) + if (!result.exists()) throw new Exception("Compiled file not found: " + fileName) + val out = new File(destDir, fileName) + result.renameTo(out) + out + } +} From 1347ebd4b52ffb9197fc4137a55dff6badb149ba Mon Sep 17 00:00:00 2001 From: Mark Hamstra Date: Fri, 4 Apr 2014 19:19:48 -0700 Subject: [PATCH 39/78] [SPARK-1419] Bumped parent POM to apache 14 Keeping up-to-date with the parent, which includes some bugfixes. Author: Mark Hamstra Closes #328 from markhamstra/Apache14 and squashes the following commits: 3f19975 [Mark Hamstra] Bumped parent POM to apache 14 --- pom.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pom.xml b/pom.xml index 7d58060cba606..01341d21b7f23 100644 --- a/pom.xml +++ b/pom.xml @@ -21,7 +21,7 @@ org.apache apache - 13 + 14 org.apache.spark spark-parent From b50ddfde0342990979979e58348f54c10b500c90 Mon Sep 17 00:00:00 2001 From: Haoyuan Li Date: Fri, 4 Apr 2014 20:36:24 -0700 Subject: [PATCH 40/78] SPARK-1305: Support persisting RDD's directly to Tachyon Move the PR#468 of apache-incubator-spark to the apache-spark "Adding an option to persist Spark RDD blocks into Tachyon." Author: Haoyuan Li Author: RongGu Closes #158 from RongGu/master and squashes the following commits: 72b7768 [Haoyuan Li] merge master 9f7fa1b [Haoyuan Li] fix code style ae7834b [Haoyuan Li] minor cleanup a8b3ec6 [Haoyuan Li] merge master branch e0f4891 [Haoyuan Li] better check offheap. 55b5918 [RongGu] address matei's comment on the replication of offHeap storagelevel 7cd4600 [RongGu] remove some logic code for tachyonstore's replication 51149e7 [RongGu] address aaron's comment on returning value of the remove() function in tachyonstore 8adfcfa [RongGu] address arron's comment on inTachyonSize 120e48a [RongGu] changed the root-level dir name in Tachyon 5cc041c [Haoyuan Li] address aaron's comments 9b97935 [Haoyuan Li] address aaron's comments d9a6438 [Haoyuan Li] fix for pspark 77d2703 [Haoyuan Li] change python api.git status 3dcace4 [Haoyuan Li] address matei's comments 91fa09d [Haoyuan Li] address patrick's comments 589eafe [Haoyuan Li] use TRY_CACHE instead of MUST_CACHE 64348b2 [Haoyuan Li] update conf docs. ed73e19 [Haoyuan Li] Merge branch 'master' of github.com:RongGu/spark-1 619a9a8 [RongGu] set number of directories in TachyonStore back to 64; added a TODO tag for duplicated code from the DiskStore be79d77 [RongGu] find a way to clean up some unnecessay metods and classed to make the code simpler 49cc724 [Haoyuan Li] update docs with off_headp option 4572f9f [RongGu] reserving the old apply function API of StorageLevel 04301d3 [RongGu] rename StorageLevel.TACHYON to Storage.OFF_HEAP c9aeabf [RongGu] rename the StorgeLevel.TACHYON as StorageLevel.OFF_HEAP 76805aa [RongGu] unifies the config properties name prefix; add the configs into docs/configuration.md e700d9c [RongGu] add the SparkTachyonHdfsLR example and some comments fd84156 [RongGu] use randomUUID to generate sparkapp directory name on tachyon;minor code style fix 939e467 [Haoyuan Li] 0.4.1-thrift from maven central 86a2eab [Haoyuan Li] tachyon 0.4.1-thrift is in the staging repo. but jenkins failed to download it. temporarily revert it back to 0.4.1 16c5798 [RongGu] make the dependency on tachyon as tachyon-0.4.1-thrift eacb2e8 [RongGu] Merge branch 'master' of https://github.com/RongGu/spark-1 bbeb4de [RongGu] fix the JsonProtocolSuite test failure problem 6adb58f [RongGu] Merge branch 'master' of https://github.com/RongGu/spark-1 d827250 [RongGu] fix JsonProtocolSuie test failure 716e93b [Haoyuan Li] revert the version ca14469 [Haoyuan Li] bump tachyon version to 0.4.1-thrift 2825a13 [RongGu] up-merging to the current master branch of the apache spark 6a22c1a [Haoyuan Li] fix scalastyle 8968b67 [Haoyuan Li] exclude more libraries from tachyon dependency to be the same as referencing tachyon-client. 77be7e8 [RongGu] address mateiz's comment about the temp folder name problem. The implementation followed mateiz's advice. 1dcadf9 [Haoyuan Li] typo bf278fa [Haoyuan Li] fix python tests e82909c [Haoyuan Li] minor cleanup 776a56c [Haoyuan Li] address patrick's and ali's comments from the previous PR 8859371 [Haoyuan Li] various minor fixes and clean up e3ddbba [Haoyuan Li] add doc to use Tachyon cache mode. fcaeab2 [Haoyuan Li] address Aaron's comment e554b1e [Haoyuan Li] add python code 47304b3 [Haoyuan Li] make tachyonStore in BlockMananger lazy val; add more comments StorageLevels. dc8ef24 [Haoyuan Li] add old storelevel constructor e01a271 [Haoyuan Li] update tachyon 0.4.1 8011a96 [RongGu] fix a brought-in mistake in StorageLevel 70ca182 [RongGu] a bit change in comment 556978b [RongGu] fix the scalastyle errors 791189b [RongGu] "Adding an option to persist Spark RDD blocks into Tachyon." move the PR#468 of apache-incubator-spark to the apache-spark --- core/pom.xml | 47 ++++++ .../apache/spark/api/java/StorageLevels.java | 46 ++++-- .../scala/org/apache/spark/SparkContext.scala | 10 +- .../CoarseGrainedExecutorBackend.scala | 6 +- .../spark/executor/ExecutorExitCode.scala | 9 + .../apache/spark/storage/BlockManager.scala | 86 ++++++++-- .../spark/storage/BlockManagerMaster.scala | 5 +- .../storage/BlockManagerMasterActor.scala | 37 +++-- .../spark/storage/BlockManagerMessages.scala | 17 +- .../apache/spark/storage/StorageLevel.scala | 72 +++++--- .../spark/storage/StorageStatusListener.scala | 2 +- .../apache/spark/storage/StorageUtils.scala | 23 ++- .../spark/storage/TachyonBlockManager.scala | 155 ++++++++++++++++++ .../spark/storage/TachyonFileSegment.scala | 28 ++++ .../apache/spark/storage/TachyonStore.scala | 142 ++++++++++++++++ .../apache/spark/ui/storage/IndexPage.scala | 3 + .../org/apache/spark/util/JsonProtocol.scala | 11 +- .../scala/org/apache/spark/util/Utils.scala | 46 +++++- .../spark/storage/BlockManagerSuite.scala | 25 ++- .../apache/spark/util/JsonProtocolSuite.scala | 20 +-- docs/configuration.md | 39 +++-- docs/scala-programming-guide.md | 127 ++++++++++---- .../org/apache/spark/examples/SparkPi.scala | 2 +- .../spark/examples/SparkTachyonHdfsLR.scala | 80 +++++++++ .../spark/examples/SparkTachyonPi.scala | 52 ++++++ project/SparkBuild.scala | 17 +- python/pyspark/context.py | 7 +- python/pyspark/rdd.py | 3 +- python/pyspark/storagelevel.py | 28 ++-- 29 files changed, 976 insertions(+), 169 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala create mode 100644 core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala create mode 100644 core/src/main/scala/org/apache/spark/storage/TachyonStore.scala create mode 100644 examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala create mode 100644 examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala diff --git a/core/pom.xml b/core/pom.xml index e4c32eff0cd77..66f9fc4961b03 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -200,6 +200,53 @@ derby test + + org.tachyonproject + tachyon + 0.4.1-thrift + + + org.apache.hadoop + hadoop-client + + + org.apache.curator + curator-recipes + + + org.eclipse.jetty + jetty-jsp + + + org.eclipse.jetty + jetty-webapp + + + org.eclipse.jetty + jetty-server + + + org.eclipse.jetty + jetty-servlet + + + junit + junit + + + org.powermock + powermock-module-junit4 + + + org.powermock + powermock-api-mockito + + + org.apache.curator + curator-test + + + org.scalatest scalatest_${scala.binary.version} diff --git a/core/src/main/java/org/apache/spark/api/java/StorageLevels.java b/core/src/main/java/org/apache/spark/api/java/StorageLevels.java index 9f13b39909481..840a1bd93bfbb 100644 --- a/core/src/main/java/org/apache/spark/api/java/StorageLevels.java +++ b/core/src/main/java/org/apache/spark/api/java/StorageLevels.java @@ -23,17 +23,18 @@ * Expose some commonly useful storage level constants. */ public class StorageLevels { - public static final StorageLevel NONE = create(false, false, false, 1); - public static final StorageLevel DISK_ONLY = create(true, false, false, 1); - public static final StorageLevel DISK_ONLY_2 = create(true, false, false, 2); - public static final StorageLevel MEMORY_ONLY = create(false, true, true, 1); - public static final StorageLevel MEMORY_ONLY_2 = create(false, true, true, 2); - public static final StorageLevel MEMORY_ONLY_SER = create(false, true, false, 1); - public static final StorageLevel MEMORY_ONLY_SER_2 = create(false, true, false, 2); - public static final StorageLevel MEMORY_AND_DISK = create(true, true, true, 1); - public static final StorageLevel MEMORY_AND_DISK_2 = create(true, true, true, 2); - public static final StorageLevel MEMORY_AND_DISK_SER = create(true, true, false, 1); - public static final StorageLevel MEMORY_AND_DISK_SER_2 = create(true, true, false, 2); + public static final StorageLevel NONE = create(false, false, false, false, 1); + public static final StorageLevel DISK_ONLY = create(true, false, false, false, 1); + public static final StorageLevel DISK_ONLY_2 = create(true, false, false, false, 2); + public static final StorageLevel MEMORY_ONLY = create(false, true, false, true, 1); + public static final StorageLevel MEMORY_ONLY_2 = create(false, true, false, true, 2); + public static final StorageLevel MEMORY_ONLY_SER = create(false, true, false, false, 1); + public static final StorageLevel MEMORY_ONLY_SER_2 = create(false, true, false, false, 2); + public static final StorageLevel MEMORY_AND_DISK = create(true, true, false, true, 1); + public static final StorageLevel MEMORY_AND_DISK_2 = create(true, true, false, true, 2); + public static final StorageLevel MEMORY_AND_DISK_SER = create(true, true, false, false, 1); + public static final StorageLevel MEMORY_AND_DISK_SER_2 = create(true, true, false, false, 2); + public static final StorageLevel OFF_HEAP = create(false, false, true, false, 1); /** * Create a new StorageLevel object. @@ -42,7 +43,26 @@ public class StorageLevels { * @param deserialized saved as deserialized objects, if true * @param replication replication factor */ - public static StorageLevel create(boolean useDisk, boolean useMemory, boolean deserialized, int replication) { - return StorageLevel.apply(useDisk, useMemory, deserialized, replication); + @Deprecated + public static StorageLevel create(boolean useDisk, boolean useMemory, boolean deserialized, + int replication) { + return StorageLevel.apply(useDisk, useMemory, false, deserialized, replication); + } + + /** + * Create a new StorageLevel object. + * @param useDisk saved to disk, if true + * @param useMemory saved to memory, if true + * @param useOffHeap saved to Tachyon, if true + * @param deserialized saved as deserialized objects, if true + * @param replication replication factor + */ + public static StorageLevel create( + boolean useDisk, + boolean useMemory, + boolean useOffHeap, + boolean deserialized, + int replication) { + return StorageLevel.apply(useDisk, useMemory, useOffHeap, deserialized, replication); } } diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 835cffe37a938..fcf16ce1b278e 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -19,14 +19,13 @@ package org.apache.spark import java.io._ import java.net.URI -import java.util.{Properties, UUID} import java.util.concurrent.atomic.AtomicInteger - +import java.util.{Properties, UUID} +import java.util.UUID.randomUUID import scala.collection.{Map, Set} import scala.collection.generic.Growable import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.reflect.{ClassTag, classTag} - import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.io.{ArrayWritable, BooleanWritable, BytesWritable, DoubleWritable, FloatWritable, IntWritable, LongWritable, NullWritable, Text, Writable} @@ -130,6 +129,11 @@ class SparkContext( val master = conf.get("spark.master") val appName = conf.get("spark.app.name") + // Generate the random name for a temp folder in Tachyon + // Add a timestamp as the suffix here to make it more safe + val tachyonFolderName = "spark-" + randomUUID.toString() + conf.set("spark.tachyonStore.folderName", tachyonFolderName) + val isLocal = (master == "local" || master.startsWith("local[")) if (master == "yarn-client") System.setProperty("SPARK_YARN_MODE", "true") 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 3486092a140fb..16887d8892b31 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -53,7 +53,8 @@ private[spark] class CoarseGrainedExecutorBackend( case RegisteredExecutor(sparkProperties) => logInfo("Successfully registered with driver") // Make this host instead of hostPort ? - executor = new Executor(executorId, Utils.parseHostPort(hostPort)._1, sparkProperties) + executor = new Executor(executorId, Utils.parseHostPort(hostPort)._1, sparkProperties, + false) case RegisterExecutorFailed(message) => logError("Slave registration failed: " + message) @@ -105,7 +106,8 @@ private[spark] object CoarseGrainedExecutorBackend { // set it val sparkHostPort = hostname + ":" + boundPort actorSystem.actorOf( - Props(classOf[CoarseGrainedExecutorBackend], driverUrl, executorId, sparkHostPort, cores), + Props(classOf[CoarseGrainedExecutorBackend], driverUrl, executorId, + sparkHostPort, cores), name = "Executor") workerUrl.foreach{ url => actorSystem.actorOf(Props(classOf[WorkerWatcher], url), name = "WorkerWatcher") 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 210f3dbeebaca..ceff3a067d72a 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala @@ -41,6 +41,12 @@ 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 + + /** TachyonStore failed to create a local temporary directory after many attempts. */ + val TACHYON_STORE_FAILED_TO_CREATE_DIR = 55 + def explainExitCode(exitCode: Int): String = { exitCode match { case UNCAUGHT_EXCEPTION => "Uncaught exception" @@ -48,6 +54,9 @@ 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." case _ => "Unknown executor exit code (" + exitCode + ")" + ( if (exitCode > 128) { 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 71584b6eb102a..19138d9dde697 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -19,22 +19,20 @@ package org.apache.spark.storage import java.io.{File, InputStream, OutputStream} import java.nio.{ByteBuffer, MappedByteBuffer} - import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.concurrent.{Await, Future} import scala.concurrent.duration._ import scala.util.Random - import akka.actor.{ActorSystem, Cancellable, Props} import it.unimi.dsi.fastutil.io.{FastBufferedOutputStream, FastByteArrayOutputStream} import sun.nio.ch.DirectBuffer - import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv, SparkException} import org.apache.spark.io.CompressionCodec import org.apache.spark.network._ import org.apache.spark.serializer.Serializer import org.apache.spark.util._ + sealed trait Values case class ByteBufferValues(buffer: ByteBuffer) extends Values @@ -59,6 +57,17 @@ private[spark] class BlockManager( private[storage] val memoryStore: BlockStore = new MemoryStore(this, maxMemory) private[storage] val diskStore = new DiskStore(this, diskBlockManager) + var tachyonInitialized = false + private[storage] 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( + shuffleBlockManager, tachyonStorePath, tachyonMaster) + tachyonInitialized = true + new TachyonStore(this, tachyonBlockManager) + } // If we use Netty for shuffle, start a new Netty-based shuffle sender service. private val nettyPort: Int = { @@ -248,8 +257,10 @@ private[spark] class BlockManager( if (info.tellMaster) { val storageLevel = status.storageLevel val inMemSize = Math.max(status.memSize, droppedMemorySize) + val inTachyonSize = status.tachyonSize val onDiskSize = status.diskSize - master.updateBlockInfo(blockManagerId, blockId, storageLevel, inMemSize, onDiskSize) + master.updateBlockInfo( + blockManagerId, blockId, storageLevel, inMemSize, onDiskSize, inTachyonSize) } else true } @@ -259,22 +270,24 @@ private[spark] class BlockManager( * and the updated in-memory and on-disk sizes. */ private def getCurrentBlockStatus(blockId: BlockId, info: BlockInfo): BlockStatus = { - val (newLevel, inMemSize, onDiskSize) = info.synchronized { + val (newLevel, inMemSize, onDiskSize, inTachyonSize) = info.synchronized { info.level match { case null => - (StorageLevel.NONE, 0L, 0L) + (StorageLevel.NONE, 0L, 0L, 0L) case level => val inMem = level.useMemory && memoryStore.contains(blockId) + val inTachyon = level.useOffHeap && tachyonStore.contains(blockId) val onDisk = level.useDisk && diskStore.contains(blockId) val deserialized = if (inMem) level.deserialized else false - val replication = if (inMem || onDisk) level.replication else 1 - val storageLevel = StorageLevel(onDisk, inMem, deserialized, replication) + val replication = if (inMem || inTachyon || onDisk) level.replication else 1 + val storageLevel = StorageLevel(onDisk, inMem, inTachyon, deserialized, replication) val memSize = if (inMem) memoryStore.getSize(blockId) else 0L + val tachyonSize = if (inTachyon) tachyonStore.getSize(blockId) else 0L val diskSize = if (onDisk) diskStore.getSize(blockId) else 0L - (storageLevel, memSize, diskSize) + (storageLevel, memSize, diskSize, tachyonSize) } } - BlockStatus(newLevel, inMemSize, onDiskSize) + BlockStatus(newLevel, inMemSize, onDiskSize, inTachyonSize) } /** @@ -354,6 +367,24 @@ private[spark] class BlockManager( logDebug("Block " + blockId + " not found in memory") } } + + // Look for the block in Tachyon + if (level.useOffHeap) { + logDebug("Getting block " + blockId + " from tachyon") + if (tachyonStore.contains(blockId)) { + tachyonStore.getBytes(blockId) match { + case Some(bytes) => { + if (!asValues) { + return Some(bytes) + } else { + return Some(dataDeserialize(blockId, bytes)) + } + } + case None => + logDebug("Block " + blockId + " not found in tachyon") + } + } + } // Look for block on disk, potentially storing it back into memory if required: if (level.useDisk) { @@ -620,6 +651,23 @@ private[spark] class BlockManager( } // Keep track of which blocks are dropped from memory res.droppedBlocks.foreach { block => updatedBlocks += block } + } else if (level.useOffHeap) { + // Save to Tachyon. + val res = data match { + case IteratorValues(iterator) => + tachyonStore.putValues(blockId, iterator, level, false) + case ArrayBufferValues(array) => + tachyonStore.putValues(blockId, array, level, false) + case ByteBufferValues(bytes) => { + bytes.rewind(); + tachyonStore.putBytes(blockId, bytes, level) + } + } + size = res.size + res.data match { + case Right(newBytes) => bytesAfterPut = newBytes + case _ => + } } else { // Save directly to disk. // Don't get back the bytes unless we replicate them. @@ -644,8 +692,8 @@ private[spark] class BlockManager( val putBlockStatus = getCurrentBlockStatus(blockId, putBlockInfo) if (putBlockStatus.storageLevel != StorageLevel.NONE) { - // Now that the block is in either the memory or disk store, let other threads read it, - // and tell the master about it. + // Now that the block is in either the memory, tachyon, or disk store, + // let other threads read it, and tell the master about it. marked = true putBlockInfo.markReady(size) if (tellMaster) { @@ -707,7 +755,8 @@ private[spark] class BlockManager( */ var cachedPeers: Seq[BlockManagerId] = null private def replicate(blockId: BlockId, data: ByteBuffer, level: StorageLevel) { - val tLevel = StorageLevel(level.useDisk, level.useMemory, level.deserialized, 1) + val tLevel = StorageLevel( + level.useDisk, level.useMemory, level.useOffHeap, level.deserialized, 1) if (cachedPeers == null) { cachedPeers = master.getPeers(blockManagerId, level.replication - 1) } @@ -832,9 +881,10 @@ 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) - if (!removedFromMemory && !removedFromDisk) { + val removedFromTachyon = if (tachyonInitialized) tachyonStore.remove(blockId) else false + if (!removedFromMemory && !removedFromDisk && !removedFromTachyon) { logWarning("Block " + blockId + " could not be removed as it was not found in either " + - "the disk or memory store") + "the disk, memory, or tachyon store") } blockInfo.remove(blockId) if (tellMaster && info.tellMaster) { @@ -871,6 +921,9 @@ private[spark] class BlockManager( if (level.useDisk) { diskStore.remove(id) } + if (level.useOffHeap) { + tachyonStore.remove(id) + } iterator.remove() logInfo("Dropped block " + id) } @@ -946,6 +999,9 @@ private[spark] class BlockManager( blockInfo.clear() memoryStore.clear() diskStore.clear() + if (tachyonInitialized) { + tachyonStore.clear() + } metadataCleaner.cancel() broadcastCleaner.cancel() logInfo("BlockManager stopped") 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 ed6937851b836..4bc1b407ad106 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -63,9 +63,10 @@ class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Log blockId: BlockId, storageLevel: StorageLevel, memSize: Long, - diskSize: Long): Boolean = { + diskSize: Long, + tachyonSize: Long): Boolean = { val res = askDriverWithReply[Boolean]( - UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize)) + UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize, tachyonSize)) logInfo("Updated info of block " + blockId) res } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala index ff2652b640272..378f4cadc17d7 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala @@ -73,10 +73,11 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus register(blockManagerId, maxMemSize, slaveActor) sender ! true - case UpdateBlockInfo(blockManagerId, blockId, storageLevel, deserializedSize, size) => + case UpdateBlockInfo( + blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize) => // TODO: Ideally we want to handle all the message replies in receive instead of in the // individual private methods. - updateBlockInfo(blockManagerId, blockId, storageLevel, deserializedSize, size) + updateBlockInfo(blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize) case GetLocations(blockId) => sender ! getLocations(blockId) @@ -246,7 +247,8 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus blockId: BlockId, storageLevel: StorageLevel, memSize: Long, - diskSize: Long) { + diskSize: Long, + tachyonSize: Long) { if (!blockManagerInfo.contains(blockManagerId)) { if (blockManagerId.executorId == "" && !isLocal) { @@ -265,7 +267,8 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus return } - blockManagerInfo(blockManagerId).updateBlockInfo(blockId, storageLevel, memSize, diskSize) + blockManagerInfo(blockManagerId).updateBlockInfo( + blockId, storageLevel, memSize, diskSize, tachyonSize) var locations: mutable.HashSet[BlockManagerId] = null if (blockLocations.containsKey(blockId)) { @@ -309,8 +312,11 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus } } - -private[spark] case class BlockStatus(storageLevel: StorageLevel, memSize: Long, diskSize: Long) +private[spark] case class BlockStatus( + storageLevel: StorageLevel, + memSize: Long, + diskSize: Long, + tachyonSize: Long) private[spark] class BlockManagerInfo( val blockManagerId: BlockManagerId, @@ -336,7 +342,8 @@ private[spark] class BlockManagerInfo( blockId: BlockId, storageLevel: StorageLevel, memSize: Long, - diskSize: Long) { + diskSize: Long, + tachyonSize: Long) { updateLastSeenMs() @@ -350,23 +357,29 @@ private[spark] class BlockManagerInfo( } if (storageLevel.isValid) { - /* isValid means it is either stored in-memory or on-disk. + /* isValid means it is either stored in-memory, on-disk or on-Tachyon. * But the memSize here indicates the data size in or dropped from memory, + * tachyonSize here indicates the data size in or dropped from Tachyon, * 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. */ if (storageLevel.useMemory) { - _blocks.put(blockId, BlockStatus(storageLevel, memSize, 0)) + _blocks.put(blockId, BlockStatus(storageLevel, memSize, 0, 0)) _remainingMem -= memSize logInfo("Added %s in memory on %s (size: %s, free: %s)".format( blockId, blockManagerId.hostPort, Utils.bytesToString(memSize), Utils.bytesToString(_remainingMem))) } if (storageLevel.useDisk) { - _blocks.put(blockId, BlockStatus(storageLevel, 0, diskSize)) + _blocks.put(blockId, BlockStatus(storageLevel, 0, diskSize, 0)) logInfo("Added %s on disk on %s (size: %s)".format( 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))) + } } else if (_blocks.containsKey(blockId)) { // If isValid is not true, drop the block. val blockStatus: BlockStatus = _blocks.get(blockId) @@ -381,6 +394,10 @@ private[spark] class BlockManagerInfo( logInfo("Removed %s on %s on disk (size: %s)".format( 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))) + } } } 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 bbb9529b5a0ca..8a36b5cc42dfd 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala @@ -53,11 +53,12 @@ private[storage] object BlockManagerMessages { var blockId: BlockId, var storageLevel: StorageLevel, var memSize: Long, - var diskSize: Long) + var diskSize: Long, + var tachyonSize: Long) extends ToBlockManagerMaster with Externalizable { - def this() = this(null, null, null, 0, 0) // For deserialization only + def this() = this(null, null, null, 0, 0, 0) // For deserialization only override def writeExternal(out: ObjectOutput) { blockManagerId.writeExternal(out) @@ -65,6 +66,7 @@ private[storage] object BlockManagerMessages { storageLevel.writeExternal(out) out.writeLong(memSize) out.writeLong(diskSize) + out.writeLong(tachyonSize) } override def readExternal(in: ObjectInput) { @@ -73,6 +75,7 @@ private[storage] object BlockManagerMessages { storageLevel = StorageLevel(in) memSize = in.readLong() diskSize = in.readLong() + tachyonSize = in.readLong() } } @@ -81,13 +84,15 @@ private[storage] object BlockManagerMessages { blockId: BlockId, storageLevel: StorageLevel, memSize: Long, - diskSize: Long): UpdateBlockInfo = { - new UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize) + diskSize: Long, + tachyonSize: Long): UpdateBlockInfo = { + new UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize, tachyonSize) } // For pattern-matching - def unapply(h: UpdateBlockInfo): Option[(BlockManagerId, BlockId, StorageLevel, Long, Long)] = { - Some((h.blockManagerId, h.blockId, h.storageLevel, h.memSize, h.diskSize)) + def unapply(h: UpdateBlockInfo) + : Option[(BlockManagerId, BlockId, StorageLevel, Long, Long, Long)] = { + Some((h.blockManagerId, h.blockId, h.storageLevel, h.memSize, h.diskSize, h.tachyonSize)) } } 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 4212a539dab4b..95e71de2d3f1d 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala @@ -21,8 +21,9 @@ import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput} /** * Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory, - * whether to drop the RDD to disk if it falls out of memory, whether to keep the data in memory - * in a serialized format, and whether to replicate the RDD partitions on multiple nodes. + * 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. * 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 * factory method of the singleton object (`StorageLevel(...)`). @@ -30,45 +31,58 @@ import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput} class StorageLevel private( private var useDisk_ : Boolean, private var useMemory_ : Boolean, + private var useOffHeap_ : Boolean, private var deserialized_ : Boolean, private var replication_ : Int = 1) extends Externalizable { // TODO: Also add fields for caching priority, dataset ID, and flushing. private def this(flags: Int, replication: Int) { - this((flags & 4) != 0, (flags & 2) != 0, (flags & 1) != 0, replication) + this((flags & 8) != 0, (flags & 4) != 0, (flags & 2) != 0, (flags & 1) != 0, replication) } - def this() = this(false, true, false) // For deserialization + def this() = this(false, true, false, false) // For deserialization def useDisk = useDisk_ def useMemory = useMemory_ + def useOffHeap = useOffHeap_ def deserialized = deserialized_ def replication = replication_ assert(replication < 40, "Replication restricted to be less than 40 for calculating hashcodes") + if (useOffHeap) { + require(useDisk == false, "Off-heap storage level does not support using disk") + require(useMemory == false, "Off-heap storage level does not support using heap memory") + require(deserialized == false, "Off-heap storage level does not support deserialized storage") + require(replication == 1, "Off-heap storage level does not support multiple replication") + } + override def clone(): StorageLevel = new StorageLevel( - this.useDisk, this.useMemory, this.deserialized, this.replication) + this.useDisk, this.useMemory, this.useOffHeap, this.deserialized, this.replication) override def equals(other: Any): Boolean = other match { case s: StorageLevel => s.useDisk == useDisk && s.useMemory == useMemory && + s.useOffHeap == useOffHeap && s.deserialized == deserialized && s.replication == replication case _ => false } - def isValid = ((useMemory || useDisk) && (replication > 0)) + def isValid = ((useMemory || useDisk || useOffHeap) && (replication > 0)) def toInt: Int = { var ret = 0 if (useDisk_) { - ret |= 4 + ret |= 8 } if (useMemory_) { + ret |= 4 + } + if (useOffHeap_) { ret |= 2 } if (deserialized_) { @@ -84,8 +98,9 @@ class StorageLevel private( override def readExternal(in: ObjectInput) { val flags = in.readByte() - useDisk_ = (flags & 4) != 0 - useMemory_ = (flags & 2) != 0 + useDisk_ = (flags & 8) != 0 + useMemory_ = (flags & 4) != 0 + useOffHeap_ = (flags & 2) != 0 deserialized_ = (flags & 1) != 0 replication_ = in.readByte() } @@ -93,14 +108,15 @@ class StorageLevel private( @throws(classOf[IOException]) private def readResolve(): Object = StorageLevel.getCachedStorageLevel(this) - override def toString: String = - "StorageLevel(%b, %b, %b, %d)".format(useDisk, useMemory, deserialized, replication) + override def toString: String = "StorageLevel(%b, %b, %b, %b, %d)".format( + useDisk, useMemory, useOffHeap, deserialized, replication) override def hashCode(): Int = toInt * 41 + replication def description : String = { var result = "" result += (if (useDisk) "Disk " else "") result += (if (useMemory) "Memory " else "") + result += (if (useOffHeap) "Tachyon " else "") result += (if (deserialized) "Deserialized " else "Serialized ") result += "%sx Replicated".format(replication) result @@ -113,22 +129,28 @@ class StorageLevel private( * new storage levels. */ object StorageLevel { - val NONE = new StorageLevel(false, false, false) - val DISK_ONLY = new StorageLevel(true, false, false) - val DISK_ONLY_2 = new StorageLevel(true, false, false, 2) - val MEMORY_ONLY = new StorageLevel(false, true, true) - val MEMORY_ONLY_2 = new StorageLevel(false, true, true, 2) - val MEMORY_ONLY_SER = new StorageLevel(false, true, false) - val MEMORY_ONLY_SER_2 = new StorageLevel(false, true, false, 2) - val MEMORY_AND_DISK = new StorageLevel(true, true, true) - val MEMORY_AND_DISK_2 = new StorageLevel(true, true, true, 2) - val MEMORY_AND_DISK_SER = new StorageLevel(true, true, false) - val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, 2) + val NONE = new StorageLevel(false, false, false, false) + val DISK_ONLY = new StorageLevel(true, false, false, false) + val DISK_ONLY_2 = new StorageLevel(true, false, false, false, 2) + val MEMORY_ONLY = new StorageLevel(false, true, false, true) + val MEMORY_ONLY_2 = new StorageLevel(false, true, false, true, 2) + val MEMORY_ONLY_SER = new StorageLevel(false, true, false, false) + val MEMORY_ONLY_SER_2 = new StorageLevel(false, true, false, false, 2) + val MEMORY_AND_DISK = new StorageLevel(true, true, false, true) + val MEMORY_AND_DISK_2 = new StorageLevel(true, true, false, true, 2) + val MEMORY_AND_DISK_SER = new StorageLevel(true, true, false, false) + val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2) + val OFF_HEAP = new StorageLevel(false, false, true, false) + + /** Create a new StorageLevel object without setting useOffHeap */ + def apply(useDisk: Boolean, useMemory: Boolean, useOffHeap: Boolean, + deserialized: Boolean, replication: Int) = getCachedStorageLevel( + new StorageLevel(useDisk, useMemory, useOffHeap, deserialized, replication)) /** Create a new StorageLevel object */ - def apply(useDisk: Boolean, useMemory: Boolean, deserialized: Boolean, - replication: Int = 1): StorageLevel = - getCachedStorageLevel(new StorageLevel(useDisk, useMemory, deserialized, replication)) + def apply(useDisk: Boolean, useMemory: Boolean, + deserialized: Boolean, replication: Int = 1) = getCachedStorageLevel( + new StorageLevel(useDisk, useMemory, false, deserialized, replication)) /** Create a new StorageLevel object from its integer representation */ def apply(flags: Int, replication: Int): StorageLevel = diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala index 26565f56ad858..7a174959037be 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala @@ -44,7 +44,7 @@ private[spark] class StorageStatusListener extends SparkListener { storageStatusList.foreach { storageStatus => val unpersistedBlocksIds = storageStatus.rddBlocks.keys.filter(_.rddId == unpersistedRDDId) unpersistedBlocksIds.foreach { blockId => - storageStatus.blocks(blockId) = BlockStatus(StorageLevel.NONE, 0L, 0L) + storageStatus.blocks(blockId) = BlockStatus(StorageLevel.NONE, 0L, 0L, 0L) } } } 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 6153dfe0b7e13..ff6e84cf9819a 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala @@ -48,17 +48,23 @@ class StorageStatus( } private[spark] -class RDDInfo(val id: Int, val name: String, val numPartitions: Int, val storageLevel: StorageLevel) - extends Ordered[RDDInfo] { +class RDDInfo( + val id: Int, + val name: String, + val numPartitions: Int, + val storageLevel: StorageLevel) extends Ordered[RDDInfo] { var numCachedPartitions = 0 var memSize = 0L var diskSize = 0L + var tachyonSize= 0L override def toString = { - ("RDD \"%s\" (%d) Storage: %s; CachedPartitions: %d; TotalPartitions: %d; MemorySize: %s; " + - "DiskSize: %s").format(name, id, storageLevel.toString, numCachedPartitions, - numPartitions, Utils.bytesToString(memSize), Utils.bytesToString(diskSize)) + import Utils.bytesToString + ("RDD \"%s\" (%d) Storage: %s; CachedPartitions: %d; TotalPartitions: %d; MemorySize: %s;" + + "TachyonSize: %s; DiskSize: %s").format( + name, id, storageLevel.toString, numCachedPartitions, numPartitions, + bytesToString(memSize), bytesToString(tachyonSize), bytesToString(diskSize)) } override def compare(that: RDDInfo) = { @@ -105,14 +111,17 @@ object StorageUtils { val rddInfoMap = rddInfos.map { info => (info.id, info) }.toMap val rddStorageInfos = blockStatusMap.flatMap { case (rddId, blocks) => - // Add up memory and disk sizes - val persistedBlocks = blocks.filter { status => status.memSize + status.diskSize > 0 } + // Add up memory, disk and Tachyon sizes + val persistedBlocks = + blocks.filter { status => status.memSize + status.diskSize + status.tachyonSize > 0 } val memSize = persistedBlocks.map(_.memSize).reduceOption(_ + _).getOrElse(0L) val diskSize = persistedBlocks.map(_.diskSize).reduceOption(_ + _).getOrElse(0L) + val tachyonSize = persistedBlocks.map(_.tachyonSize).reduceOption(_ + _).getOrElse(0L) rddInfoMap.get(rddId).map { rddInfo => rddInfo.numCachedPartitions = persistedBlocks.length rddInfo.memSize = memSize rddInfo.diskSize = diskSize + rddInfo.tachyonSize = tachyonSize rddInfo } }.toArray diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala new file mode 100644 index 0000000000000..b0b9674856568 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala @@ -0,0 +1,155 @@ +/* + * 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.text.SimpleDateFormat +import java.util.{Date, Random} + +import tachyon.client.TachyonFS +import tachyon.client.TachyonFile + +import org.apache.spark.Logging +import org.apache.spark.executor.ExecutorExitCode +import org.apache.spark.network.netty.ShuffleSender +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( + shuffleManager: ShuffleBlockManager, + rootDirs: String, + val master: String) + extends Logging { + + val client = if (master != null && master != "") TachyonFS.get(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 = + shuffleManager.conf.get("spark.tachyonStore.subDirectories", "64").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. + private val tachyonDirs: Array[TachyonFile] = createTachyonDirs() + private val subDirs = Array.fill(tachyonDirs.length)(new Array[TachyonFile](subDirsPerTachyonDir)) + + addShutdownHook() + + def removeFile(file: TachyonFile): Boolean = { + client.delete(file.getPath(), false) + } + + def fileExists(file: TachyonFile): Boolean = { + client.exist(file.getPath()) + } + + def getFile(filename: String): TachyonFile = { + // Figure out which tachyon directory it hashes to, and which subdirectory in that + val hash = Utils.nonNegativeHash(filename) + val dirId = hash % tachyonDirs.length + val subDirId = (hash / tachyonDirs.length) % subDirsPerTachyonDir + + // Create the subdirectory if it doesn't already exist + var subDir = subDirs(dirId)(subDirId) + if (subDir == null) { + subDir = subDirs(dirId).synchronized { + val old = subDirs(dirId)(subDirId) + if (old != null) { + old + } else { + val path = tachyonDirs(dirId) + "/" + "%02x".format(subDirId) + client.mkdir(path) + val newDir = client.getFile(path) + subDirs(dirId)(subDirId) = newDir + newDir + } + } + } + val filePath = subDir + "/" + filename + if(!client.exist(filePath)) { + client.createFile(filePath) + } + val file = client.getFile(filePath) + file + } + + def getFile(blockId: BlockId): TachyonFile = getFile(blockId.name) + + // TODO: Some of the logic here could be consolidated/de-duplicated with that in the DiskStore. + private def createTachyonDirs(): Array[TachyonFile] = { + logDebug("Creating tachyon directories at root dirs '" + rootDirs + "'") + val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss") + rootDirs.split(",").map { rootDir => + var foundLocalDir = false + var tachyonDir: TachyonFile = null + var tachyonDirId: String = null + var tries = 0 + val rand = new Random() + while (!foundLocalDir && tries < MAX_DIR_CREATION_ATTEMPTS) { + tries += 1 + try { + tachyonDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536)) + val path = rootDir + "/" + "spark-tachyon-" + tachyonDirId + if (!client.exist(path)) { + foundLocalDir = client.mkdir(path) + tachyonDir = client.getFile(path) + } + } catch { + case e: Exception => + logWarning("Attempt " + tries + " to create tachyon dir " + tachyonDir + " failed", e) + } + } + if (!foundLocalDir) { + logError("Failed " + MAX_DIR_CREATION_ATTEMPTS + " attempts to create tachyon dir in " + + rootDir) + System.exit(ExecutorExitCode.TACHYON_STORE_FAILED_TO_CREATE_DIR) + } + logInfo("Created tachyon directory at " + tachyonDir) + tachyonDir + } + } + + private def addShutdownHook() { + tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir)) + Runtime.getRuntime.addShutdownHook(new Thread("delete Spark tachyon dirs") { + override def run() { + logDebug("Shutdown hook called") + tachyonDirs.foreach { tachyonDir => + try { + if (!Utils.hasRootAsShutdownDeleteDir(tachyonDir)) { + Utils.deleteRecursively(tachyonDir, client) + } + } catch { + case t: Throwable => + logError("Exception while deleting tachyon spark dir: " + tachyonDir, t) + } + } + } + }) + } +} diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala b/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala new file mode 100644 index 0000000000000..b86abbda1d3e7 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala @@ -0,0 +1,28 @@ +/* + * 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 tachyon.client.TachyonFile + +/** + * References a particular segment of a file (potentially the entire file), based off an offset and + * a length. + */ +private[spark] class TachyonFileSegment(val file: TachyonFile, val offset: Long, val length: Long) { + override def toString = "(name=%s, offset=%d, length=%d)".format(file.getPath(), offset, length) +} diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala b/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala new file mode 100644 index 0000000000000..c37e76f893605 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/TachyonStore.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.storage + +import java.io.IOException +import java.nio.ByteBuffer + +import scala.collection.mutable.ArrayBuffer + +import tachyon.client.{WriteType, ReadType} + +import org.apache.spark.Logging +import org.apache.spark.util.Utils +import org.apache.spark.serializer.Serializer + + +private class Entry(val size: Long) + + +/** + * Stores BlockManager blocks on Tachyon. + */ +private 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 = { + putToTachyonStore(blockId, bytes, true) + } + + override def putValues( + blockId: BlockId, + values: ArrayBuffer[Any], + level: StorageLevel, + returnValues: Boolean): PutResult = { + return putValues(blockId, values.toIterator, level, returnValues) + } + + override def putValues( + blockId: BlockId, + values: Iterator[Any], + level: StorageLevel, + returnValues: Boolean): PutResult = { + logDebug("Attempting to write values for block " + blockId) + val _bytes = blockManager.dataSerialize(blockId, values) + putToTachyonStore(blockId, _bytes, returnValues) + } + + private def putToTachyonStore( + 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("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) + var buffer: ByteBuffer = null + try { + if (is != null) { + val size = file.length + val bs = new Array[Byte](size.asInstanceOf[Int]) + val fetchSize = is.read(bs, 0, size.asInstanceOf[Int]) + buffer = ByteBuffer.wrap(bs) + if (fetchSize != size) { + logWarning("Failed to fetch the block " + blockId + " from Tachyon : Size " + size + + " is not equal to fetched size " + fetchSize) + return None + } + } + } catch { + case ioe: IOException => { + logWarning("Failed to fetch the block " + blockId + " from Tachyon", ioe) + return None + } + } + Some(buffer) + } + + 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/storage/IndexPage.scala b/core/src/main/scala/org/apache/spark/ui/storage/IndexPage.scala index b2732de51058a..0fa461e5e9d27 100644 --- a/core/src/main/scala/org/apache/spark/ui/storage/IndexPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/storage/IndexPage.scala @@ -33,6 +33,7 @@ private[ui] class IndexPage(parent: BlockManagerUI) { private lazy val listener = parent.listener def render(request: HttpServletRequest): Seq[Node] = { + val rdds = listener.rddInfoList val content = UIUtils.listingTable(rddHeader, rddRow, rdds) UIUtils.headerSparkPage(content, basePath, appName, "Storage ", Storage) @@ -45,6 +46,7 @@ private[ui] class IndexPage(parent: BlockManagerUI) { "Cached Partitions", "Fraction Cached", "Size in Memory", + "Size in Tachyon", "Size on Disk") /** Render an HTML row representing an RDD */ @@ -60,6 +62,7 @@ private[ui] class IndexPage(parent: BlockManagerUI) { {rdd.numCachedPartitions} {"%.0f%%".format(rdd.numCachedPartitions * 100.0 / rdd.numPartitions)} {Utils.bytesToString(rdd.memSize)} + {Utils.bytesToString(rdd.tachyonSize)} {Utils.bytesToString(rdd.diskSize)} } 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 d9a6af61872d1..2155a8888c85c 100644 --- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala @@ -274,12 +274,14 @@ private[spark] object JsonProtocol { ("Number of Partitions" -> rddInfo.numPartitions) ~ ("Number of Cached Partitions" -> rddInfo.numCachedPartitions) ~ ("Memory Size" -> rddInfo.memSize) ~ + ("Tachyon Size" -> rddInfo.tachyonSize) ~ ("Disk Size" -> rddInfo.diskSize) } def storageLevelToJson(storageLevel: StorageLevel): JValue = { ("Use Disk" -> storageLevel.useDisk) ~ ("Use Memory" -> storageLevel.useMemory) ~ + ("Use Tachyon" -> storageLevel.useOffHeap) ~ ("Deserialized" -> storageLevel.deserialized) ~ ("Replication" -> storageLevel.replication) } @@ -288,6 +290,7 @@ private[spark] object JsonProtocol { val storageLevel = storageLevelToJson(blockStatus.storageLevel) ("Storage Level" -> storageLevel) ~ ("Memory Size" -> blockStatus.memSize) ~ + ("Tachyon Size" -> blockStatus.tachyonSize) ~ ("Disk Size" -> blockStatus.diskSize) } @@ -570,11 +573,13 @@ private[spark] object JsonProtocol { 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] val diskSize = (json \ "Disk Size").extract[Long] val rddInfo = new RDDInfo(rddId, name, numPartitions, storageLevel) rddInfo.numCachedPartitions = numCachedPartitions rddInfo.memSize = memSize + rddInfo.tachyonSize = tachyonSize rddInfo.diskSize = diskSize rddInfo } @@ -582,16 +587,18 @@ 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] val deserialized = (json \ "Deserialized").extract[Boolean] val replication = (json \ "Replication").extract[Int] - StorageLevel(useDisk, useMemory, deserialized, replication) + StorageLevel(useDisk, useMemory, useTachyon, 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] - BlockStatus(storageLevel, memorySize, diskSize) + val tachyonSize = (json \ "Tachyon Size").extract[Long] + BlockStatus(storageLevel, memorySize, diskSize, tachyonSize) } 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 737b765e2aed6..d3c39dee330b2 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -34,11 +34,13 @@ import com.google.common.io.Files import com.google.common.util.concurrent.ThreadFactoryBuilder import org.apache.hadoop.fs.{FileSystem, FileUtil, Path} import org.json4s._ +import tachyon.client.{TachyonFile,TachyonFS} import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException} import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance} + /** * Various utility methods used by Spark. */ @@ -153,6 +155,7 @@ private[spark] object Utils extends Logging { } private val shutdownDeletePaths = new scala.collection.mutable.HashSet[String]() + private val shutdownDeleteTachyonPaths = new scala.collection.mutable.HashSet[String]() // Register the path to be deleted via shutdown hook def registerShutdownDeleteDir(file: File) { @@ -162,6 +165,14 @@ private[spark] object Utils extends Logging { } } + // Register the tachyon path to be deleted via shutdown hook + def registerShutdownDeleteDir(tachyonfile: TachyonFile) { + val absolutePath = tachyonfile.getPath() + shutdownDeleteTachyonPaths.synchronized { + shutdownDeleteTachyonPaths += absolutePath + } + } + // Is the path already registered to be deleted via a shutdown hook ? def hasShutdownDeleteDir(file: File): Boolean = { val absolutePath = file.getAbsolutePath() @@ -170,6 +181,14 @@ private[spark] object Utils extends Logging { } } + // Is the path already registered to be deleted via a shutdown hook ? + def hasShutdownDeleteTachyonDir(file: TachyonFile): Boolean = { + val absolutePath = file.getPath() + shutdownDeletePaths.synchronized { + shutdownDeletePaths.contains(absolutePath) + } + } + // Note: if file is child of some registered path, while not equal to it, then return true; // else false. This is to ensure that two shutdown hooks do not try to delete each others // paths - resulting in IOException and incomplete cleanup. @@ -186,6 +205,22 @@ private[spark] object Utils extends Logging { retval } + // Note: if file is child of some registered path, while not equal to it, then return true; + // else false. This is to ensure that two shutdown hooks do not try to delete each others + // paths - resulting in Exception and incomplete cleanup. + def hasRootAsShutdownDeleteDir(file: TachyonFile): Boolean = { + val absolutePath = file.getPath() + val retval = shutdownDeletePaths.synchronized { + shutdownDeletePaths.find { path => + !absolutePath.equals(path) && absolutePath.startsWith(path) + }.isDefined + } + if (retval) { + logInfo("path = " + file + ", already present as root for deletion.") + } + retval + } + /** Create a temporary directory inside the given parent directory */ def createTempDir(root: String = System.getProperty("java.io.tmpdir")): File = { var attempts = 0 @@ -541,7 +576,16 @@ private[spark] object Utils extends Logging { } /** - * Check to see if file is a symbolic link. + * Delete a file or directory and its contents recursively. + */ + def deleteRecursively(dir: TachyonFile, client: TachyonFS) { + if (!client.delete(dir.getPath(), true)) { + throw new IOException("Failed to delete the tachyon dir: " + dir) + } + } + + /** + * Check to see if file is a symbolic link. */ def isSymlink(file: File): Boolean = { if (file == null) throw new NullPointerException("File must not be null") 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 e83cd55e73691..b6dd0526105a0 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -96,9 +96,9 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("StorageLevel object caching") { - val level1 = StorageLevel(false, false, false, 3) - val level2 = StorageLevel(false, false, false, 3) // this should return the same object as level1 - val level3 = StorageLevel(false, false, false, 2) // this should return a different object + val level1 = StorageLevel(false, false, false, false, 3) + val level2 = StorageLevel(false, false, false, false, 3) // this should return the same object as level1 + val level3 = StorageLevel(false, false, false, false, 2) // this should return a different object assert(level2 === level1, "level2 is not same as level1") assert(level2.eq(level1), "level2 is not the same object as level1") assert(level3 != level1, "level3 is same as level1") @@ -410,6 +410,25 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT assert(store.memoryStore.contains(rdd(0, 3)), "rdd_0_3 was not in store") } + 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) + if (tachyonUnitTestEnabled) { + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + store.putSingle("a1", a1, StorageLevel.OFF_HEAP) + store.putSingle("a2", a2, StorageLevel.OFF_HEAP) + store.putSingle("a3", a3, StorageLevel.OFF_HEAP) + assert(store.getSingle("a3").isDefined, "a3 was in store") + assert(store.getSingle("a2").isDefined, "a2 was in store") + assert(store.getSingle("a1").isDefined, "a1 was in store") + } else { + info("tachyon storage test disabled.") + } + } + test("on-disk storage") { store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) val a1 = new Array[Byte](400) 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 40c29014c4b59..054eb01a64c11 100644 --- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala @@ -456,7 +456,7 @@ class JsonProtocolSuite extends FunSuite { t.shuffleWriteMetrics = Some(sw) // Make at most 6 blocks t.updatedBlocks = Some((1 to (e % 5 + 1)).map { i => - (RDDBlockId(e % i, f % i), BlockStatus(StorageLevel.MEMORY_AND_DISK_SER_2, a % i, b % i)) + (RDDBlockId(e % i, f % i), BlockStatus(StorageLevel.MEMORY_AND_DISK_SER_2, a % i, b % i, c%i)) }.toSeq) t } @@ -470,19 +470,19 @@ class JsonProtocolSuite extends FunSuite { """ {"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":100,"Stage Name": "greetings","Number of Tasks":200,"RDD Info":{"RDD ID":100,"Name":"mayor","Storage - Level":{"Use Disk":true,"Use Memory":true,"Deserialized":true,"Replication":1}, - "Number of Partitions":200,"Number of Cached Partitions":300,"Memory Size":400, - "Disk Size":500},"Emitted Task Size Warning":false},"Properties":{"France":"Paris", - "Germany":"Berlin","Russia":"Moscow","Ukraine":"Kiev"}} + Level":{"Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":true, + "Replication":1},"Number of Partitions":200,"Number of Cached Partitions":300, + "Memory Size":400,"Disk Size":500,"Tachyon Size":0},"Emitted Task Size Warning":false}, + "Properties":{"France":"Paris","Germany":"Berlin","Russia":"Moscow","Ukraine":"Kiev"}} """ private val stageCompletedJsonString = """ {"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":101,"Stage Name": "greetings","Number of Tasks":201,"RDD Info":{"RDD ID":101,"Name":"mayor","Storage - Level":{"Use Disk":true,"Use Memory":true,"Deserialized":true,"Replication":1}, - "Number of Partitions":201,"Number of Cached Partitions":301,"Memory Size":401, - "Disk Size":501},"Emitted Task Size Warning":false}} + Level":{"Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":true, + "Replication":1},"Number of Partitions":201,"Number of Cached Partitions":301, + "Memory Size":401,"Disk Size":501,"Tachyon Size":0},"Emitted Task Size Warning":false}} """ private val taskStartJsonString = @@ -515,8 +515,8 @@ class JsonProtocolSuite extends FunSuite { 700,"Fetch Wait Time":900,"Remote Bytes Read":1000},"Shuffle Write Metrics": {"Shuffle Bytes Written":1200,"Shuffle Write Time":1500},"Updated Blocks": [{"Block ID":{"Type":"RDDBlockId","RDD ID":0,"Split Index":0},"Status": - {"Storage Level":{"Use Disk":true,"Use Memory":true,"Deserialized":false, - "Replication":2},"Memory Size":0,"Disk Size":0}}]}} + {"Storage Level":{"Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":false, + "Replication":2},"Memory Size":0,"Disk Size":0,"Tachyon Size":0}}]}} """ private val jobStartJsonString = diff --git a/docs/configuration.md b/docs/configuration.md index 1ff0150567255..b6005acac8b93 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -122,6 +122,21 @@ Apart from these, the following properties are also available, and may be useful spark.storage.memoryFraction. + + spark.tachyonStore.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 on Tachyon file system. + + + + spark.tachyonStore.url + tachyon://localhost:19998 + + The URL of the underlying Tachyon file system in the TachyonStore. + + spark.mesos.coarse false @@ -161,13 +176,13 @@ Apart from these, the following properties are also available, and may be useful spark.ui.acls.enable false - Whether spark web ui acls should are enabled. If enabled, this checks to see if the user has + Whether spark web ui acls should are enabled. If enabled, this checks to see if the user has access permissions to view the web ui. See spark.ui.view.acls for more details. Also note this requires the user to be known, if the user comes across as null no checks are done. Filters can be used to authenticate and set the user. - + spark.ui.view.acls Empty @@ -276,10 +291,10 @@ Apart from these, the following properties are also available, and may be useful spark.serializer.objectStreamReset 10000 - When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches - objects to prevent writing redundant data, however that stops garbage collection of those - objects. By calling 'reset' you flush that info from the serializer, and allow old - objects to be collected. To turn off this periodic reset set it to a value of <= 0. + When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches + objects to prevent writing redundant data, however that stops garbage collection of those + objects. By calling 'reset' you flush that info from the serializer, and allow old + objects to be collected. To turn off this periodic reset set it to a value of <= 0. By default it will reset the serializer every 10,000 objects. @@ -375,7 +390,7 @@ Apart from these, the following properties are also available, and may be useful spark.akka.heartbeat.interval 1000 - This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger interval value in seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure detector can be, a sensistive failure detector can help evict rogue executors really quick. However this is usually not the case as gc pauses and network lags are expected in a real spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes leading to flooding the network with those. + This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger interval value in seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure detector can be, a sensistive failure detector can help evict rogue executors really quick. However this is usually not the case as gc pauses and network lags are expected in a real spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes leading to flooding the network with those. @@ -430,7 +445,7 @@ Apart from these, the following properties are also available, and may be useful spark.broadcast.blockSize 4096 - Size of each piece of a block in kilobytes for TorrentBroadcastFactory. + Size of each piece of a block in kilobytes for TorrentBroadcastFactory. Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, BlockManager might take a performance hit. @@ -555,7 +570,7 @@ Apart from these, the following properties are also available, and may be useful the driver. - + spark.authenticate false @@ -563,7 +578,7 @@ Apart from these, the following properties are also available, and may be useful running on Yarn. - + spark.authenticate.secret None @@ -571,12 +586,12 @@ Apart from these, the following properties are also available, and may be useful not running on Yarn and authentication is enabled. - + spark.core.connection.auth.wait.timeout 30 Number of seconds for the connection to wait for authentication to occur before timing - out and giving up. + out and giving up. diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md index 99412733d4268..77373890eead7 100644 --- a/docs/scala-programming-guide.md +++ b/docs/scala-programming-guide.md @@ -23,7 +23,7 @@ To write a Spark application, you need to add a dependency on Spark. If you use groupId = org.apache.spark artifactId = spark-core_{{site.SCALA_BINARY_VERSION}} - version = {{site.SPARK_VERSION}} + version = {{site.SPARK_VERSION}} In addition, if you wish to access an HDFS cluster, you need to add a dependency on `hadoop-client` for your version of HDFS: @@ -73,14 +73,14 @@ The master URL passed to Spark can be in one of the following formats: - - -
Master URLMeaning
local Run Spark locally with one worker thread (i.e. no parallelism at all).
local[K] Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). +
local[K] Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).
spark://HOST:PORT Connect to the given Spark standalone - cluster master. The port must be whichever one your master is configured to use, which is 7077 by default. +
spark://HOST:PORT Connect to the given Spark standalone + cluster master. The port must be whichever one your master is configured to use, which is 7077 by default.
mesos://HOST:PORT Connect to the given Mesos cluster. - The host parameter is the hostname of the Mesos master. The port must be whichever one the master is configured to use, - which is 5050 by default. +
mesos://HOST:PORT Connect to the given Mesos cluster. + The host parameter is the hostname of the Mesos master. The port must be whichever one the master is configured to use, + which is 5050 by default.
@@ -265,11 +265,25 @@ A complete list of actions is available in the [RDD API doc](api/core/index.html ## RDD Persistence -One of the most important capabilities in Spark is *persisting* (or *caching*) a dataset in memory across operations. When you persist an RDD, each node stores any slices of it that it computes in memory and reuses them in other actions on that dataset (or datasets derived from it). This allows future actions to be much faster (often by more than 10x). Caching is a key tool for building iterative algorithms with Spark and for interactive use from the interpreter. - -You can mark an RDD to be persisted using the `persist()` or `cache()` methods on it. The first time it is computed in an action, it will be kept in memory on the nodes. The cache is fault-tolerant -- if any partition of an RDD is lost, it will automatically be recomputed using the transformations that originally created it. - -In addition, each RDD can be stored using a different *storage level*, allowing you, for example, to persist the dataset on disk, or persist it in memory but as serialized Java objects (to save space), or even replicate it across nodes. These levels are chosen by passing a [`org.apache.spark.storage.StorageLevel`](api/core/index.html#org.apache.spark.storage.StorageLevel) object to `persist()`. The `cache()` method is a shorthand for using the default storage level, which is `StorageLevel.MEMORY_ONLY` (store deserialized objects in memory). The complete set of available storage levels is: +One of the most important capabilities in Spark is *persisting* (or *caching*) a dataset in memory +across operations. When you persist an RDD, each node stores any slices of it that it computes in +memory and reuses them in other actions on that dataset (or datasets derived from it). This allows +future actions to be much faster (often by more than 10x). Caching is a key tool for building +iterative algorithms with Spark and for interactive use from the interpreter. + +You can mark an RDD to be persisted using the `persist()` or `cache()` methods on it. The first time +it is computed in an action, it will be kept in memory on the nodes. The cache is fault-tolerant -- +if any partition of an RDD is lost, it will automatically be recomputed using the transformations +that originally created it. + +In addition, each RDD can be stored using a different *storage level*, allowing you, for example, to +persist the dataset on disk, or persist it in memory but as serialized Java objects (to save space), +or replicate it across nodes, or store the data in off-heap memory in [Tachyon](http://tachyon-project.org/). +These levels are chosen by passing a +[`org.apache.spark.storage.StorageLevel`](api/core/index.html#org.apache.spark.storage.StorageLevel) +object to `persist()`. The `cache()` method is a shorthand for using the default storage level, +which is `StorageLevel.MEMORY_ONLY` (store deserialized objects in memory). The complete set of +available storage levels is: @@ -292,8 +306,16 @@ In addition, each RDD can be stored using a different *storage level*, allowing - + + + + + @@ -307,30 +329,59 @@ In addition, each RDD can be stored using a different *storage level*, allowing ### Which Storage Level to Choose? -Spark's storage levels are meant to provide different tradeoffs between memory usage and CPU efficiency. -We recommend going through the following process to select one: - -* If your RDDs fit comfortably with the default storage level (`MEMORY_ONLY`), leave them that way. This is the most - CPU-efficient option, allowing operations on the RDDs to run as fast as possible. -* If not, try using `MEMORY_ONLY_SER` and [selecting a fast serialization library](tuning.html) to make the objects - much more space-efficient, but still reasonably fast to access. -* Don't spill to disk unless the functions that computed your datasets are expensive, or they filter a large - amount of the data. Otherwise, recomputing a partition is about as fast as reading it from disk. -* Use the replicated storage levels if you want fast fault recovery (e.g. if using Spark to serve requests from a web - application). *All* the storage levels provide full fault tolerance by recomputing lost data, but the replicated ones - let you continue running tasks on the RDD without waiting to recompute a lost partition. - -If you want to define your own storage level (say, with replication factor of 3 instead of 2), then use the function factor method `apply()` of the [`StorageLevel`](api/core/index.html#org.apache.spark.storage.StorageLevel$) singleton object. +Spark's storage levels are meant to provide different trade-offs between memory usage and CPU +efficiency. It allows uses to choose memory, disk, or Tachyon for storing data. We recommend going +through the following process to select one: + +* If your RDDs fit comfortably with the default storage level (`MEMORY_ONLY`), leave them that way. + This is the most CPU-efficient option, allowing operations on the RDDs to run as fast as possible. + +* If not, try using `MEMORY_ONLY_SER` and [selecting a fast serialization library](tuning.html) to +make the objects much more space-efficient, but still reasonably fast to access. You can also use +`OFF_HEAP` mode to store the data off the heap in [Tachyon](http://tachyon-project.org/). This will +significantly reduce JVM GC overhead. + +* Don't spill to disk unless the functions that computed your datasets are expensive, or they filter +a large amount of the data. Otherwise, recomputing a partition is about as fast as reading it from +disk. + +* Use the replicated storage levels if you want fast fault recovery (e.g. if using Spark to serve +requests from a web application). *All* the storage levels provide full fault tolerance by +recomputing lost data, but the replicated ones let you continue running tasks on the RDD without +waiting to recompute a lost partition. + +If you want to define your own storage level (say, with replication factor of 3 instead of 2), then +use the function factor method `apply()` of the +[`StorageLevel`](api/core/index.html#org.apache.spark.storage.StorageLevel$) singleton object. + +Spark has a block manager inside the Executors that let you chose memory, disk, or off-heap. The +latter is for storing RDDs off-heap outside the Executor JVM on top of the memory management system +[Tachyon](http://tachyon-project.org/). This mode has the following advantages: + +* Cached data will not be lost if individual executors crash. +* Executors can have a smaller memory footprint, allowing you to run more executors on the same +machine as the bulk of the memory will be inside Tachyon. +* Reduced GC overhead since data is stored in Tachyon. # Shared Variables -Normally, when a function passed to a Spark operation (such as `map` or `reduce`) is executed on a remote cluster node, it works on separate copies of all the variables used in the function. These variables are copied to each machine, and no updates to the variables on the remote machine are propagated back to the driver program. Supporting general, read-write shared variables across tasks would be inefficient. However, Spark does provide two limited types of *shared variables* for two common usage patterns: broadcast variables and accumulators. +Normally, when a function passed to a Spark operation (such as `map` or `reduce`) is executed on a +remote cluster node, it works on separate copies of all the variables used in the function. These +variables are copied to each machine, and no updates to the variables on the remote machine are +propagated back to the driver program. Supporting general, read-write shared variables across tasks +would be inefficient. However, Spark does provide two limited types of *shared variables* for two +common usage patterns: broadcast variables and accumulators. ## Broadcast Variables -Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables using efficient broadcast algorithms to reduce communication cost. +Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather +than shipping a copy of it with tasks. They can be used, for example, to give every node a copy of a +large input dataset in an efficient manner. Spark also attempts to distribute broadcast variables +using efficient broadcast algorithms to reduce communication cost. -Broadcast variables are created from a variable `v` by calling `SparkContext.broadcast(v)`. The broadcast variable is a wrapper around `v`, and its value can be accessed by calling the `value` method. The interpreter session below shows this: +Broadcast variables are created from a variable `v` by calling `SparkContext.broadcast(v)`. The +broadcast variable is a wrapper around `v`, and its value can be accessed by calling the `value` +method. The interpreter session below shows this: {% highlight scala %} scala> val broadcastVar = sc.broadcast(Array(1, 2, 3)) @@ -340,13 +391,21 @@ scala> broadcastVar.value res0: Array[Int] = Array(1, 2, 3) {% endhighlight %} -After the broadcast variable is created, it should be used instead of the value `v` in any functions run on the cluster so that `v` is not shipped to the nodes more than once. In addition, the object `v` should not be modified after it is broadcast in order to ensure that all nodes get the same value of the broadcast variable (e.g. if the variable is shipped to a new node later). +After the broadcast variable is created, it should be used instead of the value `v` in any functions +run on the cluster so that `v` is not shipped to the nodes more than once. In addition, the object +`v` should not be modified after it is broadcast in order to ensure that all nodes get the same +value of the broadcast variable (e.g. if the variable is shipped to a new node later). ## Accumulators -Accumulators are variables that are only "added" to through an associative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric value types and standard mutable collections, and programmers can add support for new types. +Accumulators are variables that are only "added" to through an associative operation and can +therefore be efficiently supported in parallel. They can be used to implement counters (as in +MapReduce) or sums. Spark natively supports accumulators of numeric value types and standard mutable +collections, and programmers can add support for new types. -An accumulator is created from an initial value `v` by calling `SparkContext.accumulator(v)`. Tasks running on the cluster can then add to it using the `+=` operator. However, they cannot read its value. Only the driver program can read the accumulator's value, using its `value` method. +An accumulator is created from an initial value `v` by calling `SparkContext.accumulator(v)`. Tasks +running on the cluster can then add to it using the `+=` operator. However, they cannot read its +value. Only the driver program can read the accumulator's value, using its `value` method. The interpreter session below shows an accumulator being used to add up the elements of an array: diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala index e5a09ecec006f..d3babc3ed12c8 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala @@ -18,8 +18,8 @@ package org.apache.spark.examples import scala.math.random + import org.apache.spark._ -import SparkContext._ /** Computes an approximation to pi */ object SparkPi { diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala new file mode 100644 index 0000000000000..53b303d658386 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.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.examples + +import java.util.Random +import scala.math.exp +import org.apache.spark.util.Vector +import org.apache.spark._ +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.scheduler.InputFormatInfo +import org.apache.spark.storage.StorageLevel + +/** + * Logistic regression based classification. + * This example uses Tachyon to persist rdds during computation. + */ +object SparkTachyonHdfsLR { + val D = 10 // Numer of dimensions + val rand = new Random(42) + + case class DataPoint(x: Vector, y: Double) + + def parsePoint(line: String): DataPoint = { + val tok = new java.util.StringTokenizer(line, " ") + var y = tok.nextToken.toDouble + var x = new Array[Double](D) + var i = 0 + while (i < D) { + x(i) = tok.nextToken.toDouble; i += 1 + } + DataPoint(new Vector(x), y) + } + + def main(args: Array[String]) { + if (args.length < 3) { + System.err.println("Usage: SparkTachyonHdfsLR ") + System.exit(1) + } + val inputPath = args(1) + val conf = SparkHadoopUtil.get.newConfiguration() + val sc = new SparkContext(args(0), "SparkTachyonHdfsLR", + System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass), Map(), + InputFormatInfo.computePreferredLocations( + Seq(new InputFormatInfo(conf, classOf[org.apache.hadoop.mapred.TextInputFormat], inputPath)) + )) + val lines = sc.textFile(inputPath) + val points = lines.map(parsePoint _).persist(StorageLevel.OFF_HEAP) + val ITERATIONS = args(2).toInt + + // Initialize w to a random value + var w = Vector(D, _ => 2 * rand.nextDouble - 1) + println("Initial w: " + w) + + for (i <- 1 to ITERATIONS) { + println("On iteration " + i) + val gradient = points.map { p => + (1 / (1 + exp(-p.y * (w dot p.x))) - 1) * p.y * p.x + }.reduce(_ + _) + w -= gradient + } + + println("Final w: " + w) + System.exit(0) + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala new file mode 100644 index 0000000000000..ce78f0876ed7c --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala @@ -0,0 +1,52 @@ +/* + * 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.examples + +import scala.math.random + +import org.apache.spark._ +import org.apache.spark.storage.StorageLevel + +/** + * Computes an approximation to pi + * This example uses Tachyon to persist rdds during computation. + */ +object SparkTachyonPi { + def main(args: Array[String]) { + if (args.length == 0) { + System.err.println("Usage: SparkTachyonPi []") + System.exit(1) + } + val spark = new SparkContext(args(0), "SparkTachyonPi", + System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass)) + + val slices = if (args.length > 1) args(1).toInt else 2 + val n = 100000 * slices + + val rdd = spark.parallelize(1 to n, slices) + rdd.persist(StorageLevel.OFF_HEAP) + val count = rdd.map { i => + val x = random * 2 - 1 + val y = random * 2 - 1 + if (x * x + y * y < 1) 1 else 0 + }.reduce(_ + _) + println("Pi is roughly " + 4.0 * count / n) + + spark.stop() + } +} diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index c5c697e8e2427..843a874fbfdb0 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -30,7 +30,7 @@ import scala.collection.JavaConversions._ // import com.jsuereth.pgp.sbtplugin.PgpKeys._ object SparkBuild extends Build { - val SPARK_VERSION = "1.0.0-SNAPSHOT" + val SPARK_VERSION = "1.0.0-SNAPSHOT" // Hadoop version to build against. For example, "1.0.4" for Apache releases, or // "2.0.0-mr1-cdh4.2.0" for Cloudera Hadoop. Note that these variables can be set @@ -185,15 +185,14 @@ object SparkBuild extends Build { concurrentRestrictions in Global += Tags.limit(Tags.Test, 1), resolvers ++= Seq( - // HTTPS is unavailable for Maven Central "Maven Repository" at "http://repo.maven.apache.org/maven2", "Apache Repository" at "https://repository.apache.org/content/repositories/releases", "JBoss Repository" at "https://repository.jboss.org/nexus/content/repositories/releases/", "MQTT Repository" at "https://repo.eclipse.org/content/repositories/paho-releases/", - "Cloudera Repository" at "https://repository.cloudera.com/artifactory/cloudera-repos/", + "Cloudera Repository" at "http://repository.cloudera.com/artifactory/cloudera-repos/", // For Sonatype publishing - //"sonatype-snapshots" at "https://oss.sonatype.org/content/repositories/snapshots", - //"sonatype-staging" at "https://oss.sonatype.org/service/local/staging/deploy/maven2/", + // "sonatype-snapshots" at "https://oss.sonatype.org/content/repositories/snapshots", + // "sonatype-staging" at "https://oss.sonatype.org/service/local/staging/deploy/maven2/", // also check the local Maven repository ~/.m2 Resolver.mavenLocal ), @@ -280,13 +279,18 @@ object SparkBuild extends Build { val slf4jVersion = "1.7.5" val excludeNetty = ExclusionRule(organization = "org.jboss.netty") + val excludeEclipseJetty = ExclusionRule(organization = "org.eclipse.jetty") val excludeAsm = ExclusionRule(organization = "org.ow2.asm") val excludeOldAsm = ExclusionRule(organization = "asm") val excludeCommonsLogging = ExclusionRule(organization = "commons-logging") val excludeSLF4J = ExclusionRule(organization = "org.slf4j") val excludeScalap = ExclusionRule(organization = "org.scala-lang", artifact = "scalap") + val excludeHadoop = ExclusionRule(organization = "org.apache.hadoop") + val excludeCurator = ExclusionRule(organization = "org.apache.curator") + val excludePowermock = ExclusionRule(organization = "org.powermock") - def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark", + + def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark", version: String = "0.9.0-incubating", crossVersion: String = "2.10"): Option[sbt.ModuleID] = { val fullId = if (crossVersion.isEmpty) id else id + "_" + crossVersion Some(organization % fullId % version) // the artifact to compare binary compatibility with @@ -323,6 +327,7 @@ object SparkBuild extends Build { "com.codahale.metrics" % "metrics-graphite" % "3.0.0", "com.twitter" %% "chill" % "0.3.1" excludeAll(excludeAsm), "com.twitter" % "chill-java" % "0.3.1" excludeAll(excludeAsm), + "org.tachyonproject" % "tachyon" % "0.4.1-thrift" excludeAll(excludeHadoop, excludeCurator, excludeEclipseJetty, excludePowermock), "com.clearspring.analytics" % "stream" % "2.5.1" ), libraryDependencies ++= maybeAvro diff --git a/python/pyspark/context.py b/python/pyspark/context.py index ff1023bbfa539..d8667e84fedff 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -423,8 +423,11 @@ def _getJavaStorageLevel(self, storageLevel): raise Exception("storageLevel must be of type pyspark.StorageLevel") newStorageLevel = self._jvm.org.apache.spark.storage.StorageLevel - return newStorageLevel(storageLevel.useDisk, storageLevel.useMemory, - storageLevel.deserialized, storageLevel.replication) + return newStorageLevel(storageLevel.useDisk, + storageLevel.useMemory, + storageLevel.useOffHeap, + storageLevel.deserialized, + storageLevel.replication) def setJobGroup(self, groupId, description): """ diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 9943296b927dc..fb27863e07f55 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -1302,11 +1302,12 @@ def getStorageLevel(self): Get the RDD's current storage level. >>> rdd1 = sc.parallelize([1,2]) >>> rdd1.getStorageLevel() - StorageLevel(False, False, False, 1) + StorageLevel(False, False, False, False, 1) """ java_storage_level = self._jrdd.getStorageLevel() storage_level = StorageLevel(java_storage_level.useDisk(), java_storage_level.useMemory(), + java_storage_level.useOffHeap(), java_storage_level.deserialized(), java_storage_level.replication()) return storage_level diff --git a/python/pyspark/storagelevel.py b/python/pyspark/storagelevel.py index c3e3a44e8e7ab..7b6660eab231b 100644 --- a/python/pyspark/storagelevel.py +++ b/python/pyspark/storagelevel.py @@ -25,23 +25,25 @@ class StorageLevel: Also contains static constants for some commonly used storage levels, such as MEMORY_ONLY. """ - def __init__(self, useDisk, useMemory, deserialized, replication = 1): + def __init__(self, useDisk, useMemory, useOffHeap, deserialized, replication = 1): self.useDisk = useDisk self.useMemory = useMemory + self.useOffHeap = useOffHeap self.deserialized = deserialized self.replication = replication def __repr__(self): - return "StorageLevel(%s, %s, %s, %s)" % ( - self.useDisk, self.useMemory, self.deserialized, self.replication) + return "StorageLevel(%s, %s, %s, %s, %s)" % ( + self.useDisk, self.useMemory, self.useOffHeap, self.deserialized, self.replication) -StorageLevel.DISK_ONLY = StorageLevel(True, False, False) -StorageLevel.DISK_ONLY_2 = StorageLevel(True, False, False, 2) -StorageLevel.MEMORY_ONLY = StorageLevel(False, True, True) -StorageLevel.MEMORY_ONLY_2 = StorageLevel(False, True, True, 2) -StorageLevel.MEMORY_ONLY_SER = StorageLevel(False, True, False) -StorageLevel.MEMORY_ONLY_SER_2 = StorageLevel(False, True, False, 2) -StorageLevel.MEMORY_AND_DISK = StorageLevel(True, True, True) -StorageLevel.MEMORY_AND_DISK_2 = StorageLevel(True, True, True, 2) -StorageLevel.MEMORY_AND_DISK_SER = StorageLevel(True, True, False) -StorageLevel.MEMORY_AND_DISK_SER_2 = StorageLevel(True, True, False, 2) +StorageLevel.DISK_ONLY = StorageLevel(True, False, False, False) +StorageLevel.DISK_ONLY_2 = StorageLevel(True, False, False, False, 2) +StorageLevel.MEMORY_ONLY = StorageLevel(False, True, False, True) +StorageLevel.MEMORY_ONLY_2 = StorageLevel(False, True, False, True, 2) +StorageLevel.MEMORY_ONLY_SER = StorageLevel(False, True, False, False) +StorageLevel.MEMORY_ONLY_SER_2 = StorageLevel(False, True, False, False, 2) +StorageLevel.MEMORY_AND_DISK = StorageLevel(True, True, False, True) +StorageLevel.MEMORY_AND_DISK_2 = StorageLevel(True, True, False, True, 2) +StorageLevel.MEMORY_AND_DISK_SER = StorageLevel(True, True, False, False) +StorageLevel.MEMORY_AND_DISK_SER_2 = StorageLevel(True, True, False, False, 2) +StorageLevel.OFF_HEAP = StorageLevel(False, False, True, False, 1) \ No newline at end of file From 8de038eb366ded2ac74f72517e40545dbbab8cdd Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Fri, 4 Apr 2014 21:15:33 -0700 Subject: [PATCH 41/78] [SQL] SPARK-1366 Consistent sql function across different types of SQLContexts Now users who want to use HiveQL should explicitly say `hiveql` or `hql`. Author: Michael Armbrust Closes #319 from marmbrus/standardizeSqlHql and squashes the following commits: de68d0e [Michael Armbrust] Fix sampling test. fbe4a54 [Michael Armbrust] Make `sql` always use spark sql parser, users of hive context can now use hql or hiveql to run queries using HiveQL instead. --- .../spark/sql/examples/HiveFromSpark.scala | 12 ++--- .../apache/spark/sql/hive/HiveContext.scala | 17 ++++--- .../org/apache/spark/sql/hive/TestHive.scala | 12 ++--- .../hive/execution/HiveComparisonTest.scala | 10 ++-- .../sql/hive/execution/HiveQuerySuite.scala | 12 ++++- .../hive/execution/HiveResolutionSuite.scala | 2 +- .../sql/hive/execution/PruningSuite.scala | 2 +- .../spark/sql/parquet/HiveParquetSuite.scala | 46 +++++++++---------- 8 files changed, 63 insertions(+), 50 deletions(-) diff --git a/examples/src/main/scala/org/apache/spark/sql/examples/HiveFromSpark.scala b/examples/src/main/scala/org/apache/spark/sql/examples/HiveFromSpark.scala index abcc1f04d4279..62329bde84481 100644 --- a/examples/src/main/scala/org/apache/spark/sql/examples/HiveFromSpark.scala +++ b/examples/src/main/scala/org/apache/spark/sql/examples/HiveFromSpark.scala @@ -33,20 +33,20 @@ object HiveFromSpark { val hiveContext = new LocalHiveContext(sc) import hiveContext._ - sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") - sql("LOAD DATA LOCAL INPATH 'src/main/resources/kv1.txt' INTO TABLE src") + hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") + hql("LOAD DATA LOCAL INPATH 'src/main/resources/kv1.txt' INTO TABLE src") // Queries are expressed in HiveQL println("Result of 'SELECT *': ") - sql("SELECT * FROM src").collect.foreach(println) + hql("SELECT * FROM src").collect.foreach(println) // Aggregation queries are also supported. - val count = sql("SELECT COUNT(*) FROM src").collect().head.getInt(0) + val count = hql("SELECT COUNT(*) FROM src").collect().head.getInt(0) println(s"COUNT(*): $count") // The results of SQL queries are themselves RDDs and support all normal RDD functions. The // items in the RDD are of type Row, which allows you to access each column by ordinal. - val rddFromSql = sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key") + val rddFromSql = hql("SELECT key, value FROM src WHERE key < 10 ORDER BY key") println("Result of RDD.map:") val rddAsStrings = rddFromSql.map { @@ -59,6 +59,6 @@ object HiveFromSpark { // Queries can then join RDD data with data stored in Hive. println("Result of SELECT *:") - sql("SELECT * FROM records r JOIN src s ON r.key = s.key").collect().foreach(println) + hql("SELECT * FROM records r JOIN src s ON r.key = s.key").collect().foreach(println) } } 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 ff8eaacded4c8..f66a667c0a942 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 @@ -67,14 +67,13 @@ class LocalHiveContext(sc: SparkContext) extends HiveContext(sc) { class HiveContext(sc: SparkContext) extends SQLContext(sc) { self => - override def parseSql(sql: String): LogicalPlan = HiveQl.parseSql(sql) - override def executePlan(plan: LogicalPlan): this.QueryExecution = + override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution = new this.QueryExecution { val logical = plan } /** * Executes a query expressed in HiveQL using Spark, returning the result as a SchemaRDD. */ - def hql(hqlQuery: String): SchemaRDD = { + def hiveql(hqlQuery: String): SchemaRDD = { val result = new SchemaRDD(this, HiveQl.parseSql(hqlQuery)) // We force query optimization to happen right away instead of letting it happen lazily like // when using the query DSL. This is so DDL commands behave as expected. This is only @@ -83,6 +82,9 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { result } + /** An alias for `hiveql`. */ + def hql(hqlQuery: String): SchemaRDD = hiveql(hqlQuery) + // Circular buffer to hold what hive prints to STDOUT and ERR. Only printed when failures occur. @transient protected val outputBuffer = new java.io.OutputStream { @@ -120,7 +122,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { /* A catalyst metadata catalog that points to the Hive Metastore. */ @transient - override lazy val catalog = new HiveMetastoreCatalog(this) with OverrideCatalog { + override protected[sql] lazy val catalog = new HiveMetastoreCatalog(this) with OverrideCatalog { override def lookupRelation( databaseName: Option[String], tableName: String, @@ -132,7 +134,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { /* An analyzer that uses the Hive metastore. */ @transient - override lazy val analyzer = new Analyzer(catalog, HiveFunctionRegistry, caseSensitive = false) + override protected[sql] lazy val analyzer = + new Analyzer(catalog, HiveFunctionRegistry, caseSensitive = false) /** * Runs the specified SQL query using Hive. @@ -214,14 +217,14 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { } @transient - override val planner = hivePlanner + override protected[sql] val planner = hivePlanner @transient protected lazy val emptyResult = sparkContext.parallelize(Seq(new GenericRow(Array[Any]()): Row), 1) /** Extends QueryExecution with hive specific features. */ - abstract class QueryExecution extends super.QueryExecution { + protected[sql] abstract class QueryExecution extends super.QueryExecution { // TODO: Create mixin for the analyzer instead of overriding things here. override lazy val optimizedPlan = optimizer(catalog.PreInsertionCasts(catalog.CreateTables(analyzed))) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala index 0a6bea0162430..2fea9702954d7 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala @@ -110,10 +110,10 @@ class TestHiveContext(sc: SparkContext) extends LocalHiveContext(sc) { val describedTable = "DESCRIBE (\\w+)".r - class SqlQueryExecution(sql: String) extends this.QueryExecution { - lazy val logical = HiveQl.parseSql(sql) - def hiveExec() = runSqlHive(sql) - override def toString = sql + "\n" + super.toString + protected[hive] class HiveQLQueryExecution(hql: String) extends this.QueryExecution { + lazy val logical = HiveQl.parseSql(hql) + def hiveExec() = runSqlHive(hql) + override def toString = hql + "\n" + super.toString } /** @@ -140,8 +140,8 @@ class TestHiveContext(sc: SparkContext) extends LocalHiveContext(sc) { case class TestTable(name: String, commands: (()=>Unit)*) - implicit class SqlCmd(sql: String) { - def cmd = () => new SqlQueryExecution(sql).stringResult(): Unit + protected[hive] implicit class SqlCmd(sql: String) { + def cmd = () => new HiveQLQueryExecution(sql).stringResult(): Unit } /** 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 18654b308d234..3cc4562a88d66 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 @@ -125,7 +125,7 @@ abstract class HiveComparisonTest } protected def prepareAnswer( - hiveQuery: TestHive.type#SqlQueryExecution, + hiveQuery: TestHive.type#HiveQLQueryExecution, answer: Seq[String]): Seq[String] = { val orderedAnswer = hiveQuery.logical match { // Clean out non-deterministic time schema info. @@ -227,7 +227,7 @@ abstract class HiveComparisonTest try { // MINOR HACK: You must run a query before calling reset the first time. - TestHive.sql("SHOW TABLES") + TestHive.hql("SHOW TABLES") if (reset) { TestHive.reset() } val hiveCacheFiles = queryList.zipWithIndex.map { @@ -256,7 +256,7 @@ abstract class HiveComparisonTest hiveCachedResults } else { - val hiveQueries = queryList.map(new TestHive.SqlQueryExecution(_)) + val hiveQueries = queryList.map(new TestHive.HiveQLQueryExecution(_)) // Make sure we can at least parse everything before attempting hive execution. hiveQueries.foreach(_.logical) val computedResults = (queryList.zipWithIndex, hiveQueries, hiveCacheFiles).zipped.map { @@ -302,7 +302,7 @@ abstract class HiveComparisonTest // Run w/ catalyst val catalystResults = queryList.zip(hiveResults).map { case (queryString, hive) => - val query = new TestHive.SqlQueryExecution(queryString) + val query = new TestHive.HiveQLQueryExecution(queryString) try { (query, prepareAnswer(query, query.stringResult())) } catch { case e: Exception => val errorMessage = @@ -359,7 +359,7 @@ abstract class HiveComparisonTest // When we encounter an error we check to see if the environment is still okay by running a simple query. // If this fails then we halt testing since something must have gone seriously wrong. try { - new TestHive.SqlQueryExecution("SELECT key FROM src").stringResult() + new TestHive.HiveQLQueryExecution("SELECT key FROM src").stringResult() TestHive.runSqlHive("SELECT key FROM src") } catch { case e: Exception => diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala index c184ebe288af4..0c27498a93507 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala @@ -23,6 +23,16 @@ import org.apache.spark.sql.hive.TestHive._ * A set of test cases expressed in Hive QL that are not covered by the tests included in the hive distribution. */ class HiveQuerySuite extends HiveComparisonTest { + + test("Query expressed in SQL") { + assert(sql("SELECT 1").collect() === Array(Seq(1))) + } + + test("Query expressed in HiveQL") { + hql("FROM src SELECT key").collect() + hiveql("FROM src SELECT key").collect() + } + createQueryTest("Simple Average", "SELECT AVG(key) FROM src") @@ -133,7 +143,7 @@ class HiveQuerySuite extends HiveComparisonTest { "SELECT * FROM src LATERAL VIEW explode(map(key+3,key+4)) D as k, v") test("sampling") { - sql("SELECT * FROM src TABLESAMPLE(0.1 PERCENT) s") + hql("SELECT * FROM src TABLESAMPLE(0.1 PERCENT) s") } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala index 40c4e23f90fb8..8883e5b16d4da 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala @@ -56,7 +56,7 @@ class HiveResolutionSuite extends HiveComparisonTest { TestHive.sparkContext.parallelize(Data(1, 2, Nested(1,2)) :: Nil) .registerAsTable("caseSensitivityTest") - sql("SELECT a, b, A, B, n.a, n.b, n.A, n.B FROM caseSensitivityTest") + hql("SELECT a, b, A, B, n.a, n.b, n.A, n.B FROM caseSensitivityTest") } /** diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala index 1318ac1968dad..d9ccb93e23923 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala @@ -136,7 +136,7 @@ class PruningSuite extends HiveComparisonTest { expectedScannedColumns: Seq[String], expectedPartValues: Seq[Seq[String]]) = { test(s"$testCaseName - pruning test") { - val plan = new TestHive.SqlQueryExecution(sql).executedPlan + val plan = new TestHive.HiveQLQueryExecution(sql).executedPlan val actualOutputColumns = plan.output.map(_.name) val (actualScannedColumns, actualPartValues) = plan.collect { case p @ HiveTableScan(columns, relation, _) => diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala index 314ca48ad8f6a..aade62eb8f84e 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/HiveParquetSuite.scala @@ -57,34 +57,34 @@ class HiveParquetSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAft } test("SELECT on Parquet table") { - val rdd = sql("SELECT * FROM testsource").collect() + val rdd = hql("SELECT * FROM testsource").collect() assert(rdd != null) assert(rdd.forall(_.size == 6)) } test("Simple column projection + filter on Parquet table") { - val rdd = sql("SELECT myboolean, mylong FROM testsource WHERE myboolean=true").collect() + val rdd = hql("SELECT myboolean, mylong FROM testsource WHERE myboolean=true").collect() assert(rdd.size === 5, "Filter returned incorrect number of rows") assert(rdd.forall(_.getBoolean(0)), "Filter returned incorrect Boolean field value") } test("Converting Hive to Parquet Table via saveAsParquetFile") { - sql("SELECT * FROM src").saveAsParquetFile(dirname.getAbsolutePath) + hql("SELECT * FROM src").saveAsParquetFile(dirname.getAbsolutePath) parquetFile(dirname.getAbsolutePath).registerAsTable("ptable") - val rddOne = sql("SELECT * FROM src").collect().sortBy(_.getInt(0)) - val rddTwo = sql("SELECT * from ptable").collect().sortBy(_.getInt(0)) + val rddOne = hql("SELECT * FROM src").collect().sortBy(_.getInt(0)) + val rddTwo = hql("SELECT * from ptable").collect().sortBy(_.getInt(0)) compareRDDs(rddOne, rddTwo, "src (Hive)", Seq("key:Int", "value:String")) } test("INSERT OVERWRITE TABLE Parquet table") { - sql("SELECT * FROM testsource").saveAsParquetFile(dirname.getAbsolutePath) + hql("SELECT * FROM testsource").saveAsParquetFile(dirname.getAbsolutePath) parquetFile(dirname.getAbsolutePath).registerAsTable("ptable") // let's do three overwrites for good measure - sql("INSERT OVERWRITE TABLE ptable SELECT * FROM testsource").collect() - sql("INSERT OVERWRITE TABLE ptable SELECT * FROM testsource").collect() - sql("INSERT OVERWRITE TABLE ptable SELECT * FROM testsource").collect() - val rddCopy = sql("SELECT * FROM ptable").collect() - val rddOrig = sql("SELECT * FROM testsource").collect() + hql("INSERT OVERWRITE TABLE ptable SELECT * FROM testsource").collect() + hql("INSERT OVERWRITE TABLE ptable SELECT * FROM testsource").collect() + hql("INSERT OVERWRITE TABLE ptable SELECT * FROM testsource").collect() + val rddCopy = hql("SELECT * FROM ptable").collect() + val rddOrig = hql("SELECT * FROM testsource").collect() assert(rddCopy.size === rddOrig.size, "INSERT OVERWRITE changed size of table??") compareRDDs(rddOrig, rddCopy, "testsource", ParquetTestData.testSchemaFieldNames) } @@ -93,13 +93,13 @@ class HiveParquetSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAft createParquetFile(dirname.getAbsolutePath, ("key", IntegerType), ("value", StringType)) .registerAsTable("tmp") val rddCopy = - sql("INSERT INTO TABLE tmp SELECT * FROM src") + hql("INSERT INTO TABLE tmp SELECT * FROM src") .collect() .sortBy[Int](_.apply(0) match { case x: Int => x case _ => 0 }) - val rddOrig = sql("SELECT * FROM src") + val rddOrig = hql("SELECT * FROM src") .collect() .sortBy(_.getInt(0)) compareRDDs(rddOrig, rddCopy, "src (Hive)", Seq("key:Int", "value:String")) @@ -108,22 +108,22 @@ class HiveParquetSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAft test("Appending to Parquet table") { createParquetFile(dirname.getAbsolutePath, ("key", IntegerType), ("value", StringType)) .registerAsTable("tmpnew") - sql("INSERT INTO TABLE tmpnew SELECT * FROM src").collect() - sql("INSERT INTO TABLE tmpnew SELECT * FROM src").collect() - sql("INSERT INTO TABLE tmpnew SELECT * FROM src").collect() - val rddCopies = sql("SELECT * FROM tmpnew").collect() - val rddOrig = sql("SELECT * FROM src").collect() + hql("INSERT INTO TABLE tmpnew SELECT * FROM src").collect() + hql("INSERT INTO TABLE tmpnew SELECT * FROM src").collect() + hql("INSERT INTO TABLE tmpnew SELECT * FROM src").collect() + val rddCopies = hql("SELECT * FROM tmpnew").collect() + val rddOrig = hql("SELECT * FROM src").collect() assert(rddCopies.size === 3 * rddOrig.size, "number of copied rows via INSERT INTO did not match correct number") } test("Appending to and then overwriting Parquet table") { createParquetFile(dirname.getAbsolutePath, ("key", IntegerType), ("value", StringType)) .registerAsTable("tmp") - sql("INSERT INTO TABLE tmp SELECT * FROM src").collect() - sql("INSERT INTO TABLE tmp SELECT * FROM src").collect() - sql("INSERT OVERWRITE TABLE tmp SELECT * FROM src").collect() - val rddCopies = sql("SELECT * FROM tmp").collect() - val rddOrig = sql("SELECT * FROM src").collect() + hql("INSERT INTO TABLE tmp SELECT * FROM src").collect() + hql("INSERT INTO TABLE tmp SELECT * FROM src").collect() + hql("INSERT OVERWRITE TABLE tmp SELECT * FROM src").collect() + val rddCopies = hql("SELECT * FROM tmp").collect() + val rddOrig = hql("SELECT * FROM src").collect() assert(rddCopies.size === rddOrig.size, "INSERT OVERWRITE did not actually overwrite") } From 0acc7a02b4323f4e0b7736bc1999bdcedab41f39 Mon Sep 17 00:00:00 2001 From: Prabeesh K Date: Fri, 4 Apr 2014 21:32:00 -0700 Subject: [PATCH 42/78] small fix ( proogram -> program ) Author: Prabeesh K Closes #331 from prabeesh/patch-3 and squashes the following commits: 9399eb5 [Prabeesh K] small fix(proogram -> program) --- docs/quick-start.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/quick-start.md b/docs/quick-start.md index 13df6beea16e8..60e8b1ba0eb46 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -124,7 +124,7 @@ object SimpleApp { } {% endhighlight %} -This program just counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the proogram. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes. +This program just counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the program. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes. This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency. This file also adds a repository that Spark depends on: From 7c18428fac1403eb9c69b61890453964b255c432 Mon Sep 17 00:00:00 2001 From: Prashant Sharma Date: Fri, 4 Apr 2014 22:49:19 -0700 Subject: [PATCH 43/78] HOTFIX for broken CI, by SPARK-1336 Learnt about `set -o pipefail` is very useful. Author: Prashant Sharma Author: Prashant Sharma Closes #321 from ScrapCodes/hf-SPARK-1336 and squashes the following commits: 9d22bc2 [Prashant Sharma] added comment why echo -e q exists. f865951 [Prashant Sharma] made error to match with word boundry so errors does not match. This is there to make sure build fails if provided SparkBuild has compile errors. 7fffdf2 [Prashant Sharma] Removed a stray line. 97379d8 [Prashant Sharma] HOTFIX for broken CI, by SPARK-1336 --- .rat-excludes | 1 + dev/run-tests | 10 +++++----- dev/scalastyle | 4 ++-- 3 files changed, 8 insertions(+), 7 deletions(-) diff --git a/.rat-excludes b/.rat-excludes index 17cf6d0ed1cf3..85bfad60fcadc 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -39,3 +39,4 @@ work .*\.q golden test.out/* +.*iml diff --git a/dev/run-tests b/dev/run-tests index fff949e04fcd7..6ad674a2ba127 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -26,13 +26,12 @@ rm -rf ./work # Fail fast set -e - +set -o pipefail if test -x "$JAVA_HOME/bin/java"; then declare java_cmd="$JAVA_HOME/bin/java" else declare java_cmd=java fi - JAVA_VERSION=$($java_cmd -version 2>&1 | sed 's/java version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q') [ "$JAVA_VERSION" -ge 18 ] && echo "" || echo "[Warn] Java 8 tests will not run because JDK version is < 1.8." @@ -49,7 +48,9 @@ dev/scalastyle echo "=========================================================================" echo "Running Spark unit tests" echo "=========================================================================" -sbt/sbt assembly test +# echo "q" is needed because sbt on encountering a build file with failure (either resolution or compilation) +# prompts the user for input either q, r, etc to quit or retry. This echo is there to make it not block. +echo -e "q\n" | sbt/sbt assembly test | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including" echo "=========================================================================" echo "Running PySpark tests" @@ -63,5 +64,4 @@ echo "=========================================================================" echo "Detecting binary incompatibilites with MiMa" echo "=========================================================================" ./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore -sbt/sbt mima-report-binary-issues | grep -v -e "info.*Resolving" - +echo -e "q\n" | sbt/sbt mima-report-binary-issues | grep -v -e "info.*Resolving" diff --git a/dev/scalastyle b/dev/scalastyle index 5a18f4d672825..19955b9aaaad3 100755 --- a/dev/scalastyle +++ b/dev/scalastyle @@ -17,8 +17,8 @@ # limitations under the License. # -sbt/sbt clean scalastyle > scalastyle.txt -ERRORS=$(cat scalastyle.txt | grep -e "error file") +echo -e "q\n" | sbt/sbt clean scalastyle > scalastyle.txt +ERRORS=$(cat scalastyle.txt | grep -e "\") if test ! -z "$ERRORS"; then echo -e "Scalastyle checks failed at following occurrences:\n$ERRORS" exit 1 From 2d0150c1a2688296346fa279b1f8d14edac935eb Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Sat, 5 Apr 2014 15:17:50 -0700 Subject: [PATCH 44/78] Remove the getStageInfo() method from SparkContext. This method exposes the Stage objects, which are private to Spark and should not be exposed to the user. This method was added in https://github.com/apache/spark/commit/01d77f329f5878b7c8672bbdc1859f3ca95d759d; ccing @squito here in case there's a good reason to keep this! Author: Kay Ousterhout Closes #308 from kayousterhout/remove_public_method and squashes the following commits: 2e2f009 [Kay Ousterhout] Remove the getStageInfo() method from SparkContext. --- core/src/main/scala/org/apache/spark/SparkContext.scala | 4 ---- .../main/scala/org/apache/spark/scheduler/DAGScheduler.scala | 2 +- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index fcf16ce1b278e..8382dd44f3484 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -731,10 +731,6 @@ class SparkContext( */ def getPersistentRDDs: Map[Int, RDD[_]] = persistentRdds.toMap - def getStageInfo: Map[Stage, StageInfo] = { - dagScheduler.stageToInfos - } - /** * Return information about blocks stored in all of the slaves */ 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 ef3d24d746829..442a95bb2c44b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -86,7 +86,7 @@ class DAGScheduler( private[scheduler] val shuffleToMapStage = new TimeStampedHashMap[Int, Stage] private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob] private[scheduler] val resultStageToJob = new HashMap[Stage, ActiveJob] - private[spark] val stageToInfos = new TimeStampedHashMap[Stage, StageInfo] + private[scheduler] val stageToInfos = new TimeStampedHashMap[Stage, StageInfo] // Stages we need to run whose parents aren't done private[scheduler] val waitingStages = new HashSet[Stage] From 6e88583aef7d8caf59d53c9fcb659a62d2cd6051 Mon Sep 17 00:00:00 2001 From: Mridul Muralidharan Date: Sat, 5 Apr 2014 15:23:37 -0700 Subject: [PATCH 45/78] [SPARK-1371] fix computePreferredLocations signature to not depend on underlying implementation Change to Map and Set - not mutable HashMap and HashSet Author: Mridul Muralidharan Closes #302 from mridulm/master and squashes the following commits: df747af [Mridul Muralidharan] Address review comments 17e2907 [Mridul Muralidharan] fix computePreferredLocations signature to not depend on underlying implementation --- .../scala/org/apache/spark/scheduler/InputFormatInfo.scala | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala index 5555585c8b4cd..b3f2cb346f7da 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala @@ -164,8 +164,7 @@ object InputFormatInfo { PS: I know the wording here is weird, hopefully it makes some sense ! */ - def computePreferredLocations(formats: Seq[InputFormatInfo]): HashMap[String, HashSet[SplitInfo]] - = { + def computePreferredLocations(formats: Seq[InputFormatInfo]): Map[String, Set[SplitInfo]] = { val nodeToSplit = new HashMap[String, HashSet[SplitInfo]] for (inputSplit <- formats) { @@ -178,6 +177,6 @@ object InputFormatInfo { } } - nodeToSplit + nodeToSplit.mapValues(_.toSet).toMap } } From 890d63bd4e16296ac70e151b3754727ea42b583c Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Sat, 5 Apr 2014 19:08:24 -0700 Subject: [PATCH 46/78] Fix for PR #195 for Java 6 Use Java 6's recommended equivalent of Java 7's Logger.getGlobal() to retain Java 6 compatibility. See PR #195 Author: Sean Owen Closes #334 from srowen/FixPR195ForJava6 and squashes the following commits: f92fbd3 [Sean Owen] Use Java 6's recommended equivalent of Java 7's Logger.getGlobal() to retain Java 6 compatibility --- .../scala/org/apache/spark/sql/parquet/ParquetRelation.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala index 114bfbb719ee9..505ad0a2c77c1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala @@ -103,7 +103,7 @@ private[sql] object ParquetRelation { SLF4JBridgeHandler.install() for(name <- loggerNames) { val logger = Logger.getLogger(name) - logger.setParent(Logger.getGlobal) + logger.setParent(Logger.getLogger(Logger.GLOBAL_LOGGER_NAME)) logger.setUseParentHandlers(true) } } From 0b855167818b9afd2d2aa9f617b9861d77b2425d Mon Sep 17 00:00:00 2001 From: Matei Zaharia Date: Sat, 5 Apr 2014 20:52:05 -0700 Subject: [PATCH 47/78] SPARK-1421. Make MLlib work on Python 2.6 The reason it wasn't working was passing a bytearray to stream.write(), which is not supported in Python 2.6 but is in 2.7. (This array came from NumPy when we converted data to send it over to Java). Now we just convert those bytearrays to strings of bytes, which preserves nonprintable characters as well. Author: Matei Zaharia Closes #335 from mateiz/mllib-python-2.6 and squashes the following commits: f26c59f [Matei Zaharia] Update docs to no longer say we need Python 2.7 a84d6af [Matei Zaharia] SPARK-1421. Make MLlib work on Python 2.6 --- docs/mllib-guide.md | 3 +-- docs/python-programming-guide.md | 2 +- python/pyspark/mllib/__init__.py | 6 +----- python/pyspark/serializers.py | 11 ++++++++++- 4 files changed, 13 insertions(+), 9 deletions(-) diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 203d235bf9663..a5e0cc50809cf 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -38,6 +38,5 @@ depends on native Fortran routines. You may need to install the if it is not already present on your nodes. MLlib will throw a linking error if it cannot detect these libraries automatically. -To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.7 or newer -and Python 2.7. +To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.7 or newer. diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index cbe7d820b455e..c2e5327324898 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -152,7 +152,7 @@ Many of the methods also contain [doctests](http://docs.python.org/2/library/doc # Libraries [MLlib](mllib-guide.html) is also available in PySpark. To use it, you'll need -[NumPy](http://www.numpy.org) version 1.7 or newer, and Python 2.7. The [MLlib guide](mllib-guide.html) contains +[NumPy](http://www.numpy.org) version 1.7 or newer. The [MLlib guide](mllib-guide.html) contains some example applications. # Where to Go from Here diff --git a/python/pyspark/mllib/__init__.py b/python/pyspark/mllib/__init__.py index b420d7a7f23ba..538ff26ce7c33 100644 --- a/python/pyspark/mllib/__init__.py +++ b/python/pyspark/mllib/__init__.py @@ -19,11 +19,7 @@ Python bindings for MLlib. """ -# MLlib currently needs Python 2.7+ and NumPy 1.7+, so complain if lower - -import sys -if sys.version_info[0:2] < (2, 7): - raise Exception("MLlib requires Python 2.7+") +# MLlib currently needs and NumPy 1.7+, so complain if lower import numpy if numpy.version.version < '1.7': diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index 4d802924df4a1..b253807974a2e 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -64,6 +64,7 @@ from itertools import chain, izip, product import marshal import struct +import sys from pyspark import cloudpickle @@ -113,6 +114,11 @@ class FramedSerializer(Serializer): where C{length} is a 32-bit integer and data is C{length} bytes. """ + def __init__(self): + # On Python 2.6, we can't write bytearrays to streams, so we need to convert them + # to strings first. Check if the version number is that old. + self._only_write_strings = sys.version_info[0:2] <= (2, 6) + def dump_stream(self, iterator, stream): for obj in iterator: self._write_with_length(obj, stream) @@ -127,7 +133,10 @@ def load_stream(self, stream): def _write_with_length(self, obj, stream): serialized = self.dumps(obj) write_int(len(serialized), stream) - stream.write(serialized) + if self._only_write_strings: + stream.write(str(serialized)) + else: + stream.write(serialized) def _read_with_length(self, stream): length = read_int(stream) From 7012ffafad8fa876aa8bcb0b848445eec6734ef1 Mon Sep 17 00:00:00 2001 From: witgo Date: Sun, 6 Apr 2014 16:03:06 -0700 Subject: [PATCH 48/78] Fix SPARK-1420 The maven build error for Spark Catalyst Author: witgo Closes #333 from witgo/SPARK-1420 and squashes the following commits: 902519e [witgo] add dependency scala-reflect to catalyst --- sql/catalyst/pom.xml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml index 0edce55a93338..9d5c6a857bb00 100644 --- a/sql/catalyst/pom.xml +++ b/sql/catalyst/pom.xml @@ -44,6 +44,10 @@ + + org.scala-lang + scala-reflect + org.apache.spark spark-core_${scala.binary.version} From e258e5040fa1905a04efcb7b3ca4a6d33e18fa61 Mon Sep 17 00:00:00 2001 From: Egor Pakhomov Date: Sun, 6 Apr 2014 16:41:23 -0700 Subject: [PATCH 49/78] [SPARK-1259] Make RDD locally iterable Author: Egor Pakhomov Closes #156 from epahomov/SPARK-1259 and squashes the following commits: 8ec8f24 [Egor Pakhomov] Make to local iterator shorter 34aa300 [Egor Pakhomov] Fix toLocalIterator docs 08363ef [Egor Pakhomov] SPARK-1259 from toLocallyIterable to toLocalIterator 6a994eb [Egor Pakhomov] SPARK-1259 Make RDD locally iterable 8be3dcf [Egor Pakhomov] SPARK-1259 Make RDD locally iterable 33ecb17 [Egor Pakhomov] SPARK-1259 Make RDD locally iterable --- .../org/apache/spark/api/java/JavaRDDLike.scala | 14 +++++++++++++- core/src/main/scala/org/apache/spark/rdd/RDD.scala | 12 ++++++++++++ .../test/java/org/apache/spark/JavaAPISuite.java | 9 +++++++++ .../test/scala/org/apache/spark/rdd/RDDSuite.scala | 1 + 4 files changed, 35 insertions(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index e03b8e78d5f52..6e8ec8e0c7629 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -17,7 +17,8 @@ package org.apache.spark.api.java -import java.util.{Comparator, List => JList} +import java.util.{Comparator, Iterator => JIterator, List => JList} +import java.lang.{Iterable => JIterable} import scala.collection.JavaConversions._ import scala.reflect.ClassTag @@ -280,6 +281,17 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { new java.util.ArrayList(arr) } + /** + * Return an iterator that contains all of the elements in this RDD. + * + * The iterator will consume as much memory as the largest partition in this RDD. + */ + def toLocalIterator(): JIterator[T] = { + import scala.collection.JavaConversions._ + rdd.toLocalIterator + } + + /** * Return an array that contains all of the elements in this RDD. * @deprecated As of Spark 1.0.0, toArray() is deprecated, use {@link #collect()} instead 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 08c42c5ee87b6..c43823bd769b7 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -661,6 +661,18 @@ abstract class RDD[T: ClassTag]( Array.concat(results: _*) } + /** + * Return an iterator that contains all of the elements in this RDD. + * + * The iterator will consume as much memory as the largest partition in this RDD. + */ + def toLocalIterator: Iterator[T] = { + def collectPartition(p: Int): Array[T] = { + sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p), allowLocal = false).head + } + (0 until partitions.length).iterator.flatMap(i => collectPartition(i)) + } + /** * Return an array that contains all of the elements in this RDD. */ diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java index 2372f2d9924a1..762405be2a8f9 100644 --- a/core/src/test/java/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java @@ -22,6 +22,7 @@ import scala.Tuple2; +import com.google.common.collect.Lists; import com.google.common.base.Optional; import com.google.common.base.Charsets; import com.google.common.io.Files; @@ -179,6 +180,14 @@ public void call(String s) { Assert.assertEquals(2, foreachCalls); } + @Test + public void toLocalIterator() { + List correct = Arrays.asList(1, 2, 3, 4); + JavaRDD rdd = sc.parallelize(correct); + List result = Lists.newArrayList(rdd.toLocalIterator()); + Assert.assertTrue(correct.equals(result)); + } + @SuppressWarnings("unchecked") @Test public void lookup() { diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala index d6b5fdc7984b4..25973348a7837 100644 --- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala @@ -33,6 +33,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { test("basic operations") { val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) assert(nums.collect().toList === List(1, 2, 3, 4)) + assert(nums.toLocalIterator.toList === List(1, 2, 3, 4)) val dups = sc.makeRDD(Array(1, 1, 2, 2, 3, 3, 4, 4), 2) assert(dups.distinct().count() === 4) assert(dups.distinct.count === 4) // Can distinct and count be called without parentheses? From 856c50f59bffbf76ad495eaab837febaf65cf02d Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Sun, 6 Apr 2014 17:40:37 -0700 Subject: [PATCH 50/78] SPARK-1387. Update build plugins, avoid plugin version warning, centralize versions Another handful of small build changes to organize and standardize a bit, and avoid warnings: - Update Maven plugin versions for good measure - Since plugins need maven 3.0.4 already, require it explicitly (<3.0.4 had some bugs anyway) - Use variables to define versions across dependencies where they should move in lock step - ... and make this consistent between Maven/SBT OK, I also updated the JIRA URL while I was at it here. Author: Sean Owen Closes #291 from srowen/SPARK-1387 and squashes the following commits: 461eca1 [Sean Owen] Couldn't resist also updating JIRA location to new one c2d5cc5 [Sean Owen] Update plugins and Maven version; use variables consistently across Maven/SBT to define dependency versions that should stay in step. --- assembly/pom.xml | 2 +- core/pom.xml | 2 - dev/audit-release/maven_app_core/pom.xml | 2 +- docs/building-with-maven.md | 2 +- examples/pom.xml | 2 +- graphx/pom.xml | 2 +- mllib/pom.xml | 2 +- pom.xml | 43 +++++++++++---------- project/SparkBuild.scala | 49 ++++++++++++++---------- streaming/pom.xml | 1 - 10 files changed, 57 insertions(+), 50 deletions(-) diff --git a/assembly/pom.xml b/assembly/pom.xml index b5e752c6cd1f6..255107a2c47cb 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -208,7 +208,7 @@ org.codehaus.mojo buildnumber-maven-plugin - 1.1 + 1.2 validate diff --git a/core/pom.xml b/core/pom.xml index 66f9fc4961b03..1f808380817c9 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -117,12 +117,10 @@ com.twitter chill_${scala.binary.version} - 0.3.1 com.twitter chill-java - 0.3.1 commons-net diff --git a/dev/audit-release/maven_app_core/pom.xml b/dev/audit-release/maven_app_core/pom.xml index 0b837c01751fe..76a381f8e17e0 100644 --- a/dev/audit-release/maven_app_core/pom.xml +++ b/dev/audit-release/maven_app_core/pom.xml @@ -49,7 +49,7 @@ maven-compiler-plugin - 2.3.2 + 3.1 diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md index 730a6e7932564..9cebaf12283fc 100644 --- a/docs/building-with-maven.md +++ b/docs/building-with-maven.md @@ -6,7 +6,7 @@ title: Building Spark with Maven * This will become a table of contents (this text will be scraped). {:toc} -Building Spark using Maven Requires Maven 3 (the build process is tested with Maven 3.0.4) and Java 1.6 or newer. +Building Spark using Maven requires Maven 3.0.4 or newer and Java 1.6 or newer. ## Setting up Maven's Memory Usage ## diff --git a/examples/pom.xml b/examples/pom.xml index a5569ff5e71f3..0b6212b5d1549 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -110,7 +110,7 @@ org.apache.hbase hbase - 0.94.6 + ${hbase.version} asm diff --git a/graphx/pom.xml b/graphx/pom.xml index 5a5022916d234..b4c67ddcd8ca9 100644 --- a/graphx/pom.xml +++ b/graphx/pom.xml @@ -54,7 +54,7 @@ org.jblas jblas - 1.2.3 + ${jblas.version} org.eclipse.jetty diff --git a/mllib/pom.xml b/mllib/pom.xml index fec1cc94b2642..e7ce00efc4af6 100644 --- a/mllib/pom.xml +++ b/mllib/pom.xml @@ -58,7 +58,7 @@ org.jblas jblas - 1.2.3 + ${jblas.version} org.scalanlp diff --git a/pom.xml b/pom.xml index 01341d21b7f23..1426e0e00214c 100644 --- a/pom.xml +++ b/pom.xml @@ -54,11 +54,11 @@ JIRA - https://spark-project.atlassian.net/browse/SPARK + https://issues.apache.org/jira/browse/SPARK - 3.0.0 + 3.0.4 @@ -123,6 +123,10 @@ 0.94.6 0.12.0 1.3.2 + 1.2.3 + 8.1.14.v20131031 + 0.3.1 + 3.0.0 64m 512m @@ -192,22 +196,22 @@ org.eclipse.jetty jetty-util - 8.1.14.v20131031 + ${jetty.version} org.eclipse.jetty jetty-security - 8.1.14.v20131031 + ${jetty.version} org.eclipse.jetty jetty-plus - 8.1.14.v20131031 + ${jetty.version} org.eclipse.jetty jetty-server - 8.1.14.v20131031 + ${jetty.version} com.google.guava @@ -273,7 +277,7 @@ com.twitter chill_${scala.binary.version} - 0.3.1 + ${chill.version} org.ow2.asm @@ -288,7 +292,7 @@ com.twitter chill-java - 0.3.1 + ${chill.version} org.ow2.asm @@ -392,27 +396,27 @@ com.codahale.metrics metrics-core - 3.0.0 + ${codahale.metrics.version} com.codahale.metrics metrics-jvm - 3.0.0 + ${codahale.metrics.version} com.codahale.metrics metrics-json - 3.0.0 + ${codahale.metrics.version} com.codahale.metrics metrics-ganglia - 3.0.0 + ${codahale.metrics.version} com.codahale.metrics metrics-graphite - 3.0.0 + ${codahale.metrics.version} org.scala-lang @@ -585,7 +589,7 @@ org.apache.maven.plugins maven-enforcer-plugin - 1.1.1 + 1.3.1 enforce-versions @@ -595,7 +599,7 @@ - 3.0.0 + 3.0.4 ${java.version} @@ -608,12 +612,12 @@ org.codehaus.mojo build-helper-maven-plugin - 1.7 + 1.8 net.alchim31.maven scala-maven-plugin - 3.1.5 + 3.1.6 scala-compile-first @@ -674,7 +678,7 @@ org.apache.maven.plugins maven-surefire-plugin - 2.12.4 + 2.17 true @@ -713,7 +717,7 @@ org.apache.maven.plugins maven-shade-plugin - 2.0 + 2.2 org.apache.maven.plugins @@ -810,7 +814,6 @@ org.apache.maven.plugins maven-jar-plugin - 2.4 diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 843a874fbfdb0..3489b43d43f0d 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -248,10 +248,10 @@ object SparkBuild extends Build { libraryDependencies ++= Seq( "io.netty" % "netty-all" % "4.0.17.Final", - "org.eclipse.jetty" % "jetty-server" % "8.1.14.v20131031", - "org.eclipse.jetty" % "jetty-util" % "8.1.14.v20131031", - "org.eclipse.jetty" % "jetty-plus" % "8.1.14.v20131031", - "org.eclipse.jetty" % "jetty-security" % "8.1.14.v20131031", + "org.eclipse.jetty" % "jetty-server" % jettyVersion, + "org.eclipse.jetty" % "jetty-util" % jettyVersion, + "org.eclipse.jetty" % "jetty-plus" % jettyVersion, + "org.eclipse.jetty" % "jetty-security" % jettyVersion, /** Workaround for SPARK-959. Dependency used by org.eclipse.jetty. Fixed in ivy 2.3.0. */ "org.eclipse.jetty.orbit" % "javax.servlet" % "3.0.0.v201112011016" artifacts Artifact("javax.servlet", "jar", "jar"), "org.scalatest" %% "scalatest" % "1.9.1" % "test", @@ -276,6 +276,13 @@ object SparkBuild extends Build { publishLocalBoth <<= Seq(publishLocal in MavenCompile, publishLocal).dependOn ) ++ net.virtualvoid.sbt.graph.Plugin.graphSettings ++ ScalaStyleSettings + val akkaVersion = "2.2.3-shaded-protobuf" + val chillVersion = "0.3.1" + val codahaleMetricsVersion = "3.0.0" + val jblasVersion = "1.2.3" + val jettyVersion = "8.1.14.v20131031" + val hiveVersion = "0.12.0" + val parquetVersion = "1.3.2" val slf4jVersion = "1.7.5" val excludeNetty = ExclusionRule(organization = "org.jboss.netty") @@ -309,9 +316,9 @@ object SparkBuild extends Build { "commons-daemon" % "commons-daemon" % "1.0.10", // workaround for bug HADOOP-9407 "com.ning" % "compress-lzf" % "1.0.0", "org.xerial.snappy" % "snappy-java" % "1.0.5", - "org.spark-project.akka" %% "akka-remote" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty), - "org.spark-project.akka" %% "akka-slf4j" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty), - "org.spark-project.akka" %% "akka-testkit" % "2.2.3-shaded-protobuf" % "test", + "org.spark-project.akka" %% "akka-remote" % akkaVersion excludeAll(excludeNetty), + "org.spark-project.akka" %% "akka-slf4j" % akkaVersion excludeAll(excludeNetty), + "org.spark-project.akka" %% "akka-testkit" % akkaVersion % "test", "org.json4s" %% "json4s-jackson" % "3.2.6" excludeAll(excludeScalap), "it.unimi.dsi" % "fastutil" % "6.4.4", "colt" % "colt" % "1.2.0", @@ -321,12 +328,12 @@ object SparkBuild extends Build { "org.apache.derby" % "derby" % "10.4.2.0" % "test", "org.apache.hadoop" % hadoopClient % hadoopVersion excludeAll(excludeNetty, excludeAsm, excludeCommonsLogging, excludeSLF4J, excludeOldAsm), "org.apache.curator" % "curator-recipes" % "2.4.0" excludeAll(excludeNetty), - "com.codahale.metrics" % "metrics-core" % "3.0.0", - "com.codahale.metrics" % "metrics-jvm" % "3.0.0", - "com.codahale.metrics" % "metrics-json" % "3.0.0", - "com.codahale.metrics" % "metrics-graphite" % "3.0.0", - "com.twitter" %% "chill" % "0.3.1" excludeAll(excludeAsm), - "com.twitter" % "chill-java" % "0.3.1" excludeAll(excludeAsm), + "com.codahale.metrics" % "metrics-core" % codahaleMetricsVersion, + "com.codahale.metrics" % "metrics-jvm" % codahaleMetricsVersion, + "com.codahale.metrics" % "metrics-json" % codahaleMetricsVersion, + "com.codahale.metrics" % "metrics-graphite" % codahaleMetricsVersion, + "com.twitter" %% "chill" % chillVersion excludeAll(excludeAsm), + "com.twitter" % "chill-java" % chillVersion excludeAll(excludeAsm), "org.tachyonproject" % "tachyon" % "0.4.1-thrift" excludeAll(excludeHadoop, excludeCurator, excludeEclipseJetty, excludePowermock), "com.clearspring.analytics" % "stream" % "2.5.1" ), @@ -370,7 +377,7 @@ object SparkBuild extends Build { name := "spark-graphx", previousArtifact := sparkPreviousArtifact("spark-graphx"), libraryDependencies ++= Seq( - "org.jblas" % "jblas" % "1.2.3" + "org.jblas" % "jblas" % jblasVersion ) ) @@ -383,7 +390,7 @@ object SparkBuild extends Build { name := "spark-mllib", previousArtifact := sparkPreviousArtifact("spark-mllib"), libraryDependencies ++= Seq( - "org.jblas" % "jblas" % "1.2.3", + "org.jblas" % "jblas" % jblasVersion, "org.scalanlp" %% "breeze" % "0.7" ) ) @@ -403,8 +410,8 @@ object SparkBuild extends Build { def sqlCoreSettings = sharedSettings ++ Seq( name := "spark-sql", libraryDependencies ++= Seq( - "com.twitter" % "parquet-column" % "1.3.2", - "com.twitter" % "parquet-hadoop" % "1.3.2" + "com.twitter" % "parquet-column" % parquetVersion, + "com.twitter" % "parquet-hadoop" % parquetVersion ) ) @@ -416,9 +423,9 @@ object SparkBuild extends Build { jarName in packageDependency <<= version map { v => "spark-hive-assembly-" + v + "-hadoop" + hadoopVersion + "-deps.jar" }, javaOptions += "-XX:MaxPermSize=1g", libraryDependencies ++= Seq( - "org.apache.hive" % "hive-metastore" % "0.12.0", - "org.apache.hive" % "hive-exec" % "0.12.0", - "org.apache.hive" % "hive-serde" % "0.12.0" + "org.apache.hive" % "hive-metastore" % hiveVersion, + "org.apache.hive" % "hive-exec" % hiveVersion, + "org.apache.hive" % "hive-serde" % hiveVersion ), // Multiple queries rely on the TestHive singleton. See comments there for more details. parallelExecution in Test := false, @@ -549,7 +556,7 @@ object SparkBuild extends Build { name := "spark-streaming-zeromq", previousArtifact := sparkPreviousArtifact("spark-streaming-zeromq"), libraryDependencies ++= Seq( - "org.spark-project.akka" %% "akka-zeromq" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty) + "org.spark-project.akka" %% "akka-zeromq" % akkaVersion excludeAll(excludeNetty) ) ) diff --git a/streaming/pom.xml b/streaming/pom.xml index 1953cc6883378..93b1c5a37aff9 100644 --- a/streaming/pom.xml +++ b/streaming/pom.xml @@ -96,7 +96,6 @@ org.apache.maven.plugins maven-jar-plugin - 2.2 From 7ce52c4a7a07b0db5e7c1312b1920efb1165ce6a Mon Sep 17 00:00:00 2001 From: Aaron Davidson Date: Sun, 6 Apr 2014 17:43:44 -0700 Subject: [PATCH 51/78] SPARK-1349: spark-shell gets its own command history Currently, spark-shell shares its command history with scala repl. This fix is simply a modification of the default FileBackedHistory file setting: https://github.com/scala/scala/blob/master/src/repl/scala/tools/nsc/interpreter/session/FileBackedHistory.scala#L77 Author: Aaron Davidson Closes #267 from aarondav/repl and squashes the following commits: f9c62d2 [Aaron Davidson] SPARK-1349: spark-shell gets its own command history separate from scala repl --- .../org/apache/spark/repl/SparkJLineReader.scala | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkJLineReader.scala b/repl/src/main/scala/org/apache/spark/repl/SparkJLineReader.scala index 946e71039088d..0db26c3407dff 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkJLineReader.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkJLineReader.scala @@ -7,8 +7,10 @@ package org.apache.spark.repl +import scala.reflect.io.{Path, File} import scala.tools.nsc._ import scala.tools.nsc.interpreter._ +import scala.tools.nsc.interpreter.session.JLineHistory.JLineFileHistory import scala.tools.jline.console.ConsoleReader import scala.tools.jline.console.completer._ @@ -25,7 +27,7 @@ class SparkJLineReader(_completion: => Completion) extends InteractiveReader { val consoleReader = new JLineConsoleReader() lazy val completion = _completion - lazy val history: JLineHistory = JLineHistory() + lazy val history: JLineHistory = new SparkJLineHistory private def term = consoleReader.getTerminal() def reset() = term.reset() @@ -78,3 +80,11 @@ class SparkJLineReader(_completion: => Completion) extends InteractiveReader { def readOneLine(prompt: String) = consoleReader readLine prompt def readOneKey(prompt: String) = consoleReader readOneKey prompt } + +/** Changes the default history file to not collide with the scala repl's. */ +class SparkJLineHistory extends JLineFileHistory { + import Properties.userHome + + def defaultFileName = ".spark_history" + override protected lazy val historyFile = File(Path(userHome) / defaultFileName) +} From 4106558435889261243d186f5f0b51c5f9e98d56 Mon Sep 17 00:00:00 2001 From: Aaron Davidson Date: Sun, 6 Apr 2014 17:48:41 -0700 Subject: [PATCH 52/78] SPARK-1314: Use SPARK_HIVE to determine if we include Hive in packaging Previously, we based our decision regarding including datanucleus jars based on the existence of a spark-hive-assembly jar, which was incidentally built whenever "sbt assembly" is run. This means that a typical and previously supported pathway would start using hive jars. This patch has the following features/bug fixes: - Use of SPARK_HIVE (default false) to determine if we should include Hive in the assembly jar. - Analagous feature in Maven with -Phive (previously, there was no support for adding Hive to any of our jars produced by Maven) - assemble-deps fixed since we no longer use a different ASSEMBLY_DIR - avoid adding log message in compute-classpath.sh to the classpath :) Still TODO before mergeable: - We need to download the datanucleus jars outside of sbt. Perhaps we can have spark-class download them if SPARK_HIVE is set similar to how sbt downloads itself. - Spark SQL documentation updates. Author: Aaron Davidson Closes #237 from aarondav/master and squashes the following commits: 5dc4329 [Aaron Davidson] Typo fixes dd4f298 [Aaron Davidson] Doc update dd1a365 [Aaron Davidson] Eliminate need for SPARK_HIVE at runtime by d/ling datanucleus from Maven a9269b5 [Aaron Davidson] [WIP] Use SPARK_HIVE to determine if we include Hive in packaging --- assembly/pom.xml | 10 ++++++++ bin/compute-classpath.sh | 35 +++++++++++++++------------- bin/spark-class | 2 -- dev/create-release/create-release.sh | 4 ++-- docs/sql-programming-guide.md | 4 ++-- pom.xml | 7 +++++- project/SparkBuild.scala | 25 +++++++++++++------- sql/hive/pom.xml | 28 ++++++++++++++++++++++ 8 files changed, 83 insertions(+), 32 deletions(-) diff --git a/assembly/pom.xml b/assembly/pom.xml index 255107a2c47cb..923bf47f7076a 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -163,6 +163,16 @@ + + hive + + + org.apache.spark + spark-hive_${scala.binary.version} + ${project.version} + + + spark-ganglia-lgpl diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh index bef42df71ce01..be37102dc069a 100755 --- a/bin/compute-classpath.sh +++ b/bin/compute-classpath.sh @@ -30,21 +30,7 @@ FWDIR="$(cd `dirname $0`/..; pwd)" # Build up classpath CLASSPATH="$SPARK_CLASSPATH:$FWDIR/conf" -# Support for interacting with Hive. Since hive pulls in a lot of dependencies that might break -# existing Spark applications, it is not included in the standard spark assembly. Instead, we only -# include it in the classpath if the user has explicitly requested it by running "sbt hive/assembly" -# Hopefully we will find a way to avoid uber-jars entirely and deploy only the needed packages in -# the future. -if [ -f "$FWDIR"/sql/hive/target/scala-$SCALA_VERSION/spark-hive-assembly-*.jar ]; then - - # Datanucleus jars do not work if only included in the uberjar as plugin.xml metadata is lost. - DATANUCLEUSJARS=$(JARS=("$FWDIR/lib_managed/jars"/datanucleus-*.jar); IFS=:; echo "${JARS[*]}") - CLASSPATH=$CLASSPATH:$DATANUCLEUSJARS - - ASSEMBLY_DIR="$FWDIR/sql/hive/target/scala-$SCALA_VERSION/" -else - ASSEMBLY_DIR="$FWDIR/assembly/target/scala-$SCALA_VERSION/" -fi +ASSEMBLY_DIR="$FWDIR/assembly/target/scala-$SCALA_VERSION" # First check if we have a dependencies jar. If so, include binary classes with the deps jar if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then @@ -59,7 +45,7 @@ if [ -f "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar ]; then CLASSPATH="$CLASSPATH:$FWDIR/sql/core/target/scala-$SCALA_VERSION/classes" CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SCALA_VERSION/classes" - DEPS_ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark*-assembly*hadoop*-deps.jar` + DEPS_ASSEMBLY_JAR=`ls "$ASSEMBLY_DIR"/spark-assembly*hadoop*-deps.jar` CLASSPATH="$CLASSPATH:$DEPS_ASSEMBLY_JAR" else # Else use spark-assembly jar from either RELEASE or assembly directory @@ -71,6 +57,23 @@ else CLASSPATH="$CLASSPATH:$ASSEMBLY_JAR" fi +# When Hive support is needed, Datanucleus jars must be included on the classpath. +# Datanucleus jars do not work if only included in the uber jar as plugin.xml metadata is lost. +# Both sbt and maven will populate "lib_managed/jars/" with the datanucleus jars when Spark is +# built with Hive, so first check if the datanucleus jars exist, and then ensure the current Spark +# assembly is built for Hive, before actually populating the CLASSPATH with the jars. +# Note that this check order is faster (by up to half a second) in the case where Hive is not used. +num_datanucleus_jars=$(ls "$FWDIR"/lib_managed/jars/ | grep "datanucleus-.*\\.jar" | wc -l) +if [ $num_datanucleus_jars -gt 0 ]; then + AN_ASSEMBLY_JAR=${ASSEMBLY_JAR:-$DEPS_ASSEMBLY_JAR} + num_hive_files=$(jar tvf "$AN_ASSEMBLY_JAR" org/apache/hadoop/hive/ql/exec 2>/dev/null | wc -l) + if [ $num_hive_files -gt 0 ]; then + echo "Spark assembly has been built with Hive, including Datanucleus jars on classpath" 1>&2 + DATANUCLEUSJARS=$(echo "$FWDIR/lib_managed/jars"/datanucleus-*.jar | tr " " :) + CLASSPATH=$CLASSPATH:$DATANUCLEUSJARS + fi +fi + # Add test classes if we're running from SBT or Maven with SPARK_TESTING set to 1 if [[ $SPARK_TESTING == 1 ]]; then CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SCALA_VERSION/test-classes" diff --git a/bin/spark-class b/bin/spark-class index 0dcf0e156cb52..76fde3e448891 100755 --- a/bin/spark-class +++ b/bin/spark-class @@ -154,5 +154,3 @@ if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then fi exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@" - - diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh index 995106f111443..bf1c5d7953bd2 100755 --- a/dev/create-release/create-release.sh +++ b/dev/create-release/create-release.sh @@ -49,14 +49,14 @@ mvn -DskipTests \ -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \ -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \ -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \ - -Pyarn -Pspark-ganglia-lgpl \ + -Pyarn -Phive -Pspark-ganglia-lgpl\ -Dtag=$GIT_TAG -DautoVersionSubmodules=true \ --batch-mode release:prepare mvn -DskipTests \ -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \ -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \ - -Pyarn -Pspark-ganglia-lgpl\ + -Pyarn -Phive -Pspark-ganglia-lgpl\ release:perform rm -rf spark diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index f849716f7a48f..a59393e1424de 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -264,8 +264,8 @@ evaluated by the SQL execution engine. A full list of the functions supported c Spark SQL also supports reading and writing data stored in [Apache Hive](http://hive.apache.org/). However, since Hive has a large number of dependencies, it is not included in the default Spark assembly. -In order to use Hive you must first run '`SPARK_HIVE=true sbt/sbt assembly/assembly`'. This command builds a new assembly -jar that includes Hive. Note that this Hive assembly jar must also be present +In order to use Hive you must first run '`SPARK_HIVE=true sbt/sbt assembly/assembly`' (or use `-Phive` for maven). +This command builds a new assembly jar that includes Hive. Note that this Hive assembly jar must also be present on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries (SerDes) in order to acccess data stored in Hive. diff --git a/pom.xml b/pom.xml index 1426e0e00214c..c03bb35c99442 100644 --- a/pom.xml +++ b/pom.xml @@ -377,7 +377,6 @@ org.apache.derby derby 10.4.2.0 - test net.liftweb @@ -580,6 +579,12 @@ + + + org.codehaus.jackson + jackson-mapper-asl + 1.8.8 + diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 3489b43d43f0d..d1e4b8b964b88 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -43,6 +43,8 @@ object SparkBuild extends Build { val DEFAULT_YARN = false + val DEFAULT_HIVE = false + // HBase version; set as appropriate. val HBASE_VERSION = "0.94.6" @@ -67,15 +69,17 @@ object SparkBuild extends Build { lazy val sql = Project("sql", file("sql/core"), settings = sqlCoreSettings) dependsOn(core, catalyst) - // Since hive is its own assembly, it depends on all of the modules. - lazy val hive = Project("hive", file("sql/hive"), settings = hiveSettings) dependsOn(sql, graphx, bagel, mllib, streaming, repl) + lazy val hive = Project("hive", file("sql/hive"), settings = hiveSettings) dependsOn(sql) + + lazy val maybeHive: Seq[ClasspathDependency] = if (isHiveEnabled) Seq(hive) else Seq() + lazy val maybeHiveRef: Seq[ProjectReference] = if (isHiveEnabled) Seq(hive) else Seq() lazy val streaming = Project("streaming", file("streaming"), settings = streamingSettings) dependsOn(core) lazy val mllib = Project("mllib", file("mllib"), settings = mllibSettings) dependsOn(core) lazy val assemblyProj = Project("assembly", file("assembly"), settings = assemblyProjSettings) - .dependsOn(core, graphx, bagel, mllib, streaming, repl, sql) dependsOn(maybeYarn: _*) dependsOn(maybeGanglia: _*) + .dependsOn(core, graphx, bagel, mllib, streaming, repl, sql) dependsOn(maybeYarn: _*) dependsOn(maybeHive: _*) dependsOn(maybeGanglia: _*) lazy val assembleDeps = TaskKey[Unit]("assemble-deps", "Build assembly of dependencies and packages Spark projects") @@ -101,6 +105,11 @@ object SparkBuild extends Build { lazy val hadoopClient = if (hadoopVersion.startsWith("0.20.") || hadoopVersion == "1.0.0") "hadoop-core" else "hadoop-client" val maybeAvro = if (hadoopVersion.startsWith("0.23.") && isYarnEnabled) Seq("org.apache.avro" % "avro" % "1.7.4") else Seq() + lazy val isHiveEnabled = Properties.envOrNone("SPARK_HIVE") match { + case None => DEFAULT_HIVE + case Some(v) => v.toBoolean + } + // Include Ganglia integration if the user has enabled Ganglia // This is isolated from the normal build due to LGPL-licensed code in the library lazy val isGangliaEnabled = Properties.envOrNone("SPARK_GANGLIA_LGPL").isDefined @@ -141,13 +150,13 @@ object SparkBuild extends Build { lazy val allExternalRefs = Seq[ProjectReference](externalTwitter, externalKafka, externalFlume, externalZeromq, externalMqtt) lazy val examples = Project("examples", file("examples"), settings = examplesSettings) - .dependsOn(core, mllib, graphx, bagel, streaming, externalTwitter, hive) dependsOn(allExternal: _*) + .dependsOn(core, mllib, graphx, bagel, streaming, hive) dependsOn(allExternal: _*) // Everything except assembly, hive, tools, java8Tests and examples belong to packageProjects - lazy val packageProjects = Seq[ProjectReference](core, repl, bagel, streaming, mllib, graphx, catalyst, sql) ++ maybeYarnRef ++ maybeGangliaRef + lazy val packageProjects = Seq[ProjectReference](core, repl, bagel, streaming, mllib, graphx, catalyst, sql) ++ maybeYarnRef ++ maybeHiveRef ++ maybeGangliaRef lazy val allProjects = packageProjects ++ allExternalRefs ++ - Seq[ProjectReference](examples, tools, assemblyProj, hive) ++ maybeJava8Tests + Seq[ProjectReference](examples, tools, assemblyProj) ++ maybeJava8Tests def sharedSettings = Defaults.defaultSettings ++ MimaBuild.mimaSettings(file(sparkHome)) ++ Seq( organization := "org.apache.spark", @@ -417,10 +426,8 @@ object SparkBuild extends Build { // Since we don't include hive in the main assembly this project also acts as an alternative // assembly jar. - def hiveSettings = sharedSettings ++ assemblyProjSettings ++ Seq( + def hiveSettings = sharedSettings ++ Seq( name := "spark-hive", - jarName in assembly <<= version map { v => "spark-hive-assembly-" + v + "-hadoop" + hadoopVersion + ".jar" }, - jarName in packageDependency <<= version map { v => "spark-hive-assembly-" + v + "-hadoop" + hadoopVersion + "-deps.jar" }, javaOptions += "-XX:MaxPermSize=1g", libraryDependencies ++= Seq( "org.apache.hive" % "hive-metastore" % hiveVersion, diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml index 63f592cb4b441..a662da76ce25a 100644 --- a/sql/hive/pom.xml +++ b/sql/hive/pom.xml @@ -63,6 +63,10 @@ hive-exec ${hive.version} + + org.codehaus.jackson + jackson-mapper-asl + org.apache.hive hive-serde @@ -87,6 +91,30 @@ org.scalatest scalatest-maven-plugin + + + + org.apache.maven.plugins + maven-dependency-plugin + 2.4 + + + copy-dependencies + package + + copy-dependencies + + + + ${basedir}/../../lib_managed/jars + false + false + true + org.datanucleus + + + + From 1440154c27ca48b5a75103eccc9057286d3f6ca8 Mon Sep 17 00:00:00 2001 From: Evan Chan Date: Sun, 6 Apr 2014 19:17:33 -0700 Subject: [PATCH 53/78] SPARK-1154: Clean up app folders in worker nodes This is a fix for [SPARK-1154](https://issues.apache.org/jira/browse/SPARK-1154). The issue is that worker nodes fill up with a huge number of app-* folders after some time. This change adds a periodic cleanup task which asynchronously deletes app directories older than a configurable TTL. Two new configuration parameters have been introduced: spark.worker.cleanup_interval spark.worker.app_data_ttl This change does not include moving the downloads of application jars to a location outside of the work directory. We will address that if we have time, but that potentially involves caching so it will come either as part of this PR or a separate PR. Author: Evan Chan Author: Kelvin Chu Closes #288 from velvia/SPARK-1154-cleanup-app-folders and squashes the following commits: 0689995 [Evan Chan] CR from @aarondav - move config, clarify for standalone mode 9f10d96 [Evan Chan] CR from @pwendell - rename configs and add cleanup.enabled f2f6027 [Evan Chan] CR from @andrewor14 553d8c2 [Kelvin Chu] change the variable name to currentTimeMillis since it actually tracks in seconds 8dc9cb5 [Kelvin Chu] Fixed a bug in Utils.findOldFiles() after merge. cb52f2b [Kelvin Chu] Change the name of findOldestFiles() to findOldFiles() 72f7d2d [Kelvin Chu] Fix a bug of Utils.findOldestFiles(). file.lastModified is returned in milliseconds. ad99955 [Kelvin Chu] Add unit test for Utils.findOldestFiles() dc1a311 [Evan Chan] Don't recompute current time with every new file e3c408e [Evan Chan] Document the two new settings b92752b [Evan Chan] SPARK-1154: Add a periodic task to clean up app directories --- .../apache/spark/deploy/DeployMessage.scala | 4 +++ .../apache/spark/deploy/worker/Worker.scala | 23 +++++++++++++++- .../scala/org/apache/spark/util/Utils.scala | 19 ++++++++++++-- .../org/apache/spark/util/UtilsSuite.scala | 15 ++++++++++- docs/configuration.md | 26 +++++++++++++++++++ 5 files changed, 83 insertions(+), 4 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala index 83ce14a0a806a..a7368f9f3dfbe 100644 --- a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala @@ -86,6 +86,10 @@ private[deploy] object DeployMessages { case class KillDriver(driverId: String) extends DeployMessage + // Worker internal + + case object WorkDirCleanup // Sent to Worker actor periodically for cleaning up app folders + // AppClient to Master case class RegisterApplication(appDescription: ApplicationDescription) diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index 8a71ddda4cb5e..bf5a8d09dd2df 100755 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -64,6 +64,12 @@ private[spark] class Worker( val REGISTRATION_TIMEOUT = 20.seconds val REGISTRATION_RETRIES = 3 + val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", true) + // How often worker will clean up old app folders + val CLEANUP_INTERVAL_MILLIS = conf.getLong("spark.worker.cleanup.interval", 60 * 30) * 1000 + // TTL for app folders/data; after TTL expires it will be cleaned up + val APP_DATA_RETENTION_SECS = conf.getLong("spark.worker.cleanup.appDataTtl", 7 * 24 * 3600) + // Index into masterUrls that we're currently trying to register with. var masterIndex = 0 @@ -179,12 +185,28 @@ private[spark] class Worker( registered = true changeMaster(masterUrl, masterWebUiUrl) context.system.scheduler.schedule(0 millis, HEARTBEAT_MILLIS millis, self, SendHeartbeat) + if (CLEANUP_ENABLED) { + context.system.scheduler.schedule(CLEANUP_INTERVAL_MILLIS millis, + CLEANUP_INTERVAL_MILLIS millis, self, WorkDirCleanup) + } case SendHeartbeat => masterLock.synchronized { if (connected) { master ! Heartbeat(workerId) } } + case WorkDirCleanup => + // Spin up a separate thread (in a future) to do the dir cleanup; don't tie up worker actor + val cleanupFuture = concurrent.future { + logInfo("Cleaning up oldest application directories in " + workDir + " ...") + Utils.findOldFiles(workDir, APP_DATA_RETENTION_SECS) + .foreach(Utils.deleteRecursively) + } + cleanupFuture onFailure { + case e: Throwable => + logError("App dir cleanup failed: " + e.getMessage, e) + } + case MasterChanged(masterUrl, masterWebUiUrl) => logInfo("Master has changed, new master is at " + masterUrl) changeMaster(masterUrl, masterWebUiUrl) @@ -331,7 +353,6 @@ private[spark] class Worker( } private[spark] object Worker { - def main(argStrings: Array[String]) { val args = new WorkerArguments(argStrings) val (actorSystem, _) = startSystemAndActor(args.host, args.port, args.webUiPort, args.cores, 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 d3c39dee330b2..4435b21a7505e 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -597,9 +597,24 @@ private[spark] object Utils extends Logging { } if (fileInCanonicalDir.getCanonicalFile().equals(fileInCanonicalDir.getAbsoluteFile())) { - return false; + return false } else { - return true; + return true + } + } + + /** + * Finds all the files in a directory whose last modified time is older than cutoff seconds. + * @param dir must be the path to a directory, or IllegalArgumentException is thrown + * @param cutoff measured in seconds. Files older than this are returned. + */ + def findOldFiles(dir: File, cutoff: Long): Seq[File] = { + val currentTimeMillis = System.currentTimeMillis + if (dir.isDirectory) { + val files = listFilesSafely(dir) + files.filter { file => file.lastModified < (currentTimeMillis - cutoff * 1000) } + } else { + throw new IllegalArgumentException(dir + " is not a directory!") } } 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 616214fb5e3a6..eb7fb6318262b 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -19,7 +19,7 @@ package org.apache.spark.util import scala.util.Random -import java.io.{ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream} +import java.io.{File, ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream} import java.nio.{ByteBuffer, ByteOrder} import com.google.common.base.Charsets @@ -154,5 +154,18 @@ class UtilsSuite extends FunSuite { val iterator = Iterator.range(0, 5) assert(Utils.getIteratorSize(iterator) === 5L) } + + test("findOldFiles") { + // create some temporary directories and files + val parent: File = Utils.createTempDir() + val child1: File = Utils.createTempDir(parent.getCanonicalPath) // The parent directory has two child directories + val child2: File = Utils.createTempDir(parent.getCanonicalPath) + // set the last modified time of child1 to 10 secs old + child1.setLastModified(System.currentTimeMillis() - (1000 * 10)) + + val result = Utils.findOldFiles(parent, 5) // find files older than 5 secs + assert(result.size.equals(1)) + assert(result(0).getCanonicalPath.equals(child1.getCanonicalPath)) + } } diff --git a/docs/configuration.md b/docs/configuration.md index b6005acac8b93..57bda20edcdf1 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -348,6 +348,32 @@ Apart from these, the following properties are also available, and may be useful receives no heartbeats. + + + + + + + + + + + + + + + From 87d0928a3301835705652c24a26096546597e156 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Sun, 6 Apr 2014 21:04:45 -0700 Subject: [PATCH 54/78] SPARK-1431: Allow merging conflicting pull requests Sometimes if there is a small conflict it's nice to be able to just manually fix it up rather than have another RTT with the contributor. Author: Patrick Wendell Closes #342 from pwendell/merge-conflicts and squashes the following commits: cdce61a [Patrick Wendell] SPARK-1431: Allow merging conflicting pull requests --- dev/merge_spark_pr.py | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py index e8f78fc5f231a..7a61943e94814 100755 --- a/dev/merge_spark_pr.py +++ b/dev/merge_spark_pr.py @@ -87,11 +87,20 @@ def merge_pr(pr_num, target_ref): run_cmd("git fetch %s %s:%s" % (PUSH_REMOTE_NAME, target_ref, target_branch_name)) run_cmd("git checkout %s" % target_branch_name) - run_cmd(['git', 'merge', pr_branch_name, '--squash']) + had_conflicts = False + try: + run_cmd(['git', 'merge', pr_branch_name, '--squash']) + except Exception as e: + msg = "Error merging: %s\nWould you like to manually fix-up this merge?" % e + continue_maybe(msg) + msg = "Okay, please fix any conflicts and 'git add' conflicting files... Finished?" + continue_maybe(msg) + had_conflicts = True commit_authors = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name, '--pretty=format:%an <%ae>']).split("\n") - distinct_authors = sorted(set(commit_authors), key=lambda x: commit_authors.count(x), reverse=True) + distinct_authors = sorted(set(commit_authors), key=lambda x: commit_authors.count(x), + reverse=True) primary_author = distinct_authors[0] commits = run_cmd(['git', 'log', 'HEAD..%s' % pr_branch_name, '--pretty=format:%h [%an] %s']).split("\n\n") @@ -105,6 +114,13 @@ def merge_pr(pr_num, target_ref): merge_message_flags += ["-m", authors] + if had_conflicts: + committer_name = run_cmd("git config --get user.name").strip() + committer_email = run_cmd("git config --get user.email").strip() + message = "This patch had conflicts when merged, resolved by\nCommitter: %s <%s>" % ( + committer_name, committer_email) + merge_message_flags += ["-m", message] + # The string "Closes #%s" string is required for GitHub to correctly close the PR merge_message_flags += ["-m", "Closes #%s from %s and squashes the following commits:" % (pr_num, pr_repo_desc)] @@ -186,8 +202,10 @@ def maybe_cherry_pick(pr_num, merge_hash, default_branch): maybe_cherry_pick(pr_num, merge_hash, latest_branch) sys.exit(0) -if bool(pr["mergeable"]) == False: - fail("Pull request %s is not mergeable in its current form" % pr_num) +if not bool(pr["mergeable"]): + msg = "Pull request %s is not mergeable in its current form.\n" % pr_num + \ + "Continue? (experts only!)" + continue_maybe(msg) print ("\n=== Pull Request #%s ===" % pr_num) print("title\t%s\nsource\t%s\ntarget\t%s\nurl\t%s" % ( From accd0999f9cb6a449434d3fc5274dd469eeecab2 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Mon, 7 Apr 2014 00:14:00 -0700 Subject: [PATCH 55/78] [SQL] SPARK-1371 Hash Aggregation Improvements Given: ```scala case class Data(a: Int, b: Int) val rdd = sparkContext .parallelize(1 to 200) .flatMap(_ => (1 to 50000).map(i => Data(i % 100, i))) rdd.registerAsTable("data") cacheTable("data") ``` Before: ``` SELECT COUNT(*) FROM data:[10000000] 16795.567ms SELECT a, SUM(b) FROM data GROUP BY a 7536.436ms SELECT SUM(b) FROM data 10954.1ms ``` After: ``` SELECT COUNT(*) FROM data:[10000000] 1372.175ms SELECT a, SUM(b) FROM data GROUP BY a 2070.446ms SELECT SUM(b) FROM data 958.969ms ``` Author: Michael Armbrust Closes #295 from marmbrus/hashAgg and squashes the following commits: ec63575 [Michael Armbrust] Add comment. d0495a9 [Michael Armbrust] Use scaladoc instead. b4a6887 [Michael Armbrust] Address review comments. a2d90ba [Michael Armbrust] Capture child output statically to avoid issues with generators and serialization. 7c13112 [Michael Armbrust] Rewrite Aggregate operator to stream input and use projections. Remove unused local RDD functions implicits. 5096f99 [Michael Armbrust] Make HiveUDAF fields transient since object inspectors are not serializable. 6a4b671 [Michael Armbrust] Add option to avoid binding operators expressions automatically. 92cca08 [Michael Armbrust] Always include serialization debug info when running tests. 1279df2 [Michael Armbrust] Increase default number of partitions. --- project/SparkBuild.scala | 1 + .../catalyst/expressions/BoundAttribute.scala | 6 + .../sql/catalyst/expressions/Projection.scala | 6 +- .../sql/catalyst/expressions/aggregates.scala | 16 +- .../rdd/PartitionLocalRDDFunctions.scala | 100 ---------- .../apache/spark/sql/execution/Exchange.scala | 2 +- .../spark/sql/execution/aggregates.scala | 183 +++++++++++++----- .../org/apache/spark/sql/hive/hiveUdfs.scala | 3 + 8 files changed, 157 insertions(+), 160 deletions(-) delete mode 100644 sql/core/src/main/scala/org/apache/spark/rdd/PartitionLocalRDDFunctions.scala diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index d1e4b8b964b88..6b8740d9f21a1 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -178,6 +178,7 @@ object SparkBuild extends Build { fork := true, javaOptions in Test += "-Dspark.home=" + sparkHome, javaOptions in Test += "-Dspark.testing=1", + 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, javaOptions += "-Xmx3g", // Show full stack trace and duration in test cases. 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 f70e80b7f27f2..37b9035df9d8c 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 @@ -48,11 +48,17 @@ case class BoundReference(ordinal: Int, baseReference: Attribute) override def apply(input: Row): Any = input(ordinal) } +/** + * Used to denote operators that do their own binding of attributes internally. + */ +trait NoBind { self: trees.TreeNode[_] => } + class BindReferences[TreeNode <: QueryPlan[TreeNode]] extends Rule[TreeNode] { import BindReferences._ def apply(plan: TreeNode): TreeNode = { plan.transform { + case n: NoBind => n.asInstanceOf[TreeNode] case leafNode if leafNode.children.isEmpty => leafNode case unaryNode if unaryNode.children.size == 1 => unaryNode.transformExpressions { case e => bindReference(e, unaryNode.children.head.output) 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 38542d3fc7290..5576ecbb65ef5 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 @@ -28,9 +28,9 @@ class Projection(expressions: Seq[Expression]) extends (Row => Row) { protected val exprArray = expressions.toArray def apply(input: Row): Row = { - val outputArray = new Array[Any](exprArray.size) + val outputArray = new Array[Any](exprArray.length) var i = 0 - while (i < exprArray.size) { + while (i < exprArray.length) { outputArray(i) = exprArray(i).apply(input) i += 1 } @@ -57,7 +57,7 @@ case class MutableProjection(expressions: Seq[Expression]) extends (Row => Row) def apply(input: Row): Row = { var i = 0 - while (i < exprArray.size) { + while (i < exprArray.length) { mutableRow(i) = exprArray(i).apply(input) i += 1 } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 7303b155cae3d..53b884a41e16b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -27,7 +27,7 @@ abstract class AggregateExpression extends Expression { * Creates a new instance that can be used to compute this aggregate expression for a group * of input rows/ */ - def newInstance: AggregateFunction + def newInstance(): AggregateFunction } /** @@ -75,7 +75,7 @@ abstract class AggregateFunction override def apply(input: Row): Any // Do we really need this? - def newInstance = makeCopy(productIterator.map { case a: AnyRef => a }.toArray) + def newInstance() = makeCopy(productIterator.map { case a: AnyRef => a }.toArray) } case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { @@ -89,7 +89,7 @@ case class Count(child: Expression) extends PartialAggregate with trees.UnaryNod SplitEvaluation(Sum(partialCount.toAttribute), partialCount :: Nil) } - override def newInstance = new CountFunction(child, this) + override def newInstance()= new CountFunction(child, this) } case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpression { @@ -98,7 +98,7 @@ case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpressi def nullable = false def dataType = IntegerType override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")}})" - override def newInstance = new CountDistinctFunction(expressions, this) + override def newInstance()= new CountDistinctFunction(expressions, this) } case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { @@ -118,7 +118,7 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN partialCount :: partialSum :: Nil) } - override def newInstance = new AverageFunction(child, this) + override def newInstance()= new AverageFunction(child, this) } case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { @@ -134,7 +134,7 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[ partialSum :: Nil) } - override def newInstance = new SumFunction(child, this) + override def newInstance()= new SumFunction(child, this) } case class SumDistinct(child: Expression) @@ -145,7 +145,7 @@ case class SumDistinct(child: Expression) def dataType = child.dataType override def toString = s"SUM(DISTINCT $child)" - override def newInstance = new SumDistinctFunction(child, this) + override def newInstance()= new SumDistinctFunction(child, this) } case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { @@ -160,7 +160,7 @@ case class First(child: Expression) extends PartialAggregate with trees.UnaryNod First(partialFirst.toAttribute), partialFirst :: Nil) } - override def newInstance = new FirstFunction(child, this) + override def newInstance()= new FirstFunction(child, this) } case class AverageFunction(expr: Expression, base: AggregateExpression) diff --git a/sql/core/src/main/scala/org/apache/spark/rdd/PartitionLocalRDDFunctions.scala b/sql/core/src/main/scala/org/apache/spark/rdd/PartitionLocalRDDFunctions.scala deleted file mode 100644 index f1230e7526ab1..0000000000000 --- a/sql/core/src/main/scala/org/apache/spark/rdd/PartitionLocalRDDFunctions.scala +++ /dev/null @@ -1,100 +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.rdd - -import scala.language.implicitConversions - -import scala.reflect._ -import scala.collection.mutable.ArrayBuffer - -import org.apache.spark.{Aggregator, InterruptibleIterator, Logging} -import org.apache.spark.util.collection.AppendOnlyMap - -/* Implicit conversions */ -import org.apache.spark.SparkContext._ - -/** - * Extra functions on RDDs that perform only local operations. These can be used when data has - * already been partitioned correctly. - */ -private[spark] class PartitionLocalRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) - extends Logging - with Serializable { - - /** - * Cogroup corresponding partitions of `this` and `other`. These two RDDs should have - * the same number of partitions. Partitions of these two RDDs are cogrouped - * according to the indexes of partitions. If we have two RDDs and - * each of them has n partitions, we will cogroup the partition i from `this` - * with the partition i from `other`. - * This function will not introduce a shuffling operation. - */ - def cogroupLocally[W](other: RDD[(K, W)]): RDD[(K, (Seq[V], Seq[W]))] = { - val cg = self.zipPartitions(other)((iter1:Iterator[(K, V)], iter2:Iterator[(K, W)]) => { - val map = new AppendOnlyMap[K, Seq[ArrayBuffer[Any]]] - - val update: (Boolean, Seq[ArrayBuffer[Any]]) => Seq[ArrayBuffer[Any]] = (hadVal, oldVal) => { - if (hadVal) oldVal else Array.fill(2)(new ArrayBuffer[Any]) - } - - val getSeq = (k: K) => { - map.changeValue(k, update) - } - - iter1.foreach { kv => getSeq(kv._1)(0) += kv._2 } - iter2.foreach { kv => getSeq(kv._1)(1) += kv._2 } - - map.iterator - }).mapValues { case Seq(vs, ws) => (vs.asInstanceOf[Seq[V]], ws.asInstanceOf[Seq[W]])} - - cg - } - - /** - * Group the values for each key within a partition of the RDD into a single sequence. - * This function will not introduce a shuffling operation. - */ - def groupByKeyLocally(): RDD[(K, Seq[V])] = { - def createCombiner(v: V) = ArrayBuffer(v) - def mergeValue(buf: ArrayBuffer[V], v: V) = buf += v - val aggregator = new Aggregator[K, V, ArrayBuffer[V]](createCombiner, mergeValue, _ ++ _) - val bufs = self.mapPartitionsWithContext((context, iter) => { - new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context)) - }, preservesPartitioning = true) - bufs.asInstanceOf[RDD[(K, Seq[V])]] - } - - /** - * Join corresponding partitions of `this` and `other`. - * If we have two RDDs and each of them has n partitions, - * we will join the partition i from `this` with the partition i from `other`. - * This function will not introduce a shuffling operation. - */ - def joinLocally[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = { - cogroupLocally(other).flatMapValues { - case (vs, ws) => for (v <- vs.iterator; w <- ws.iterator) yield (v, w) - } - } -} - -private[spark] object PartitionLocalRDDFunctions { - implicit def rddToPartitionLocalRDDFunctions[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]) = - new PartitionLocalRDDFunctions(rdd) -} - - diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala index 869673b1fe978..450c142c0baa4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala @@ -76,7 +76,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una */ object AddExchange extends Rule[SparkPlan] { // TODO: Determine the number of partitions. - val numPartitions = 8 + val numPartitions = 150 def apply(plan: SparkPlan): SparkPlan = plan.transformUp { case operator: SparkPlan => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala index 8515a18f18c55..2a4f7b5670457 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala @@ -17,14 +17,13 @@ package org.apache.spark.sql.execution +import java.util.HashMap + import org.apache.spark.SparkContext import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ -/* Implicit conversions */ -import org.apache.spark.rdd.PartitionLocalRDDFunctions._ - /** * Groups input data by `groupingExpressions` and computes the `aggregateExpressions` for each * group. @@ -40,7 +39,7 @@ case class Aggregate( groupingExpressions: Seq[Expression], aggregateExpressions: Seq[NamedExpression], child: SparkPlan)(@transient sc: SparkContext) - extends UnaryNode { + extends UnaryNode with NoBind { override def requiredChildDistribution = if (partial) { @@ -55,61 +54,149 @@ case class Aggregate( override def otherCopyArgs = sc :: Nil + // HACK: Generators don't correctly preserve their output through serializations so we grab + // out child's output attributes statically here. + val childOutput = child.output + def output = aggregateExpressions.map(_.toAttribute) - /* Replace all aggregate expressions with spark functions that will compute the result. */ - def createAggregateImplementations() = aggregateExpressions.map { agg => - val impl = agg transform { - case a: AggregateExpression => a.newInstance + /** + * An aggregate that needs to be computed for each row in a group. + * + * @param unbound Unbound version of this aggregate, used for result substitution. + * @param aggregate A bound copy of this aggregate used to create a new aggregation buffer. + * @param resultAttribute An attribute used to refer to the result of this aggregate in the final + * output. + */ + case class ComputedAggregate( + unbound: AggregateExpression, + aggregate: AggregateExpression, + resultAttribute: AttributeReference) + + /** A list of aggregates that need to be computed for each group. */ + @transient + lazy val computedAggregates = aggregateExpressions.flatMap { agg => + agg.collect { + case a: AggregateExpression => + ComputedAggregate( + a, + BindReferences.bindReference(a, childOutput).asInstanceOf[AggregateExpression], + AttributeReference(s"aggResult:$a", a.dataType, nullable = true)()) } + }.toArray + + /** The schema of the result of all aggregate evaluations */ + @transient + lazy val computedSchema = computedAggregates.map(_.resultAttribute) + + /** Creates a new aggregate buffer for a group. */ + def newAggregateBuffer(): Array[AggregateFunction] = { + val buffer = new Array[AggregateFunction](computedAggregates.length) + var i = 0 + while (i < computedAggregates.length) { + buffer(i) = computedAggregates(i).aggregate.newInstance() + i += 1 + } + buffer + } - val remainingAttributes = impl.collect { case a: Attribute => a } - // If any references exist that are not inside agg functions then the must be grouping exprs - // in this case we must rebind them to the grouping tuple. - if (remainingAttributes.nonEmpty) { - val unaliasedAggregateExpr = agg transform { case Alias(c, _) => c } - - // An exact match with a grouping expression - val exactGroupingExpr = groupingExpressions.indexOf(unaliasedAggregateExpr) match { - case -1 => None - case ordinal => Some(BoundReference(ordinal, Alias(impl, "AGGEXPR")().toAttribute)) - } + /** Named attributes used to substitute grouping attributes into the final result. */ + @transient + lazy val namedGroups = groupingExpressions.map { + case ne: NamedExpression => ne -> ne.toAttribute + case e => e -> Alias(e, s"groupingExpr:$e")().toAttribute + } - exactGroupingExpr.getOrElse( - sys.error(s"$agg is not in grouping expressions: $groupingExpressions")) - } else { - impl + /** + * A map of substitutions that are used to insert the aggregate expressions and grouping + * expression into the final result expression. + */ + @transient + lazy val resultMap = + (computedAggregates.map { agg => agg.unbound -> agg.resultAttribute} ++ namedGroups).toMap + + /** + * Substituted version of aggregateExpressions expressions which are used to compute final + * output rows given a group and the result of all aggregate computations. + */ + @transient + lazy val resultExpressions = aggregateExpressions.map { agg => + agg.transform { + case e: Expression if resultMap.contains(e) => resultMap(e) } } def execute() = attachTree(this, "execute") { - // TODO: If the child of it is an [[catalyst.execution.Exchange]], - // do not evaluate the groupingExpressions again since we have evaluated it - // in the [[catalyst.execution.Exchange]]. - val grouped = child.execute().mapPartitions { iter => - val buildGrouping = new Projection(groupingExpressions) - iter.map(row => (buildGrouping(row), row.copy())) - }.groupByKeyLocally() - - val result = grouped.map { case (group, rows) => - val aggImplementations = createAggregateImplementations() - - // Pull out all the functions so we can feed each row into them. - val aggFunctions = aggImplementations.flatMap(_ collect { case f: AggregateFunction => f }) - - rows.foreach { row => - aggFunctions.foreach(_.update(row)) + if (groupingExpressions.isEmpty) { + child.execute().mapPartitions { iter => + val buffer = newAggregateBuffer() + var currentRow: Row = null + while (iter.hasNext) { + currentRow = iter.next() + var i = 0 + while (i < buffer.length) { + buffer(i).update(currentRow) + i += 1 + } + } + val resultProjection = new Projection(resultExpressions, computedSchema) + val aggregateResults = new GenericMutableRow(computedAggregates.length) + + var i = 0 + while (i < buffer.length) { + aggregateResults(i) = buffer(i).apply(EmptyRow) + i += 1 + } + + Iterator(resultProjection(aggregateResults)) } - buildRow(aggImplementations.map(_.apply(group))) - } - - // TODO: THIS BREAKS PIPELINING, DOUBLE COMPUTES THE ANSWER, AND USES TOO MUCH MEMORY... - if (groupingExpressions.isEmpty && result.count == 0) { - // When there there is no output to the Aggregate operator, we still output an empty row. - val aggImplementations = createAggregateImplementations() - sc.makeRDD(buildRow(aggImplementations.map(_.apply(null))) :: Nil) } else { - result + child.execute().mapPartitions { iter => + val hashTable = new HashMap[Row, Array[AggregateFunction]] + val groupingProjection = new MutableProjection(groupingExpressions, childOutput) + + var currentRow: Row = null + while (iter.hasNext) { + currentRow = iter.next() + val currentGroup = groupingProjection(currentRow) + var currentBuffer = hashTable.get(currentGroup) + if (currentBuffer == null) { + currentBuffer = newAggregateBuffer() + hashTable.put(currentGroup.copy(), currentBuffer) + } + + var i = 0 + while (i < currentBuffer.length) { + currentBuffer(i).update(currentRow) + i += 1 + } + } + + new Iterator[Row] { + private[this] val hashTableIter = hashTable.entrySet().iterator() + private[this] val aggregateResults = new GenericMutableRow(computedAggregates.length) + private[this] val resultProjection = + new MutableProjection(resultExpressions, computedSchema ++ namedGroups.map(_._2)) + private[this] val joinedRow = new JoinedRow + + override final def hasNext: Boolean = hashTableIter.hasNext + + override final def next(): Row = { + val currentEntry = hashTableIter.next() + val currentGroup = currentEntry.getKey + val currentBuffer = currentEntry.getValue + + var i = 0 + while (i < currentBuffer.length) { + // Evaluating an aggregate buffer returns the result. No row is required since we + // already added all rows in the group using update. + aggregateResults(i) = currentBuffer(i).apply(EmptyRow) + i += 1 + } + resultProjection(joinedRow(aggregateResults, currentGroup)) + } + } + } } } } 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 44901db3f963b..2c607455c8e4b 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 @@ -337,13 +337,16 @@ case class HiveGenericUdaf( type UDFType = AbstractGenericUDAFResolver + @transient protected lazy val resolver: AbstractGenericUDAFResolver = createFunction(name) + @transient protected lazy val objectInspector = { resolver.getEvaluator(children.map(_.dataType.toTypeInfo).toArray) .init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors.toArray) } + @transient protected lazy val inspectors = children.map(_.dataType).map(toInspector) def dataType: DataType = inspectorToDataType(objectInspector) From b5bae849dbdf833a80746b705732b8c86971fddc Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Mon, 7 Apr 2014 01:46:50 -0700 Subject: [PATCH 56/78] [SQL] SPARK-1427 Fix toString for SchemaRDD NativeCommands. Author: Michael Armbrust Closes #343 from marmbrus/toStringFix and squashes the following commits: 37198fe [Michael Armbrust] Fix toString for SchemaRDD NativeCommands. --- .../src/main/scala/org/apache/spark/sql/SQLContext.scala | 2 ++ .../src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala | 3 +-- .../main/scala/org/apache/spark/sql/hive/HiveContext.scala | 6 ++++++ .../apache/spark/sql/hive/execution/HiveQuerySuite.scala | 4 ++++ 4 files changed, 13 insertions(+), 2 deletions(-) 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 36059c6630aa4..3193787680d16 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 @@ -224,6 +224,8 @@ class SQLContext(@transient val sparkContext: SparkContext) protected def stringOrError[A](f: => A): String = try f.toString catch { case e: Throwable => e.toString } + def simpleString: String = stringOrError(executedPlan) + override def toString: String = s"""== Logical Plan == |${stringOrError(analyzed)} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala index 840803a52c1cf..3dd9897c0d3b8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala @@ -41,8 +41,7 @@ trait SchemaRDDLike { override def toString = s"""${super.toString} |== Query Plan == - |${queryExecution.executedPlan}""".stripMargin.trim - + |${queryExecution.simpleString}""".stripMargin.trim /** * Saves the contents of this `SchemaRDD` as a parquet file, preserving the schema. Files that 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 f66a667c0a942..353458432b210 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 @@ -297,5 +297,11 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { val asString = result.map(_.zip(types).map(toHiveString)).map(_.mkString("\t")).toSeq asString } + + override def simpleString: String = + logical match { + case _: NativeCommand => "" + case _ => executedPlan.toString + } } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala index 0c27498a93507..a09667ac84b01 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala @@ -146,4 +146,8 @@ class HiveQuerySuite extends HiveComparisonTest { hql("SELECT * FROM src TABLESAMPLE(0.1 PERCENT) s") } + test("SchemaRDD toString") { + hql("SHOW TABLES").toString + hql("SELECT * FROM src").toString + } } From a3c51c6ea2320efdeb2a6a5c1cd11d714f8994aa Mon Sep 17 00:00:00 2001 From: Davis Shepherd Date: Mon, 7 Apr 2014 10:02:00 -0700 Subject: [PATCH 57/78] SPARK-1432: Make sure that all metadata fields are properly cleaned While working on spark-1337 with @pwendell, we noticed that not all of the metadata maps in JobProgessListener were being properly cleaned. This could lead to a (hypothetical) memory leak issue should a job run long enough. This patch aims to address the issue. Author: Davis Shepherd Closes #338 from dgshep/master and squashes the following commits: a77b65c [Davis Shepherd] In the contex of SPARK-1337: Make sure that all metadata fields are properly cleaned --- .../scala/org/apache/spark/ui/jobs/JobProgressListener.scala | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) 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 cd4be57227a16..048f671c8788f 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 @@ -83,7 +83,6 @@ private[ui] class JobProgressListener(conf: SparkConf) extends SparkListener { if (stages.size > retainedStages) { val toRemove = math.max(retainedStages / 10, 1) stages.take(toRemove).foreach { s => - stageIdToTaskData.remove(s.stageId) stageIdToTime.remove(s.stageId) stageIdToShuffleRead.remove(s.stageId) stageIdToShuffleWrite.remove(s.stageId) @@ -92,8 +91,10 @@ private[ui] class JobProgressListener(conf: SparkConf) extends SparkListener { stageIdToTasksActive.remove(s.stageId) stageIdToTasksComplete.remove(s.stageId) stageIdToTasksFailed.remove(s.stageId) + stageIdToTaskData.remove(s.stageId) + stageIdToExecutorSummaries.remove(s.stageId) stageIdToPool.remove(s.stageId) - if (stageIdToDescription.contains(s.stageId)) {stageIdToDescription.remove(s.stageId)} + stageIdToDescription.remove(s.stageId) } stages.trimStart(toRemove) } From 83f2a2f14e4145a04672e42216d43100a66b1fc2 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 7 Apr 2014 10:45:31 -0700 Subject: [PATCH 58/78] [sql] Rename Expression.apply to eval for better readability. Also used this opportunity to add a bunch of override's and made some members private. Author: Reynold Xin Closes #340 from rxin/eval and squashes the following commits: a7c7ca7 [Reynold Xin] Fixed conflicts in merge. 9069de6 [Reynold Xin] Merge branch 'master' into eval 3ccc313 [Reynold Xin] Merge branch 'master' into eval 1a47e10 [Reynold Xin] Renamed apply to eval for generators and added a bunch of override's. ea061de [Reynold Xin] Rename Expression.apply to eval for better readability. --- .../catalyst/expressions/BoundAttribute.scala | 2 +- .../spark/sql/catalyst/expressions/Cast.scala | 4 +- .../sql/catalyst/expressions/Expression.scala | 26 ++--- .../sql/catalyst/expressions/Projection.scala | 5 +- .../spark/sql/catalyst/expressions/Row.scala | 4 +- .../sql/catalyst/expressions/ScalaUdf.scala | 8 +- .../catalyst/expressions/WrapDynamic.scala | 2 +- .../sql/catalyst/expressions/aggregates.scala | 96 +++++++++---------- .../sql/catalyst/expressions/arithmetic.scala | 12 +-- .../catalyst/expressions/complexTypes.scala | 14 +-- .../sql/catalyst/expressions/generators.scala | 20 ++-- .../sql/catalyst/expressions/literals.scala | 6 +- .../expressions/namedExpressions.scala | 2 +- .../catalyst/expressions/nullFunctions.scala | 12 +-- .../sql/catalyst/expressions/predicates.scala | 46 ++++----- .../expressions/stringOperations.scala | 10 +- .../sql/catalyst/optimizer/Optimizer.scala | 2 +- .../ExpressionEvaluationSuite.scala | 8 +- .../apache/spark/sql/execution/Generate.scala | 8 +- .../spark/sql/execution/aggregates.scala | 4 +- .../spark/sql/execution/basicOperators.scala | 2 +- .../apache/spark/sql/execution/TgfSuite.scala | 6 +- .../apache/spark/sql/hive/hiveOperators.scala | 4 +- .../org/apache/spark/sql/hive/hiveUdfs.scala | 12 +-- 24 files changed, 156 insertions(+), 159 deletions(-) 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 37b9035df9d8c..4ebf6c4584b94 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 @@ -45,7 +45,7 @@ case class BoundReference(ordinal: Int, baseReference: Attribute) override def toString = s"$baseReference:$ordinal" - override def apply(input: Row): Any = input(ordinal) + override def eval(input: Row): Any = input(ordinal) } /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala index 941b53fe70d23..89226999ca005 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala @@ -185,8 +185,8 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression { case DoubleType => castToDouble } - override def apply(input: Row): Any = { - val evaluated = child.apply(input) + override def eval(input: Row): Any = { + val evaluated = child.eval(input) if (evaluated == null) { null } else { 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 a3d19525503ba..f190bd0cca375 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 @@ -17,8 +17,8 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.trees 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.catalyst.types.{DataType, FractionalType, IntegralType, NumericType, NativeType} @@ -50,7 +50,7 @@ abstract class Expression extends TreeNode[Expression] { def references: Set[Attribute] /** Returns the result of evaluating this expression on a given input Row */ - def apply(input: Row = null): EvaluatedType = + def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") /** @@ -73,7 +73,7 @@ abstract class Expression extends TreeNode[Expression] { */ @inline def n1(e: Expression, i: Row, f: ((Numeric[Any], Any) => Any)): Any = { - val evalE = e.apply(i) + val evalE = e.eval(i) if (evalE == null) { null } else { @@ -102,11 +102,11 @@ abstract class Expression extends TreeNode[Expression] { throw new TreeNodeException(this, s"Types do not match ${e1.dataType} != ${e2.dataType}") } - val evalE1 = e1.apply(i) + val evalE1 = e1.eval(i) if(evalE1 == null) { null } else { - val evalE2 = e2.apply(i) + val evalE2 = e2.eval(i) if (evalE2 == null) { null } else { @@ -135,11 +135,11 @@ abstract class Expression extends TreeNode[Expression] { throw new TreeNodeException(this, s"Types do not match ${e1.dataType} != ${e2.dataType}") } - val evalE1 = e1.apply(i: Row) + val evalE1 = e1.eval(i: Row) if(evalE1 == null) { null } else { - val evalE2 = e2.apply(i: Row) + val evalE2 = e2.eval(i: Row) if (evalE2 == null) { null } else { @@ -168,11 +168,11 @@ abstract class Expression extends TreeNode[Expression] { throw new TreeNodeException(this, s"Types do not match ${e1.dataType} != ${e2.dataType}") } - val evalE1 = e1.apply(i) + val evalE1 = e1.eval(i) if(evalE1 == null) { null } else { - val evalE2 = e2.apply(i) + val evalE2 = e2.eval(i) if (evalE2 == null) { null } else { @@ -205,11 +205,11 @@ abstract class Expression extends TreeNode[Expression] { throw new TreeNodeException(this, s"Types do not match ${e1.dataType} != ${e2.dataType}") } - val evalE1 = e1.apply(i) + val evalE1 = e1.eval(i) if(evalE1 == null) { null } else { - val evalE2 = e2.apply(i) + val evalE2 = e2.eval(i) if (evalE2 == null) { null } else { @@ -231,7 +231,7 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express override def foldable = left.foldable && right.foldable - def references = left.references ++ right.references + override def references = left.references ++ right.references override def toString = s"($left $symbol $right)" } @@ -243,5 +243,5 @@ abstract class LeafExpression extends Expression with trees.LeafNode[Expression] abstract class UnaryExpression extends Expression with trees.UnaryNode[Expression] { self: Product => - def references = child.references + override def references = child.references } 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 5576ecbb65ef5..c9b7cea6a3e5f 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 @@ -27,11 +27,12 @@ class Projection(expressions: Seq[Expression]) extends (Row => Row) { this(expressions.map(BindReferences.bindReference(_, inputSchema))) protected val exprArray = expressions.toArray + def apply(input: Row): Row = { val outputArray = new Array[Any](exprArray.length) var i = 0 while (i < exprArray.length) { - outputArray(i) = exprArray(i).apply(input) + outputArray(i) = exprArray(i).eval(input) i += 1 } new GenericRow(outputArray) @@ -58,7 +59,7 @@ case class MutableProjection(expressions: Seq[Expression]) extends (Row => Row) def apply(input: Row): Row = { var i = 0 while (i < exprArray.length) { - mutableRow(i) = exprArray(i).apply(input) + mutableRow(i) = exprArray(i).eval(input) i += 1 } mutableRow diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala index 9f4d84466e6ac..0f06ea088e1a1 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala @@ -212,8 +212,8 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[Row] { var i = 0 while (i < ordering.size) { val order = ordering(i) - val left = order.child.apply(a) - val right = order.child.apply(b) + val left = order.child.eval(a) + val right = order.child.eval(b) if (left == null && right == null) { // Both null, continue looking. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala index f53d8504b083f..5e089f7618e0a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala @@ -27,13 +27,13 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi def references = children.flatMap(_.references).toSet def nullable = true - override def apply(input: Row): Any = { + override def eval(input: Row): Any = { children.size match { - case 1 => function.asInstanceOf[(Any) => Any](children(0).apply(input)) + case 1 => function.asInstanceOf[(Any) => Any](children(0).eval(input)) case 2 => function.asInstanceOf[(Any, Any) => Any]( - children(0).apply(input), - children(1).apply(input)) + children(0).eval(input), + children(1).eval(input)) } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala index 9828d0b9bd8b2..e787c59e75723 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala @@ -30,7 +30,7 @@ case class WrapDynamic(children: Seq[Attribute]) extends Expression { def references = children.toSet def dataType = DynamicType - override def apply(input: Row): DynamicRow = input match { + override def eval(input: Row): DynamicRow = input match { // Avoid copy for generic rows. case g: GenericRow => new DynamicRow(children, g.values) case otherRowType => new DynamicRow(children, otherRowType.toArray) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 53b884a41e16b..5edcea14278c7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -43,7 +43,7 @@ case class SplitEvaluation( partialEvaluations: Seq[NamedExpression]) /** - * An [[AggregateExpression]] that can be partially computed without seeing all relevent tuples. + * An [[AggregateExpression]] that can be partially computed without seeing all relevant tuples. * These partial evaluations can then be combined to compute the actual answer. */ abstract class PartialAggregate extends AggregateExpression { @@ -63,28 +63,28 @@ abstract class AggregateFunction extends AggregateExpression with Serializable with trees.LeafNode[Expression] { self: Product => - type EvaluatedType = Any + override type EvaluatedType = Any /** Base should return the generic aggregate expression that this function is computing */ val base: AggregateExpression - def references = base.references - def nullable = base.nullable - def dataType = base.dataType + override def references = base.references + override def nullable = base.nullable + override def dataType = base.dataType def update(input: Row): Unit - override def apply(input: Row): Any + override def eval(input: Row): Any // Do we really need this? - def newInstance() = makeCopy(productIterator.map { case a: AnyRef => a }.toArray) + override def newInstance() = makeCopy(productIterator.map { case a: AnyRef => a }.toArray) } case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - def references = child.references - def nullable = false - def dataType = IntegerType + override def references = child.references + override def nullable = false + override def dataType = IntegerType override def toString = s"COUNT($child)" - def asPartial: SplitEvaluation = { + override def asPartial: SplitEvaluation = { val partialCount = Alias(Count(child), "PartialCount")() SplitEvaluation(Sum(partialCount.toAttribute), partialCount :: Nil) } @@ -93,18 +93,18 @@ case class Count(child: Expression) extends PartialAggregate with trees.UnaryNod } case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpression { - def children = expressions - def references = expressions.flatMap(_.references).toSet - def nullable = false - def dataType = IntegerType + override def children = expressions + override def references = expressions.flatMap(_.references).toSet + override def nullable = false + override def dataType = IntegerType override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")}})" override def newInstance()= new CountDistinctFunction(expressions, this) } case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - def references = child.references - def nullable = false - def dataType = DoubleType + override def references = child.references + override def nullable = false + override def dataType = DoubleType override def toString = s"AVG($child)" override def asPartial: SplitEvaluation = { @@ -122,9 +122,9 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN } case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - def references = child.references - def nullable = false - def dataType = child.dataType + override def references = child.references + override def nullable = false + override def dataType = child.dataType override def toString = s"SUM($child)" override def asPartial: SplitEvaluation = { @@ -140,18 +140,18 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[ case class SumDistinct(child: Expression) extends AggregateExpression with trees.UnaryNode[Expression] { - def references = child.references - def nullable = false - def dataType = child.dataType + override def references = child.references + override def nullable = false + override def dataType = child.dataType override def toString = s"SUM(DISTINCT $child)" override def newInstance()= new SumDistinctFunction(child, this) } case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - def references = child.references - def nullable = child.nullable - def dataType = child.dataType + override def references = child.references + override def nullable = child.nullable + override def dataType = child.dataType override def toString = s"FIRST($child)" override def asPartial: SplitEvaluation = { @@ -169,17 +169,15 @@ case class AverageFunction(expr: Expression, base: AggregateExpression) def this() = this(null, null) // Required for serialization. private var count: Long = _ - private val sum = MutableLiteral(Cast(Literal(0), expr.dataType).apply(EmptyRow)) + private val sum = MutableLiteral(Cast(Literal(0), expr.dataType).eval(EmptyRow)) private val sumAsDouble = Cast(sum, DoubleType) - - private val addFunction = Add(sum, expr) - override def apply(input: Row): Any = - sumAsDouble.apply(EmptyRow).asInstanceOf[Double] / count.toDouble + override def eval(input: Row): Any = + sumAsDouble.eval(EmptyRow).asInstanceOf[Double] / count.toDouble - def update(input: Row): Unit = { + override def update(input: Row): Unit = { count += 1 sum.update(addFunction, input) } @@ -190,28 +188,28 @@ case class CountFunction(expr: Expression, base: AggregateExpression) extends Ag var count: Int = _ - def update(input: Row): Unit = { - val evaluatedExpr = expr.map(_.apply(input)) + override def update(input: Row): Unit = { + val evaluatedExpr = expr.map(_.eval(input)) if (evaluatedExpr.map(_ != null).reduceLeft(_ || _)) { count += 1 } } - override def apply(input: Row): Any = count + override def eval(input: Row): Any = count } case class SumFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { def this() = this(null, null) // Required for serialization. - private val sum = MutableLiteral(Cast(Literal(0), expr.dataType).apply(null)) + private val sum = MutableLiteral(Cast(Literal(0), expr.dataType).eval(null)) private val addFunction = Add(sum, expr) - def update(input: Row): Unit = { + override def update(input: Row): Unit = { sum.update(addFunction, input) } - override def apply(input: Row): Any = sum.apply(null) + override def eval(input: Row): Any = sum.eval(null) } case class SumDistinctFunction(expr: Expression, base: AggregateExpression) @@ -219,16 +217,16 @@ case class SumDistinctFunction(expr: Expression, base: AggregateExpression) def this() = this(null, null) // Required for serialization. - val seen = new scala.collection.mutable.HashSet[Any]() + private val seen = new scala.collection.mutable.HashSet[Any]() - def update(input: Row): Unit = { - val evaluatedExpr = expr.apply(input) + override def update(input: Row): Unit = { + val evaluatedExpr = expr.eval(input) if (evaluatedExpr != null) { seen += evaluatedExpr } } - override def apply(input: Row): Any = + override def eval(input: Row): Any = seen.reduceLeft(base.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].plus) } @@ -239,14 +237,14 @@ case class CountDistinctFunction(expr: Seq[Expression], base: AggregateExpressio val seen = new scala.collection.mutable.HashSet[Any]() - def update(input: Row): Unit = { - val evaluatedExpr = expr.map(_.apply(input)) + override def update(input: Row): Unit = { + val evaluatedExpr = expr.map(_.eval(input)) if (evaluatedExpr.map(_ != null).reduceLeft(_ && _)) { seen += evaluatedExpr } } - override def apply(input: Row): Any = seen.size + override def eval(input: Row): Any = seen.size } case class FirstFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { @@ -254,11 +252,11 @@ case class FirstFunction(expr: Expression, base: AggregateExpression) extends Ag var result: Any = null - def update(input: Row): Unit = { + override def update(input: Row): Unit = { if (result == null) { - result = expr.apply(input) + result = expr.eval(input) } } - override def apply(input: Row): Any = result + override def eval(input: Row): Any = result } 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 fba056e7c07e3..c79c1847cedf5 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 @@ -28,7 +28,7 @@ case class UnaryMinus(child: Expression) extends UnaryExpression { def nullable = child.nullable override def toString = s"-$child" - override def apply(input: Row): Any = { + override def eval(input: Row): Any = { n1(child, input, _.negate(_)) } } @@ -55,25 +55,25 @@ abstract class BinaryArithmetic extends BinaryExpression { case class Add(left: Expression, right: Expression) extends BinaryArithmetic { def symbol = "+" - override def apply(input: Row): Any = n2(input, left, right, _.plus(_, _)) + override def eval(input: Row): Any = n2(input, left, right, _.plus(_, _)) } case class Subtract(left: Expression, right: Expression) extends BinaryArithmetic { def symbol = "-" - override def apply(input: Row): Any = n2(input, left, right, _.minus(_, _)) + override def eval(input: Row): Any = n2(input, left, right, _.minus(_, _)) } case class Multiply(left: Expression, right: Expression) extends BinaryArithmetic { def symbol = "*" - override def apply(input: Row): Any = n2(input, left, right, _.times(_, _)) + override def eval(input: Row): Any = n2(input, left, right, _.times(_, _)) } case class Divide(left: Expression, right: Expression) extends BinaryArithmetic { def symbol = "/" - override def apply(input: Row): Any = dataType match { + override def eval(input: Row): Any = dataType match { case _: FractionalType => f2(input, left, right, _.div(_, _)) case _: IntegralType => i2(input, left , right, _.quot(_, _)) } @@ -83,5 +83,5 @@ case class Divide(left: Expression, right: Expression) extends BinaryArithmetic case class Remainder(left: Expression, right: Expression) extends BinaryArithmetic { def symbol = "%" - override def apply(input: Row): Any = i2(input, left, right, _.rem(_, _)) + override def eval(input: Row): Any = i2(input, left, right, _.rem(_, _)) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala index ab96618d73df7..c947155cb701c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala @@ -39,10 +39,10 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression { override def toString = s"$child[$ordinal]" - override def apply(input: Row): Any = { + override def eval(input: Row): Any = { if (child.dataType.isInstanceOf[ArrayType]) { - val baseValue = child.apply(input).asInstanceOf[Seq[_]] - val o = ordinal.apply(input).asInstanceOf[Int] + val baseValue = child.eval(input).asInstanceOf[Seq[_]] + val o = ordinal.eval(input).asInstanceOf[Int] if (baseValue == null) { null } else if (o >= baseValue.size || o < 0) { @@ -51,8 +51,8 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression { baseValue(o) } } else { - val baseValue = child.apply(input).asInstanceOf[Map[Any, _]] - val key = ordinal.apply(input) + val baseValue = child.eval(input).asInstanceOf[Map[Any, _]] + val key = ordinal.eval(input) if (baseValue == null) { null } else { @@ -85,8 +85,8 @@ case class GetField(child: Expression, fieldName: String) extends UnaryExpressio override lazy val resolved = childrenResolved && child.dataType.isInstanceOf[StructType] - override def apply(input: Row): Any = { - val baseValue = child.apply(input).asInstanceOf[Row] + override def eval(input: Row): Any = { + val baseValue = child.eval(input).asInstanceOf[Row] if (baseValue == null) null else baseValue(ordinal) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala index e9b491b10a5f2..dd78614754e12 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala @@ -35,17 +35,17 @@ import org.apache.spark.sql.catalyst.types._ * requested. The attributes produced by this function will be automatically copied anytime rules * result in changes to the Generator or its children. */ -abstract class Generator extends Expression with (Row => TraversableOnce[Row]) { +abstract class Generator extends Expression { self: Product => - type EvaluatedType = TraversableOnce[Row] + override type EvaluatedType = TraversableOnce[Row] - lazy val dataType = + override lazy val dataType = ArrayType(StructType(output.map(a => StructField(a.name, a.dataType, a.nullable)))) - def nullable = false + override def nullable = false - def references = children.flatMap(_.references).toSet + override def references = children.flatMap(_.references).toSet /** * Should be overridden by specific generators. Called only once for each instance to ensure @@ -63,7 +63,7 @@ abstract class Generator extends Expression with (Row => TraversableOnce[Row]) { } /** Should be implemented by child classes to perform specific Generators. */ - def apply(input: Row): TraversableOnce[Row] + override def eval(input: Row): TraversableOnce[Row] /** Overridden `makeCopy` also copies the attributes that are produced by this generator. */ override def makeCopy(newArgs: Array[AnyRef]): this.type = { @@ -83,7 +83,7 @@ case class Explode(attributeNames: Seq[String], child: Expression) child.resolved && (child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType]) - lazy val elementTypes = child.dataType match { + private lazy val elementTypes = child.dataType match { case ArrayType(et) => et :: Nil case MapType(kt,vt) => kt :: vt :: Nil } @@ -100,13 +100,13 @@ case class Explode(attributeNames: Seq[String], child: Expression) } } - override def apply(input: Row): TraversableOnce[Row] = { + override def eval(input: Row): TraversableOnce[Row] = { child.dataType match { case ArrayType(_) => - val inputArray = child.apply(input).asInstanceOf[Seq[Any]] + val inputArray = child.eval(input).asInstanceOf[Seq[Any]] if (inputArray == null) Nil else inputArray.map(v => new GenericRow(Array(v))) case MapType(_, _) => - val inputMap = child.apply(input).asInstanceOf[Map[Any,Any]] + val inputMap = child.eval(input).asInstanceOf[Map[Any,Any]] if (inputMap == null) Nil else inputMap.map { case (k,v) => new GenericRow(Array(k,v)) } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala index d879b2b5e8ba1..e15e16d633365 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala @@ -57,7 +57,7 @@ case class Literal(value: Any, dataType: DataType) extends LeafExpression { override def toString = if (value != null) value.toString else "null" type EvaluatedType = Any - override def apply(input: Row):Any = value + override def eval(input: Row):Any = value } // TODO: Specialize @@ -69,8 +69,8 @@ case class MutableLiteral(var value: Any, nullable: Boolean = true) extends Leaf def references = Set.empty def update(expression: Expression, input: Row) = { - value = expression.apply(input) + value = expression.eval(input) } - override def apply(input: Row) = value + override def eval(input: Row) = value } 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 69c8bed309c18..eb4bc8e755284 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 @@ -79,7 +79,7 @@ case class Alias(child: Expression, name: String) type EvaluatedType = Any - override def apply(input: Row) = child.apply(input) + override def eval(input: Row) = child.eval(input) def dataType = child.dataType def nullable = child.nullable diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala index 5a47768dcb4a1..ce6d99c911ab3 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala @@ -41,11 +41,11 @@ case class Coalesce(children: Seq[Expression]) extends Expression { throw new UnresolvedException(this, "Coalesce cannot have children of different types.") } - override def apply(input: Row): Any = { + override def eval(input: Row): Any = { var i = 0 var result: Any = null while(i < children.size && result == null) { - result = children(i).apply(input) + result = children(i).eval(input) i += 1 } result @@ -57,8 +57,8 @@ case class IsNull(child: Expression) extends Predicate with trees.UnaryNode[Expr override def foldable = child.foldable def nullable = false - override def apply(input: Row): Any = { - child.apply(input) == null + override def eval(input: Row): Any = { + child.eval(input) == null } } @@ -68,7 +68,7 @@ case class IsNotNull(child: Expression) extends Predicate with trees.UnaryNode[E def nullable = false override def toString = s"IS NOT NULL $child" - override def apply(input: Row): Any = { - child.apply(input) != null + override def eval(input: Row): Any = { + child.eval(input) != null } } 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 b74809e5ca67d..da5b2cf5b0362 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 @@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.types.{BooleanType, StringType, TimestampTy object InterpretedPredicate { def apply(expression: Expression): (Row => Boolean) = { - (r: Row) => expression.apply(r).asInstanceOf[Boolean] + (r: Row) => expression.eval(r).asInstanceOf[Boolean] } } @@ -54,8 +54,8 @@ case class Not(child: Expression) extends Predicate with trees.UnaryNode[Express def nullable = child.nullable override def toString = s"NOT $child" - override def apply(input: Row): Any = { - child.apply(input) match { + override def eval(input: Row): Any = { + child.eval(input) match { case null => null case b: Boolean => !b } @@ -71,18 +71,18 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate { def nullable = true // TODO: Figure out correct nullability semantics of IN. override def toString = s"$value IN ${list.mkString("(", ",", ")")}" - override def apply(input: Row): Any = { - val evaluatedValue = value.apply(input) - list.exists(e => e.apply(input) == evaluatedValue) + override def eval(input: Row): Any = { + val evaluatedValue = value.eval(input) + list.exists(e => e.eval(input) == evaluatedValue) } } case class And(left: Expression, right: Expression) extends BinaryPredicate { def symbol = "&&" - override def apply(input: Row): Any = { - val l = left.apply(input) - val r = right.apply(input) + override def eval(input: Row): Any = { + val l = left.eval(input) + val r = right.eval(input) if (l == false || r == false) { false } else if (l == null || r == null ) { @@ -96,9 +96,9 @@ case class And(left: Expression, right: Expression) extends BinaryPredicate { case class Or(left: Expression, right: Expression) extends BinaryPredicate { def symbol = "||" - override def apply(input: Row): Any = { - val l = left.apply(input) - val r = right.apply(input) + override def eval(input: Row): Any = { + val l = left.eval(input) + val r = right.eval(input) if (l == true || r == true) { true } else if (l == null || r == null) { @@ -115,31 +115,31 @@ abstract class BinaryComparison extends BinaryPredicate { case class Equals(left: Expression, right: Expression) extends BinaryComparison { def symbol = "=" - override def apply(input: Row): Any = { - val l = left.apply(input) - val r = right.apply(input) + override def eval(input: Row): Any = { + val l = left.eval(input) + val r = right.eval(input) if (l == null || r == null) null else l == r } } case class LessThan(left: Expression, right: Expression) extends BinaryComparison { def symbol = "<" - override def apply(input: Row): Any = c2(input, left, right, _.lt(_, _)) + override def eval(input: Row): Any = c2(input, left, right, _.lt(_, _)) } case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { def symbol = "<=" - override def apply(input: Row): Any = c2(input, left, right, _.lteq(_, _)) + override def eval(input: Row): Any = c2(input, left, right, _.lteq(_, _)) } case class GreaterThan(left: Expression, right: Expression) extends BinaryComparison { def symbol = ">" - override def apply(input: Row): Any = c2(input, left, right, _.gt(_, _)) + override def eval(input: Row): Any = c2(input, left, right, _.gt(_, _)) } case class GreaterThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { def symbol = ">=" - override def apply(input: Row): Any = c2(input, left, right, _.gteq(_, _)) + override def eval(input: Row): Any = c2(input, left, right, _.gteq(_, _)) } case class If(predicate: Expression, trueValue: Expression, falseValue: Expression) @@ -159,11 +159,11 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi } type EvaluatedType = Any - override def apply(input: Row): Any = { - if (predicate(input).asInstanceOf[Boolean]) { - trueValue.apply(input) + override def eval(input: Row): Any = { + if (predicate.eval(input).asInstanceOf[Boolean]) { + trueValue.eval(input) } else { - falseValue.apply(input) + falseValue.eval(input) } } 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 42b7a9b125b7a..a27c71db1b999 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 @@ -22,8 +22,6 @@ import java.util.regex.Pattern import org.apache.spark.sql.catalyst.types.DataType import org.apache.spark.sql.catalyst.types.StringType import org.apache.spark.sql.catalyst.types.BooleanType -import org.apache.spark.sql.catalyst.trees.TreeNode -import org.apache.spark.sql.catalyst.errors.`package`.TreeNodeException trait StringRegexExpression { @@ -52,12 +50,12 @@ trait StringRegexExpression { protected def pattern(str: String) = if(cache == null) compile(str) else cache - override def apply(input: Row): Any = { - val l = left.apply(input) - if(l == null) { + override def eval(input: Row): Any = { + val l = left.eval(input) + if (l == null) { null } else { - val r = right.apply(input) + val r = right.eval(input) if(r == null) { null } else { 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 3dd6818029bcf..37b23ba58289c 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 @@ -45,7 +45,7 @@ object ConstantFolding extends Rule[LogicalPlan] { case q: LogicalPlan => q transformExpressionsDown { // Skip redundant folding of literals. case l: Literal => l - case e if e.foldable => Literal(e.apply(null), e.dataType) + case e if e.foldable => Literal(e.eval(null), e.dataType) } } } 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 43876033d327b..92987405aa313 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 @@ -29,7 +29,7 @@ import org.apache.spark.sql.catalyst.dsl.expressions._ class ExpressionEvaluationSuite extends FunSuite { test("literals") { - assert((Literal(1) + Literal(1)).apply(null) === 2) + assert((Literal(1) + Literal(1)).eval(null) === 2) } /** @@ -62,7 +62,7 @@ class ExpressionEvaluationSuite extends FunSuite { notTrueTable.foreach { case (v, answer) => val expr = Not(Literal(v, BooleanType)) - val result = expr.apply(null) + val result = expr.eval(null) if (result != answer) fail(s"$expr should not evaluate to $result, expected: $answer") } } @@ -105,7 +105,7 @@ class ExpressionEvaluationSuite extends FunSuite { truthTable.foreach { case (l,r,answer) => val expr = op(Literal(l, BooleanType), Literal(r, BooleanType)) - val result = expr.apply(null) + val result = expr.eval(null) if (result != answer) fail(s"$expr should not evaluate to $result, expected: $answer") } @@ -113,7 +113,7 @@ class ExpressionEvaluationSuite extends FunSuite { } def evaluate(expression: Expression, inputRow: Row = EmptyRow): Any = { - expression.apply(inputRow) + expression.eval(inputRow) } def checkEvaluation(expression: Expression, expected: Any, inputRow: Row = EmptyRow): Unit = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala index e902e6ced521d..cff4887936ae1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala @@ -36,10 +36,10 @@ case class Generate( child: SparkPlan) extends UnaryNode { - def output = + override def output = if (join) child.output ++ generator.output else generator.output - def execute() = { + override def execute() = { if (join) { child.execute().mapPartitions { iter => val nullValues = Seq.fill(generator.output.size)(Literal(null)) @@ -52,7 +52,7 @@ case class Generate( val joinedRow = new JoinedRow iter.flatMap {row => - val outputRows = generator(row) + val outputRows = generator.eval(row) if (outer && outputRows.isEmpty) { outerProjection(row) :: Nil } else { @@ -61,7 +61,7 @@ case class Generate( } } } else { - child.execute().mapPartitions(iter => iter.flatMap(generator)) + child.execute().mapPartitions(iter => iter.flatMap(row => generator.eval(row))) } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala index 2a4f7b5670457..0890faa33b507 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala @@ -144,7 +144,7 @@ case class Aggregate( var i = 0 while (i < buffer.length) { - aggregateResults(i) = buffer(i).apply(EmptyRow) + aggregateResults(i) = buffer(i).eval(EmptyRow) i += 1 } @@ -190,7 +190,7 @@ case class Aggregate( while (i < currentBuffer.length) { // Evaluating an aggregate buffer returns the result. No row is required since we // already added all rows in the group using update. - aggregateResults(i) = currentBuffer(i).apply(EmptyRow) + aggregateResults(i) = currentBuffer(i).eval(EmptyRow) i += 1 } resultProjection(joinedRow(aggregateResults, currentGroup)) 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 524e5022ee14b..ab2e62463764a 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 @@ -41,7 +41,7 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { override def output = child.output override def execute() = child.execute().mapPartitions { iter => - iter.filter(condition.apply(_).asInstanceOf[Boolean]) + iter.filter(condition.eval(_).asInstanceOf[Boolean]) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TgfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TgfSuite.scala index ca5c8b8eb63dc..e55648b8ed15a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TgfSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TgfSuite.scala @@ -39,9 +39,9 @@ case class ExampleTGF(input: Seq[Attribute] = Seq('name, 'age)) extends Generato val Seq(nameAttr, ageAttr) = input - override def apply(input: Row): TraversableOnce[Row] = { - val name = nameAttr.apply(input) - val age = ageAttr.apply(input).asInstanceOf[Int] + override def eval(input: Row): TraversableOnce[Row] = { + val name = nameAttr.eval(input) + val age = ageAttr.eval(input).asInstanceOf[Int] Iterator( new GenericRow(Array[Any](s"$name is $age years old")), diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala index e2d9d8de2572a..821fb22112f87 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveOperators.scala @@ -106,7 +106,7 @@ case class HiveTableScan( } private def castFromString(value: String, dataType: DataType) = { - Cast(Literal(value), dataType).apply(null) + Cast(Literal(value), dataType).eval(null) } @transient @@ -134,7 +134,7 @@ case class HiveTableScan( // Only partitioned values are needed here, since the predicate has already been bound to // partition key attribute references. val row = new GenericRow(castedValues.toArray) - shouldKeep.apply(row).asInstanceOf[Boolean] + shouldKeep.eval(row).asInstanceOf[Boolean] } } } 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 2c607455c8e4b..f9b437d435eba 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 @@ -190,8 +190,8 @@ case class HiveSimpleUdf(name: String, children: Seq[Expression]) extends HiveUd } // TODO: Finish input output types. - override def apply(input: Row): Any = { - val evaluatedChildren = children.map(_.apply(input)) + override def eval(input: Row): Any = { + val evaluatedChildren = children.map(_.eval(input)) // Wrap the function arguments in the expected types. val args = evaluatedChildren.zip(wrappers).map { case (arg, wrapper) => wrapper(arg) @@ -216,12 +216,12 @@ case class HiveGenericUdf( val dataType: DataType = inspectorToDataType(returnInspector) - override def apply(input: Row): Any = { + override def eval(input: Row): Any = { returnInspector // Make sure initialized. val args = children.map { v => new DeferredObject { override def prepare(i: Int) = {} - override def get(): AnyRef = wrap(v.apply(input)) + override def get(): AnyRef = wrap(v.eval(input)) } }.toArray unwrap(function.evaluate(args)) @@ -406,7 +406,7 @@ case class HiveGenericUdtf( } } - override def apply(input: Row): TraversableOnce[Row] = { + override def eval(input: Row): TraversableOnce[Row] = { outputInspectors // Make sure initialized. val inputProjection = new Projection(children) @@ -460,7 +460,7 @@ case class HiveUdafFunction( private val buffer = function.getNewAggregationBuffer.asInstanceOf[GenericUDAFEvaluator.AbstractAggregationBuffer] - override def apply(input: Row): Any = unwrapData(function.evaluate(buffer), returnInspector) + override def eval(input: Row): Any = unwrapData(function.evaluate(buffer), returnInspector) @transient val inputProjection = new Projection(exprs) From 9dd8b9166225cbcfcab1f39268244c2feb42a658 Mon Sep 17 00:00:00 2001 From: Sandy Ryza Date: Mon, 7 Apr 2014 13:28:14 -0500 Subject: [PATCH 59/78] SPARK-1252. On YARN, use container-log4j.properties for executors container-log4j.properties is a file that YARN provides so that containers can have log4j.properties distinct from that of the NodeManagers. Logs now go to syslog, and stderr and stdout just have the process's standard err and standard out. I tested this on pseudo-distributed clusters for both yarn (Hadoop 2.2) and yarn-alpha (Hadoop 0.23.7)/ Author: Sandy Ryza Closes #148 from sryza/sandy-spark-1252 and squashes the following commits: c0043b8 [Sandy Ryza] Put log4j.properties file under common 55823da [Sandy Ryza] Add license headers to new files 10934b8 [Sandy Ryza] Add log4j-spark-container.properties and support SPARK_LOG4J_CONF e74450b [Sandy Ryza] SPARK-1252. On YARN, use container-log4j.properties for executors --- .../spark/deploy/yarn/ExecutorRunnable.scala | 3 ++- .../log4j-spark-container.properties | 24 +++++++++++++++++++ .../apache/spark/deploy/yarn/ClientBase.scala | 8 +++++-- .../deploy/yarn/ExecutorRunnableUtil.scala | 7 +++++- .../deploy/yarn/YarnSparkHadoopUtil.scala | 7 ++++++ yarn/pom.xml | 6 +++++ .../spark/deploy/yarn/ExecutorRunnable.scala | 3 ++- 7 files changed, 53 insertions(+), 5 deletions(-) create mode 100644 yarn/common/src/main/resources/log4j-spark-container.properties diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala index 981e8b05f602d..3469b7decedf6 100644 --- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala +++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala @@ -81,7 +81,8 @@ class ExecutorRunnable( credentials.writeTokenStorageToStream(dob) ctx.setContainerTokens(ByteBuffer.wrap(dob.getData())) - val commands = prepareCommand(masterAddress, slaveId, hostname, executorMemory, executorCores) + val commands = prepareCommand(masterAddress, slaveId, hostname, executorMemory, executorCores, + localResources.contains(ClientBase.LOG4J_PROP)) logInfo("Setting up executor with commands: " + commands) ctx.setCommands(commands) diff --git a/yarn/common/src/main/resources/log4j-spark-container.properties b/yarn/common/src/main/resources/log4j-spark-container.properties new file mode 100644 index 0000000000000..a1e37a0be27dd --- /dev/null +++ b/yarn/common/src/main/resources/log4j-spark-container.properties @@ -0,0 +1,24 @@ +# +# Licensed 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. See accompanying LICENSE file. + +# Set everything to be logged to the console +log4j.rootCategory=INFO, console +log4j.appender.console=org.apache.log4j.ConsoleAppender +log4j.appender.console.target=System.err +log4j.appender.console.layout=org.apache.log4j.PatternLayout +log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n + +# Settings to quiet third party logs that are too verbose +log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO +log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala index bc267900fcf1d..eb42922aea228 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala @@ -266,11 +266,11 @@ trait ClientBase extends Logging { localResources: HashMap[String, LocalResource], stagingDir: String): HashMap[String, String] = { logInfo("Setting up the launch environment") - val log4jConfLocalRes = localResources.getOrElse(ClientBase.LOG4J_PROP, null) val env = new HashMap[String, String]() - ClientBase.populateClasspath(yarnConf, sparkConf, log4jConfLocalRes != null, env) + ClientBase.populateClasspath(yarnConf, sparkConf, localResources.contains(ClientBase.LOG4J_PROP), + env) env("SPARK_YARN_MODE") = "true" env("SPARK_YARN_STAGING_DIR") = stagingDir env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName() @@ -344,6 +344,10 @@ trait ClientBase extends Logging { JAVA_OPTS += " " + env("SPARK_JAVA_OPTS") } + if (!localResources.contains(ClientBase.LOG4J_PROP)) { + JAVA_OPTS += " " + YarnSparkHadoopUtil.getLoggingArgsForContainerCommandLine() + } + // Command for the ApplicationMaster val commands = List[String]( Environment.JAVA_HOME.$() + "/bin/java" + diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala index 2079697d8160e..b3696c5fe7183 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala @@ -50,7 +50,8 @@ trait ExecutorRunnableUtil extends Logging { slaveId: String, hostname: String, executorMemory: Int, - executorCores: Int) = { + executorCores: Int, + userSpecifiedLogFile: Boolean) = { // Extra options for the JVM var JAVA_OPTS = "" // Set the JVM memory @@ -63,6 +64,10 @@ trait ExecutorRunnableUtil extends Logging { JAVA_OPTS += " -Djava.io.tmpdir=" + new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR) + " " + if (!userSpecifiedLogFile) { + JAVA_OPTS += " " + YarnSparkHadoopUtil.getLoggingArgsForContainerCommandLine() + } + // Commenting it out for now - so that people can refer to the properties if required. Remove // it once cpuset version is pushed out. // The context is, default gc for server class machines end up using all cores to do gc - hence diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala index 4c6e1dcd6dac3..314a7550ada71 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -22,6 +22,7 @@ import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.security.Credentials import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.conf.Configuration import org.apache.spark.deploy.SparkHadoopUtil @@ -67,3 +68,9 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil { } } + +object YarnSparkHadoopUtil { + def getLoggingArgsForContainerCommandLine(): String = { + "-Dlog4j.configuration=log4j-spark-container.properties" + } +} diff --git a/yarn/pom.xml b/yarn/pom.xml index 35e31760c1f02..3342cb65edcd1 100644 --- a/yarn/pom.xml +++ b/yarn/pom.xml @@ -167,6 +167,12 @@ target/scala-${scala.binary.version}/classes target/scala-${scala.binary.version}/test-classes + + + + ../common/src/main/resources + + diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala index 53c403f7d0913..81d9d1b5c9280 100644 --- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala +++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala @@ -78,7 +78,8 @@ class ExecutorRunnable( credentials.writeTokenStorageToStream(dob) ctx.setTokens(ByteBuffer.wrap(dob.getData())) - val commands = prepareCommand(masterAddress, slaveId, hostname, executorMemory, executorCores) + val commands = prepareCommand(masterAddress, slaveId, hostname, executorMemory, executorCores, + localResources.contains(ClientBase.LOG4J_PROP)) logInfo("Setting up executor with commands: " + commands) ctx.setCommands(commands) From 2a2ca48be61ed0d72c4347e1c042a264b94db3e8 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Mon, 7 Apr 2014 12:47:27 -0700 Subject: [PATCH 60/78] HOTFIX: Disable actor input stream test. This test makes incorrect assumptions about the behavior of Thread.sleep(). Author: Patrick Wendell Closes #347 from pwendell/stream-tests and squashes the following commits: 10e09e0 [Patrick Wendell] HOTFIX: Disable actor input stream. --- .../scala/org/apache/spark/streaming/InputStreamsSuite.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) 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 7df206241beb6..389b23d4d5e4b 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -144,8 +144,8 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") } - - test("actor input stream") { + // TODO: This test makes assumptions about Thread.sleep() and is flaky + ignore("actor input stream") { // Start the server val testServer = new TestServer() val port = testServer.port From 0307db0f55b714930c7ea118d5451190ea8c1a94 Mon Sep 17 00:00:00 2001 From: Aaron Davidson Date: Mon, 7 Apr 2014 13:06:30 -0700 Subject: [PATCH 61/78] SPARK-1099: Introduce local[*] mode to infer number of cores This is the default mode for running spark-shell and pyspark, intended to allow users running spark for the first time to see the performance benefits of using multiple cores, while not breaking backwards compatibility for users who use "local" mode and expect exactly 1 core. Author: Aaron Davidson Closes #182 from aarondav/110 and squashes the following commits: a88294c [Aaron Davidson] Rebased changes for new spark-shell a9f393e [Aaron Davidson] SPARK-1099: Introduce local[*] mode to infer number of cores --- bin/spark-shell | 4 ++-- core/src/main/scala/org/apache/spark/SparkContext.scala | 9 ++++++--- .../spark/SparkContextSchedulerCreationSuite.scala | 8 ++++++++ docs/python-programming-guide.md | 7 ++++--- docs/scala-programming-guide.md | 5 +++-- python/pyspark/shell.py | 2 +- .../main/scala/org/apache/spark/repl/SparkILoop.scala | 2 +- 7 files changed, 25 insertions(+), 12 deletions(-) diff --git a/bin/spark-shell b/bin/spark-shell index 535ee3ccd8269..ea12d256b23a1 100755 --- a/bin/spark-shell +++ b/bin/spark-shell @@ -34,7 +34,7 @@ set -o posix FWDIR="$(cd `dirname $0`/..; pwd)" SPARK_REPL_OPTS="${SPARK_REPL_OPTS:-""}" -DEFAULT_MASTER="local" +DEFAULT_MASTER="local[*]" MASTER=${MASTER:-""} info_log=0 @@ -64,7 +64,7 @@ ${txtbld}OPTIONS${txtrst}: is followed by m for megabytes or g for gigabytes, e.g. "1g". -dm --driver-memory : The memory used by the Spark Shell, the number is followed by m for megabytes or g for gigabytes, e.g. "1g". - -m --master : A full string that describes the Spark Master, defaults to "local" + -m --master : A full string that describes the Spark Master, defaults to "local[*]" e.g. "spark://localhost:7077". --log-conf : Enables logging of the supplied SparkConf as INFO at start of the Spark Context. diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 8382dd44f3484..e5ebd350eeced 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -1285,8 +1285,8 @@ object SparkContext extends Logging { /** Creates a task scheduler based on a given master URL. Extracted for testing. */ private def createTaskScheduler(sc: SparkContext, master: String): TaskScheduler = { - // Regular expression used for local[N] master format - val LOCAL_N_REGEX = """local\[([0-9]+)\]""".r + // Regular expression used for local[N] and local[*] master formats + val LOCAL_N_REGEX = """local\[([0-9\*]+)\]""".r // Regular expression for local[N, maxRetries], used in tests with failing tasks val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+)\s*,\s*([0-9]+)\]""".r // Regular expression for simulating a Spark cluster of [N, cores, memory] locally @@ -1309,8 +1309,11 @@ object SparkContext extends Logging { scheduler case LOCAL_N_REGEX(threads) => + def localCpuCount = Runtime.getRuntime.availableProcessors() + // local[*] estimates the number of cores on the machine; local[N] uses exactly N threads. + val threadCount = if (threads == "*") localCpuCount else threads.toInt val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) - val backend = new LocalBackend(scheduler, threads.toInt) + val backend = new LocalBackend(scheduler, threadCount) scheduler.initialize(backend) scheduler diff --git a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala index b543471a5d35b..94fba102865b3 100644 --- a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala @@ -51,6 +51,14 @@ class SparkContextSchedulerCreationSuite } } + test("local-*") { + val sched = createTaskScheduler("local[*]") + sched.backend match { + case s: LocalBackend => assert(s.totalCores === Runtime.getRuntime.availableProcessors()) + case _ => fail() + } + } + test("local-n") { val sched = createTaskScheduler("local[5]") assert(sched.maxTaskFailures === 1) diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index c2e5327324898..888631e7025b0 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -82,15 +82,16 @@ The Python shell can be used explore data interactively and is a simple way to l >>> help(pyspark) # Show all pyspark functions {% endhighlight %} -By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on a single core. -To connect to a non-local cluster, or use multiple cores, set the `MASTER` environment variable. +By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on all of +your machine's logical cores. +To connect to a non-local cluster, or to specify a number of cores, set the `MASTER` environment variable. For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.html): {% highlight bash %} $ MASTER=spark://IP:PORT ./bin/pyspark {% endhighlight %} -Or, to use four cores on the local machine: +Or, to use exactly four cores on the local machine: {% highlight bash %} $ MASTER=local[4] ./bin/pyspark diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md index 77373890eead7..a07cd2e0a32a2 100644 --- a/docs/scala-programming-guide.md +++ b/docs/scala-programming-guide.md @@ -54,7 +54,7 @@ object for more advanced configuration. The `master` parameter is a string specifying a [Spark or Mesos cluster URL](#master-urls) to connect to, or a special "local" string to run in local mode, as described below. `appName` is a name for your application, which will be shown in the cluster web UI. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later. -In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `bin/spark-shell` on four cores, use +In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `bin/spark-shell` on exactly four cores, use {% highlight bash %} $ MASTER=local[4] ./bin/spark-shell @@ -74,6 +74,7 @@ The master URL passed to Spark can be in one of the following formats:
Storage LevelMeaning
MEMORY_AND_DISK_SER Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of recomputing them - on the fly each time they're needed. Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of + recomputing them on the fly each time they're needed.
OFF_HEAP Store RDD in a serialized format in Tachyon. + This is generally more space-efficient than deserialized objects, especially when using a + fast serializer, but more CPU-intensive to read. + This also significantly reduces the overheads of GC. +
DISK_ONLY
spark.worker.cleanup.enabledtrue + Enable periodic cleanup of worker / application directories. Note that this only affects standalone + mode, as YARN works differently. +
spark.worker.cleanup.interval1800 (30 minutes) + Controls the interval, in seconds, at which the worker cleans up old application work dirs + on the local machine. +
spark.worker.cleanup.appDataTtl7 * 24 * 3600 (7 days) + The number of seconds to retain application work directories on each worker. This is a Time To Live + and should depend on the amount of available disk space you have. Application logs and jars are + downloaded to each application work dir. Over time, the work dirs can quickly fill up disk space, + especially if you run jobs very frequently. +
spark.akka.frameSize 10
Master URLMeaning
local Run Spark locally with one worker thread (i.e. no parallelism at all).
local[K] Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). +
local[*] Run Spark locally with as many worker threads as logical cores on your machine.
spark://HOST:PORT Connect to the given Spark standalone cluster master. The port must be whichever one your master is configured to use, which is 7077 by default. @@ -84,7 +85,7 @@ The master URL passed to Spark can be in one of the following formats:
-If no master URL is specified, the spark shell defaults to "local". +If no master URL is specified, the spark shell defaults to "local[*]". For running on YARN, Spark launches an instance of the standalone deploy cluster within YARN; see [running on YARN](running-on-yarn.html) for details. diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py index 3d779faf1fa44..35e48276e3cb9 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.py @@ -29,7 +29,7 @@ # this is the equivalent of ADD_JARS add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("ADD_FILES") != None else None -sc = SparkContext(os.environ.get("MASTER", "local"), "PySparkShell", pyFiles=add_files) +sc = SparkContext(os.environ.get("MASTER", "local[*]"), "PySparkShell", pyFiles=add_files) print """Welcome to ____ __ diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala index 9b1da195002c2..5a367b6bb79de 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -963,7 +963,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, case Some(m) => m case None => { val prop = System.getenv("MASTER") - if (prop != null) prop else "local" + if (prop != null) prop else "local[*]" } } master From 14c9238aa7173ba663a999ef320d8cffb73306c4 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 7 Apr 2014 18:38:44 -0700 Subject: [PATCH 62/78] [sql] Rename execution/aggregates.scala Aggregate.scala, and added a bunch of private[this] to variables. Author: Reynold Xin Closes #348 from rxin/aggregate and squashes the following commits: f4bc36f [Reynold Xin] Rename execution/aggregates.scala Aggregate.scala, and added a bunch of private[this] to variables. --- .../{aggregates.scala => Aggregate.scala} | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) rename sql/core/src/main/scala/org/apache/spark/sql/execution/{aggregates.scala => Aggregate.scala} (92%) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala similarity index 92% rename from sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala rename to sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala index 0890faa33b507..3a4f071eebedf 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregates.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala @@ -56,9 +56,9 @@ case class Aggregate( // HACK: Generators don't correctly preserve their output through serializations so we grab // out child's output attributes statically here. - val childOutput = child.output + private[this] val childOutput = child.output - def output = aggregateExpressions.map(_.toAttribute) + override def output = aggregateExpressions.map(_.toAttribute) /** * An aggregate that needs to be computed for each row in a group. @@ -75,7 +75,7 @@ case class Aggregate( /** A list of aggregates that need to be computed for each group. */ @transient - lazy val computedAggregates = aggregateExpressions.flatMap { agg => + private[this] lazy val computedAggregates = aggregateExpressions.flatMap { agg => agg.collect { case a: AggregateExpression => ComputedAggregate( @@ -87,10 +87,10 @@ case class Aggregate( /** The schema of the result of all aggregate evaluations */ @transient - lazy val computedSchema = computedAggregates.map(_.resultAttribute) + private[this] lazy val computedSchema = computedAggregates.map(_.resultAttribute) /** Creates a new aggregate buffer for a group. */ - def newAggregateBuffer(): Array[AggregateFunction] = { + private[this] def newAggregateBuffer(): Array[AggregateFunction] = { val buffer = new Array[AggregateFunction](computedAggregates.length) var i = 0 while (i < computedAggregates.length) { @@ -102,7 +102,7 @@ case class Aggregate( /** Named attributes used to substitute grouping attributes into the final result. */ @transient - lazy val namedGroups = groupingExpressions.map { + private[this] lazy val namedGroups = groupingExpressions.map { case ne: NamedExpression => ne -> ne.toAttribute case e => e -> Alias(e, s"groupingExpr:$e")().toAttribute } @@ -112,7 +112,7 @@ case class Aggregate( * expression into the final result expression. */ @transient - lazy val resultMap = + private[this] lazy val resultMap = (computedAggregates.map { agg => agg.unbound -> agg.resultAttribute} ++ namedGroups).toMap /** @@ -120,13 +120,13 @@ case class Aggregate( * output rows given a group and the result of all aggregate computations. */ @transient - lazy val resultExpressions = aggregateExpressions.map { agg => + private[this] lazy val resultExpressions = aggregateExpressions.map { agg => agg.transform { case e: Expression if resultMap.contains(e) => resultMap(e) } } - def execute() = attachTree(this, "execute") { + override def execute() = attachTree(this, "execute") { if (groupingExpressions.isEmpty) { child.execute().mapPartitions { iter => val buffer = newAggregateBuffer() From 55dfd5dcdbf3a9bfddb2108c8325bda3100eb33d Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 7 Apr 2014 18:39:18 -0700 Subject: [PATCH 63/78] Removed the default eval implementation from Expression, and added a bunch of override's in classes I touched. It is more robust to not provide a default implementation for Expression's. Author: Reynold Xin Closes #350 from rxin/eval-default and squashes the following commits: 0a83b8f [Reynold Xin] Removed the default eval implementation from Expression, and added a bunch of override's in classes I touched. --- .../sql/catalyst/analysis/unresolved.scala | 52 ++++++++++++------- .../sql/catalyst/expressions/Expression.scala | 3 +- .../sql/catalyst/expressions/SortOrder.scala | 11 +++- .../sql/catalyst/expressions/aggregates.scala | 8 +++ .../expressions/namedExpressions.scala | 21 +++++--- .../plans/physical/partitioning.scala | 32 ++++++++---- .../ExpressionEvaluationSuite.scala | 5 +- .../optimizer/ConstantFoldingSuite.scala | 2 +- 8 files changed, 89 insertions(+), 45 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala index 41e9bcef3cd7f..d629172a7426e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala @@ -18,7 +18,8 @@ package org.apache.spark.sql.catalyst.analysis import org.apache.spark.sql.catalyst.{errors, trees} -import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, Expression, NamedExpression} +import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.BaseRelation import org.apache.spark.sql.catalyst.trees.TreeNode @@ -36,7 +37,7 @@ case class UnresolvedRelation( databaseName: Option[String], tableName: String, alias: Option[String] = None) extends BaseRelation { - def output = Nil + override def output = Nil override lazy val resolved = false } @@ -44,26 +45,33 @@ case class UnresolvedRelation( * Holds the name of an attribute that has yet to be resolved. */ case class UnresolvedAttribute(name: String) extends Attribute with trees.LeafNode[Expression] { - def exprId = throw new UnresolvedException(this, "exprId") - def dataType = throw new UnresolvedException(this, "dataType") - def nullable = throw new UnresolvedException(this, "nullable") - def qualifiers = throw new UnresolvedException(this, "qualifiers") + override def exprId = throw new UnresolvedException(this, "exprId") + override def dataType = throw new UnresolvedException(this, "dataType") + override def nullable = throw new UnresolvedException(this, "nullable") + override def qualifiers = throw new UnresolvedException(this, "qualifiers") override lazy val resolved = false - def newInstance = this - def withQualifiers(newQualifiers: Seq[String]) = this + override def newInstance = this + override def withQualifiers(newQualifiers: Seq[String]) = this + + // Unresolved attributes 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" } case class UnresolvedFunction(name: String, children: Seq[Expression]) extends Expression { - def exprId = throw new UnresolvedException(this, "exprId") - def dataType = throw new UnresolvedException(this, "dataType") + override def dataType = throw new UnresolvedException(this, "dataType") override def foldable = throw new UnresolvedException(this, "foldable") - def nullable = throw new UnresolvedException(this, "nullable") - def qualifiers = throw new UnresolvedException(this, "qualifiers") - def references = children.flatMap(_.references).toSet + override def nullable = throw new UnresolvedException(this, "nullable") + override def references = children.flatMap(_.references).toSet 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: ${this.nodeName}") + override def toString = s"'$name(${children.mkString(",")})" } @@ -79,15 +87,15 @@ case class Star( mapFunction: Attribute => Expression = identity[Attribute]) extends Attribute with trees.LeafNode[Expression] { - def name = throw new UnresolvedException(this, "exprId") - def exprId = throw new UnresolvedException(this, "exprId") - def dataType = throw new UnresolvedException(this, "dataType") - def nullable = throw new UnresolvedException(this, "nullable") - def qualifiers = throw new UnresolvedException(this, "qualifiers") + override def name = throw new UnresolvedException(this, "exprId") + override def exprId = throw new UnresolvedException(this, "exprId") + override def dataType = throw new UnresolvedException(this, "dataType") + override def nullable = throw new UnresolvedException(this, "nullable") + override def qualifiers = throw new UnresolvedException(this, "qualifiers") override lazy val resolved = false - def newInstance = this - def withQualifiers(newQualifiers: Seq[String]) = this + override def newInstance = this + override def withQualifiers(newQualifiers: Seq[String]) = this def expand(input: Seq[Attribute]): Seq[NamedExpression] = { val expandedAttributes: Seq[Attribute] = table match { @@ -104,5 +112,9 @@ case class Star( mappedAttributes } + // Star gets expanded at runtime so we never evaluate a Star. + override def eval(input: Row = null): EvaluatedType = + throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") + override def toString = table.map(_ + ".").getOrElse("") + "*" } 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 f190bd0cca375..8a1db8e796816 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 @@ -50,8 +50,7 @@ abstract class Expression extends TreeNode[Expression] { def references: Set[Attribute] /** Returns the result of evaluating this expression on a given input Row */ - def eval(input: Row = null): EvaluatedType = - throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") + def eval(input: Row = null): EvaluatedType /** * Returns `true` if this expression and all its children have been resolved to a specific schema diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala index d5d93778f4b8d..08b2f11d20f5e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql.catalyst.expressions +import org.apache.spark.sql.catalyst.errors.TreeNodeException + abstract sealed class SortDirection case object Ascending extends SortDirection case object Descending extends SortDirection @@ -26,7 +28,12 @@ case object Descending extends SortDirection * transformations over expression will descend into its child. */ case class SortOrder(child: Expression, direction: SortDirection) extends UnaryExpression { - def dataType = child.dataType - def nullable = child.nullable + override def dataType = child.dataType + override def nullable = child.nullable + + // SortOrder itself is never evaluated. + override def eval(input: Row = null): EvaluatedType = + throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") + override def toString = s"$child ${if (direction == Ascending) "ASC" else "DESC"}" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 5edcea14278c7..b152f95f96c70 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.types._ import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.catalyst.errors.TreeNodeException abstract class AggregateExpression extends Expression { self: Product => @@ -28,6 +29,13 @@ abstract class AggregateExpression extends Expression { * of input rows/ */ def newInstance(): AggregateFunction + + /** + * [[AggregateExpression.eval]] should never be invoked because [[AggregateExpression]]'s are + * replaced with a physical aggregate operator at runtime. + */ + override def eval(input: Row = null): EvaluatedType = + throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } /** 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 eb4bc8e755284..a8145c37c20fa 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 @@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute +import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.types._ object NamedExpression { @@ -58,9 +59,9 @@ abstract class Attribute extends NamedExpression { def withQualifiers(newQualifiers: Seq[String]): Attribute - def references = Set(this) def toAttribute = this def newInstance: Attribute + override def references = Set(this) } /** @@ -77,15 +78,15 @@ case class Alias(child: Expression, name: String) (val exprId: ExprId = NamedExpression.newExprId, val qualifiers: Seq[String] = Nil) extends NamedExpression with trees.UnaryNode[Expression] { - type EvaluatedType = Any + override type EvaluatedType = Any override def eval(input: Row) = child.eval(input) - def dataType = child.dataType - def nullable = child.nullable - def references = child.references + override def dataType = child.dataType + override def nullable = child.nullable + override def references = child.references - def toAttribute = { + override def toAttribute = { if (resolved) { AttributeReference(name, child.dataType, child.nullable)(exprId, qualifiers) } else { @@ -127,7 +128,7 @@ case class AttributeReference(name: String, dataType: DataType, nullable: Boolea h } - def newInstance = AttributeReference(name, dataType, nullable)(qualifiers = qualifiers) + override def newInstance = AttributeReference(name, dataType, nullable)(qualifiers = qualifiers) /** * Returns a copy of this [[AttributeReference]] with changed nullability. @@ -143,7 +144,7 @@ case class AttributeReference(name: String, dataType: DataType, nullable: Boolea /** * Returns a copy of this [[AttributeReference]] with new qualifiers. */ - def withQualifiers(newQualifiers: Seq[String]) = { + override def withQualifiers(newQualifiers: Seq[String]) = { if (newQualifiers == qualifiers) { this } else { @@ -151,5 +152,9 @@ case class AttributeReference(name: String, dataType: DataType, nullable: Boolea } } + // Unresolved attributes 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#${exprId.id}$typeSuffix" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala index 8893744eb2e7a..ffb3a92f8f340 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala @@ -17,7 +17,8 @@ package org.apache.spark.sql.catalyst.plans.physical -import org.apache.spark.sql.catalyst.expressions.{Expression, SortOrder} +import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.catalyst.expressions.{Expression, Row, SortOrder} import org.apache.spark.sql.catalyst.types.IntegerType /** @@ -139,12 +140,12 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) extends Expression with Partitioning { - def children = expressions - def references = expressions.flatMap(_.references).toSet - def nullable = false - def dataType = IntegerType + override def children = expressions + override def references = expressions.flatMap(_.references).toSet + override def nullable = false + override def dataType = IntegerType - lazy val clusteringSet = expressions.toSet + private[this] lazy val clusteringSet = expressions.toSet override def satisfies(required: Distribution): Boolean = required match { case UnspecifiedDistribution => true @@ -158,6 +159,9 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) case h: HashPartitioning if h == this => true case _ => false } + + override def eval(input: Row = null): EvaluatedType = + throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } /** @@ -168,17 +172,20 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) * partition. * - Each partition will have a `min` and `max` row, relative to the given ordering. All rows * that are in between `min` and `max` in this `ordering` will reside in this partition. + * + * This class extends expression primarily so that transformations over expression will descend + * into its child. */ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int) extends Expression with Partitioning { - def children = ordering - def references = ordering.flatMap(_.references).toSet - def nullable = false - def dataType = IntegerType + override def children = ordering + override def references = ordering.flatMap(_.references).toSet + override def nullable = false + override def dataType = IntegerType - lazy val clusteringSet = ordering.map(_.child).toSet + private[this] lazy val clusteringSet = ordering.map(_.child).toSet override def satisfies(required: Distribution): Boolean = required match { case UnspecifiedDistribution => true @@ -195,4 +202,7 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int) case r: RangePartitioning if r == this => true case _ => false } + + override def eval(input: Row): EvaluatedType = + throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } 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 92987405aa313..31be6c4ef1b0b 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 @@ -100,7 +100,10 @@ class ExpressionEvaluationSuite extends FunSuite { (null, false, null) :: (null, null, null) :: Nil) - def booleanLogicTest(name: String, op: (Expression, Expression) => Expression, truthTable: Seq[(Any, Any, Any)]) { + def booleanLogicTest( + name: String, + op: (Expression, Expression) => Expression, + truthTable: Seq[(Any, Any, Any)]) { test(s"3VL $name") { truthTable.foreach { case (l,r,answer) => 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 2ab14f48ccc8a..20dfba847790c 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 @@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor -import org.apache.spark.sql.catalyst.types.IntegerType +import org.apache.spark.sql.catalyst.types.{DoubleType, IntegerType} // For implicit conversions import org.apache.spark.sql.catalyst.dsl.plans._ From 31e6fff03730bb915a836d77dcd43d098afd1dbd Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 7 Apr 2014 18:40:08 -0700 Subject: [PATCH 64/78] Added eval for Rand (without any support for user-defined seed). Author: Reynold Xin Closes #349 from rxin/rand and squashes the following commits: fd11322 [Reynold Xin] Added eval for Rand (without any support for user-defined seed). --- .../spark/sql/catalyst/expressions/Rand.scala | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala index 0bde621602944..38f836f0a1a0e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala @@ -17,11 +17,18 @@ package org.apache.spark.sql.catalyst.expressions +import java.util.Random import org.apache.spark.sql.catalyst.types.DoubleType + case object Rand extends LeafExpression { - def dataType = DoubleType - def nullable = false - def references = Set.empty + override def dataType = DoubleType + override def nullable = false + override def references = Set.empty + + private[this] lazy val rand = new Random + + override def eval(input: Row = null) = rand.nextDouble().asInstanceOf[EvaluatedType] + override def toString = "RAND()" } From f27e56aa612538188a8550fe72ee20b8b13304d7 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 7 Apr 2014 19:28:24 -0700 Subject: [PATCH 65/78] Change timestamp cast semantics. When cast to numeric types, return the unix time in seconds (instead of millis). @marmbrus @chenghao-intel Author: Reynold Xin Closes #352 from rxin/timestamp-cast and squashes the following commits: 18aacd3 [Reynold Xin] Fixed precision for double. 2adb235 [Reynold Xin] Change timestamp cast semantics. When cast to numeric types, return the unix time in seconds (instead of millis). --- .../spark/sql/catalyst/dsl/package.scala | 2 +- .../spark/sql/catalyst/expressions/Cast.scala | 23 ++++++++++------ .../ExpressionEvaluationSuite.scala | 27 ++++++++++++++++--- 3 files changed, 40 insertions(+), 12 deletions(-) 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 2d62e4cbbce01..987befe8e22ee 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 @@ -104,7 +104,7 @@ package object dsl { implicit class DslSymbol(sym: Symbol) extends ImplicitAttribute { def s = sym.name } // TODO more implicit class for literal? implicit class DslString(val s: String) extends ImplicitOperators { - def expr: Expression = Literal(s) + override def expr: Expression = Literal(s) def attr = analysis.UnresolvedAttribute(s) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala index 89226999ca005..17118499d0c87 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala @@ -87,7 +87,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression { private def decimalToTimestamp(d: BigDecimal) = { val seconds = d.longValue() - val bd = (d - seconds) * (1000000000) + val bd = (d - seconds) * 1000000000 val nanos = bd.intValue() // Convert to millis @@ -96,18 +96,23 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression { // remaining fractional portion as nanos t.setNanos(nanos) - t } - private def timestampToDouble(t: Timestamp) = (t.getSeconds() + t.getNanos().toDouble / 1000) + // Timestamp to long, converting milliseconds to seconds + private def timestampToLong(ts: Timestamp) = ts.getTime / 1000 + + private def timestampToDouble(ts: Timestamp) = { + // First part is the seconds since the beginning of time, followed by nanosecs. + ts.getTime / 1000 + ts.getNanos.toDouble / 1000000000 + } def castToLong: Any => Any = child.dataType match { case StringType => nullOrCast[String](_, s => try s.toLong catch { case _: NumberFormatException => null }) case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) - case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toLong) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t)) case DecimalType => nullOrCast[BigDecimal](_, _.toLong) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toLong(b) } @@ -117,7 +122,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression { case _: NumberFormatException => null }) case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) - case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toInt) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t).toInt) case DecimalType => nullOrCast[BigDecimal](_, _.toInt) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b) } @@ -127,7 +132,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression { case _: NumberFormatException => null }) case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) - case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toShort) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t).toShort) case DecimalType => nullOrCast[BigDecimal](_, _.toShort) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toShort } @@ -137,7 +142,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression { case _: NumberFormatException => null }) case BooleanType => nullOrCast[Boolean](_, b => if(b) 1 else 0) - case TimestampType => nullOrCast[Timestamp](_, t => timestampToDouble(t).toByte) + case TimestampType => nullOrCast[Timestamp](_, t => timestampToLong(t).toByte) case DecimalType => nullOrCast[BigDecimal](_, _.toByte) case x: NumericType => b => x.numeric.asInstanceOf[Numeric[Any]].toInt(b).toByte } @@ -147,7 +152,9 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression { case _: NumberFormatException => null }) case BooleanType => nullOrCast[Boolean](_, b => if(b) BigDecimal(1) else BigDecimal(0)) - case TimestampType => nullOrCast[Timestamp](_, t => BigDecimal(timestampToDouble(t))) + case TimestampType => + // Note that we lose precision here. + nullOrCast[Timestamp](_, t => BigDecimal(timestampToDouble(t))) case x: NumericType => b => BigDecimal(x.numeric.asInstanceOf[Numeric[Any]].toDouble(b)) } 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 31be6c4ef1b0b..888a19d79f7e4 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 @@ -201,7 +201,7 @@ class ExpressionEvaluationSuite extends FunSuite { val sts = "1970-01-01 00:00:01.0" val ts = Timestamp.valueOf(sts) - + checkEvaluation("abdef" cast StringType, "abdef") checkEvaluation("abdef" cast DecimalType, null) checkEvaluation("abdef" cast TimestampType, null) @@ -209,7 +209,6 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(Literal(1) cast LongType, 1) checkEvaluation(Cast(Literal(1) cast TimestampType, LongType), 1) - checkEvaluation(Cast(Literal(BigDecimal(1)) cast TimestampType, DecimalType), 1) checkEvaluation(Cast(Literal(1.toDouble) cast TimestampType, DoubleType), 1.toDouble) checkEvaluation(Cast(Literal(sts) cast TimestampType, StringType), sts) @@ -240,12 +239,34 @@ class ExpressionEvaluationSuite extends FunSuite { intercept[Exception] {evaluate(Literal(1) cast BinaryType, null)} } - + test("timestamp") { val ts1 = new Timestamp(12) val ts2 = new Timestamp(123) checkEvaluation(Literal("ab") < Literal("abc"), true) checkEvaluation(Literal(ts1) < Literal(ts2), true) } + + test("timestamp casting") { + val millis = 15 * 1000 + 2 + val ts = new Timestamp(millis) + val ts1 = new Timestamp(15 * 1000) // a timestamp without the milliseconds part + checkEvaluation(Cast(ts, ShortType), 15) + checkEvaluation(Cast(ts, IntegerType), 15) + checkEvaluation(Cast(ts, LongType), 15) + checkEvaluation(Cast(ts, FloatType), 15.002f) + checkEvaluation(Cast(ts, DoubleType), 15.002) + checkEvaluation(Cast(Cast(ts, ShortType), TimestampType), ts1) + checkEvaluation(Cast(Cast(ts, IntegerType), TimestampType), ts1) + checkEvaluation(Cast(Cast(ts, LongType), TimestampType), ts1) + checkEvaluation(Cast(Cast(millis.toFloat / 1000, TimestampType), FloatType), + millis.toFloat / 1000) + checkEvaluation(Cast(Cast(millis.toDouble / 1000, TimestampType), DoubleType), + millis.toDouble / 1000) + checkEvaluation(Cast(Literal(BigDecimal(1)) cast TimestampType, DecimalType), 1) + + // A test for higher precision than millis + checkEvaluation(Cast(Cast(0.00000001, TimestampType), DoubleType), 0.00000001) + } } From 0d0493fcf7fc86d30b0ddd4e2c5a293c5c88eb9d Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Mon, 7 Apr 2014 22:24:12 -0700 Subject: [PATCH 66/78] [SPARK-1402] Added 3 more compression schemes JIRA issue: [SPARK-1402](https://issues.apache.org/jira/browse/SPARK-1402) This PR provides 3 more compression schemes for Spark SQL in-memory columnar storage: * `BooleanBitSet` * `IntDelta` * `LongDelta` Now there are 6 compression schemes in total, including the no-op `PassThrough` scheme. Also fixed a bug in PR #286: not all compression schemes are added as available schemes when accessing an in-memory column, and when a column is compressed with an unrecognised scheme, `ColumnAccessor` throws exception. Author: Cheng Lian Closes #330 from liancheng/moreCompressionSchemes and squashes the following commits: 1d037b8 [Cheng Lian] Fixed SPARK-1436: in-memory column byte buffer must be able to be accessed multiple times d7c0e8f [Cheng Lian] Added test suite for IntegralDelta (IntDelta & LongDelta) 3c1ad7a [Cheng Lian] Added test suite for BooleanBitSet, refactored other test suites 44fe4b2 [Cheng Lian] Refactored CompressionScheme, added 3 more compression schemes. --- .../spark/sql/columnar/ColumnAccessor.scala | 23 +- .../spark/sql/columnar/ColumnStats.scala | 6 + .../CompressibleColumnBuilder.scala | 6 +- .../compression/CompressionScheme.scala | 28 +- .../compression/compressionSchemes.scala | 266 +++++++++++++++--- .../sql/columnar/ColumnarQuerySuite.scala | 8 + .../compression/BooleanBitSetSuite.scala | 98 +++++++ .../compression/DictionaryEncodingSuite.scala | 122 ++++---- .../compression/IntegralDeltaSuite.scala | 115 ++++++++ .../compression/RunLengthEncodingSuite.scala | 87 +++--- .../TestCompressibleColumnBuilder.scala | 6 +- 11 files changed, 586 insertions(+), 179 deletions(-) create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala index ffd4894b5213d..3c39e1d350fa8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala @@ -100,20 +100,21 @@ private[sql] class GenericColumnAccessor(buffer: ByteBuffer) private[sql] object ColumnAccessor { def apply(buffer: ByteBuffer): ColumnAccessor = { + val dup = buffer.duplicate().order(ByteOrder.nativeOrder) // The first 4 bytes in the buffer indicate the column type. - val columnTypeId = buffer.getInt() + val columnTypeId = dup.getInt() columnTypeId match { - case INT.typeId => new IntColumnAccessor(buffer) - case LONG.typeId => new LongColumnAccessor(buffer) - case FLOAT.typeId => new FloatColumnAccessor(buffer) - case DOUBLE.typeId => new DoubleColumnAccessor(buffer) - case BOOLEAN.typeId => new BooleanColumnAccessor(buffer) - case BYTE.typeId => new ByteColumnAccessor(buffer) - case SHORT.typeId => new ShortColumnAccessor(buffer) - case STRING.typeId => new StringColumnAccessor(buffer) - case BINARY.typeId => new BinaryColumnAccessor(buffer) - case GENERIC.typeId => new GenericColumnAccessor(buffer) + case INT.typeId => new IntColumnAccessor(dup) + case LONG.typeId => new LongColumnAccessor(dup) + case FLOAT.typeId => new FloatColumnAccessor(dup) + case DOUBLE.typeId => new DoubleColumnAccessor(dup) + case BOOLEAN.typeId => new BooleanColumnAccessor(dup) + case BYTE.typeId => new ByteColumnAccessor(dup) + case SHORT.typeId => new ShortColumnAccessor(dup) + case STRING.typeId => new StringColumnAccessor(dup) + case BINARY.typeId => new BinaryColumnAccessor(dup) + case GENERIC.typeId => new GenericColumnAccessor(dup) } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala index 30c6bdc7912fc..95602d321dc6f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala @@ -20,6 +20,12 @@ package org.apache.spark.sql.columnar import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.types._ +/** + * Used to collect statistical information when building in-memory columns. + * + * NOTE: we intentionally avoid using `Ordering[T]` to compare values here because `Ordering[T]` + * brings significant performance penalty. + */ private[sql] sealed abstract class ColumnStats[T <: DataType, JvmType] extends Serializable { /** * Closed lower bound of this column. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala index 3ac4b358ddf83..fd3b1adf9687a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala @@ -47,9 +47,9 @@ private[sql] trait CompressibleColumnBuilder[T <: NativeType] import CompressionScheme._ - val compressionEncoders = schemes.filter(_.supports(columnType)).map(_.encoder) + val compressionEncoders = schemes.filter(_.supports(columnType)).map(_.encoder[T]) - protected def isWorthCompressing(encoder: Encoder) = { + protected def isWorthCompressing(encoder: Encoder[T]) = { encoder.compressionRatio < 0.8 } @@ -70,7 +70,7 @@ private[sql] trait CompressibleColumnBuilder[T <: NativeType] abstract override def build() = { val rawBuffer = super.build() - val encoder = { + val encoder: Encoder[T] = { val candidate = compressionEncoders.minBy(_.compressionRatio) if (isWorthCompressing(candidate)) candidate else PassThrough.encoder } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala index d3a4ac8df926b..c605a8e4434e3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala @@ -22,10 +22,8 @@ import java.nio.ByteBuffer import org.apache.spark.sql.catalyst.types.NativeType import org.apache.spark.sql.columnar.{ColumnType, NativeColumnType} -private[sql] trait Encoder { - def gatherCompressibilityStats[T <: NativeType]( - value: T#JvmType, - columnType: ColumnType[T, T#JvmType]) {} +private[sql] trait Encoder[T <: NativeType] { + def gatherCompressibilityStats(value: T#JvmType, columnType: NativeColumnType[T]) {} def compressedSize: Int @@ -35,10 +33,7 @@ private[sql] trait Encoder { if (uncompressedSize > 0) compressedSize.toDouble / uncompressedSize else 1.0 } - def compress[T <: NativeType]( - from: ByteBuffer, - to: ByteBuffer, - columnType: ColumnType[T, T#JvmType]): ByteBuffer + def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[T]): ByteBuffer } private[sql] trait Decoder[T <: NativeType] extends Iterator[T#JvmType] @@ -48,7 +43,7 @@ private[sql] trait CompressionScheme { def supports(columnType: ColumnType[_, _]): Boolean - def encoder: Encoder + def encoder[T <: NativeType]: Encoder[T] def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]): Decoder[T] } @@ -58,15 +53,18 @@ private[sql] trait WithCompressionSchemes { } private[sql] trait AllCompressionSchemes extends WithCompressionSchemes { - override val schemes: Seq[CompressionScheme] = { - Seq(PassThrough, RunLengthEncoding, DictionaryEncoding) - } + override val schemes: Seq[CompressionScheme] = CompressionScheme.all } private[sql] object CompressionScheme { - def apply(typeId: Int): CompressionScheme = typeId match { - case PassThrough.typeId => PassThrough - case _ => throw new UnsupportedOperationException() + val all: Seq[CompressionScheme] = + Seq(PassThrough, RunLengthEncoding, DictionaryEncoding, BooleanBitSet, IntDelta, LongDelta) + + private val typeIdToScheme = all.map(scheme => scheme.typeId -> scheme).toMap + + def apply(typeId: Int): CompressionScheme = { + typeIdToScheme.getOrElse(typeId, throw new UnsupportedOperationException( + s"Unrecognized compression scheme type ID: $typeId")) } def copyColumnHeader(from: ByteBuffer, to: ByteBuffer) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala index dc2c153faf8ad..df8220b556edd 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala @@ -24,7 +24,7 @@ import scala.reflect.ClassTag import scala.reflect.runtime.universe.runtimeMirror import org.apache.spark.sql.catalyst.expressions.GenericMutableRow -import org.apache.spark.sql.catalyst.types.NativeType +import org.apache.spark.sql.catalyst.types._ import org.apache.spark.sql.columnar._ private[sql] case object PassThrough extends CompressionScheme { @@ -32,22 +32,18 @@ private[sql] case object PassThrough extends CompressionScheme { override def supports(columnType: ColumnType[_, _]) = true - override def encoder = new this.Encoder + override def encoder[T <: NativeType] = new this.Encoder[T] override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { new this.Decoder(buffer, columnType) } - class Encoder extends compression.Encoder { + class Encoder[T <: NativeType] extends compression.Encoder[T] { override def uncompressedSize = 0 override def compressedSize = 0 - override def compress[T <: NativeType]( - from: ByteBuffer, - to: ByteBuffer, - columnType: ColumnType[T, T#JvmType]) = { - + override def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[T]) = { // Writes compression type ID and copies raw contents to.putInt(PassThrough.typeId).put(from).rewind() to @@ -64,9 +60,9 @@ private[sql] case object PassThrough extends CompressionScheme { } private[sql] case object RunLengthEncoding extends CompressionScheme { - override def typeId = 1 + override val typeId = 1 - override def encoder = new this.Encoder + override def encoder[T <: NativeType] = new this.Encoder[T] override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { new this.Decoder(buffer, columnType) @@ -77,7 +73,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { case _ => false } - class Encoder extends compression.Encoder { + class Encoder[T <: NativeType] extends compression.Encoder[T] { private var _uncompressedSize = 0 private var _compressedSize = 0 @@ -89,10 +85,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { override def compressedSize = _compressedSize - override def gatherCompressibilityStats[T <: NativeType]( - value: T#JvmType, - columnType: ColumnType[T, T#JvmType]) { - + override def gatherCompressibilityStats(value: T#JvmType, columnType: NativeColumnType[T]) { val actualSize = columnType.actualSize(value) _uncompressedSize += actualSize @@ -111,11 +104,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { } } - override def compress[T <: NativeType]( - from: ByteBuffer, - to: ByteBuffer, - columnType: ColumnType[T, T#JvmType]) = { - + override def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[T]) = { to.putInt(RunLengthEncoding.typeId) if (from.hasRemaining) { @@ -172,23 +161,23 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { } private[sql] case object DictionaryEncoding extends CompressionScheme { - override def typeId: Int = 2 + override val typeId = 2 // 32K unique values allowed - private val MAX_DICT_SIZE = Short.MaxValue - 1 + val MAX_DICT_SIZE = Short.MaxValue override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { - new this.Decoder[T](buffer, columnType) + new this.Decoder(buffer, columnType) } - override def encoder = new this.Encoder + override def encoder[T <: NativeType] = new this.Encoder[T] override def supports(columnType: ColumnType[_, _]) = columnType match { case INT | LONG | STRING => true case _ => false } - class Encoder extends compression.Encoder{ + class Encoder[T <: NativeType] extends compression.Encoder[T] { // Size of the input, uncompressed, in bytes. Note that we only count until the dictionary // overflows. private var _uncompressedSize = 0 @@ -201,7 +190,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { private var count = 0 // The reverse mapping of _dictionary, i.e. mapping encoded integer to the value itself. - private var values = new mutable.ArrayBuffer[Any](1024) + private var values = new mutable.ArrayBuffer[T#JvmType](1024) // The dictionary that maps a value to the encoded short integer. private val dictionary = mutable.HashMap.empty[Any, Short] @@ -210,10 +199,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { // to store dictionary element count. private var dictionarySize = 4 - override def gatherCompressibilityStats[T <: NativeType]( - value: T#JvmType, - columnType: ColumnType[T, T#JvmType]) { - + override def gatherCompressibilityStats(value: T#JvmType, columnType: NativeColumnType[T]) { if (!overflow) { val actualSize = columnType.actualSize(value) count += 1 @@ -234,11 +220,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { } } - override def compress[T <: NativeType]( - from: ByteBuffer, - to: ByteBuffer, - columnType: ColumnType[T, T#JvmType]) = { - + override def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[T]) = { if (overflow) { throw new IllegalStateException( "Dictionary encoding should not be used because of dictionary overflow.") @@ -249,7 +231,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { var i = 0 while (i < values.length) { - columnType.append(values(i).asInstanceOf[T#JvmType], to) + columnType.append(values(i), to) i += 1 } @@ -286,3 +268,215 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { override def hasNext = buffer.hasRemaining } } + +private[sql] case object BooleanBitSet extends CompressionScheme { + override val typeId = 3 + + val BITS_PER_LONG = 64 + + override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + new this.Decoder(buffer).asInstanceOf[compression.Decoder[T]] + } + + override def encoder[T <: NativeType] = (new this.Encoder).asInstanceOf[compression.Encoder[T]] + + override def supports(columnType: ColumnType[_, _]) = columnType == BOOLEAN + + class Encoder extends compression.Encoder[BooleanType.type] { + private var _uncompressedSize = 0 + + override def gatherCompressibilityStats( + value: Boolean, + columnType: NativeColumnType[BooleanType.type]) { + + _uncompressedSize += BOOLEAN.defaultSize + } + + override def compress( + from: ByteBuffer, + to: ByteBuffer, + columnType: NativeColumnType[BooleanType.type]) = { + + to.putInt(BooleanBitSet.typeId) + // Total element count (1 byte per Boolean value) + .putInt(from.remaining) + + while (from.remaining >= BITS_PER_LONG) { + var word = 0: Long + var i = 0 + + while (i < BITS_PER_LONG) { + if (BOOLEAN.extract(from)) { + word |= (1: Long) << i + } + i += 1 + } + + to.putLong(word) + } + + if (from.hasRemaining) { + var word = 0: Long + var i = 0 + + while (from.hasRemaining) { + if (BOOLEAN.extract(from)) { + word |= (1: Long) << i + } + i += 1 + } + + to.putLong(word) + } + + to.rewind() + to + } + + override def uncompressedSize = _uncompressedSize + + override def compressedSize = { + val extra = if (_uncompressedSize % BITS_PER_LONG == 0) 0 else 1 + (_uncompressedSize / BITS_PER_LONG + extra) * 8 + 4 + } + } + + class Decoder(buffer: ByteBuffer) extends compression.Decoder[BooleanType.type] { + private val count = buffer.getInt() + + private var currentWord = 0: Long + + private var visited: Int = 0 + + override def next(): Boolean = { + val bit = visited % BITS_PER_LONG + + visited += 1 + if (bit == 0) { + currentWord = buffer.getLong() + } + + ((currentWord >> bit) & 1) != 0 + } + + override def hasNext: Boolean = visited < count + } +} + +private[sql] sealed abstract class IntegralDelta[I <: IntegralType] extends CompressionScheme { + override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = { + new this.Decoder(buffer, columnType.asInstanceOf[NativeColumnType[I]]) + .asInstanceOf[compression.Decoder[T]] + } + + override def encoder[T <: NativeType] = (new this.Encoder).asInstanceOf[compression.Encoder[T]] + + /** + * Computes `delta = x - y`, returns `(true, delta)` if `delta` can fit into a single byte, or + * `(false, 0: Byte)` otherwise. + */ + protected def byteSizedDelta(x: I#JvmType, y: I#JvmType): (Boolean, Byte) + + /** + * Simply computes `x + delta` + */ + protected def addDelta(x: I#JvmType, delta: Byte): I#JvmType + + class Encoder extends compression.Encoder[I] { + private var _compressedSize: Int = 0 + + private var _uncompressedSize: Int = 0 + + private var prev: I#JvmType = _ + + private var initial = true + + override def gatherCompressibilityStats(value: I#JvmType, columnType: NativeColumnType[I]) { + _uncompressedSize += columnType.defaultSize + + if (initial) { + initial = false + prev = value + _compressedSize += 1 + columnType.defaultSize + } else { + val (smallEnough, _) = byteSizedDelta(value, prev) + _compressedSize += (if (smallEnough) 1 else 1 + columnType.defaultSize) + } + } + + override def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[I]) = { + to.putInt(typeId) + + if (from.hasRemaining) { + val prev = columnType.extract(from) + + to.put(Byte.MinValue) + columnType.append(prev, to) + + while (from.hasRemaining) { + val current = columnType.extract(from) + val (smallEnough, delta) = byteSizedDelta(current, prev) + + if (smallEnough) { + to.put(delta) + } else { + to.put(Byte.MinValue) + columnType.append(current, to) + } + } + } + + to.rewind() + to + } + + override def uncompressedSize = _uncompressedSize + + override def compressedSize = _compressedSize + } + + class Decoder(buffer: ByteBuffer, columnType: NativeColumnType[I]) + extends compression.Decoder[I] { + + private var prev: I#JvmType = _ + + override def next() = { + val delta = buffer.get() + + if (delta > Byte.MinValue) { + addDelta(prev, delta) + } else { + prev = columnType.extract(buffer) + prev + } + } + + override def hasNext = buffer.hasRemaining + } +} + +private[sql] case object IntDelta extends IntegralDelta[IntegerType.type] { + override val typeId = 4 + + override def supports(columnType: ColumnType[_, _]) = columnType == INT + + override protected def addDelta(x: Int, delta: Byte) = x + delta + + override protected def byteSizedDelta(x: Int, y: Int): (Boolean, Byte) = { + val delta = x - y + if (delta < Byte.MaxValue) (true, delta.toByte) else (false, 0: Byte) + } +} + +private[sql] case object LongDelta extends IntegralDelta[LongType.type] { + override val typeId = 5 + + override def supports(columnType: ColumnType[_, _]) = columnType == LONG + + override protected def addDelta(x: Long, delta: Byte) = x + delta + + override protected def byteSizedDelta(x: Long, y: Long): (Boolean, Byte) = { + val delta = x - y + if (delta < Byte.MaxValue) (true, delta.toByte) else (false, 0: Byte) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarQuerySuite.scala index 70b2e851737f8..2ed4cf2170f9d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarQuerySuite.scala @@ -31,4 +31,12 @@ class ColumnarQuerySuite extends QueryTest { checkAnswer(scan, testData.collect().toSeq) } + + test("SPARK-1436 regression: in-memory columns must be able to be accessed multiple times") { + val plan = TestSQLContext.executePlan(testData.logicalPlan).executedPlan + val scan = SparkLogicalPlan(InMemoryColumnarTableScan(plan.output, plan)) + + checkAnswer(scan, testData.collect().toSeq) + checkAnswer(scan, testData.collect().toSeq) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala new file mode 100644 index 0000000000000..a754f98f7fbf1 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala @@ -0,0 +1,98 @@ +/* + * 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.columnar.compression + +import org.scalatest.FunSuite + +import org.apache.spark.sql.Row +import org.apache.spark.sql.columnar.{BOOLEAN, BooleanColumnStats} +import org.apache.spark.sql.columnar.ColumnarTestUtils._ + +class BooleanBitSetSuite extends FunSuite { + import BooleanBitSet._ + + def skeleton(count: Int) { + // ------------- + // Tests encoder + // ------------- + + val builder = TestCompressibleColumnBuilder(new BooleanColumnStats, BOOLEAN, BooleanBitSet) + val rows = Seq.fill[Row](count)(makeRandomRow(BOOLEAN)) + val values = rows.map(_.head) + + rows.foreach(builder.appendFrom(_, 0)) + val buffer = builder.build() + + // Column type ID + null count + null positions + val headerSize = CompressionScheme.columnHeaderSize(buffer) + + // Compression scheme ID + element count + bitset words + val compressedSize = 4 + 4 + { + val extra = if (count % BITS_PER_LONG == 0) 0 else 1 + (count / BITS_PER_LONG + extra) * 8 + } + + // 4 extra bytes for compression scheme type ID + expectResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity) + + // Skips column header + buffer.position(headerSize) + expectResult(BooleanBitSet.typeId, "Wrong compression scheme ID")(buffer.getInt()) + expectResult(count, "Wrong element count")(buffer.getInt()) + + var word = 0: Long + for (i <- 0 until count) { + val bit = i % BITS_PER_LONG + word = if (bit == 0) buffer.getLong() else word + expectResult(values(i), s"Wrong value in compressed buffer, index=$i") { + (word & ((1: Long) << bit)) != 0 + } + } + + // ------------- + // Tests decoder + // ------------- + + // Rewinds, skips column header and 4 more bytes for compression scheme ID + buffer.rewind().position(headerSize + 4) + + val decoder = BooleanBitSet.decoder(buffer, BOOLEAN) + values.foreach(expectResult(_, "Wrong decoded value")(decoder.next())) + assert(!decoder.hasNext) + } + + test(s"$BooleanBitSet: empty") { + skeleton(0) + } + + test(s"$BooleanBitSet: less than 1 word") { + skeleton(BITS_PER_LONG - 1) + } + + test(s"$BooleanBitSet: exactly 1 word") { + skeleton(BITS_PER_LONG) + } + + test(s"$BooleanBitSet: multiple whole words") { + skeleton(BITS_PER_LONG * 2) + } + + test(s"$BooleanBitSet: multiple words and 1 more bit") { + skeleton(BITS_PER_LONG * 2 + 1) + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala index 184691ab5b46a..eab27987e08ea 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala @@ -24,7 +24,6 @@ import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.types.NativeType import org.apache.spark.sql.columnar._ import org.apache.spark.sql.columnar.ColumnarTestUtils._ -import org.apache.spark.sql.catalyst.expressions.GenericMutableRow class DictionaryEncodingSuite extends FunSuite { testDictionaryEncoding(new IntColumnStats, INT) @@ -41,73 +40,82 @@ class DictionaryEncodingSuite extends FunSuite { (0 until buffer.getInt()).map(columnType.extract(buffer) -> _.toShort).toMap } - test(s"$DictionaryEncoding with $typeName: simple case") { + def stableDistinct(seq: Seq[Int]): Seq[Int] = if (seq.isEmpty) { + Seq.empty + } else { + seq.head +: seq.tail.filterNot(_ == seq.head) + } + + def skeleton(uniqueValueCount: Int, inputSeq: Seq[Int]) { // ------------- // Tests encoder // ------------- val builder = TestCompressibleColumnBuilder(columnStats, columnType, DictionaryEncoding) - val (values, rows) = makeUniqueValuesAndSingleValueRows(columnType, 2) - - builder.initialize(0) - builder.appendFrom(rows(0), 0) - builder.appendFrom(rows(1), 0) - builder.appendFrom(rows(0), 0) - builder.appendFrom(rows(1), 0) - - val buffer = builder.build() - val headerSize = CompressionScheme.columnHeaderSize(buffer) - // 4 extra bytes for dictionary size - val dictionarySize = 4 + values.map(columnType.actualSize).sum - // 4 `Short`s, 2 bytes each - val compressedSize = dictionarySize + 2 * 4 - // 4 extra bytes for compression scheme type ID - expectResult(headerSize + 4 + compressedSize, "Wrong buffer capacity")(buffer.capacity) - - // Skips column header - buffer.position(headerSize) - expectResult(DictionaryEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt()) - - val dictionary = buildDictionary(buffer) - Array[Short](0, 1).foreach { i => - expectResult(i, "Wrong dictionary entry")(dictionary(values(i))) - } - - Array[Short](0, 1, 0, 1).foreach { - expectResult(_, "Wrong column element value")(buffer.getShort()) + val (values, rows) = makeUniqueValuesAndSingleValueRows(columnType, uniqueValueCount) + val dictValues = stableDistinct(inputSeq) + + inputSeq.foreach(i => builder.appendFrom(rows(i), 0)) + + if (dictValues.length > DictionaryEncoding.MAX_DICT_SIZE) { + withClue("Dictionary overflowed, compression should fail") { + intercept[Throwable] { + builder.build() + } + } + } else { + val buffer = builder.build() + val headerSize = CompressionScheme.columnHeaderSize(buffer) + // 4 extra bytes for dictionary size + val dictionarySize = 4 + values.map(columnType.actualSize).sum + // 2 bytes for each `Short` + val compressedSize = 4 + dictionarySize + 2 * inputSeq.length + // 4 extra bytes for compression scheme type ID + expectResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity) + + // Skips column header + buffer.position(headerSize) + expectResult(DictionaryEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt()) + + val dictionary = buildDictionary(buffer).toMap + + dictValues.foreach { i => + expectResult(i, "Wrong dictionary entry") { + dictionary(values(i)) + } + } + + inputSeq.foreach { i => + expectResult(i.toShort, "Wrong column element value")(buffer.getShort()) + } + + // ------------- + // Tests decoder + // ------------- + + // Rewinds, skips column header and 4 more bytes for compression scheme ID + buffer.rewind().position(headerSize + 4) + + val decoder = DictionaryEncoding.decoder(buffer, columnType) + + inputSeq.foreach { i => + expectResult(values(i), "Wrong decoded value")(decoder.next()) + } + + assert(!decoder.hasNext) } - - // ------------- - // Tests decoder - // ------------- - - // Rewinds, skips column header and 4 more bytes for compression scheme ID - buffer.rewind().position(headerSize + 4) - - val decoder = new DictionaryEncoding.Decoder[T](buffer, columnType) - - Array[Short](0, 1, 0, 1).foreach { i => - expectResult(values(i), "Wrong decoded value")(decoder.next()) - } - - assert(!decoder.hasNext) } - } - test(s"$DictionaryEncoding: overflow") { - val builder = TestCompressibleColumnBuilder(new IntColumnStats, INT, DictionaryEncoding) - builder.initialize(0) + test(s"$DictionaryEncoding with $typeName: empty") { + skeleton(0, Seq.empty) + } - (0 to Short.MaxValue).foreach { n => - val row = new GenericMutableRow(1) - row.setInt(0, n) - builder.appendFrom(row, 0) + test(s"$DictionaryEncoding with $typeName: simple case") { + skeleton(2, Seq(0, 1, 0, 1)) } - withClue("Dictionary overflowed, encoding should fail") { - intercept[Throwable] { - builder.build() - } + test(s"$DictionaryEncoding with $typeName: dictionary overflow") { + skeleton(DictionaryEncoding.MAX_DICT_SIZE + 1, 0 to DictionaryEncoding.MAX_DICT_SIZE) } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala new file mode 100644 index 0000000000000..1390e5eef6106 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala @@ -0,0 +1,115 @@ +/* + * 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.columnar.compression + +import org.scalatest.FunSuite + +import org.apache.spark.sql.catalyst.expressions.GenericMutableRow +import org.apache.spark.sql.catalyst.types.IntegralType +import org.apache.spark.sql.columnar._ + +class IntegralDeltaSuite extends FunSuite { + testIntegralDelta(new IntColumnStats, INT, IntDelta) + testIntegralDelta(new LongColumnStats, LONG, LongDelta) + + def testIntegralDelta[I <: IntegralType]( + columnStats: NativeColumnStats[I], + columnType: NativeColumnType[I], + scheme: IntegralDelta[I]) { + + def skeleton(input: Seq[I#JvmType]) { + // ------------- + // Tests encoder + // ------------- + + val builder = TestCompressibleColumnBuilder(columnStats, columnType, scheme) + val deltas = if (input.isEmpty) { + Seq.empty[Long] + } else { + (input.tail, input.init).zipped.map { + case (x: Int, y: Int) => (x - y).toLong + case (x: Long, y: Long) => x - y + } + } + + input.map { value => + val row = new GenericMutableRow(1) + columnType.setField(row, 0, value) + builder.appendFrom(row, 0) + } + + val buffer = builder.build() + // Column type ID + null count + null positions + val headerSize = CompressionScheme.columnHeaderSize(buffer) + + // Compression scheme ID + compressed contents + val compressedSize = 4 + (if (deltas.isEmpty) { + 0 + } else { + val oneBoolean = columnType.defaultSize + 1 + oneBoolean + deltas.map { + d => if (math.abs(d) < Byte.MaxValue) 1 else 1 + oneBoolean + }.sum + }) + + // 4 extra bytes for compression scheme type ID + expectResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity) + + buffer.position(headerSize) + expectResult(scheme.typeId, "Wrong compression scheme ID")(buffer.getInt()) + + if (input.nonEmpty) { + expectResult(Byte.MinValue, "The first byte should be an escaping mark")(buffer.get()) + expectResult(input.head, "The first value is wrong")(columnType.extract(buffer)) + + (input.tail, deltas).zipped.foreach { (value, delta) => + if (delta < Byte.MaxValue) { + expectResult(delta, "Wrong delta")(buffer.get()) + } else { + expectResult(Byte.MinValue, "Expecting escaping mark here")(buffer.get()) + expectResult(value, "Wrong value")(columnType.extract(buffer)) + } + } + } + + // ------------- + // Tests decoder + // ------------- + + // Rewinds, skips column header and 4 more bytes for compression scheme ID + buffer.rewind().position(headerSize + 4) + + val decoder = scheme.decoder(buffer, columnType) + input.foreach(expectResult(_, "Wrong decoded value")(decoder.next())) + assert(!decoder.hasNext) + } + + test(s"$scheme: empty column") { + skeleton(Seq.empty) + } + + test(s"$scheme: simple case") { + val input = columnType match { + case INT => Seq(1: Int, 2: Int, 130: Int) + case LONG => Seq(1: Long, 2: Long, 130: Long) + } + + skeleton(input.map(_.asInstanceOf[I#JvmType])) + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala index 2089ad120d4f2..89f9b60a4397b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala @@ -37,34 +37,39 @@ class RunLengthEncodingSuite extends FunSuite { val typeName = columnType.getClass.getSimpleName.stripSuffix("$") - test(s"$RunLengthEncoding with $typeName: simple case") { + def skeleton(uniqueValueCount: Int, inputRuns: Seq[(Int, Int)]) { // ------------- // Tests encoder // ------------- val builder = TestCompressibleColumnBuilder(columnStats, columnType, RunLengthEncoding) - val (values, rows) = makeUniqueValuesAndSingleValueRows(columnType, 2) - - builder.initialize(0) - builder.appendFrom(rows(0), 0) - builder.appendFrom(rows(0), 0) - builder.appendFrom(rows(1), 0) - builder.appendFrom(rows(1), 0) + val (values, rows) = makeUniqueValuesAndSingleValueRows(columnType, uniqueValueCount) + val inputSeq = inputRuns.flatMap { case (index, run) => + Seq.fill(run)(index) + } + inputSeq.foreach(i => builder.appendFrom(rows(i), 0)) val buffer = builder.build() + + // Column type ID + null count + null positions val headerSize = CompressionScheme.columnHeaderSize(buffer) - // 4 extra bytes each run for run length - val compressedSize = values.map(columnType.actualSize(_) + 4).sum + + // Compression scheme ID + compressed contents + val compressedSize = 4 + inputRuns.map { case (index, _) => + // 4 extra bytes each run for run length + columnType.actualSize(values(index)) + 4 + }.sum + // 4 extra bytes for compression scheme type ID - expectResult(headerSize + 4 + compressedSize, "Wrong buffer capacity")(buffer.capacity) + expectResult(headerSize + compressedSize, "Wrong buffer capacity")(buffer.capacity) // Skips column header buffer.position(headerSize) expectResult(RunLengthEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt()) - Array(0, 1).foreach { i => - expectResult(values(i), "Wrong column element value")(columnType.extract(buffer)) - expectResult(2, "Wrong run length")(buffer.getInt()) + inputRuns.foreach { case (index, run) => + expectResult(values(index), "Wrong column element value")(columnType.extract(buffer)) + expectResult(run, "Wrong run length")(buffer.getInt()) } // ------------- @@ -74,57 +79,29 @@ class RunLengthEncodingSuite extends FunSuite { // Rewinds, skips column header and 4 more bytes for compression scheme ID buffer.rewind().position(headerSize + 4) - val decoder = new RunLengthEncoding.Decoder[T](buffer, columnType) + val decoder = RunLengthEncoding.decoder(buffer, columnType) - Array(0, 0, 1, 1).foreach { i => + inputSeq.foreach { i => expectResult(values(i), "Wrong decoded value")(decoder.next()) } assert(!decoder.hasNext) } - test(s"$RunLengthEncoding with $typeName: run length == 1") { - // ------------- - // Tests encoder - // ------------- - - val builder = TestCompressibleColumnBuilder(columnStats, columnType, RunLengthEncoding) - val (values, rows) = makeUniqueValuesAndSingleValueRows(columnType, 2) - - builder.initialize(0) - builder.appendFrom(rows(0), 0) - builder.appendFrom(rows(1), 0) - - val buffer = builder.build() - val headerSize = CompressionScheme.columnHeaderSize(buffer) - // 4 bytes each run for run length - val compressedSize = values.map(columnType.actualSize(_) + 4).sum - // 4 bytes for compression scheme type ID - expectResult(headerSize + 4 + compressedSize, "Wrong buffer capacity")(buffer.capacity) - - // Skips column header - buffer.position(headerSize) - expectResult(RunLengthEncoding.typeId, "Wrong compression scheme ID")(buffer.getInt()) - - Array(0, 1).foreach { i => - expectResult(values(i), "Wrong column element value")(columnType.extract(buffer)) - expectResult(1, "Wrong run length")(buffer.getInt()) - } - - // ------------- - // Tests decoder - // ------------- - - // Rewinds, skips column header and 4 more bytes for compression scheme ID - buffer.rewind().position(headerSize + 4) + test(s"$RunLengthEncoding with $typeName: empty column") { + skeleton(0, Seq.empty) + } - val decoder = new RunLengthEncoding.Decoder[T](buffer, columnType) + test(s"$RunLengthEncoding with $typeName: simple case") { + skeleton(2, Seq(0 -> 2, 1 ->2)) + } - Array(0, 1).foreach { i => - expectResult(values(i), "Wrong decoded value")(decoder.next()) - } + test(s"$RunLengthEncoding with $typeName: run length == 1") { + skeleton(2, Seq(0 -> 1, 1 ->1)) + } - assert(!decoder.hasNext) + test(s"$RunLengthEncoding with $typeName: single long run") { + skeleton(1, Seq(0 -> 1000)) } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala index e0ec812863dcf..81bf5e99d19b9 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala @@ -28,7 +28,7 @@ class TestCompressibleColumnBuilder[T <: NativeType]( with NullableColumnBuilder with CompressibleColumnBuilder[T] { - override protected def isWorthCompressing(encoder: Encoder) = true + override protected def isWorthCompressing(encoder: Encoder[T]) = true } object TestCompressibleColumnBuilder { @@ -37,7 +37,9 @@ object TestCompressibleColumnBuilder { columnType: NativeColumnType[T], scheme: CompressionScheme) = { - new TestCompressibleColumnBuilder(columnStats, columnType, Seq(scheme)) + val builder = new TestCompressibleColumnBuilder(columnStats, columnType, Seq(scheme)) + builder.initialize(0) + builder } } From 11eabbe125b2ee572fad359c33c93f5e6fdf0b2d Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Mon, 7 Apr 2014 23:40:21 -0700 Subject: [PATCH 67/78] [SPARK-1103] Automatic garbage collection of RDD, shuffle and broadcast data MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This PR allows Spark to automatically cleanup metadata and data related to persisted RDDs, shuffles and broadcast variables when the corresponding RDDs, shuffles and broadcast variables fall out of scope from the driver program. This is still a work in progress as broadcast cleanup has not been implemented. **Implementation Details** A new class `ContextCleaner` is responsible cleaning all the state. It is instantiated as part of a `SparkContext`. RDD and ShuffleDependency classes have overridden `finalize()` function that gets called whenever their instances go out of scope. The `finalize()` function enqueues the object’s identifier (i.e. RDD ID, shuffle ID, etc.) with the `ContextCleaner`, which is a very short and cheap operation and should not significantly affect the garbage collection mechanism. The `ContextCleaner`, on a different thread, performs the cleanup, whose details are given below. *RDD cleanup:* `ContextCleaner` calls `RDD.unpersist()` is used to cleanup persisted RDDs. Regarding metadata, the DAGScheduler automatically cleans up all metadata related to a RDD after all jobs have completed. Only the `SparkContext.persistentRDDs` keeps strong references to persisted RDDs. The `TimeStampedHashMap` used for that has been replaced by `TimeStampedWeakValueHashMap` that keeps only weak references to the RDDs, allowing them to be garbage collected. *Shuffle cleanup:* New BlockManager message `RemoveShuffle()` asks the `BlockManagerMaster` and currently active `BlockManager`s to delete all the disk blocks related to the shuffle ID. `ContextCleaner` cleans up shuffle data using this message and also cleans up the metadata in the `MapOutputTracker` of the driver. The `MapOutputTracker` at the workers, that caches the shuffle metadata, maintains a `BoundedHashMap` to limit the shuffle information it caches. Refetching the shuffle information from the driver is not too costly. *Broadcast cleanup:* To be done. [This PR](https://github.com/apache/incubator-spark/pull/543/) adds mechanism for explicit cleanup of broadcast variables. `Broadcast.finalize()` will enqueue its own ID with ContextCleaner and the PRs mechanism will be used to unpersist the Broadcast data. *Other cleanup:* `ShuffleMapTask` and `ResultTask` caches tasks and used TTL based cleanup (using `TimeStampedHashMap`), so nothing got cleaned up if TTL was not set. Instead, they now use `BoundedHashMap` to keep a limited number of map output information. Cost of repopulating the cache if necessary is very small. **Current state of implementation** Implemented RDD and shuffle cleanup. Things left to be done are. - Cleaning up for broadcast variable still to be done. - Automatic cleaning up keys with empty weak refs as values in `TimeStampedWeakValueHashMap` Author: Tathagata Das Author: Andrew Or Author: Roman Pastukhov Closes #126 from tdas/state-cleanup and squashes the following commits: 61b8d6e [Tathagata Das] Fixed issue with Tachyon + new BlockManager methods. f489fdc [Tathagata Das] Merge remote-tracking branch 'apache/master' into state-cleanup d25a86e [Tathagata Das] Fixed stupid typo. cff023c [Tathagata Das] Fixed issues based on Andrew's comments. 4d05314 [Tathagata Das] Scala style fix. 2b95b5e [Tathagata Das] Added more documentation on Broadcast implementations, specially which blocks are told about to the driver. Also, fixed Broadcast API to hide destroy functionality. 41c9ece [Tathagata Das] Added more unit tests for BlockManager, DiskBlockManager, and ContextCleaner. 6222697 [Tathagata Das] Fixed bug and adding unit test for removeBroadcast in BlockManagerSuite. 104a89a [Tathagata Das] Fixed failing BroadcastSuite unit tests by introducing blocking for removeShuffle and removeBroadcast in BlockManager* a430f06 [Tathagata Das] Fixed compilation errors. b27f8e8 [Tathagata Das] Merge pull request #3 from andrewor14/cleanup cd72d19 [Andrew Or] Make automatic cleanup configurable (not documented) ada45f0 [Andrew Or] Merge branch 'state-cleanup' of github.com:tdas/spark into cleanup a2cc8bc [Tathagata Das] Merge remote-tracking branch 'apache/master' into state-cleanup c5b1d98 [Andrew Or] Address Patrick's comments a6460d4 [Andrew Or] Merge github.com:apache/spark into cleanup 762a4d8 [Tathagata Das] Merge pull request #1 from andrewor14/cleanup f0aabb1 [Andrew Or] Correct semantics for TimeStampedWeakValueHashMap + add tests 5016375 [Andrew Or] Address TD's comments 7ed72fb [Andrew Or] Fix style test fail + remove verbose test message regarding broadcast 634a097 [Andrew Or] Merge branch 'state-cleanup' of github.com:tdas/spark into cleanup 7edbc98 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into state-cleanup 8557c12 [Andrew Or] Merge github.com:apache/spark into cleanup e442246 [Andrew Or] Merge github.com:apache/spark into cleanup 88904a3 [Andrew Or] Make TimeStampedWeakValueHashMap a wrapper of TimeStampedHashMap fbfeec8 [Andrew Or] Add functionality to query executors for their local BlockStatuses 34f436f [Andrew Or] Generalize BroadcastBlockId to remove BroadcastHelperBlockId 0d17060 [Andrew Or] Import, comments, and style fixes (minor) c92e4d9 [Andrew Or] Merge github.com:apache/spark into cleanup f201a8d [Andrew Or] Test broadcast cleanup in ContextCleanerSuite + remove BoundedHashMap e95479c [Andrew Or] Add tests for unpersisting broadcast 544ac86 [Andrew Or] Clean up broadcast blocks through BlockManager* d0edef3 [Andrew Or] Add framework for broadcast cleanup ba52e00 [Andrew Or] Refactor broadcast classes c7ccef1 [Andrew Or] Merge branch 'bc-unpersist-merge' of github.com:ignatich/incubator-spark into cleanup 6c9dcf6 [Tathagata Das] Added missing Apache license d2f8b97 [Tathagata Das] Removed duplicate unpersistRDD. a007307 [Tathagata Das] Merge remote-tracking branch 'apache/master' into state-cleanup 620eca3 [Tathagata Das] Changes based on PR comments. f2881fd [Tathagata Das] Changed ContextCleaner to use ReferenceQueue instead of finalizer e1fba5f [Tathagata Das] Style fix 892b952 [Tathagata Das] Removed use of BoundedHashMap, and made BlockManagerSlaveActor cleanup shuffle metadata in MapOutputTrackerWorker. a7260d3 [Tathagata Das] Added try-catch in context cleaner and null value cleaning in TimeStampedWeakValueHashMap. e61daa0 [Tathagata Das] Modifications based on the comments on PR 126. ae9da88 [Tathagata Das] Removed unncessary TimeStampedHashMap from DAGScheduler, added try-catches in finalize() methods, and replaced ArrayBlockingQueue to LinkedBlockingQueue to avoid blocking in Java's finalizing thread. cb0a5a6 [Tathagata Das] Fixed docs and styles. a24fefc [Tathagata Das] Merge remote-tracking branch 'apache/master' into state-cleanup 8512612 [Tathagata Das] Changed TimeStampedHashMap to use WrappedJavaHashMap. e427a9e [Tathagata Das] Added ContextCleaner to automatically clean RDDs and shuffles when they fall out of scope. Also replaced TimeStampedHashMap to BoundedHashMaps and TimeStampedWeakValueHashMap for the necessary hashmap behavior. 80dd977 [Roman Pastukhov] Fix for Broadcast unpersist patch. 1e752f1 [Roman Pastukhov] Added unpersist method to Broadcast. --- .../org/apache/spark/ContextCleaner.scala | 192 ++++++++ .../scala/org/apache/spark/Dependency.scala | 2 + .../org/apache/spark/MapOutputTracker.scala | 148 ++++--- .../scala/org/apache/spark/SparkContext.scala | 23 +- .../scala/org/apache/spark/SparkEnv.scala | 25 +- .../apache/spark/broadcast/Broadcast.scala | 107 +++-- .../spark/broadcast/BroadcastFactory.scala | 3 +- .../spark/broadcast/BroadcastManager.scala | 66 +++ .../spark/broadcast/HttpBroadcast.scala | 128 ++++-- .../broadcast/HttpBroadcastFactory.scala | 45 ++ .../spark/broadcast/TorrentBroadcast.scala | 162 ++++--- .../broadcast/TorrentBroadcastFactory.scala | 46 ++ .../spark/network/ConnectionManager.scala | 1 - .../main/scala/org/apache/spark/rdd/RDD.scala | 5 +- .../apache/spark/scheduler/DAGScheduler.scala | 38 +- .../apache/spark/scheduler/ResultTask.scala | 16 +- .../spark/scheduler/ShuffleMapTask.scala | 14 +- .../spark/scheduler/TaskSchedulerImpl.scala | 2 +- .../org/apache/spark/storage/BlockId.scala | 24 +- .../apache/spark/storage/BlockManager.scala | 67 ++- .../spark/storage/BlockManagerMaster.scala | 84 +++- .../storage/BlockManagerMasterActor.scala | 107 ++++- .../spark/storage/BlockManagerMessages.scala | 20 +- .../storage/BlockManagerSlaveActor.scala | 60 ++- .../spark/storage/DiskBlockManager.scala | 14 + .../spark/storage/ShuffleBlockManager.scala | 44 +- .../apache/spark/storage/ThreadingTest.scala | 6 +- .../apache/spark/util/MetadataCleaner.scala | 19 +- .../spark/util/TimeStampedHashMap.scala | 109 ++--- .../util/TimeStampedWeakValueHashMap.scala | 170 +++++++ .../scala/org/apache/spark/util/Utils.scala | 8 +- .../org/apache/spark/AkkaUtilsSuite.scala | 8 +- .../org/apache/spark/BroadcastSuite.scala | 311 +++++++++++-- .../apache/spark/ContextCleanerSuite.scala | 415 ++++++++++++++++++ .../apache/spark/MapOutputTrackerSuite.scala | 25 +- .../spark/storage/BlockManagerSuite.scala | 243 ++++++++-- .../spark/storage/DiskBlockManagerSuite.scala | 10 +- .../apache/spark/util/JsonProtocolSuite.scala | 5 +- .../spark/util/TimeStampedHashMapSuite.scala | 264 +++++++++++ .../spark/streaming/dstream/DStream.scala | 4 +- 40 files changed, 2571 insertions(+), 469 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/ContextCleaner.scala create mode 100644 core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala create mode 100644 core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala create mode 100644 core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala create mode 100644 core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala create mode 100644 core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala create mode 100644 core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala diff --git a/core/src/main/scala/org/apache/spark/ContextCleaner.scala b/core/src/main/scala/org/apache/spark/ContextCleaner.scala new file mode 100644 index 0000000000000..54e08d7866f75 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/ContextCleaner.scala @@ -0,0 +1,192 @@ +/* + * 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 java.lang.ref.{ReferenceQueue, WeakReference} + +import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer} + +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.rdd.RDD + +/** + * Classes that represent cleaning tasks. + */ +private sealed trait CleanupTask +private case class CleanRDD(rddId: Int) extends CleanupTask +private case class CleanShuffle(shuffleId: Int) extends CleanupTask +private case class CleanBroadcast(broadcastId: Long) extends CleanupTask + +/** + * A WeakReference associated with a CleanupTask. + * + * When the referent object becomes only weakly reachable, the corresponding + * CleanupTaskWeakReference is automatically added to the given reference queue. + */ +private class CleanupTaskWeakReference( + val task: CleanupTask, + referent: AnyRef, + referenceQueue: ReferenceQueue[AnyRef]) + extends WeakReference(referent, referenceQueue) + +/** + * An asynchronous cleaner for RDD, shuffle, and broadcast state. + * + * This maintains a weak reference for each RDD, ShuffleDependency, and Broadcast of interest, + * to be processed when the associated object goes out of scope of the application. Actual + * cleanup is performed in a separate daemon thread. + */ +private[spark] class ContextCleaner(sc: SparkContext) extends Logging { + + private val referenceBuffer = new ArrayBuffer[CleanupTaskWeakReference] + with SynchronizedBuffer[CleanupTaskWeakReference] + + private val referenceQueue = new ReferenceQueue[AnyRef] + + private val listeners = new ArrayBuffer[CleanerListener] + with SynchronizedBuffer[CleanerListener] + + private val cleaningThread = new Thread() { override def run() { keepCleaning() }} + + /** + * Whether the cleaning thread will block on cleanup tasks. + * This is set to true only for tests. + */ + private val blockOnCleanupTasks = sc.conf.getBoolean( + "spark.cleaner.referenceTracking.blocking", false) + + @volatile private var stopped = false + + /** Attach a listener object to get information of when objects are cleaned. */ + def attachListener(listener: CleanerListener) { + listeners += listener + } + + /** Start the cleaner. */ + def start() { + cleaningThread.setDaemon(true) + cleaningThread.setName("Spark Context Cleaner") + cleaningThread.start() + } + + /** Stop the cleaner. */ + def stop() { + stopped = true + } + + /** Register a RDD for cleanup when it is garbage collected. */ + def registerRDDForCleanup(rdd: RDD[_]) { + registerForCleanup(rdd, CleanRDD(rdd.id)) + } + + /** Register a ShuffleDependency for cleanup when it is garbage collected. */ + def registerShuffleForCleanup(shuffleDependency: ShuffleDependency[_, _]) { + registerForCleanup(shuffleDependency, CleanShuffle(shuffleDependency.shuffleId)) + } + + /** Register a Broadcast for cleanup when it is garbage collected. */ + def registerBroadcastForCleanup[T](broadcast: Broadcast[T]) { + registerForCleanup(broadcast, CleanBroadcast(broadcast.id)) + } + + /** Register an object for cleanup. */ + private def registerForCleanup(objectForCleanup: AnyRef, task: CleanupTask) { + referenceBuffer += new CleanupTaskWeakReference(task, objectForCleanup, referenceQueue) + } + + /** Keep cleaning RDD, shuffle, and broadcast state. */ + private def keepCleaning() { + while (!stopped) { + try { + val reference = Option(referenceQueue.remove(ContextCleaner.REF_QUEUE_POLL_TIMEOUT)) + .map(_.asInstanceOf[CleanupTaskWeakReference]) + reference.map(_.task).foreach { task => + logDebug("Got cleaning task " + task) + referenceBuffer -= reference.get + task match { + case CleanRDD(rddId) => + doCleanupRDD(rddId, blocking = blockOnCleanupTasks) + case CleanShuffle(shuffleId) => + doCleanupShuffle(shuffleId, blocking = blockOnCleanupTasks) + case CleanBroadcast(broadcastId) => + doCleanupBroadcast(broadcastId, blocking = blockOnCleanupTasks) + } + } + } catch { + case t: Throwable => logError("Error in cleaning thread", t) + } + } + } + + /** Perform RDD cleanup. */ + def doCleanupRDD(rddId: Int, blocking: Boolean) { + try { + logDebug("Cleaning RDD " + rddId) + sc.unpersistRDD(rddId, blocking) + listeners.foreach(_.rddCleaned(rddId)) + logInfo("Cleaned RDD " + rddId) + } catch { + case t: Throwable => logError("Error cleaning RDD " + rddId, t) + } + } + + /** Perform shuffle cleanup, asynchronously. */ + def doCleanupShuffle(shuffleId: Int, blocking: Boolean) { + try { + logDebug("Cleaning shuffle " + shuffleId) + mapOutputTrackerMaster.unregisterShuffle(shuffleId) + blockManagerMaster.removeShuffle(shuffleId, blocking) + listeners.foreach(_.shuffleCleaned(shuffleId)) + logInfo("Cleaned shuffle " + shuffleId) + } catch { + case t: Throwable => logError("Error cleaning shuffle " + shuffleId, t) + } + } + + /** Perform broadcast cleanup. */ + def doCleanupBroadcast(broadcastId: Long, blocking: Boolean) { + try { + logDebug("Cleaning broadcast " + broadcastId) + broadcastManager.unbroadcast(broadcastId, true, blocking) + listeners.foreach(_.broadcastCleaned(broadcastId)) + logInfo("Cleaned broadcast " + broadcastId) + } catch { + case t: Throwable => logError("Error cleaning broadcast " + broadcastId, t) + } + } + + private def blockManagerMaster = sc.env.blockManager.master + private def broadcastManager = sc.env.broadcastManager + private def mapOutputTrackerMaster = sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster] + + // Used for testing. These methods explicitly blocks until cleanup is completed + // to ensure that more reliable testing. +} + +private object ContextCleaner { + private val REF_QUEUE_POLL_TIMEOUT = 100 +} + +/** + * Listener class used for testing when any item has been cleaned by the Cleaner class. + */ +private[spark] trait CleanerListener { + def rddCleaned(rddId: Int) + def shuffleCleaned(shuffleId: Int) + def broadcastCleaned(broadcastId: Long) +} diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala index 3132dcf745e19..1cd629c15bd46 100644 --- a/core/src/main/scala/org/apache/spark/Dependency.scala +++ b/core/src/main/scala/org/apache/spark/Dependency.scala @@ -55,6 +55,8 @@ class ShuffleDependency[K, V]( extends Dependency(rdd.asInstanceOf[RDD[Product2[K, V]]]) { val shuffleId: Int = rdd.context.newShuffleId() + + rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this)) } diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index 80cbf951cb70e..ee82d9fa7874b 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -20,21 +20,21 @@ package org.apache.spark import java.io._ import java.util.zip.{GZIPInputStream, GZIPOutputStream} -import scala.collection.mutable.HashSet +import scala.collection.mutable.{HashSet, HashMap, Map} import scala.concurrent.Await import akka.actor._ import akka.pattern.ask - import org.apache.spark.scheduler.MapStatus import org.apache.spark.storage.BlockManagerId -import org.apache.spark.util.{AkkaUtils, MetadataCleaner, MetadataCleanerType, TimeStampedHashMap} +import org.apache.spark.util._ private[spark] sealed trait MapOutputTrackerMessage private[spark] case class GetMapOutputStatuses(shuffleId: Int) extends MapOutputTrackerMessage private[spark] case object StopMapOutputTracker extends MapOutputTrackerMessage +/** Actor class for MapOutputTrackerMaster */ private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster, conf: SparkConf) extends Actor with Logging { val maxAkkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf) @@ -65,26 +65,41 @@ private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster } } -private[spark] class MapOutputTracker(conf: SparkConf) extends Logging { - +/** + * Class that keeps track of the location of the map output of + * a stage. This is abstract because different versions of MapOutputTracker + * (driver and worker) use different HashMap to store its metadata. + */ +private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging { private val timeout = AkkaUtils.askTimeout(conf) - // Set to the MapOutputTrackerActor living on the driver + /** Set to the MapOutputTrackerActor living on the driver. */ var trackerActor: ActorRef = _ - protected val mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]] + /** + * This HashMap has different behavior for the master and the workers. + * + * On the master, it serves as the source of map outputs recorded from ShuffleMapTasks. + * On the workers, it simply serves as a cache, in which a miss triggers a fetch from the + * master's corresponding HashMap. + */ + protected val mapStatuses: Map[Int, Array[MapStatus]] - // Incremented every time a fetch fails so that client nodes know to clear - // their cache of map output locations if this happens. + /** + * Incremented every time a fetch fails so that client nodes know to clear + * their cache of map output locations if this happens. + */ protected var epoch: Long = 0 - protected val epochLock = new java.lang.Object + protected val epochLock = new AnyRef - private val metadataCleaner = - new MetadataCleaner(MetadataCleanerType.MAP_OUTPUT_TRACKER, this.cleanup, conf) + /** Remembers which map output locations are currently being fetched on a worker. */ + private val fetching = new HashSet[Int] - // Send a message to the trackerActor and get its result within a default timeout, or - // throw a SparkException if this fails. - private def askTracker(message: Any): Any = { + /** + * Send a message to the trackerActor and get its result within a default timeout, or + * throw a SparkException if this fails. + */ + protected def askTracker(message: Any): Any = { try { val future = trackerActor.ask(message)(timeout) Await.result(future, timeout) @@ -94,17 +109,17 @@ private[spark] class MapOutputTracker(conf: SparkConf) extends Logging { } } - // Send a one-way message to the trackerActor, to which we expect it to reply with true. - private def communicate(message: Any) { + /** Send a one-way message to the trackerActor, to which we expect it to reply with true. */ + protected def sendTracker(message: Any) { if (askTracker(message) != true) { throw new SparkException("Error reply received from MapOutputTracker") } } - // Remembers which map output locations are currently being fetched on a worker - private val fetching = new HashSet[Int] - - // Called on possibly remote nodes to get the server URIs and output sizes for a given shuffle + /** + * Called from executors to get the server URIs and output sizes of the map outputs of + * a given shuffle. + */ def getServerStatuses(shuffleId: Int, reduceId: Int): Array[(BlockManagerId, Long)] = { val statuses = mapStatuses.get(shuffleId).orNull if (statuses == null) { @@ -152,8 +167,7 @@ private[spark] class MapOutputTracker(conf: SparkConf) extends Logging { fetchedStatuses.synchronized { return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, fetchedStatuses) } - } - else { + } else { throw new FetchFailedException(null, shuffleId, -1, reduceId, new Exception("Missing all output locations for shuffle " + shuffleId)) } @@ -164,27 +178,18 @@ private[spark] class MapOutputTracker(conf: SparkConf) extends Logging { } } - protected def cleanup(cleanupTime: Long) { - mapStatuses.clearOldValues(cleanupTime) - } - - def stop() { - communicate(StopMapOutputTracker) - mapStatuses.clear() - metadataCleaner.cancel() - trackerActor = null - } - - // Called to get current epoch number + /** Called to get current epoch number. */ def getEpoch: Long = { epochLock.synchronized { return epoch } } - // Called on workers to update the epoch number, potentially clearing old outputs - // because of a fetch failure. (Each worker task calls this with the latest epoch - // number on the master at the time it was created.) + /** + * Called from executors to update the epoch number, potentially clearing old outputs + * because of a fetch failure. Each worker task calls this with the latest epoch + * number on the master at the time it was created. + */ def updateEpoch(newEpoch: Long) { epochLock.synchronized { if (newEpoch > epoch) { @@ -194,17 +199,40 @@ private[spark] class MapOutputTracker(conf: SparkConf) extends Logging { } } } + + /** Unregister shuffle data. */ + def unregisterShuffle(shuffleId: Int) { + mapStatuses.remove(shuffleId) + } + + /** Stop the tracker. */ + def stop() { } } +/** + * MapOutputTracker for the driver. This uses TimeStampedHashMap to keep track of map + * output information, which allows old output information based on a TTL. + */ private[spark] class MapOutputTrackerMaster(conf: SparkConf) extends MapOutputTracker(conf) { - // Cache a serialized version of the output statuses for each shuffle to send them out faster + /** Cache a serialized version of the output statuses for each shuffle to send them out faster */ private var cacheEpoch = epoch - private val cachedSerializedStatuses = new TimeStampedHashMap[Int, Array[Byte]] + + /** + * Timestamp based HashMap for storing mapStatuses and cached serialized statuses in the master, + * so that statuses are dropped only by explicit de-registering or by TTL-based cleaning (if set). + * Other than these two scenarios, nothing should be dropped from this HashMap. + */ + protected val mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]]() + private val cachedSerializedStatuses = new TimeStampedHashMap[Int, Array[Byte]]() + + // For cleaning up TimeStampedHashMaps + private val metadataCleaner = + new MetadataCleaner(MetadataCleanerType.MAP_OUTPUT_TRACKER, this.cleanup, conf) def registerShuffle(shuffleId: Int, numMaps: Int) { - if (mapStatuses.putIfAbsent(shuffleId, new Array[MapStatus](numMaps)).isDefined) { + if (mapStatuses.put(shuffleId, new Array[MapStatus](numMaps)).isDefined) { throw new IllegalArgumentException("Shuffle ID " + shuffleId + " registered twice") } } @@ -216,6 +244,7 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf) } } + /** Register multiple map output information for the given shuffle */ def registerMapOutputs(shuffleId: Int, statuses: Array[MapStatus], changeEpoch: Boolean = false) { mapStatuses.put(shuffleId, Array[MapStatus]() ++ statuses) if (changeEpoch) { @@ -223,6 +252,7 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf) } } + /** Unregister map output information of the given shuffle, mapper and block manager */ def unregisterMapOutput(shuffleId: Int, mapId: Int, bmAddress: BlockManagerId) { val arrayOpt = mapStatuses.get(shuffleId) if (arrayOpt.isDefined && arrayOpt.get != null) { @@ -238,6 +268,17 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf) } } + /** Unregister shuffle data */ + override def unregisterShuffle(shuffleId: Int) { + mapStatuses.remove(shuffleId) + cachedSerializedStatuses.remove(shuffleId) + } + + /** Check if the given shuffle is being tracked */ + def containsShuffle(shuffleId: Int): Boolean = { + cachedSerializedStatuses.contains(shuffleId) || mapStatuses.contains(shuffleId) + } + def incrementEpoch() { epochLock.synchronized { epoch += 1 @@ -274,23 +315,26 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf) bytes } - protected override def cleanup(cleanupTime: Long) { - super.cleanup(cleanupTime) - cachedSerializedStatuses.clearOldValues(cleanupTime) - } - override def stop() { - super.stop() + sendTracker(StopMapOutputTracker) + mapStatuses.clear() + trackerActor = null + metadataCleaner.cancel() cachedSerializedStatuses.clear() } - override def updateEpoch(newEpoch: Long) { - // This might be called on the MapOutputTrackerMaster if we're running in local mode. + private def cleanup(cleanupTime: Long) { + mapStatuses.clearOldValues(cleanupTime) + cachedSerializedStatuses.clearOldValues(cleanupTime) } +} - def has(shuffleId: Int): Boolean = { - cachedSerializedStatuses.get(shuffleId).isDefined || mapStatuses.contains(shuffleId) - } +/** + * MapOutputTracker for the workers, which fetches map output information from the driver's + * MapOutputTrackerMaster. + */ +private[spark] class MapOutputTrackerWorker(conf: SparkConf) extends MapOutputTracker(conf) { + protected val mapStatuses = new HashMap[Int, Array[MapStatus]] } private[spark] object MapOutputTracker { diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index e5ebd350eeced..d7124616d3bfb 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -45,7 +45,7 @@ import org.apache.spark.scheduler.cluster.mesos.{CoarseMesosSchedulerBackend, Me import org.apache.spark.scheduler.local.LocalBackend import org.apache.spark.storage.{BlockManagerSource, RDDInfo, StorageStatus, StorageUtils} import org.apache.spark.ui.SparkUI -import org.apache.spark.util.{ClosureCleaner, MetadataCleaner, MetadataCleanerType, TimeStampedHashMap, Utils} +import org.apache.spark.util.{ClosureCleaner, MetadataCleaner, MetadataCleanerType, TimeStampedWeakValueHashMap, Utils} /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark @@ -157,7 +157,7 @@ class SparkContext( private[spark] val addedJars = HashMap[String, Long]() // Keeps track of all persisted RDDs - private[spark] val persistentRdds = new TimeStampedHashMap[Int, RDD[_]] + private[spark] val persistentRdds = new TimeStampedWeakValueHashMap[Int, RDD[_]] private[spark] val metadataCleaner = new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, conf) @@ -233,6 +233,15 @@ class SparkContext( @volatile private[spark] var dagScheduler = new DAGScheduler(this) dagScheduler.start() + private[spark] val cleaner: Option[ContextCleaner] = { + if (conf.getBoolean("spark.cleaner.referenceTracking", true)) { + Some(new ContextCleaner(this)) + } else { + None + } + } + cleaner.foreach(_.start()) + postEnvironmentUpdate() /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */ @@ -679,7 +688,11 @@ class SparkContext( * [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions. * The variable will be sent to each cluster only once. */ - def broadcast[T](value: T): Broadcast[T] = env.broadcastManager.newBroadcast[T](value, isLocal) + def broadcast[T](value: T): Broadcast[T] = { + val bc = env.broadcastManager.newBroadcast[T](value, isLocal) + cleaner.foreach(_.registerBroadcastForCleanup(bc)) + bc + } /** * Add a file to be downloaded with this Spark job on every node. @@ -789,8 +802,7 @@ class SparkContext( /** * Unpersist an RDD from memory and/or disk storage */ - private[spark] def unpersistRDD(rdd: RDD[_], blocking: Boolean = true) { - val rddId = rdd.id + private[spark] def unpersistRDD(rddId: Int, blocking: Boolean = true) { env.blockManager.master.removeRdd(rddId, blocking) persistentRdds.remove(rddId) listenerBus.post(SparkListenerUnpersistRDD(rddId)) @@ -869,6 +881,7 @@ class SparkContext( dagScheduler = null if (dagSchedulerCopy != null) { metadataCleaner.cancel() + cleaner.foreach(_.stop()) dagSchedulerCopy.stop() listenerBus.stop() taskScheduler = null diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 5ceac28fe7afb..9ea123f174b95 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -180,12 +180,24 @@ object SparkEnv extends Logging { } } + val mapOutputTracker = if (isDriver) { + new MapOutputTrackerMaster(conf) + } else { + new MapOutputTrackerWorker(conf) + } + + // Have to assign trackerActor after initialization as MapOutputTrackerActor + // requires the MapOutputTracker itself + mapOutputTracker.trackerActor = registerOrLookup( + "MapOutputTracker", + new MapOutputTrackerMasterActor(mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf)) + val blockManagerMaster = new BlockManagerMaster(registerOrLookup( "BlockManagerMaster", new BlockManagerMasterActor(isLocal, conf, listenerBus)), conf) val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster, - serializer, conf, securityManager) + serializer, conf, securityManager, mapOutputTracker) val connectionManager = blockManager.connectionManager @@ -193,17 +205,6 @@ object SparkEnv extends Logging { val cacheManager = new CacheManager(blockManager) - // Have to assign trackerActor after initialization as MapOutputTrackerActor - // requires the MapOutputTracker itself - val mapOutputTracker = if (isDriver) { - new MapOutputTrackerMaster(conf) - } else { - new MapOutputTracker(conf) - } - mapOutputTracker.trackerActor = registerOrLookup( - "MapOutputTracker", - new MapOutputTrackerMasterActor(mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf)) - val shuffleFetcher = instantiateClass[ShuffleFetcher]( "spark.shuffle.fetcher", "org.apache.spark.BlockStoreShuffleFetcher") diff --git a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala index e3c3a12d16f2a..738a3b1bed7f3 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala @@ -18,9 +18,8 @@ package org.apache.spark.broadcast import java.io.Serializable -import java.util.concurrent.atomic.AtomicLong -import org.apache.spark._ +import org.apache.spark.SparkException /** * A broadcast variable. Broadcast variables allow the programmer to keep a read-only variable @@ -29,7 +28,8 @@ import org.apache.spark._ * attempts to distribute broadcast variables using efficient broadcast algorithms to reduce * communication cost. * - * Broadcast variables are created from a variable `v` by calling [[SparkContext#broadcast]]. + * Broadcast variables are created from a variable `v` by calling + * [[org.apache.spark.SparkContext#broadcast]]. * The broadcast variable is a wrapper around `v`, and its value can be accessed by calling the * `value` method. The interpreter session below shows this: * @@ -51,49 +51,80 @@ import org.apache.spark._ * @tparam T Type of the data contained in the broadcast variable. */ abstract class Broadcast[T](val id: Long) extends Serializable { - def value: T - // We cannot have an abstract readObject here due to some weird issues with - // readObject having to be 'private' in sub-classes. + /** + * Flag signifying whether the broadcast variable is valid + * (that is, not already destroyed) or not. + */ + @volatile private var _isValid = true - override def toString = "Broadcast(" + id + ")" -} - -private[spark] -class BroadcastManager(val _isDriver: Boolean, conf: SparkConf, securityManager: SecurityManager) - extends Logging with Serializable { - - private var initialized = false - private var broadcastFactory: BroadcastFactory = null - - initialize() - - // Called by SparkContext or Executor before using Broadcast - private def initialize() { - synchronized { - if (!initialized) { - val broadcastFactoryClass = conf.get( - "spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") - - broadcastFactory = - Class.forName(broadcastFactoryClass).newInstance.asInstanceOf[BroadcastFactory] + /** Get the broadcasted value. */ + def value: T = { + assertValid() + getValue() + } - // Initialize appropriate BroadcastFactory and BroadcastObject - broadcastFactory.initialize(isDriver, conf, securityManager) + /** + * Asynchronously delete cached copies of this broadcast on the executors. + * If the broadcast is used after this is called, it will need to be re-sent to each executor. + */ + def unpersist() { + unpersist(blocking = false) + } - initialized = true - } - } + /** + * Delete cached copies of this broadcast on the executors. If the broadcast is used after + * this is called, it will need to be re-sent to each executor. + * @param blocking Whether to block until unpersisting has completed + */ + def unpersist(blocking: Boolean) { + assertValid() + doUnpersist(blocking) } - def stop() { - broadcastFactory.stop() + /** + * Destroy all data and metadata related to this broadcast variable. Use this with caution; + * once a broadcast variable has been destroyed, it cannot be used again. + */ + private[spark] def destroy(blocking: Boolean) { + assertValid() + _isValid = false + doDestroy(blocking) } - private val nextBroadcastId = new AtomicLong(0) + /** + * Whether this Broadcast is actually usable. This should be false once persisted state is + * removed from the driver. + */ + private[spark] def isValid: Boolean = { + _isValid + } - def newBroadcast[T](value_ : T, isLocal: Boolean) = - broadcastFactory.newBroadcast[T](value_, isLocal, nextBroadcastId.getAndIncrement()) + /** + * Actually get the broadcasted value. Concrete implementations of Broadcast class must + * define their own way to get the value. + */ + private[spark] def getValue(): T + + /** + * Actually unpersist the broadcasted value on the executors. Concrete implementations of + * Broadcast class must define their own logic to unpersist their own data. + */ + private[spark] def doUnpersist(blocking: Boolean) + + /** + * Actually destroy all data and metadata related to this broadcast variable. + * Implementation of Broadcast class must define their own logic to destroy their own + * state. + */ + private[spark] def doDestroy(blocking: Boolean) + + /** Check if this broadcast is valid. If not valid, exception is thrown. */ + private[spark] def assertValid() { + if (!_isValid) { + throw new SparkException("Attempted to use %s after it has been destroyed!".format(toString)) + } + } - def isDriver = _isDriver + override def toString = "Broadcast(" + id + ")" } diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala index 6beecaeced5be..c7f7c59cfb449 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala @@ -27,7 +27,8 @@ import org.apache.spark.SparkConf * entire Spark job. */ trait BroadcastFactory { - def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager): Unit + def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager): Unit def newBroadcast[T](value: T, isLocal: Boolean, id: Long): Broadcast[T] + def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean): Unit def stop(): Unit } diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala new file mode 100644 index 0000000000000..cf62aca4d45e8 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala @@ -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.broadcast + +import java.util.concurrent.atomic.AtomicLong + +import org.apache.spark._ + +private[spark] class BroadcastManager( + val isDriver: Boolean, + conf: SparkConf, + securityManager: SecurityManager) + extends Logging { + + private var initialized = false + private var broadcastFactory: BroadcastFactory = null + + initialize() + + // Called by SparkContext or Executor before using Broadcast + private def initialize() { + synchronized { + if (!initialized) { + val broadcastFactoryClass = + conf.get("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") + + broadcastFactory = + Class.forName(broadcastFactoryClass).newInstance.asInstanceOf[BroadcastFactory] + + // Initialize appropriate BroadcastFactory and BroadcastObject + broadcastFactory.initialize(isDriver, conf, securityManager) + + initialized = true + } + } + } + + def stop() { + broadcastFactory.stop() + } + + private val nextBroadcastId = new AtomicLong(0) + + def newBroadcast[T](value_ : T, isLocal: Boolean) = { + broadcastFactory.newBroadcast[T](value_, isLocal, nextBroadcastId.getAndIncrement()) + } + + def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean) { + broadcastFactory.unbroadcast(id, removeFromDriver, blocking) + } +} diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala index e8eb04bb10469..f6a8a8af91e4b 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -17,34 +17,65 @@ package org.apache.spark.broadcast -import java.io.{File, FileOutputStream, ObjectInputStream, OutputStream} -import java.net.{URL, URLConnection, URI} +import java.io.{File, FileOutputStream, ObjectInputStream, ObjectOutputStream, OutputStream} +import java.net.{URI, URL, URLConnection} import java.util.concurrent.TimeUnit -import it.unimi.dsi.fastutil.io.FastBufferedInputStream -import it.unimi.dsi.fastutil.io.FastBufferedOutputStream +import it.unimi.dsi.fastutil.io.{FastBufferedInputStream, FastBufferedOutputStream} -import org.apache.spark.{SparkConf, HttpServer, Logging, SecurityManager, SparkEnv} +import org.apache.spark.{HttpServer, Logging, SecurityManager, SparkConf, SparkEnv} import org.apache.spark.io.CompressionCodec import org.apache.spark.storage.{BroadcastBlockId, StorageLevel} import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashSet, Utils} +/** + * A [[org.apache.spark.broadcast.Broadcast]] implementation that uses HTTP server + * as a broadcast mechanism. The first time a HTTP broadcast variable (sent as part of a + * task) is deserialized in the executor, the broadcasted data is fetched from the driver + * (through a HTTP server running at the driver) and stored in the BlockManager of the + * executor to speed up future accesses. + */ private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long) extends Broadcast[T](id) with Logging with Serializable { - def value = value_ + def getValue = value_ - def blockId = BroadcastBlockId(id) + val blockId = BroadcastBlockId(id) + /* + * Broadcasted data is also stored in the BlockManager of the driver. The BlockManagerMaster + * does not need to be told about this block as not only need to know about this data block. + */ HttpBroadcast.synchronized { - SparkEnv.get.blockManager.putSingle(blockId, value_, StorageLevel.MEMORY_AND_DISK, false) + SparkEnv.get.blockManager.putSingle( + blockId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false) } if (!isLocal) { HttpBroadcast.write(id, value_) } - // Called by JVM when deserializing an object + /** + * Remove all persisted state associated with this HTTP broadcast on the executors. + */ + def doUnpersist(blocking: Boolean) { + HttpBroadcast.unpersist(id, removeFromDriver = false, blocking) + } + + /** + * Remove all persisted state associated with this HTTP broadcast on the executors and driver. + */ + def doDestroy(blocking: Boolean) { + HttpBroadcast.unpersist(id, removeFromDriver = true, blocking) + } + + /** Used by the JVM when serializing this object. */ + private def writeObject(out: ObjectOutputStream) { + assertValid() + out.defaultWriteObject() + } + + /** Used by the JVM when deserializing this object. */ private def readObject(in: ObjectInputStream) { in.defaultReadObject() HttpBroadcast.synchronized { @@ -54,7 +85,13 @@ private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolea logInfo("Started reading broadcast variable " + id) val start = System.nanoTime value_ = HttpBroadcast.read[T](id) - SparkEnv.get.blockManager.putSingle(blockId, value_, StorageLevel.MEMORY_AND_DISK, false) + /* + * We cache broadcast data in the BlockManager so that subsequent tasks using it + * do not need to re-fetch. This data is only used locally and no other node + * needs to fetch this block, so we don't notify the master. + */ + SparkEnv.get.blockManager.putSingle( + blockId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false) val time = (System.nanoTime - start) / 1e9 logInfo("Reading broadcast variable " + id + " took " + time + " s") } @@ -63,23 +100,8 @@ private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolea } } -/** - * A [[BroadcastFactory]] implementation that uses a HTTP server as the broadcast medium. - */ -class HttpBroadcastFactory extends BroadcastFactory { - def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) { - HttpBroadcast.initialize(isDriver, conf, securityMgr) - } - - def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = - new HttpBroadcast[T](value_, isLocal, id) - - def stop() { HttpBroadcast.stop() } -} - -private object HttpBroadcast extends Logging { +private[spark] object HttpBroadcast extends Logging { private var initialized = false - private var broadcastDir: File = null private var compress: Boolean = false private var bufferSize: Int = 65536 @@ -89,11 +111,9 @@ private object HttpBroadcast extends Logging { // TODO: This shouldn't be a global variable so that multiple SparkContexts can coexist private val files = new TimeStampedHashSet[String] - private var cleaner: MetadataCleaner = null - private val httpReadTimeout = TimeUnit.MILLISECONDS.convert(5, TimeUnit.MINUTES).toInt - private var compressionCodec: CompressionCodec = null + private var cleaner: MetadataCleaner = null def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) { synchronized { @@ -136,8 +156,10 @@ private object HttpBroadcast extends Logging { logInfo("Broadcast server started at " + serverUri) } + def getFile(id: Long) = new File(broadcastDir, BroadcastBlockId(id).name) + def write(id: Long, value: Any) { - val file = new File(broadcastDir, BroadcastBlockId(id).name) + val file = getFile(id) val out: OutputStream = { if (compress) { compressionCodec.compressedOutputStream(new FileOutputStream(file)) @@ -160,7 +182,7 @@ private object HttpBroadcast extends Logging { if (securityManager.isAuthenticationEnabled()) { logDebug("broadcast security enabled") val newuri = Utils.constructURIForAuthentication(new URI(url), securityManager) - uc = newuri.toURL().openConnection() + uc = newuri.toURL.openConnection() uc.setAllowUserInteraction(false) } else { logDebug("broadcast not using security") @@ -169,7 +191,7 @@ private object HttpBroadcast extends Logging { val in = { uc.setReadTimeout(httpReadTimeout) - val inputStream = uc.getInputStream(); + val inputStream = uc.getInputStream if (compress) { compressionCodec.compressedInputStream(inputStream) } else { @@ -183,20 +205,48 @@ private object HttpBroadcast extends Logging { obj } - def cleanup(cleanupTime: Long) { + /** + * Remove all persisted blocks associated with this HTTP broadcast on the executors. + * If removeFromDriver is true, also remove these persisted blocks on the driver + * and delete the associated broadcast file. + */ + def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = synchronized { + SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking) + if (removeFromDriver) { + val file = getFile(id) + files.remove(file.toString) + deleteBroadcastFile(file) + } + } + + /** + * Periodically clean up old broadcasts by removing the associated map entries and + * deleting the associated files. + */ + private def cleanup(cleanupTime: Long) { val iterator = files.internalMap.entrySet().iterator() while(iterator.hasNext) { val entry = iterator.next() val (file, time) = (entry.getKey, entry.getValue) if (time < cleanupTime) { - try { - iterator.remove() - new File(file.toString).delete() - logInfo("Deleted broadcast file '" + file + "'") - } catch { - case e: Exception => logWarning("Could not delete broadcast file '" + file + "'", e) + iterator.remove() + deleteBroadcastFile(new File(file.toString)) + } + } + } + + private def deleteBroadcastFile(file: File) { + try { + if (file.exists) { + if (file.delete()) { + logInfo("Deleted broadcast file: %s".format(file)) + } else { + logWarning("Could not delete broadcast file: %s".format(file)) } } + } catch { + case e: Exception => + logError("Exception while deleting broadcast file: %s".format(file), e) } } } diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala new file mode 100644 index 0000000000000..e3f6cdc6154dd --- /dev/null +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala @@ -0,0 +1,45 @@ +/* + * 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.broadcast + +import org.apache.spark.{SecurityManager, SparkConf} + +/** + * A [[org.apache.spark.broadcast.BroadcastFactory]] implementation that uses a + * HTTP server as the broadcast mechanism. Refer to + * [[org.apache.spark.broadcast.HttpBroadcast]] for more details about this mechanism. + */ +class HttpBroadcastFactory extends BroadcastFactory { + def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) { + HttpBroadcast.initialize(isDriver, conf, securityMgr) + } + + def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = + new HttpBroadcast[T](value_, isLocal, id) + + def stop() { HttpBroadcast.stop() } + + /** + * Remove all persisted state associated with the HTTP broadcast with the given ID. + * @param removeFromDriver Whether to remove state from the driver + * @param blocking Whether to block until unbroadcasted + */ + def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean) { + HttpBroadcast.unpersist(id, removeFromDriver, blocking) + } +} diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala index 2595c15104e87..2b32546c6854d 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala @@ -17,24 +17,43 @@ package org.apache.spark.broadcast -import java.io._ +import java.io.{ByteArrayInputStream, ObjectInputStream, ObjectOutputStream} import scala.math import scala.util.Random -import org.apache.spark._ -import org.apache.spark.storage.{BroadcastBlockId, BroadcastHelperBlockId, StorageLevel} +import org.apache.spark.{Logging, SparkConf, SparkEnv, SparkException} +import org.apache.spark.storage.{BroadcastBlockId, StorageLevel} import org.apache.spark.util.Utils +/** + * A [[org.apache.spark.broadcast.Broadcast]] implementation that uses a BitTorrent-like + * protocol to do a distributed transfer of the broadcasted data to the executors. + * The mechanism is as follows. The driver divides the serializes the broadcasted data, + * divides it into smaller chunks, and stores them in the BlockManager of the driver. + * These chunks are reported to the BlockManagerMaster so that all the executors can + * learn the location of those chunks. The first time the broadcast variable (sent as + * part of task) is deserialized at a executor, all the chunks are fetched using + * the BlockManager. When all the chunks are fetched (initially from the driver's + * BlockManager), they are combined and deserialized to recreate the broadcasted data. + * However, the chunks are also stored in the BlockManager and reported to the + * BlockManagerMaster. As more executors fetch the chunks, BlockManagerMaster learns + * multiple locations for each chunk. Hence, subsequent fetches of each chunk will be + * made to other executors who already have those chunks, resulting in a distributed + * fetching. This prevents the driver from being the bottleneck in sending out multiple + * copies of the broadcast data (one per executor) as done by the + * [[org.apache.spark.broadcast.HttpBroadcast]]. + */ private[spark] class TorrentBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long) -extends Broadcast[T](id) with Logging with Serializable { + extends Broadcast[T](id) with Logging with Serializable { - def value = value_ + def getValue = value_ - def broadcastId = BroadcastBlockId(id) + val broadcastId = BroadcastBlockId(id) TorrentBroadcast.synchronized { - SparkEnv.get.blockManager.putSingle(broadcastId, value_, StorageLevel.MEMORY_AND_DISK, false) + SparkEnv.get.blockManager.putSingle( + broadcastId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false) } @transient var arrayOfBlocks: Array[TorrentBlock] = null @@ -46,32 +65,52 @@ extends Broadcast[T](id) with Logging with Serializable { sendBroadcast() } - def sendBroadcast() { - var tInfo = TorrentBroadcast.blockifyObject(value_) + /** + * Remove all persisted state associated with this Torrent broadcast on the executors. + */ + def doUnpersist(blocking: Boolean) { + TorrentBroadcast.unpersist(id, removeFromDriver = false, blocking) + } + + /** + * Remove all persisted state associated with this Torrent broadcast on the executors + * and driver. + */ + def doDestroy(blocking: Boolean) { + TorrentBroadcast.unpersist(id, removeFromDriver = true, blocking) + } + def sendBroadcast() { + val tInfo = TorrentBroadcast.blockifyObject(value_) totalBlocks = tInfo.totalBlocks totalBytes = tInfo.totalBytes hasBlocks = tInfo.totalBlocks // Store meta-info - val metaId = BroadcastHelperBlockId(broadcastId, "meta") + val metaId = BroadcastBlockId(id, "meta") val metaInfo = TorrentInfo(null, totalBlocks, totalBytes) TorrentBroadcast.synchronized { SparkEnv.get.blockManager.putSingle( - metaId, metaInfo, StorageLevel.MEMORY_AND_DISK, true) + metaId, metaInfo, StorageLevel.MEMORY_AND_DISK, tellMaster = true) } // Store individual pieces for (i <- 0 until totalBlocks) { - val pieceId = BroadcastHelperBlockId(broadcastId, "piece" + i) + val pieceId = BroadcastBlockId(id, "piece" + i) TorrentBroadcast.synchronized { SparkEnv.get.blockManager.putSingle( - pieceId, tInfo.arrayOfBlocks(i), StorageLevel.MEMORY_AND_DISK, true) + pieceId, tInfo.arrayOfBlocks(i), StorageLevel.MEMORY_AND_DISK, tellMaster = true) } } } - // Called by JVM when deserializing an object + /** Used by the JVM when serializing this object. */ + private def writeObject(out: ObjectOutputStream) { + assertValid() + out.defaultWriteObject() + } + + /** Used by the JVM when deserializing this object. */ private def readObject(in: ObjectInputStream) { in.defaultReadObject() TorrentBroadcast.synchronized { @@ -86,18 +125,22 @@ extends Broadcast[T](id) with Logging with Serializable { // Initialize @transient variables that will receive garbage values from the master. resetWorkerVariables() - if (receiveBroadcast(id)) { + if (receiveBroadcast()) { value_ = TorrentBroadcast.unBlockifyObject[T](arrayOfBlocks, totalBytes, totalBlocks) - // Store the merged copy in cache so that the next worker doesn't need to rebuild it. - // This creates a tradeoff between memory usage and latency. - // Storing copy doubles the memory footprint; not storing doubles deserialization cost. + /* Store the merged copy in cache so that the next worker doesn't need to rebuild it. + * This creates a trade-off between memory usage and latency. Storing copy doubles + * the memory footprint; not storing doubles deserialization cost. Also, + * this does not need to be reported to BlockManagerMaster since other executors + * does not need to access this block (they only need to fetch the chunks, + * which are reported). + */ SparkEnv.get.blockManager.putSingle( - broadcastId, value_, StorageLevel.MEMORY_AND_DISK, false) + broadcastId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false) // Remove arrayOfBlocks from memory once value_ is on local cache resetWorkerVariables() - } else { + } else { logError("Reading broadcast variable " + id + " failed") } @@ -114,9 +157,10 @@ extends Broadcast[T](id) with Logging with Serializable { hasBlocks = 0 } - def receiveBroadcast(variableID: Long): Boolean = { - // Receive meta-info - val metaId = BroadcastHelperBlockId(broadcastId, "meta") + def receiveBroadcast(): Boolean = { + // Receive meta-info about the size of broadcast data, + // the number of chunks it is divided into, etc. + val metaId = BroadcastBlockId(id, "meta") var attemptId = 10 while (attemptId > 0 && totalBlocks == -1) { TorrentBroadcast.synchronized { @@ -138,17 +182,21 @@ extends Broadcast[T](id) with Logging with Serializable { return false } - // Receive actual blocks + /* + * Fetch actual chunks of data. Note that all these chunks are stored in + * the BlockManager and reported to the master, so that other executors + * can find out and pull the chunks from this executor. + */ val recvOrder = new Random().shuffle(Array.iterate(0, totalBlocks)(_ + 1).toList) for (pid <- recvOrder) { - val pieceId = BroadcastHelperBlockId(broadcastId, "piece" + pid) + val pieceId = BroadcastBlockId(id, "piece" + pid) TorrentBroadcast.synchronized { SparkEnv.get.blockManager.getSingle(pieceId) match { case Some(x) => arrayOfBlocks(pid) = x.asInstanceOf[TorrentBlock] hasBlocks += 1 SparkEnv.get.blockManager.putSingle( - pieceId, arrayOfBlocks(pid), StorageLevel.MEMORY_AND_DISK, true) + pieceId, arrayOfBlocks(pid), StorageLevel.MEMORY_AND_DISK, tellMaster = true) case None => throw new SparkException("Failed to get " + pieceId + " of " + broadcastId) @@ -156,16 +204,16 @@ extends Broadcast[T](id) with Logging with Serializable { } } - (hasBlocks == totalBlocks) + hasBlocks == totalBlocks } } -private object TorrentBroadcast -extends Logging { - +private[spark] object TorrentBroadcast extends Logging { + private lazy val BLOCK_SIZE = conf.getInt("spark.broadcast.blockSize", 4096) * 1024 private var initialized = false private var conf: SparkConf = null + def initialize(_isDriver: Boolean, conf: SparkConf) { TorrentBroadcast.conf = conf // TODO: we might have to fix it in tests synchronized { @@ -179,39 +227,37 @@ extends Logging { initialized = false } - lazy val BLOCK_SIZE = conf.getInt("spark.broadcast.blockSize", 4096) * 1024 - def blockifyObject[T](obj: T): TorrentInfo = { val byteArray = Utils.serialize[T](obj) val bais = new ByteArrayInputStream(byteArray) - var blockNum = (byteArray.length / BLOCK_SIZE) + var blockNum = byteArray.length / BLOCK_SIZE if (byteArray.length % BLOCK_SIZE != 0) { blockNum += 1 } - var retVal = new Array[TorrentBlock](blockNum) - var blockID = 0 + val blocks = new Array[TorrentBlock](blockNum) + var blockId = 0 for (i <- 0 until (byteArray.length, BLOCK_SIZE)) { val thisBlockSize = math.min(BLOCK_SIZE, byteArray.length - i) - var tempByteArray = new Array[Byte](thisBlockSize) - val hasRead = bais.read(tempByteArray, 0, thisBlockSize) + val tempByteArray = new Array[Byte](thisBlockSize) + bais.read(tempByteArray, 0, thisBlockSize) - retVal(blockID) = new TorrentBlock(blockID, tempByteArray) - blockID += 1 + blocks(blockId) = new TorrentBlock(blockId, tempByteArray) + blockId += 1 } bais.close() - val tInfo = TorrentInfo(retVal, blockNum, byteArray.length) - tInfo.hasBlocks = blockNum - - tInfo + val info = TorrentInfo(blocks, blockNum, byteArray.length) + info.hasBlocks = blockNum + info } - def unBlockifyObject[T](arrayOfBlocks: Array[TorrentBlock], - totalBytes: Int, - totalBlocks: Int): T = { + def unBlockifyObject[T]( + arrayOfBlocks: Array[TorrentBlock], + totalBytes: Int, + totalBlocks: Int): T = { val retByteArray = new Array[Byte](totalBytes) for (i <- 0 until totalBlocks) { System.arraycopy(arrayOfBlocks(i).byteArray, 0, retByteArray, @@ -220,6 +266,13 @@ extends Logging { Utils.deserialize[T](retByteArray, Thread.currentThread.getContextClassLoader) } + /** + * Remove all persisted blocks associated with this torrent broadcast on the executors. + * If removeFromDriver is true, also remove these persisted blocks on the driver. + */ + def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = synchronized { + SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking) + } } private[spark] case class TorrentBlock( @@ -228,25 +281,10 @@ private[spark] case class TorrentBlock( extends Serializable private[spark] case class TorrentInfo( - @transient arrayOfBlocks : Array[TorrentBlock], + @transient arrayOfBlocks: Array[TorrentBlock], totalBlocks: Int, totalBytes: Int) extends Serializable { @transient var hasBlocks = 0 } - -/** - * A [[BroadcastFactory]] that creates a torrent-based implementation of broadcast. - */ -class TorrentBroadcastFactory extends BroadcastFactory { - - def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) { - TorrentBroadcast.initialize(isDriver, conf) - } - - def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = - new TorrentBroadcast[T](value_, isLocal, id) - - def stop() { TorrentBroadcast.stop() } -} diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala new file mode 100644 index 0000000000000..d216b58718148 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala @@ -0,0 +1,46 @@ +/* + * 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.broadcast + +import org.apache.spark.{SecurityManager, SparkConf} + +/** + * A [[org.apache.spark.broadcast.Broadcast]] implementation that uses a BitTorrent-like + * protocol to do a distributed transfer of the broadcasted data to the executors. Refer to + * [[org.apache.spark.broadcast.TorrentBroadcast]] for more details. + */ +class TorrentBroadcastFactory extends BroadcastFactory { + + def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) { + TorrentBroadcast.initialize(isDriver, conf) + } + + def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = + new TorrentBroadcast[T](value_, isLocal, id) + + def stop() { TorrentBroadcast.stop() } + + /** + * Remove all persisted state associated with the torrent broadcast with the given ID. + * @param removeFromDriver Whether to remove state from the driver. + * @param blocking Whether to block until unbroadcasted + */ + def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean) { + TorrentBroadcast.unpersist(id, removeFromDriver, blocking) + } +} diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala index 6b0a972f0bbe0..bdf586351ac14 100644 --- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala @@ -17,7 +17,6 @@ package org.apache.spark.network -import java.net._ import java.nio._ import java.nio.channels._ import java.nio.channels.spi._ 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 c43823bd769b7..bf3c57ad41eb2 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -138,6 +138,8 @@ abstract class RDD[T: ClassTag]( "Cannot change storage level of an RDD after it was already assigned a level") } sc.persistRDD(this) + // Register the RDD with the ContextCleaner for automatic GC-based cleanup + sc.cleaner.foreach(_.registerRDDForCleanup(this)) storageLevel = newLevel this } @@ -156,7 +158,7 @@ abstract class RDD[T: ClassTag]( */ def unpersist(blocking: Boolean = true): RDD[T] = { logInfo("Removing RDD " + id + " from persistence list") - sc.unpersistRDD(this, blocking) + sc.unpersistRDD(id, blocking) storageLevel = StorageLevel.NONE this } @@ -1141,5 +1143,4 @@ abstract class RDD[T: ClassTag]( def toJavaRDD() : JavaRDD[T] = { new JavaRDD(this)(elementClassTag) } - } 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 442a95bb2c44b..6368665f249ee 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -32,7 +32,7 @@ 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.{BlockId, BlockManager, BlockManagerMaster, RDDBlockId} -import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashMap, Utils} +import org.apache.spark.util.Utils /** * The high-level scheduling layer that implements stage-oriented scheduling. It computes a DAG of @@ -80,13 +80,13 @@ class DAGScheduler( private[scheduler] def numTotalJobs: Int = nextJobId.get() private val nextStageId = new AtomicInteger(0) - private[scheduler] val jobIdToStageIds = new TimeStampedHashMap[Int, HashSet[Int]] - private[scheduler] val stageIdToJobIds = new TimeStampedHashMap[Int, HashSet[Int]] - private[scheduler] val stageIdToStage = new TimeStampedHashMap[Int, Stage] - private[scheduler] val shuffleToMapStage = new TimeStampedHashMap[Int, Stage] + private[scheduler] val jobIdToStageIds = new HashMap[Int, HashSet[Int]] + private[scheduler] val stageIdToJobIds = new HashMap[Int, HashSet[Int]] + private[scheduler] val stageIdToStage = new HashMap[Int, Stage] + private[scheduler] val shuffleToMapStage = new HashMap[Int, Stage] private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob] private[scheduler] val resultStageToJob = new HashMap[Stage, ActiveJob] - private[scheduler] val stageToInfos = new TimeStampedHashMap[Stage, StageInfo] + private[scheduler] val stageToInfos = new HashMap[Stage, StageInfo] // Stages we need to run whose parents aren't done private[scheduler] val waitingStages = new HashSet[Stage] @@ -98,7 +98,7 @@ class DAGScheduler( private[scheduler] val failedStages = new HashSet[Stage] // Missing tasks from each stage - private[scheduler] val pendingTasks = new TimeStampedHashMap[Stage, HashSet[Task[_]]] + private[scheduler] val pendingTasks = new HashMap[Stage, HashSet[Task[_]]] private[scheduler] val activeJobs = new HashSet[ActiveJob] @@ -113,9 +113,6 @@ class DAGScheduler( // stray messages to detect. private val failedEpoch = new HashMap[String, Long] - private val metadataCleaner = - new MetadataCleaner(MetadataCleanerType.DAG_SCHEDULER, this.cleanup, env.conf) - taskScheduler.setDAGScheduler(this) /** @@ -258,7 +255,7 @@ class DAGScheduler( : Stage = { val stage = newStage(rdd, numTasks, Some(shuffleDep), jobId, callSite) - if (mapOutputTracker.has(shuffleDep.shuffleId)) { + if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) { val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId) val locs = MapOutputTracker.deserializeMapStatuses(serLocs) for (i <- 0 until locs.size) { @@ -390,6 +387,9 @@ class DAGScheduler( stageIdToStage -= stageId stageIdToJobIds -= stageId + ShuffleMapTask.removeStage(stageId) + ResultTask.removeStage(stageId) + logDebug("After removal of stage %d, remaining stages = %d" .format(stageId, stageIdToStage.size)) } @@ -1084,26 +1084,10 @@ class DAGScheduler( Nil } - private def cleanup(cleanupTime: Long) { - Map( - "stageIdToStage" -> stageIdToStage, - "shuffleToMapStage" -> shuffleToMapStage, - "pendingTasks" -> pendingTasks, - "stageToInfos" -> stageToInfos, - "jobIdToStageIds" -> jobIdToStageIds, - "stageIdToJobIds" -> stageIdToJobIds). - foreach { case (s, t) => - val sizeBefore = t.size - t.clearOldValues(cleanupTime) - logInfo("%s %d --> %d".format(s, sizeBefore, t.size)) - } - } - def stop() { if (eventProcessActor != null) { eventProcessActor ! StopDAGScheduler } - metadataCleaner.cancel() taskScheduler.stop() } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala index 3fc6cc9850feb..083fb895d8696 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala @@ -20,21 +20,17 @@ package org.apache.spark.scheduler import java.io._ import java.util.zip.{GZIPInputStream, GZIPOutputStream} +import scala.collection.mutable.HashMap + import org.apache.spark._ -import org.apache.spark.rdd.RDD -import org.apache.spark.rdd.RDDCheckpointData -import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashMap} +import org.apache.spark.rdd.{RDD, RDDCheckpointData} private[spark] object ResultTask { // A simple map between the stage id to the serialized byte array of a task. // Served as a cache for task serialization because serialization can be // expensive on the master node if it needs to launch thousands of tasks. - val serializedInfoCache = new TimeStampedHashMap[Int, Array[Byte]] - - // TODO: This object shouldn't have global variables - val metadataCleaner = new MetadataCleaner( - MetadataCleanerType.RESULT_TASK, serializedInfoCache.clearOldValues, new SparkConf) + private val serializedInfoCache = new HashMap[Int, Array[Byte]] def serializeInfo(stageId: Int, rdd: RDD[_], func: (TaskContext, Iterator[_]) => _): Array[Byte] = { @@ -67,6 +63,10 @@ private[spark] object ResultTask { (rdd, func) } + def removeStage(stageId: Int) { + serializedInfoCache.remove(stageId) + } + def clearCache() { synchronized { serializedInfoCache.clear() diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala index 2a9edf4a76b97..23f3b3e824762 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala @@ -24,22 +24,16 @@ import scala.collection.mutable.HashMap import org.apache.spark._ import org.apache.spark.executor.ShuffleWriteMetrics -import org.apache.spark.rdd.RDD -import org.apache.spark.rdd.RDDCheckpointData +import org.apache.spark.rdd.{RDD, RDDCheckpointData} import org.apache.spark.serializer.Serializer import org.apache.spark.storage._ -import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashMap} private[spark] object ShuffleMapTask { // A simple map between the stage id to the serialized byte array of a task. // Served as a cache for task serialization because serialization can be // expensive on the master node if it needs to launch thousands of tasks. - val serializedInfoCache = new TimeStampedHashMap[Int, Array[Byte]] - - // TODO: This object shouldn't have global variables - val metadataCleaner = new MetadataCleaner( - MetadataCleanerType.SHUFFLE_MAP_TASK, serializedInfoCache.clearOldValues, new SparkConf) + private val serializedInfoCache = new HashMap[Int, Array[Byte]] def serializeInfo(stageId: Int, rdd: RDD[_], dep: ShuffleDependency[_,_]): Array[Byte] = { synchronized { @@ -80,6 +74,10 @@ private[spark] object ShuffleMapTask { HashMap(set.toSeq: _*) } + def removeStage(stageId: Int) { + serializedInfoCache.remove(stageId) + } + def clearCache() { synchronized { serializedInfoCache.clear() 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 a92922166f595..acd152dda89d4 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -42,7 +42,7 @@ import org.apache.spark.scheduler.SchedulingMode.SchedulingMode * * THREADING: SchedulerBackends and task-submitting clients can call this class from multiple * threads, so it needs locks in public API methods to maintain its state. In addition, some - * SchedulerBackends sycnchronize on themselves when they want to send events here, and then + * SchedulerBackends synchronize on themselves when they want to send events here, and then * acquire a lock on us, so we need to make sure that we don't try to lock the backend while * we are holding a lock on ourselves. */ diff --git a/core/src/main/scala/org/apache/spark/storage/BlockId.scala b/core/src/main/scala/org/apache/spark/storage/BlockId.scala index 301d784b350a3..cffea28fbf794 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockId.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockId.scala @@ -34,7 +34,7 @@ private[spark] sealed abstract class BlockId { def asRDDId = if (isRDD) Some(asInstanceOf[RDDBlockId]) else None def isRDD = isInstanceOf[RDDBlockId] def isShuffle = isInstanceOf[ShuffleBlockId] - def isBroadcast = isInstanceOf[BroadcastBlockId] || isInstanceOf[BroadcastHelperBlockId] + def isBroadcast = isInstanceOf[BroadcastBlockId] override def toString = name override def hashCode = name.hashCode @@ -48,18 +48,13 @@ private[spark] case class RDDBlockId(rddId: Int, splitIndex: Int) extends BlockI def name = "rdd_" + rddId + "_" + splitIndex } -private[spark] -case class ShuffleBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId { +private[spark] case class ShuffleBlockId(shuffleId: Int, mapId: Int, reduceId: Int) + extends BlockId { def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId } -private[spark] case class BroadcastBlockId(broadcastId: Long) extends BlockId { - def name = "broadcast_" + broadcastId -} - -private[spark] -case class BroadcastHelperBlockId(broadcastId: BroadcastBlockId, hType: String) extends BlockId { - def name = broadcastId.name + "_" + hType +private[spark] case class BroadcastBlockId(broadcastId: Long, field: String = "") extends BlockId { + def name = "broadcast_" + broadcastId + (if (field == "") "" else "_" + field) } private[spark] case class TaskResultBlockId(taskId: Long) extends BlockId { @@ -83,8 +78,7 @@ private[spark] case class TestBlockId(id: String) extends BlockId { private[spark] object BlockId { val RDD = "rdd_([0-9]+)_([0-9]+)".r val SHUFFLE = "shuffle_([0-9]+)_([0-9]+)_([0-9]+)".r - val BROADCAST = "broadcast_([0-9]+)".r - val BROADCAST_HELPER = "broadcast_([0-9]+)_([A-Za-z0-9]+)".r + val BROADCAST = "broadcast_([0-9]+)([_A-Za-z0-9]*)".r val TASKRESULT = "taskresult_([0-9]+)".r val STREAM = "input-([0-9]+)-([0-9]+)".r val TEST = "test_(.*)".r @@ -95,10 +89,8 @@ private[spark] object BlockId { RDDBlockId(rddId.toInt, splitIndex.toInt) case SHUFFLE(shuffleId, mapId, reduceId) => ShuffleBlockId(shuffleId.toInt, mapId.toInt, reduceId.toInt) - case BROADCAST(broadcastId) => - BroadcastBlockId(broadcastId.toLong) - case BROADCAST_HELPER(broadcastId, hType) => - BroadcastHelperBlockId(BroadcastBlockId(broadcastId.toLong), hType) + case BROADCAST(broadcastId, field) => + BroadcastBlockId(broadcastId.toLong, field.stripPrefix("_")) case TASKRESULT(taskId) => TaskResultBlockId(taskId.toLong) case STREAM(streamId, uniqueId) => 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 19138d9dde697..b021564477c47 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -19,20 +19,22 @@ package org.apache.spark.storage import java.io.{File, InputStream, OutputStream} import java.nio.{ByteBuffer, MappedByteBuffer} + import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.concurrent.{Await, Future} import scala.concurrent.duration._ import scala.util.Random + import akka.actor.{ActorSystem, Cancellable, Props} import it.unimi.dsi.fastutil.io.{FastBufferedOutputStream, FastByteArrayOutputStream} import sun.nio.ch.DirectBuffer -import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv, SparkException} + +import org.apache.spark.{Logging, MapOutputTracker, SecurityManager, SparkConf, SparkEnv, SparkException} import org.apache.spark.io.CompressionCodec import org.apache.spark.network._ import org.apache.spark.serializer.Serializer import org.apache.spark.util._ - sealed trait Values case class ByteBufferValues(buffer: ByteBuffer) extends Values @@ -46,7 +48,8 @@ private[spark] class BlockManager( val defaultSerializer: Serializer, maxMemory: Long, val conf: SparkConf, - securityManager: SecurityManager) + securityManager: SecurityManager, + mapOutputTracker: MapOutputTracker) extends Logging { val shuffleBlockManager = new ShuffleBlockManager(this) @@ -55,7 +58,7 @@ private[spark] class BlockManager( private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo] - private[storage] val memoryStore: BlockStore = new MemoryStore(this, maxMemory) + private[storage] val memoryStore = new MemoryStore(this, maxMemory) private[storage] val diskStore = new DiskStore(this, diskBlockManager) var tachyonInitialized = false private[storage] lazy val tachyonStore: TachyonStore = { @@ -98,7 +101,7 @@ private[spark] class BlockManager( val heartBeatFrequency = BlockManager.getHeartBeatFrequency(conf) - val slaveActor = actorSystem.actorOf(Props(new BlockManagerSlaveActor(this)), + val slaveActor = actorSystem.actorOf(Props(new BlockManagerSlaveActor(this, mapOutputTracker)), name = "BlockManagerActor" + BlockManager.ID_GENERATOR.next) // Pending re-registration action being executed asynchronously or null if none @@ -137,9 +140,10 @@ private[spark] class BlockManager( master: BlockManagerMaster, serializer: Serializer, conf: SparkConf, - securityManager: SecurityManager) = { + securityManager: SecurityManager, + mapOutputTracker: MapOutputTracker) = { this(execId, actorSystem, master, serializer, BlockManager.getMaxMemory(conf), - conf, securityManager) + conf, securityManager, mapOutputTracker) } /** @@ -217,9 +221,26 @@ private[spark] class BlockManager( } /** - * Get storage level of local block. If no info exists for the block, then returns null. + * 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. + */ + 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 + BlockStatus(info.level, memSize, diskSize, 0L) + } + } + + /** + * Get the ids of existing blocks that match the given filter. Note that this will + * query the blocks stored in the disk block manager (that the block manager + * may not know of). */ - def getLevel(blockId: BlockId): StorageLevel = blockInfo.get(blockId).map(_.level).orNull + def getMatchingBlockIds(filter: BlockId => Boolean): Seq[BlockId] = { + (blockInfo.keys ++ diskBlockManager.getAllBlocks()).filter(filter).toSeq + } /** * Tell the master about the current storage status of a block. This will send a block update @@ -525,9 +546,8 @@ private[spark] class BlockManager( /** * A short circuited method to get a block writer that can write data directly to disk. - * The Block will be appended to the File specified by filename. - * This is currently used for writing shuffle files out. Callers should handle error - * cases. + * The Block will be appended to the File specified by filename. This is currently used for + * writing shuffle files out. Callers should handle error cases. */ def getDiskWriter( blockId: BlockId, @@ -863,11 +883,22 @@ private[spark] class BlockManager( * @return The number of blocks removed. */ def removeRdd(rddId: Int): Int = { - // TODO: Instead of doing a linear scan on the blockInfo map, create another map that maps - // from RDD.id to blocks. + // TODO: Avoid a linear scan by creating another mapping of RDD.id to blocks. logInfo("Removing RDD " + rddId) val blocksToRemove = blockInfo.keys.flatMap(_.asRDDId).filter(_.rddId == rddId) - blocksToRemove.foreach(blockId => removeBlock(blockId, tellMaster = false)) + blocksToRemove.foreach { blockId => removeBlock(blockId, tellMaster = false) } + blocksToRemove.size + } + + /** + * Remove all blocks belonging to the given broadcast. + */ + def removeBroadcast(broadcastId: Long, tellMaster: Boolean): Int = { + logInfo("Removing broadcast " + broadcastId) + val blocksToRemove = blockInfo.keys.collect { + case bid @ BroadcastBlockId(`broadcastId`, _) => bid + } + blocksToRemove.foreach { blockId => removeBlock(blockId, tellMaster) } blocksToRemove.size } @@ -908,10 +939,10 @@ private[spark] class BlockManager( } private def dropOldBlocks(cleanupTime: Long, shouldDrop: (BlockId => Boolean)) { - val iterator = blockInfo.internalMap.entrySet().iterator() + val iterator = blockInfo.getEntrySet.iterator while (iterator.hasNext) { val entry = iterator.next() - val (id, info, time) = (entry.getKey, entry.getValue._1, entry.getValue._2) + val (id, info, time) = (entry.getKey, entry.getValue.value, entry.getValue.timestamp) if (time < cleanupTime && shouldDrop(id)) { info.synchronized { val level = info.level @@ -935,7 +966,7 @@ private[spark] class BlockManager( def shouldCompress(blockId: BlockId): Boolean = blockId match { case ShuffleBlockId(_, _, _) => compressShuffle - case BroadcastBlockId(_) => compressBroadcast + case BroadcastBlockId(_, _) => compressBroadcast case RDDBlockId(_, _) => compressRdds case TempBlockId(_) => compressShuffleSpill case _ => false 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 4bc1b407ad106..7897fade2df2b 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -81,6 +81,14 @@ class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Log askDriverWithReply[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) } + /** + * Check if block manager master has a block. Note that this can be used to check for only + * those blocks that are reported to block manager master. + */ + def contains(blockId: BlockId) = { + !getLocations(blockId).isEmpty + } + /** Get ids of other nodes in the cluster from the driver */ def getPeers(blockManagerId: BlockManagerId, numPeers: Int): Seq[BlockManagerId] = { val result = askDriverWithReply[Seq[BlockManagerId]](GetPeers(blockManagerId, numPeers)) @@ -99,12 +107,10 @@ class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Log askDriverWithReply(RemoveBlock(blockId)) } - /** - * Remove all blocks belonging to the given RDD. - */ + /** Remove all blocks belonging to the given RDD. */ def removeRdd(rddId: Int, blocking: Boolean) { val future = askDriverWithReply[Future[Seq[Int]]](RemoveRdd(rddId)) - future onFailure { + future.onFailure { case e: Throwable => logError("Failed to remove RDD " + rddId, e) } if (blocking) { @@ -112,6 +118,31 @@ class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Log } } + /** Remove all blocks belonging to the given shuffle. */ + def removeShuffle(shuffleId: Int, blocking: Boolean) { + val future = askDriverWithReply[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) + future.onFailure { + case e: Throwable => logError("Failed to remove shuffle " + shuffleId, e) + } + if (blocking) { + Await.result(future, timeout) + } + } + + /** Remove all blocks belonging to the given broadcast. */ + def removeBroadcast(broadcastId: Long, removeFromMaster: Boolean, blocking: Boolean) { + val future = askDriverWithReply[Future[Seq[Int]]]( + RemoveBroadcast(broadcastId, removeFromMaster)) + future.onFailure { + case e: Throwable => + logError("Failed to remove broadcast " + broadcastId + + " with removeFromMaster = " + removeFromMaster, e) + } + if (blocking) { + Await.result(future, timeout) + } + } + /** * Return the memory status for each block manager, in the form of a map from * the block manager's id to two long values. The first value is the maximum @@ -126,6 +157,51 @@ class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Log askDriverWithReply[Array[StorageStatus]](GetStorageStatus) } + /** + * Return the block's status on all block managers, if any. NOTE: This is a + * potentially expensive operation and should only be used for testing. + * + * If askSlaves is true, this invokes the master to query each block manager for the most + * updated block statuses. This is useful when the master is not informed of the given block + * by all block managers. + */ + def getBlockStatus( + blockId: BlockId, + askSlaves: Boolean = true): Map[BlockManagerId, BlockStatus] = { + val msg = GetBlockStatus(blockId, askSlaves) + /* + * To avoid potential deadlocks, the use of Futures is necessary, because the master actor + * should not block on waiting for a block manager, which can in turn be waiting for the + * master actor for a response to a prior message. + */ + val response = askDriverWithReply[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) + val (blockManagerIds, futures) = response.unzip + val result = Await.result(Future.sequence(futures), timeout) + if (result == null) { + throw new SparkException("BlockManager returned null for BlockStatus query: " + blockId) + } + val blockStatus = result.asInstanceOf[Iterable[Option[BlockStatus]]] + blockManagerIds.zip(blockStatus).flatMap { case (blockManagerId, status) => + status.map { s => (blockManagerId, s) } + }.toMap + } + + /** + * Return a list of ids of existing blocks such that the ids match the given filter. NOTE: This + * is a potentially expensive operation and should only be used for testing. + * + * If askSlaves is true, this invokes the master to query each block manager for the most + * updated block statuses. This is useful when the master is not informed of the given block + * by all block managers. + */ + def getMatchingBlockIds( + filter: BlockId => Boolean, + askSlaves: Boolean): Seq[BlockId] = { + val msg = GetMatchingBlockIds(filter, askSlaves) + val future = askDriverWithReply[Future[Seq[BlockId]]](msg) + Await.result(future, timeout) + } + /** Stop the driver actor, called only on the Spark driver node */ def stop() { if (driverActor != null) { diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala index 378f4cadc17d7..c57b6e8391b13 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala @@ -94,9 +94,21 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus case GetStorageStatus => sender ! storageStatus + case GetBlockStatus(blockId, askSlaves) => + sender ! blockStatus(blockId, askSlaves) + + case GetMatchingBlockIds(filter, askSlaves) => + sender ! getMatchingBlockIds(filter, askSlaves) + case RemoveRdd(rddId) => sender ! removeRdd(rddId) + case RemoveShuffle(shuffleId) => + sender ! removeShuffle(shuffleId) + + case RemoveBroadcast(broadcastId, removeFromDriver) => + sender ! removeBroadcast(broadcastId, removeFromDriver) + case RemoveBlock(blockId) => removeBlockFromWorkers(blockId) sender ! true @@ -140,9 +152,41 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus // The dispatcher is used as an implicit argument into the Future sequence construction. import context.dispatcher val removeMsg = RemoveRdd(rddId) - Future.sequence(blockManagerInfo.values.map { bm => - bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Int] - }.toSeq) + Future.sequence( + blockManagerInfo.values.map { bm => + bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Int] + }.toSeq + ) + } + + private def removeShuffle(shuffleId: Int): Future[Seq[Boolean]] = { + // Nothing to do in the BlockManagerMasterActor data structures + import context.dispatcher + val removeMsg = RemoveShuffle(shuffleId) + Future.sequence( + blockManagerInfo.values.map { bm => + bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Boolean] + }.toSeq + ) + } + + /** + * Delegate RemoveBroadcast messages to each BlockManager because the master may not notified + * of all broadcast blocks. If removeFromDriver is false, broadcast blocks are only removed + * from the executors, but not from the driver. + */ + private def removeBroadcast(broadcastId: Long, removeFromDriver: Boolean): Future[Seq[Int]] = { + // TODO: Consolidate usages of + import context.dispatcher + val removeMsg = RemoveBroadcast(broadcastId, removeFromDriver) + val requiredBlockManagers = blockManagerInfo.values.filter { info => + removeFromDriver || info.blockManagerId.executorId != "" + } + Future.sequence( + requiredBlockManagers.map { bm => + bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Int] + }.toSeq + ) } private def removeBlockManager(blockManagerId: BlockManagerId) { @@ -225,6 +269,61 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus }.toArray } + /** + * Return the block's status for all block managers, if any. NOTE: This is a + * potentially expensive operation and should only be used for testing. + * + * If askSlaves is true, the master queries each block manager for the most updated block + * statuses. This is useful when the master is not informed of the given block by all block + * managers. + */ + private def blockStatus( + blockId: BlockId, + askSlaves: Boolean): Map[BlockManagerId, Future[Option[BlockStatus]]] = { + import context.dispatcher + val getBlockStatus = GetBlockStatus(blockId) + /* + * Rather than blocking on the block status query, master actor should simply return + * Futures to avoid potential deadlocks. This can arise if there exists a block manager + * that is also waiting for this master actor's response to a previous message. + */ + blockManagerInfo.values.map { info => + val blockStatusFuture = + if (askSlaves) { + info.slaveActor.ask(getBlockStatus)(akkaTimeout).mapTo[Option[BlockStatus]] + } else { + Future { info.getStatus(blockId) } + } + (info.blockManagerId, blockStatusFuture) + }.toMap + } + + /** + * Return the ids of blocks present in all the block managers that match the given filter. + * NOTE: This is a potentially expensive operation and should only be used for testing. + * + * If askSlaves is true, the master queries each block manager for the most updated block + * statuses. This is useful when the master is not informed of the given block by all block + * managers. + */ + private def getMatchingBlockIds( + filter: BlockId => Boolean, + askSlaves: Boolean): Future[Seq[BlockId]] = { + import context.dispatcher + val getMatchingBlockIds = GetMatchingBlockIds(filter) + Future.sequence( + blockManagerInfo.values.map { info => + val future = + if (askSlaves) { + info.slaveActor.ask(getMatchingBlockIds)(akkaTimeout).mapTo[Seq[BlockId]] + } else { + Future { info.blocks.keys.filter(filter).toSeq } + } + future + } + ).map(_.flatten.toSeq) + } + private def register(id: BlockManagerId, maxMemSize: Long, slaveActor: ActorRef) { if (!blockManagerInfo.contains(id)) { blockManagerIdByExecutor.get(id.executorId) match { @@ -334,6 +433,8 @@ private[spark] class BlockManagerInfo( logInfo("Registering block manager %s with %s RAM".format( blockManagerId.hostPort, Utils.bytesToString(maxMem))) + def getStatus(blockId: BlockId) = Option(_blocks.get(blockId)) + def updateLastSeenMs() { _lastSeenMs = System.currentTimeMillis() } 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 8a36b5cc42dfd..2b53bf33b5fba 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala @@ -34,6 +34,13 @@ private[storage] object BlockManagerMessages { // Remove all blocks belonging to a specific RDD. case class RemoveRdd(rddId: Int) extends ToBlockManagerSlave + // Remove all blocks belonging to a specific shuffle. + case class RemoveShuffle(shuffleId: Int) extends ToBlockManagerSlave + + // Remove all blocks belonging to a specific broadcast. + case class RemoveBroadcast(broadcastId: Long, removeFromDriver: Boolean = true) + extends ToBlockManagerSlave + ////////////////////////////////////////////////////////////////////////////////// // Messages from slaves to the master. @@ -80,7 +87,8 @@ private[storage] object BlockManagerMessages { } object UpdateBlockInfo { - def apply(blockManagerId: BlockManagerId, + def apply( + blockManagerId: BlockManagerId, blockId: BlockId, storageLevel: StorageLevel, memSize: Long, @@ -108,7 +116,13 @@ private[storage] object BlockManagerMessages { case object GetMemoryStatus extends ToBlockManagerMaster - case object ExpireDeadHosts extends ToBlockManagerMaster - case object GetStorageStatus extends ToBlockManagerMaster + + case class GetBlockStatus(blockId: BlockId, askSlaves: Boolean = true) + extends ToBlockManagerMaster + + case class GetMatchingBlockIds(filter: BlockId => Boolean, askSlaves: Boolean = true) + extends ToBlockManagerMaster + + case object ExpireDeadHosts extends ToBlockManagerMaster } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala index bcfb82d3c7336..6d4db064dff58 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala @@ -17,8 +17,11 @@ package org.apache.spark.storage -import akka.actor.Actor +import scala.concurrent.Future +import akka.actor.{ActorRef, Actor} + +import org.apache.spark.{Logging, MapOutputTracker} import org.apache.spark.storage.BlockManagerMessages._ /** @@ -26,14 +29,59 @@ import org.apache.spark.storage.BlockManagerMessages._ * this is used to remove blocks from the slave's BlockManager. */ private[storage] -class BlockManagerSlaveActor(blockManager: BlockManager) extends Actor { - override def receive = { +class BlockManagerSlaveActor( + blockManager: BlockManager, + mapOutputTracker: MapOutputTracker) + extends Actor with Logging { + + import context.dispatcher + // Operations that involve removing blocks may be slow and should be done asynchronously + override def receive = { case RemoveBlock(blockId) => - blockManager.removeBlock(blockId) + doAsync[Boolean]("removing block " + blockId, sender) { + blockManager.removeBlock(blockId) + true + } case RemoveRdd(rddId) => - val numBlocksRemoved = blockManager.removeRdd(rddId) - sender ! numBlocksRemoved + doAsync[Int]("removing RDD " + rddId, sender) { + blockManager.removeRdd(rddId) + } + + case RemoveShuffle(shuffleId) => + doAsync[Boolean]("removing shuffle " + shuffleId, sender) { + if (mapOutputTracker != null) { + mapOutputTracker.unregisterShuffle(shuffleId) + } + blockManager.shuffleBlockManager.removeShuffle(shuffleId) + } + + case RemoveBroadcast(broadcastId, tellMaster) => + doAsync[Int]("removing broadcast " + broadcastId, sender) { + blockManager.removeBroadcast(broadcastId, tellMaster) + } + + case GetBlockStatus(blockId, _) => + sender ! blockManager.getStatus(blockId) + + case GetMatchingBlockIds(filter, _) => + sender ! blockManager.getMatchingBlockIds(filter) + } + + private def doAsync[T](actionMessage: String, responseActor: ActorRef)(body: => T) { + val future = Future { + logDebug(actionMessage) + body + } + future.onSuccess { case response => + logDebug("Done " + actionMessage + ", response is " + response) + responseActor ! response + logDebug("Sent response: " + response + " to " + responseActor) + } + future.onFailure { case t: Throwable => + logError("Error in " + actionMessage, t) + responseActor ! null.asInstanceOf[T] + } } } diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala index f3e1c38744d78..7a24c8f57f43b 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -90,6 +90,20 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD def getFile(blockId: BlockId): File = getFile(blockId.name) + /** Check if disk block manager has a block. */ + def containsBlock(blockId: BlockId): Boolean = { + getBlockLocation(blockId).file.exists() + } + + /** List all the blocks currently stored on disk by the disk manager. */ + def getAllBlocks(): Seq[BlockId] = { + // Get all the files inside the array of array of directories + subDirs.flatten.filter(_ != null).flatMap { dir => + val files = dir.list() + if (files != null) files else Seq.empty + }.map(BlockId.apply) + } + /** Produces a unique block id and File suitable for intermediate results. */ def createTempBlock(): (TempBlockId, File) = { var blockId = new TempBlockId(UUID.randomUUID()) diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala index bb07c8cb134cc..4cd4cdbd9909d 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala @@ -169,23 +169,43 @@ class ShuffleBlockManager(blockManager: BlockManager) extends Logging { throw new IllegalStateException("Failed to find shuffle block: " + id) } + /** Remove all the blocks / files and metadata related to a particular shuffle. */ + def removeShuffle(shuffleId: ShuffleId): Boolean = { + // Do not change the ordering of this, if shuffleStates should be removed only + // after the corresponding shuffle blocks have been removed + val cleaned = removeShuffleBlocks(shuffleId) + shuffleStates.remove(shuffleId) + cleaned + } + + /** Remove all the blocks / files related to a particular shuffle. */ + private def removeShuffleBlocks(shuffleId: ShuffleId): Boolean = { + shuffleStates.get(shuffleId) match { + case Some(state) => + if (consolidateShuffleFiles) { + for (fileGroup <- state.allFileGroups; file <- fileGroup.files) { + file.delete() + } + } else { + for (mapId <- state.completedMapTasks; reduceId <- 0 until state.numBuckets) { + val blockId = new ShuffleBlockId(shuffleId, mapId, reduceId) + blockManager.diskBlockManager.getFile(blockId).delete() + } + } + logInfo("Deleted all files for shuffle " + shuffleId) + true + case None => + logInfo("Could not find files for shuffle " + shuffleId + " for deleting") + false + } + } + private def physicalFileName(shuffleId: Int, bucketId: Int, fileId: Int) = { "merged_shuffle_%d_%d_%d".format(shuffleId, bucketId, fileId) } private def cleanup(cleanupTime: Long) { - shuffleStates.clearOldValues(cleanupTime, (shuffleId, state) => { - if (consolidateShuffleFiles) { - for (fileGroup <- state.allFileGroups; file <- fileGroup.files) { - file.delete() - } - } else { - for (mapId <- state.completedMapTasks; reduceId <- 0 until state.numBuckets) { - val blockId = new ShuffleBlockId(shuffleId, mapId, reduceId) - blockManager.diskBlockManager.getFile(blockId).delete() - } - } - }) + shuffleStates.clearOldValues(cleanupTime, (shuffleId, state) => removeShuffleBlocks(shuffleId)) } } diff --git a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala index 226ed2a132b00..a107c5182b3be 100644 --- a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala +++ b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala @@ -22,7 +22,7 @@ import java.util.concurrent.ArrayBlockingQueue import akka.actor._ import util.Random -import org.apache.spark.{SecurityManager, SparkConf} +import org.apache.spark.{MapOutputTrackerMaster, SecurityManager, SparkConf} import org.apache.spark.scheduler.LiveListenerBus import org.apache.spark.serializer.KryoSerializer @@ -48,7 +48,7 @@ private[spark] object ThreadingTest { val block = (1 to blockSize).map(_ => Random.nextInt()) val level = randomLevel() val startTime = System.currentTimeMillis() - manager.put(blockId, block.iterator, level, true) + manager.put(blockId, block.iterator, level, tellMaster = true) println("Pushed block " + blockId + " in " + (System.currentTimeMillis - startTime) + " ms") queue.add((blockId, block)) } @@ -101,7 +101,7 @@ private[spark] object ThreadingTest { conf) val blockManager = new BlockManager( "", actorSystem, blockManagerMaster, serializer, 1024 * 1024, conf, - new SecurityManager(conf)) + new SecurityManager(conf), new MapOutputTrackerMaster(conf)) val producers = (1 to numProducers).map(i => new ProducerThread(blockManager, i)) val consumers = producers.map(p => new ConsumerThread(blockManager, p.queue)) producers.foreach(_.start) diff --git a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala index 0448919e09161..7ebed5105b9fd 100644 --- a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala @@ -62,8 +62,8 @@ private[spark] class MetadataCleaner( private[spark] object MetadataCleanerType extends Enumeration { - val MAP_OUTPUT_TRACKER, SPARK_CONTEXT, HTTP_BROADCAST, DAG_SCHEDULER, RESULT_TASK, - SHUFFLE_MAP_TASK, BLOCK_MANAGER, SHUFFLE_BLOCK_MANAGER, BROADCAST_VARS = Value + val MAP_OUTPUT_TRACKER, SPARK_CONTEXT, HTTP_BROADCAST, BLOCK_MANAGER, + SHUFFLE_BLOCK_MANAGER, BROADCAST_VARS = Value type MetadataCleanerType = Value @@ -78,15 +78,16 @@ private[spark] object MetadataCleaner { conf.getInt("spark.cleaner.ttl", -1) } - def getDelaySeconds(conf: SparkConf, cleanerType: MetadataCleanerType.MetadataCleanerType): Int = - { - conf.get(MetadataCleanerType.systemProperty(cleanerType), getDelaySeconds(conf).toString) - .toInt + def getDelaySeconds( + conf: SparkConf, + cleanerType: MetadataCleanerType.MetadataCleanerType): Int = { + conf.get(MetadataCleanerType.systemProperty(cleanerType), getDelaySeconds(conf).toString).toInt } - def setDelaySeconds(conf: SparkConf, cleanerType: MetadataCleanerType.MetadataCleanerType, - delay: Int) - { + def setDelaySeconds( + conf: SparkConf, + cleanerType: MetadataCleanerType.MetadataCleanerType, + delay: Int) { conf.set(MetadataCleanerType.systemProperty(cleanerType), delay.toString) } diff --git a/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala b/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala index ddbd084ed7f01..8de75ba9a9c92 100644 --- a/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala @@ -17,48 +17,54 @@ package org.apache.spark.util +import java.util.Set +import java.util.Map.Entry import java.util.concurrent.ConcurrentHashMap -import scala.collection.JavaConversions -import scala.collection.immutable -import scala.collection.mutable.Map +import scala.collection.{JavaConversions, mutable} import org.apache.spark.Logging +private[spark] case class TimeStampedValue[V](value: V, timestamp: Long) + /** * This is a custom implementation of scala.collection.mutable.Map which stores the insertion * timestamp along with each key-value pair. If specified, the timestamp of each pair can be * updated every time it is accessed. Key-value pairs whose timestamp are older than a particular * threshold time can then be removed using the clearOldValues method. This is intended to * be a drop-in replacement of scala.collection.mutable.HashMap. - * @param updateTimeStampOnGet When enabled, the timestamp of a pair will be - * updated when it is accessed + * + * @param updateTimeStampOnGet Whether timestamp of a pair will be updated when it is accessed */ -class TimeStampedHashMap[A, B](updateTimeStampOnGet: Boolean = false) - extends Map[A, B]() with Logging { - val internalMap = new ConcurrentHashMap[A, (B, Long)]() +private[spark] class TimeStampedHashMap[A, B](updateTimeStampOnGet: Boolean = false) + extends mutable.Map[A, B]() with Logging { + + private val internalMap = new ConcurrentHashMap[A, TimeStampedValue[B]]() def get(key: A): Option[B] = { val value = internalMap.get(key) if (value != null && updateTimeStampOnGet) { - internalMap.replace(key, value, (value._1, currentTime)) + internalMap.replace(key, value, TimeStampedValue(value.value, currentTime)) } - Option(value).map(_._1) + Option(value).map(_.value) } def iterator: Iterator[(A, B)] = { - val jIterator = internalMap.entrySet().iterator() - JavaConversions.asScalaIterator(jIterator).map(kv => (kv.getKey, kv.getValue._1)) + val jIterator = getEntrySet.iterator + JavaConversions.asScalaIterator(jIterator).map(kv => (kv.getKey, kv.getValue.value)) } - override def + [B1 >: B](kv: (A, B1)): Map[A, B1] = { + def getEntrySet: Set[Entry[A, TimeStampedValue[B]]] = internalMap.entrySet + + override def + [B1 >: B](kv: (A, B1)): mutable.Map[A, B1] = { val newMap = new TimeStampedHashMap[A, B1] - newMap.internalMap.putAll(this.internalMap) - newMap.internalMap.put(kv._1, (kv._2, currentTime)) + val oldInternalMap = this.internalMap.asInstanceOf[ConcurrentHashMap[A, TimeStampedValue[B1]]] + newMap.internalMap.putAll(oldInternalMap) + kv match { case (a, b) => newMap.internalMap.put(a, TimeStampedValue(b, currentTime)) } newMap } - override def - (key: A): Map[A, B] = { + override def - (key: A): mutable.Map[A, B] = { val newMap = new TimeStampedHashMap[A, B] newMap.internalMap.putAll(this.internalMap) newMap.internalMap.remove(key) @@ -66,17 +72,10 @@ class TimeStampedHashMap[A, B](updateTimeStampOnGet: Boolean = false) } override def += (kv: (A, B)): this.type = { - internalMap.put(kv._1, (kv._2, currentTime)) + kv match { case (a, b) => internalMap.put(a, TimeStampedValue(b, currentTime)) } this } - // Should we return previous value directly or as Option ? - def putIfAbsent(key: A, value: B): Option[B] = { - val prev = internalMap.putIfAbsent(key, (value, currentTime)) - if (prev != null) Some(prev._1) else None - } - - override def -= (key: A): this.type = { internalMap.remove(key) this @@ -87,53 +86,65 @@ class TimeStampedHashMap[A, B](updateTimeStampOnGet: Boolean = false) } override def apply(key: A): B = { - val value = internalMap.get(key) - if (value == null) throw new NoSuchElementException() - value._1 + get(key).getOrElse { throw new NoSuchElementException() } } - override def filter(p: ((A, B)) => Boolean): Map[A, B] = { - JavaConversions.mapAsScalaConcurrentMap(internalMap).map(kv => (kv._1, kv._2._1)).filter(p) + override def filter(p: ((A, B)) => Boolean): mutable.Map[A, B] = { + JavaConversions.mapAsScalaConcurrentMap(internalMap) + .map { case (k, TimeStampedValue(v, t)) => (k, v) } + .filter(p) } - override def empty: Map[A, B] = new TimeStampedHashMap[A, B]() + override def empty: mutable.Map[A, B] = new TimeStampedHashMap[A, B]() override def size: Int = internalMap.size override def foreach[U](f: ((A, B)) => U) { - val iterator = internalMap.entrySet().iterator() - while(iterator.hasNext) { - val entry = iterator.next() - val kv = (entry.getKey, entry.getValue._1) + val it = getEntrySet.iterator + while(it.hasNext) { + val entry = it.next() + val kv = (entry.getKey, entry.getValue.value) f(kv) } } - def toMap: immutable.Map[A, B] = iterator.toMap + def putIfAbsent(key: A, value: B): Option[B] = { + val prev = internalMap.putIfAbsent(key, TimeStampedValue(value, currentTime)) + Option(prev).map(_.value) + } + + def putAll(map: Map[A, B]) { + map.foreach { case (k, v) => update(k, v) } + } + + def toMap: Map[A, B] = iterator.toMap - /** - * Removes old key-value pairs that have timestamp earlier than `threshTime`, - * calling the supplied function on each such entry before removing. - */ def clearOldValues(threshTime: Long, f: (A, B) => Unit) { - val iterator = internalMap.entrySet().iterator() - while (iterator.hasNext) { - val entry = iterator.next() - if (entry.getValue._2 < threshTime) { - f(entry.getKey, entry.getValue._1) + val it = getEntrySet.iterator + while (it.hasNext) { + val entry = it.next() + if (entry.getValue.timestamp < threshTime) { + f(entry.getKey, entry.getValue.value) logDebug("Removing key " + entry.getKey) - iterator.remove() + it.remove() } } } - /** - * Removes old key-value pairs that have timestamp earlier than `threshTime` - */ + /** Removes old key-value pairs that have timestamp earlier than `threshTime`. */ def clearOldValues(threshTime: Long) { clearOldValues(threshTime, (_, _) => ()) } - private def currentTime: Long = System.currentTimeMillis() + private def currentTime: Long = System.currentTimeMillis + // For testing + + def getTimeStampedValue(key: A): Option[TimeStampedValue[B]] = { + Option(internalMap.get(key)) + } + + def getTimestamp(key: A): Option[Long] = { + getTimeStampedValue(key).map(_.timestamp) + } } diff --git a/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala b/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala new file mode 100644 index 0000000000000..b65017d6806c6 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala @@ -0,0 +1,170 @@ +/* + * 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.lang.ref.WeakReference +import java.util.concurrent.atomic.AtomicInteger + +import scala.collection.mutable + +import org.apache.spark.Logging + +/** + * A wrapper of TimeStampedHashMap that ensures the values are weakly referenced and timestamped. + * + * If the value is garbage collected and the weak reference is null, get() will return a + * non-existent value. These entries are removed from the map periodically (every N inserts), as + * their values are no longer strongly reachable. Further, key-value pairs whose timestamps are + * older than a particular threshold can be removed using the clearOldValues method. + * + * TimeStampedWeakValueHashMap exposes a scala.collection.mutable.Map interface, which allows it + * to be a drop-in replacement for Scala HashMaps. Internally, it uses a Java ConcurrentHashMap, + * so all operations on this HashMap are thread-safe. + * + * @param updateTimeStampOnGet Whether timestamp of a pair will be updated when it is accessed. + */ +private[spark] class TimeStampedWeakValueHashMap[A, B](updateTimeStampOnGet: Boolean = false) + extends mutable.Map[A, B]() with Logging { + + import TimeStampedWeakValueHashMap._ + + private val internalMap = new TimeStampedHashMap[A, WeakReference[B]](updateTimeStampOnGet) + private val insertCount = new AtomicInteger(0) + + /** Return a map consisting only of entries whose values are still strongly reachable. */ + private def nonNullReferenceMap = internalMap.filter { case (_, ref) => ref.get != null } + + def get(key: A): Option[B] = internalMap.get(key) + + def iterator: Iterator[(A, B)] = nonNullReferenceMap.iterator + + override def + [B1 >: B](kv: (A, B1)): mutable.Map[A, B1] = { + val newMap = new TimeStampedWeakValueHashMap[A, B1] + val oldMap = nonNullReferenceMap.asInstanceOf[mutable.Map[A, WeakReference[B1]]] + newMap.internalMap.putAll(oldMap.toMap) + newMap.internalMap += kv + newMap + } + + override def - (key: A): mutable.Map[A, B] = { + val newMap = new TimeStampedWeakValueHashMap[A, B] + newMap.internalMap.putAll(nonNullReferenceMap.toMap) + newMap.internalMap -= key + newMap + } + + override def += (kv: (A, B)): this.type = { + internalMap += kv + if (insertCount.incrementAndGet() % CLEAR_NULL_VALUES_INTERVAL == 0) { + clearNullValues() + } + this + } + + override def -= (key: A): this.type = { + internalMap -= key + this + } + + override def update(key: A, value: B) = this += ((key, value)) + + override def apply(key: A): B = internalMap.apply(key) + + override def filter(p: ((A, B)) => Boolean): mutable.Map[A, B] = nonNullReferenceMap.filter(p) + + override def empty: mutable.Map[A, B] = new TimeStampedWeakValueHashMap[A, B]() + + override def size: Int = internalMap.size + + override def foreach[U](f: ((A, B)) => U) = nonNullReferenceMap.foreach(f) + + def putIfAbsent(key: A, value: B): Option[B] = internalMap.putIfAbsent(key, value) + + def toMap: Map[A, B] = iterator.toMap + + /** Remove old key-value pairs with timestamps earlier than `threshTime`. */ + def clearOldValues(threshTime: Long) = internalMap.clearOldValues(threshTime) + + /** Remove entries with values that are no longer strongly reachable. */ + def clearNullValues() { + val it = internalMap.getEntrySet.iterator + while (it.hasNext) { + val entry = it.next() + if (entry.getValue.value.get == null) { + logDebug("Removing key " + entry.getKey + " because it is no longer strongly reachable.") + it.remove() + } + } + } + + // For testing + + def getTimestamp(key: A): Option[Long] = { + internalMap.getTimeStampedValue(key).map(_.timestamp) + } + + def getReference(key: A): Option[WeakReference[B]] = { + internalMap.getTimeStampedValue(key).map(_.value) + } +} + +/** + * Helper methods for converting to and from WeakReferences. + */ +private object TimeStampedWeakValueHashMap { + + // Number of inserts after which entries with null references are removed + val CLEAR_NULL_VALUES_INTERVAL = 100 + + /* Implicit conversion methods to WeakReferences. */ + + implicit def toWeakReference[V](v: V): WeakReference[V] = new WeakReference[V](v) + + implicit def toWeakReferenceTuple[K, V](kv: (K, V)): (K, WeakReference[V]) = { + kv match { case (k, v) => (k, toWeakReference(v)) } + } + + implicit def toWeakReferenceFunction[K, V, R](p: ((K, V)) => R): ((K, WeakReference[V])) => R = { + (kv: (K, WeakReference[V])) => p(kv) + } + + /* Implicit conversion methods from WeakReferences. */ + + implicit def fromWeakReference[V](ref: WeakReference[V]): V = ref.get + + implicit def fromWeakReferenceOption[V](v: Option[WeakReference[V]]): Option[V] = { + v match { + case Some(ref) => Option(fromWeakReference(ref)) + case None => None + } + } + + implicit def fromWeakReferenceTuple[K, V](kv: (K, WeakReference[V])): (K, V) = { + kv match { case (k, v) => (k, fromWeakReference(v)) } + } + + implicit def fromWeakReferenceIterator[K, V]( + it: Iterator[(K, WeakReference[V])]): Iterator[(K, V)] = { + it.map(fromWeakReferenceTuple) + } + + implicit def fromWeakReferenceMap[K, V]( + map: mutable.Map[K, WeakReference[V]]) : mutable.Map[K, V] = { + mutable.Map(map.mapValues(fromWeakReference).toSeq: _*) + } +} 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 4435b21a7505e..59da51f3e0297 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -499,10 +499,10 @@ private[spark] object Utils extends Logging { private val hostPortParseResults = new ConcurrentHashMap[String, (String, Int)]() def parseHostPort(hostPort: String): (String, Int) = { - { - // Check cache first. - val cached = hostPortParseResults.get(hostPort) - if (cached != null) return cached + // Check cache first. + val cached = hostPortParseResults.get(hostPort) + if (cached != null) { + return cached } val indx: Int = hostPort.lastIndexOf(':') diff --git a/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala b/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala index d2e303d81c4c8..c5f24c66ce0c1 100644 --- a/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/AkkaUtilsSuite.scala @@ -56,7 +56,7 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext { val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = conf, securityManager = securityManagerBad) - val slaveTracker = new MapOutputTracker(conf) + val slaveTracker = new MapOutputTrackerWorker(conf) val selection = slaveSystem.actorSelection( s"akka.tcp://spark@localhost:$boundPort/user/MapOutputTracker") val timeout = AkkaUtils.lookupTimeout(conf) @@ -93,7 +93,7 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext { val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = badconf, securityManager = securityManagerBad) - val slaveTracker = new MapOutputTracker(conf) + val slaveTracker = new MapOutputTrackerWorker(conf) val selection = slaveSystem.actorSelection( s"akka.tcp://spark@localhost:$boundPort/user/MapOutputTracker") val timeout = AkkaUtils.lookupTimeout(conf) @@ -147,7 +147,7 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext { val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = goodconf, securityManager = securityManagerGood) - val slaveTracker = new MapOutputTracker(conf) + val slaveTracker = new MapOutputTrackerWorker(conf) val selection = slaveSystem.actorSelection( s"akka.tcp://spark@localhost:$boundPort/user/MapOutputTracker") val timeout = AkkaUtils.lookupTimeout(conf) @@ -200,7 +200,7 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext { val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = badconf, securityManager = securityManagerBad) - val slaveTracker = new MapOutputTracker(conf) + val slaveTracker = new MapOutputTrackerWorker(conf) val selection = slaveSystem.actorSelection( s"akka.tcp://spark@localhost:$boundPort/user/MapOutputTracker") val timeout = AkkaUtils.lookupTimeout(conf) diff --git a/core/src/test/scala/org/apache/spark/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/BroadcastSuite.scala index 96ba3929c1685..c9936256a5b95 100644 --- a/core/src/test/scala/org/apache/spark/BroadcastSuite.scala +++ b/core/src/test/scala/org/apache/spark/BroadcastSuite.scala @@ -19,68 +19,297 @@ package org.apache.spark import org.scalatest.FunSuite -class BroadcastSuite extends FunSuite with LocalSparkContext { +import org.apache.spark.storage._ +import org.apache.spark.broadcast.{Broadcast, HttpBroadcast} +import org.apache.spark.storage.BroadcastBlockId +class BroadcastSuite extends FunSuite with LocalSparkContext { - override def afterEach() { - super.afterEach() - System.clearProperty("spark.broadcast.factory") - } + private val httpConf = broadcastConf("HttpBroadcastFactory") + private val torrentConf = broadcastConf("TorrentBroadcastFactory") test("Using HttpBroadcast locally") { - System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") - sc = new SparkContext("local", "test") - val list = List(1, 2, 3, 4) - val listBroadcast = sc.broadcast(list) - val results = sc.parallelize(1 to 2).map(x => (x, listBroadcast.value.sum)) - assert(results.collect.toSet === Set((1, 10), (2, 10))) + sc = new SparkContext("local", "test", httpConf) + val list = List[Int](1, 2, 3, 4) + val broadcast = sc.broadcast(list) + val results = sc.parallelize(1 to 2).map(x => (x, broadcast.value.sum)) + assert(results.collect().toSet === Set((1, 10), (2, 10))) } test("Accessing HttpBroadcast variables from multiple threads") { - System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") - sc = new SparkContext("local[10]", "test") - val list = List(1, 2, 3, 4) - val listBroadcast = sc.broadcast(list) - val results = sc.parallelize(1 to 10).map(x => (x, listBroadcast.value.sum)) - assert(results.collect.toSet === (1 to 10).map(x => (x, 10)).toSet) + sc = new SparkContext("local[10]", "test", httpConf) + val list = List[Int](1, 2, 3, 4) + val broadcast = sc.broadcast(list) + val results = sc.parallelize(1 to 10).map(x => (x, broadcast.value.sum)) + assert(results.collect().toSet === (1 to 10).map(x => (x, 10)).toSet) } test("Accessing HttpBroadcast variables in a local cluster") { - System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") val numSlaves = 4 - sc = new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test") - val list = List(1, 2, 3, 4) - val listBroadcast = sc.broadcast(list) - val results = sc.parallelize(1 to numSlaves).map(x => (x, listBroadcast.value.sum)) - assert(results.collect.toSet === (1 to numSlaves).map(x => (x, 10)).toSet) + sc = new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", httpConf) + val list = List[Int](1, 2, 3, 4) + val broadcast = sc.broadcast(list) + val results = sc.parallelize(1 to numSlaves).map(x => (x, broadcast.value.sum)) + assert(results.collect().toSet === (1 to numSlaves).map(x => (x, 10)).toSet) } test("Using TorrentBroadcast locally") { - System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.TorrentBroadcastFactory") - sc = new SparkContext("local", "test") - val list = List(1, 2, 3, 4) - val listBroadcast = sc.broadcast(list) - val results = sc.parallelize(1 to 2).map(x => (x, listBroadcast.value.sum)) - assert(results.collect.toSet === Set((1, 10), (2, 10))) + sc = new SparkContext("local", "test", torrentConf) + val list = List[Int](1, 2, 3, 4) + val broadcast = sc.broadcast(list) + val results = sc.parallelize(1 to 2).map(x => (x, broadcast.value.sum)) + assert(results.collect().toSet === Set((1, 10), (2, 10))) } test("Accessing TorrentBroadcast variables from multiple threads") { - System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.TorrentBroadcastFactory") - sc = new SparkContext("local[10]", "test") - val list = List(1, 2, 3, 4) - val listBroadcast = sc.broadcast(list) - val results = sc.parallelize(1 to 10).map(x => (x, listBroadcast.value.sum)) - assert(results.collect.toSet === (1 to 10).map(x => (x, 10)).toSet) + sc = new SparkContext("local[10]", "test", torrentConf) + val list = List[Int](1, 2, 3, 4) + val broadcast = sc.broadcast(list) + val results = sc.parallelize(1 to 10).map(x => (x, broadcast.value.sum)) + assert(results.collect().toSet === (1 to 10).map(x => (x, 10)).toSet) } test("Accessing TorrentBroadcast variables in a local cluster") { - System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast.TorrentBroadcastFactory") val numSlaves = 4 - sc = new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test") - val list = List(1, 2, 3, 4) - val listBroadcast = sc.broadcast(list) - val results = sc.parallelize(1 to numSlaves).map(x => (x, listBroadcast.value.sum)) - assert(results.collect.toSet === (1 to numSlaves).map(x => (x, 10)).toSet) + sc = new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", torrentConf) + val list = List[Int](1, 2, 3, 4) + val broadcast = sc.broadcast(list) + val results = sc.parallelize(1 to numSlaves).map(x => (x, broadcast.value.sum)) + assert(results.collect().toSet === (1 to numSlaves).map(x => (x, 10)).toSet) + } + + test("Unpersisting HttpBroadcast on executors only in local mode") { + testUnpersistHttpBroadcast(distributed = false, removeFromDriver = false) + } + + test("Unpersisting HttpBroadcast on executors and driver in local mode") { + testUnpersistHttpBroadcast(distributed = false, removeFromDriver = true) + } + + test("Unpersisting HttpBroadcast on executors only in distributed mode") { + testUnpersistHttpBroadcast(distributed = true, removeFromDriver = false) + } + + test("Unpersisting HttpBroadcast on executors and driver in distributed mode") { + testUnpersistHttpBroadcast(distributed = true, removeFromDriver = true) + } + + test("Unpersisting TorrentBroadcast on executors only in local mode") { + testUnpersistTorrentBroadcast(distributed = false, removeFromDriver = false) + } + + test("Unpersisting TorrentBroadcast on executors and driver in local mode") { + testUnpersistTorrentBroadcast(distributed = false, removeFromDriver = true) + } + + test("Unpersisting TorrentBroadcast on executors only in distributed mode") { + testUnpersistTorrentBroadcast(distributed = true, removeFromDriver = false) + } + + test("Unpersisting TorrentBroadcast on executors and driver in distributed mode") { + testUnpersistTorrentBroadcast(distributed = true, removeFromDriver = true) + } + /** + * Verify the persistence of state associated with an HttpBroadcast in either local mode or + * local-cluster mode (when distributed = true). + * + * This test creates a broadcast variable, uses it on all executors, and then unpersists it. + * In between each step, this test verifies that the broadcast blocks and the broadcast file + * are present only on the expected nodes. + */ + private def testUnpersistHttpBroadcast(distributed: Boolean, removeFromDriver: Boolean) { + val numSlaves = if (distributed) 2 else 0 + + def getBlockIds(id: Long) = Seq[BroadcastBlockId](BroadcastBlockId(id)) + + // Verify that the broadcast file is created, and blocks are persisted only on the driver + def afterCreation(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) { + assert(blockIds.size === 1) + val statuses = bmm.getBlockStatus(blockIds.head, askSlaves = true) + assert(statuses.size === 1) + statuses.head match { case (bm, status) => + assert(bm.executorId === "", "Block should only be on the driver") + assert(status.storageLevel === StorageLevel.MEMORY_AND_DISK) + assert(status.memSize > 0, "Block should be in memory store on the driver") + assert(status.diskSize === 0, "Block should not be in disk store on the driver") + } + if (distributed) { + // this file is only generated in distributed mode + assert(HttpBroadcast.getFile(blockIds.head.broadcastId).exists, "Broadcast file not found!") + } + } + + // Verify that blocks are persisted in both the executors and the driver + def afterUsingBroadcast(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) { + assert(blockIds.size === 1) + val statuses = bmm.getBlockStatus(blockIds.head, askSlaves = true) + assert(statuses.size === numSlaves + 1) + statuses.foreach { case (_, status) => + assert(status.storageLevel === StorageLevel.MEMORY_AND_DISK) + assert(status.memSize > 0, "Block should be in memory store") + assert(status.diskSize === 0, "Block should not be in disk store") + } + } + + // Verify that blocks are unpersisted on all executors, and on all nodes if removeFromDriver + // is true. In the latter case, also verify that the broadcast file is deleted on the driver. + def afterUnpersist(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) { + assert(blockIds.size === 1) + val statuses = bmm.getBlockStatus(blockIds.head, askSlaves = true) + val expectedNumBlocks = if (removeFromDriver) 0 else 1 + val possiblyNot = if (removeFromDriver) "" else " not" + assert(statuses.size === expectedNumBlocks, + "Block should%s be unpersisted on the driver".format(possiblyNot)) + if (distributed && removeFromDriver) { + // this file is only generated in distributed mode + assert(!HttpBroadcast.getFile(blockIds.head.broadcastId).exists, + "Broadcast file should%s be deleted".format(possiblyNot)) + } + } + + testUnpersistBroadcast(distributed, numSlaves, httpConf, getBlockIds, afterCreation, + afterUsingBroadcast, afterUnpersist, removeFromDriver) + } + + /** + * Verify the persistence of state associated with an TorrentBroadcast in a local-cluster. + * + * This test creates a broadcast variable, uses it on all executors, and then unpersists it. + * In between each step, this test verifies that the broadcast blocks are present only on the + * expected nodes. + */ + private def testUnpersistTorrentBroadcast(distributed: Boolean, removeFromDriver: Boolean) { + val numSlaves = if (distributed) 2 else 0 + + def getBlockIds(id: Long) = { + val broadcastBlockId = BroadcastBlockId(id) + val metaBlockId = BroadcastBlockId(id, "meta") + // Assume broadcast value is small enough to fit into 1 piece + val pieceBlockId = BroadcastBlockId(id, "piece0") + if (distributed) { + // the metadata and piece blocks are generated only in distributed mode + Seq[BroadcastBlockId](broadcastBlockId, metaBlockId, pieceBlockId) + } else { + Seq[BroadcastBlockId](broadcastBlockId) + } + } + + // Verify that blocks are persisted only on the driver + def afterCreation(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) { + blockIds.foreach { blockId => + val statuses = bmm.getBlockStatus(blockIds.head, askSlaves = true) + assert(statuses.size === 1) + statuses.head match { case (bm, status) => + assert(bm.executorId === "", "Block should only be on the driver") + assert(status.storageLevel === StorageLevel.MEMORY_AND_DISK) + assert(status.memSize > 0, "Block should be in memory store on the driver") + assert(status.diskSize === 0, "Block should not be in disk store on the driver") + } + } + } + + // Verify that blocks are persisted in both the executors and the driver + def afterUsingBroadcast(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) { + blockIds.foreach { blockId => + val statuses = bmm.getBlockStatus(blockId, askSlaves = true) + if (blockId.field == "meta") { + // Meta data is only on the driver + assert(statuses.size === 1) + statuses.head match { case (bm, _) => assert(bm.executorId === "") } + } else { + // Other blocks are on both the executors and the driver + assert(statuses.size === numSlaves + 1, + blockId + " has " + statuses.size + " statuses: " + statuses.mkString(",")) + statuses.foreach { case (_, status) => + assert(status.storageLevel === StorageLevel.MEMORY_AND_DISK) + assert(status.memSize > 0, "Block should be in memory store") + assert(status.diskSize === 0, "Block should not be in disk store") + } + } + } + } + + // Verify that blocks are unpersisted on all executors, and on all nodes if removeFromDriver + // is true. + def afterUnpersist(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) { + val expectedNumBlocks = if (removeFromDriver) 0 else 1 + val possiblyNot = if (removeFromDriver) "" else " not" + blockIds.foreach { blockId => + val statuses = bmm.getBlockStatus(blockId, askSlaves = true) + assert(statuses.size === expectedNumBlocks, + "Block should%s be unpersisted on the driver".format(possiblyNot)) + } + } + + testUnpersistBroadcast(distributed, numSlaves, torrentConf, getBlockIds, afterCreation, + afterUsingBroadcast, afterUnpersist, removeFromDriver) + } + + /** + * This test runs in 4 steps: + * + * 1) Create broadcast variable, and verify that all state is persisted on the driver. + * 2) Use the broadcast variable on all executors, and verify that all state is persisted + * on both the driver and the executors. + * 3) Unpersist the broadcast, and verify that all state is removed where they should be. + * 4) [Optional] If removeFromDriver is false, we verify that the broadcast is re-usable. + */ + private def testUnpersistBroadcast( + distributed: Boolean, + numSlaves: Int, // used only when distributed = true + broadcastConf: SparkConf, + getBlockIds: Long => Seq[BroadcastBlockId], + afterCreation: (Seq[BroadcastBlockId], BlockManagerMaster) => Unit, + afterUsingBroadcast: (Seq[BroadcastBlockId], BlockManagerMaster) => Unit, + afterUnpersist: (Seq[BroadcastBlockId], BlockManagerMaster) => Unit, + removeFromDriver: Boolean) { + + sc = if (distributed) { + new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", broadcastConf) + } else { + new SparkContext("local", "test", broadcastConf) + } + val blockManagerMaster = sc.env.blockManager.master + val list = List[Int](1, 2, 3, 4) + + // Create broadcast variable + val broadcast = sc.broadcast(list) + val blocks = getBlockIds(broadcast.id) + afterCreation(blocks, blockManagerMaster) + + // Use broadcast variable on all executors + val partitions = 10 + assert(partitions > numSlaves) + val results = sc.parallelize(1 to partitions, partitions).map(x => (x, broadcast.value.sum)) + assert(results.collect().toSet === (1 to partitions).map(x => (x, list.sum)).toSet) + afterUsingBroadcast(blocks, blockManagerMaster) + + // Unpersist broadcast + if (removeFromDriver) { + broadcast.destroy(blocking = true) + } else { + broadcast.unpersist(blocking = true) + } + afterUnpersist(blocks, blockManagerMaster) + + // If the broadcast is removed from driver, all subsequent uses of the broadcast variable + // should throw SparkExceptions. Otherwise, the result should be the same as before. + if (removeFromDriver) { + // Using this variable on the executors crashes them, which hangs the test. + // Instead, crash the driver by directly accessing the broadcast value. + intercept[SparkException] { broadcast.value } + intercept[SparkException] { broadcast.unpersist() } + intercept[SparkException] { broadcast.destroy(blocking = true) } + } else { + val results = sc.parallelize(1 to partitions, partitions).map(x => (x, broadcast.value.sum)) + assert(results.collect().toSet === (1 to partitions).map(x => (x, list.sum)).toSet) + } } + /** Helper method to create a SparkConf that uses the given broadcast factory. */ + private def broadcastConf(factoryName: String): SparkConf = { + val conf = new SparkConf + conf.set("spark.broadcast.factory", "org.apache.spark.broadcast.%s".format(factoryName)) + conf + } } diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala new file mode 100644 index 0000000000000..e50981cf6fb20 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala @@ -0,0 +1,415 @@ +/* + * 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 java.lang.ref.WeakReference + +import scala.collection.mutable.{HashSet, SynchronizedSet} +import scala.util.Random + +import org.scalatest.{BeforeAndAfter, FunSuite} +import org.scalatest.concurrent.Eventually +import org.scalatest.concurrent.Eventually._ +import org.scalatest.time.SpanSugar._ + +import org.apache.spark.SparkContext._ +import org.apache.spark.rdd.RDD +import org.apache.spark.storage.{BlockId, BroadcastBlockId, RDDBlockId, ShuffleBlockId} + +class ContextCleanerSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { + + implicit val defaultTimeout = timeout(10000 millis) + val conf = new SparkConf() + .setMaster("local[2]") + .setAppName("ContextCleanerSuite") + .set("spark.cleaner.referenceTracking.blocking", "true") + + before { + sc = new SparkContext(conf) + } + + after { + if (sc != null) { + sc.stop() + sc = null + } + } + + + test("cleanup RDD") { + val rdd = newRDD.persist() + val collected = rdd.collect().toList + val tester = new CleanerTester(sc, rddIds = Seq(rdd.id)) + + // Explicit cleanup + cleaner.doCleanupRDD(rdd.id, blocking = true) + tester.assertCleanup() + + // Verify that RDDs can be re-executed after cleaning up + assert(rdd.collect().toList === collected) + } + + test("cleanup shuffle") { + val (rdd, shuffleDeps) = newRDDWithShuffleDependencies + val collected = rdd.collect().toList + val tester = new CleanerTester(sc, shuffleIds = shuffleDeps.map(_.shuffleId)) + + // Explicit cleanup + shuffleDeps.foreach(s => cleaner.doCleanupShuffle(s.shuffleId, blocking = true)) + tester.assertCleanup() + + // Verify that shuffles can be re-executed after cleaning up + assert(rdd.collect().toList === collected) + } + + test("cleanup broadcast") { + val broadcast = newBroadcast + val tester = new CleanerTester(sc, broadcastIds = Seq(broadcast.id)) + + // Explicit cleanup + cleaner.doCleanupBroadcast(broadcast.id, blocking = true) + tester.assertCleanup() + } + + test("automatically cleanup RDD") { + var rdd = newRDD.persist() + rdd.count() + + // Test that GC does not cause RDD cleanup due to a strong reference + val preGCTester = new CleanerTester(sc, rddIds = Seq(rdd.id)) + runGC() + intercept[Exception] { + preGCTester.assertCleanup()(timeout(1000 millis)) + } + + // Test that GC causes RDD cleanup after dereferencing the RDD + val postGCTester = new CleanerTester(sc, rddIds = Seq(rdd.id)) + rdd = null // Make RDD out of scope + runGC() + postGCTester.assertCleanup() + } + + test("automatically cleanup shuffle") { + var rdd = newShuffleRDD + rdd.count() + + // Test that GC does not cause shuffle cleanup due to a strong reference + val preGCTester = new CleanerTester(sc, shuffleIds = Seq(0)) + runGC() + intercept[Exception] { + preGCTester.assertCleanup()(timeout(1000 millis)) + } + + // Test that GC causes shuffle cleanup after dereferencing the RDD + val postGCTester = new CleanerTester(sc, shuffleIds = Seq(0)) + rdd = null // Make RDD out of scope, so that corresponding shuffle goes out of scope + runGC() + postGCTester.assertCleanup() + } + + test("automatically cleanup broadcast") { + var broadcast = newBroadcast + + // Test that GC does not cause broadcast cleanup due to a strong reference + val preGCTester = new CleanerTester(sc, broadcastIds = Seq(broadcast.id)) + runGC() + intercept[Exception] { + preGCTester.assertCleanup()(timeout(1000 millis)) + } + + // Test that GC causes broadcast cleanup after dereferencing the broadcast variable + val postGCTester = new CleanerTester(sc, broadcastIds = Seq(broadcast.id)) + broadcast = null // Make broadcast variable out of scope + runGC() + postGCTester.assertCleanup() + } + + test("automatically cleanup RDD + shuffle + broadcast") { + val numRdds = 100 + val numBroadcasts = 4 // Broadcasts are more costly + val rddBuffer = (1 to numRdds).map(i => randomRdd).toBuffer + val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast).toBuffer + val rddIds = sc.persistentRdds.keys.toSeq + val shuffleIds = 0 until sc.newShuffleId + val broadcastIds = 0L until numBroadcasts + + val preGCTester = new CleanerTester(sc, rddIds, shuffleIds, broadcastIds) + runGC() + intercept[Exception] { + preGCTester.assertCleanup()(timeout(1000 millis)) + } + + // Test that GC triggers the cleanup of all variables after the dereferencing them + val postGCTester = new CleanerTester(sc, rddIds, shuffleIds, broadcastIds) + broadcastBuffer.clear() + rddBuffer.clear() + runGC() + postGCTester.assertCleanup() + } + + test("automatically cleanup RDD + shuffle + broadcast in distributed mode") { + sc.stop() + + val conf2 = new SparkConf() + .setMaster("local-cluster[2, 1, 512]") + .setAppName("ContextCleanerSuite") + .set("spark.cleaner.referenceTracking.blocking", "true") + sc = new SparkContext(conf2) + + val numRdds = 10 + val numBroadcasts = 4 // Broadcasts are more costly + val rddBuffer = (1 to numRdds).map(i => randomRdd).toBuffer + val broadcastBuffer = (1 to numBroadcasts).map(i => randomBroadcast).toBuffer + val rddIds = sc.persistentRdds.keys.toSeq + val shuffleIds = 0 until sc.newShuffleId + val broadcastIds = 0L until numBroadcasts + + val preGCTester = new CleanerTester(sc, rddIds, shuffleIds, broadcastIds) + runGC() + intercept[Exception] { + preGCTester.assertCleanup()(timeout(1000 millis)) + } + + // Test that GC triggers the cleanup of all variables after the dereferencing them + val postGCTester = new CleanerTester(sc, rddIds, shuffleIds, broadcastIds) + broadcastBuffer.clear() + rddBuffer.clear() + runGC() + postGCTester.assertCleanup() + } + + //------ Helper functions ------ + + def newRDD = sc.makeRDD(1 to 10) + def newPairRDD = newRDD.map(_ -> 1) + def newShuffleRDD = newPairRDD.reduceByKey(_ + _) + def newBroadcast = sc.broadcast(1 to 100) + def newRDDWithShuffleDependencies: (RDD[_], Seq[ShuffleDependency[_, _]]) = { + def getAllDependencies(rdd: RDD[_]): Seq[Dependency[_]] = { + rdd.dependencies ++ rdd.dependencies.flatMap { dep => + getAllDependencies(dep.rdd) + } + } + val rdd = newShuffleRDD + + // Get all the shuffle dependencies + val shuffleDeps = getAllDependencies(rdd) + .filter(_.isInstanceOf[ShuffleDependency[_, _]]) + .map(_.asInstanceOf[ShuffleDependency[_, _]]) + (rdd, shuffleDeps) + } + + def randomRdd = { + val rdd: RDD[_] = Random.nextInt(3) match { + case 0 => newRDD + case 1 => newShuffleRDD + case 2 => newPairRDD.join(newPairRDD) + } + if (Random.nextBoolean()) rdd.persist() + rdd.count() + rdd + } + + def randomBroadcast = { + sc.broadcast(Random.nextInt(Int.MaxValue)) + } + + /** Run GC and make sure it actually has run */ + def runGC() { + val weakRef = new WeakReference(new Object()) + val startTime = System.currentTimeMillis + System.gc() // Make a best effort to run the garbage collection. It *usually* runs GC. + // Wait until a weak reference object has been GCed + while(System.currentTimeMillis - startTime < 10000 && weakRef.get != null) { + System.gc() + Thread.sleep(200) + } + } + + def cleaner = sc.cleaner.get +} + + +/** Class to test whether RDDs, shuffles, etc. have been successfully cleaned. */ +class CleanerTester( + sc: SparkContext, + rddIds: Seq[Int] = Seq.empty, + shuffleIds: Seq[Int] = Seq.empty, + broadcastIds: Seq[Long] = Seq.empty) + extends Logging { + + val toBeCleanedRDDIds = new HashSet[Int] with SynchronizedSet[Int] ++= rddIds + val toBeCleanedShuffleIds = new HashSet[Int] with SynchronizedSet[Int] ++= shuffleIds + val toBeCleanedBroadcstIds = new HashSet[Long] with SynchronizedSet[Long] ++= broadcastIds + val isDistributed = !sc.isLocal + + val cleanerListener = new CleanerListener { + def rddCleaned(rddId: Int): Unit = { + toBeCleanedRDDIds -= rddId + logInfo("RDD "+ rddId + " cleaned") + } + + def shuffleCleaned(shuffleId: Int): Unit = { + toBeCleanedShuffleIds -= shuffleId + logInfo("Shuffle " + shuffleId + " cleaned") + } + + def broadcastCleaned(broadcastId: Long): Unit = { + toBeCleanedBroadcstIds -= broadcastId + logInfo("Broadcast" + broadcastId + " cleaned") + } + } + + val MAX_VALIDATION_ATTEMPTS = 10 + val VALIDATION_ATTEMPT_INTERVAL = 100 + + logInfo("Attempting to validate before cleanup:\n" + uncleanedResourcesToString) + preCleanupValidate() + sc.cleaner.get.attachListener(cleanerListener) + + /** Assert that all the stuff has been cleaned up */ + def assertCleanup()(implicit waitTimeout: Eventually.Timeout) { + try { + eventually(waitTimeout, interval(100 millis)) { + assert(isAllCleanedUp) + } + postCleanupValidate() + } finally { + logInfo("Resources left from cleaning up:\n" + uncleanedResourcesToString) + } + } + + /** Verify that RDDs, shuffles, etc. occupy resources */ + private def preCleanupValidate() { + assert(rddIds.nonEmpty || shuffleIds.nonEmpty || broadcastIds.nonEmpty, "Nothing to cleanup") + + // Verify the RDDs have been persisted and blocks are present + rddIds.foreach { rddId => + assert( + sc.persistentRdds.contains(rddId), + "RDD " + rddId + " have not been persisted, cannot start cleaner test" + ) + + assert( + !getRDDBlocks(rddId).isEmpty, + "Blocks of RDD " + rddId + " cannot be found in block manager, " + + "cannot start cleaner test" + ) + } + + // Verify the shuffle ids are registered and blocks are present + shuffleIds.foreach { shuffleId => + assert( + mapOutputTrackerMaster.containsShuffle(shuffleId), + "Shuffle " + shuffleId + " have not been registered, cannot start cleaner test" + ) + + assert( + !getShuffleBlocks(shuffleId).isEmpty, + "Blocks of shuffle " + shuffleId + " cannot be found in block manager, " + + "cannot start cleaner test" + ) + } + + // Verify that the broadcast blocks are present + broadcastIds.foreach { broadcastId => + assert( + !getBroadcastBlocks(broadcastId).isEmpty, + "Blocks of broadcast " + broadcastId + "cannot be found in block manager, " + + "cannot start cleaner test" + ) + } + } + + /** + * Verify that RDDs, shuffles, etc. do not occupy resources. Tests multiple times as there is + * as there is not guarantee on how long it will take clean up the resources. + */ + private def postCleanupValidate() { + // Verify the RDDs have been persisted and blocks are present + rddIds.foreach { rddId => + assert( + !sc.persistentRdds.contains(rddId), + "RDD " + rddId + " was not cleared from sc.persistentRdds" + ) + + assert( + getRDDBlocks(rddId).isEmpty, + "Blocks of RDD " + rddId + " were not cleared from block manager" + ) + } + + // Verify the shuffle ids are registered and blocks are present + shuffleIds.foreach { shuffleId => + assert( + !mapOutputTrackerMaster.containsShuffle(shuffleId), + "Shuffle " + shuffleId + " was not deregistered from map output tracker" + ) + + assert( + getShuffleBlocks(shuffleId).isEmpty, + "Blocks of shuffle " + shuffleId + " were not cleared from block manager" + ) + } + + // Verify that the broadcast blocks are present + broadcastIds.foreach { broadcastId => + assert( + getBroadcastBlocks(broadcastId).isEmpty, + "Blocks of broadcast " + broadcastId + " were not cleared from block manager" + ) + } + } + + private def uncleanedResourcesToString = { + s""" + |\tRDDs = ${toBeCleanedRDDIds.toSeq.sorted.mkString("[", ", ", "]")} + |\tShuffles = ${toBeCleanedShuffleIds.toSeq.sorted.mkString("[", ", ", "]")} + |\tBroadcasts = ${toBeCleanedBroadcstIds.toSeq.sorted.mkString("[", ", ", "]")} + """.stripMargin + } + + private def isAllCleanedUp = + toBeCleanedRDDIds.isEmpty && + toBeCleanedShuffleIds.isEmpty && + toBeCleanedBroadcstIds.isEmpty + + private def getRDDBlocks(rddId: Int): Seq[BlockId] = { + blockManager.master.getMatchingBlockIds( _ match { + case RDDBlockId(`rddId`, _) => true + case _ => false + }, askSlaves = true) + } + + private def getShuffleBlocks(shuffleId: Int): Seq[BlockId] = { + blockManager.master.getMatchingBlockIds( _ match { + case ShuffleBlockId(`shuffleId`, _, _) => true + case _ => false + }, askSlaves = true) + } + + private def getBroadcastBlocks(broadcastId: Long): Seq[BlockId] = { + blockManager.master.getMatchingBlockIds( _ match { + case BroadcastBlockId(`broadcastId`, _) => true + case _ => false + }, askSlaves = true) + } + + private def blockManager = sc.env.blockManager + private def mapOutputTrackerMaster = sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster] +} diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala index a5bd72eb0a122..6b2571cd9295e 100644 --- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala @@ -57,12 +57,13 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { tracker.stop() } - test("master register and fetch") { + test("master register shuffle and fetch") { val actorSystem = ActorSystem("test") val tracker = new MapOutputTrackerMaster(conf) tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) tracker.registerShuffle(10, 2) + assert(tracker.containsShuffle(10)) val compressedSize1000 = MapOutputTracker.compressSize(1000L) val compressedSize10000 = MapOutputTracker.compressSize(10000L) val size1000 = MapOutputTracker.decompressSize(compressedSize1000) @@ -77,7 +78,25 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { tracker.stop() } - test("master register and unregister and fetch") { + test("master register and unregister shuffle") { + val actorSystem = ActorSystem("test") + val tracker = new MapOutputTrackerMaster(conf) + tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) + tracker.registerShuffle(10, 2) + val compressedSize1000 = MapOutputTracker.compressSize(1000L) + val compressedSize10000 = MapOutputTracker.compressSize(10000L) + tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000, 0), + Array(compressedSize1000, compressedSize10000))) + tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000, 0), + Array(compressedSize10000, compressedSize1000))) + assert(tracker.containsShuffle(10)) + assert(tracker.getServerStatuses(10, 0).nonEmpty) + tracker.unregisterShuffle(10) + assert(!tracker.containsShuffle(10)) + assert(tracker.getServerStatuses(10, 0).isEmpty) + } + + test("master register shuffle and unregister map output and fetch") { val actorSystem = ActorSystem("test") val tracker = new MapOutputTrackerMaster(conf) tracker.trackerActor = @@ -114,7 +133,7 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = conf, securityManager = new SecurityManager(conf)) - val slaveTracker = new MapOutputTracker(conf) + val slaveTracker = new MapOutputTrackerWorker(conf) val selection = slaveSystem.actorSelection( s"akka.tcp://spark@localhost:$boundPort/user/MapOutputTracker") val timeout = AkkaUtils.lookupTimeout(conf) 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 b6dd0526105a0..e10ec7d2624a0 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -28,7 +28,7 @@ import org.scalatest.concurrent.Timeouts._ import org.scalatest.matchers.ShouldMatchers._ import org.scalatest.time.SpanSugar._ -import org.apache.spark.{SecurityManager, SparkConf} +import org.apache.spark.{MapOutputTrackerMaster, SecurityManager, SparkConf} import org.apache.spark.scheduler.LiveListenerBus import org.apache.spark.serializer.{JavaSerializer, KryoSerializer} import org.apache.spark.util.{AkkaUtils, ByteBufferInputStream, SizeEstimator, Utils} @@ -42,6 +42,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT var oldArch: String = null conf.set("spark.authenticate", "false") val securityMgr = new SecurityManager(conf) + val mapOutputTracker = new MapOutputTrackerMaster(conf) // Reuse a serializer across tests to avoid creating a new thread-local buffer on each test conf.set("spark.kryoserializer.buffer.mb", "1") @@ -130,7 +131,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("master + 1 manager interaction") { - store = new BlockManager("", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -160,9 +162,10 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("master + 2 managers interaction") { - store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) store2 = new BlockManager("exec2", actorSystem, master, new KryoSerializer(conf), 2000, conf, - securityMgr) + securityMgr, mapOutputTracker) val peers = master.getPeers(store.blockManagerId, 1) assert(peers.size === 1, "master did not return the other manager as a peer") @@ -177,7 +180,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("removing block") { - store = new BlockManager("", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -225,7 +229,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("removing rdd") { - store = new BlockManager("", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -257,9 +262,82 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT master.getLocations(rdd(0, 1)) should have size 0 } + test("removing broadcast") { + store = new BlockManager("", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) + val driverStore = store + val executorStore = new BlockManager("executor", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + val a4 = new Array[Byte](400) + + val broadcast0BlockId = BroadcastBlockId(0) + val broadcast1BlockId = BroadcastBlockId(1) + val broadcast2BlockId = BroadcastBlockId(2) + val broadcast2BlockId2 = BroadcastBlockId(2, "_") + + // insert broadcast blocks in both the stores + Seq(driverStore, executorStore).foreach { case s => + s.putSingle(broadcast0BlockId, a1, StorageLevel.DISK_ONLY) + s.putSingle(broadcast1BlockId, a2, StorageLevel.DISK_ONLY) + s.putSingle(broadcast2BlockId, a3, StorageLevel.DISK_ONLY) + s.putSingle(broadcast2BlockId2, a4, StorageLevel.DISK_ONLY) + } + + // verify whether the blocks exist in both the stores + Seq(driverStore, executorStore).foreach { case s => + s.getLocal(broadcast0BlockId) should not be (None) + s.getLocal(broadcast1BlockId) should not be (None) + s.getLocal(broadcast2BlockId) should not be (None) + s.getLocal(broadcast2BlockId2) should not be (None) + } + + // remove broadcast 0 block only from executors + master.removeBroadcast(0, removeFromMaster = false, blocking = true) + + // only broadcast 0 block should be removed from the executor store + executorStore.getLocal(broadcast0BlockId) should be (None) + executorStore.getLocal(broadcast1BlockId) should not be (None) + executorStore.getLocal(broadcast2BlockId) should not be (None) + + // nothing should be removed from the driver store + driverStore.getLocal(broadcast0BlockId) should not be (None) + driverStore.getLocal(broadcast1BlockId) should not be (None) + driverStore.getLocal(broadcast2BlockId) should not be (None) + + // remove broadcast 0 block from the driver as well + master.removeBroadcast(0, removeFromMaster = true, blocking = true) + driverStore.getLocal(broadcast0BlockId) should be (None) + driverStore.getLocal(broadcast1BlockId) should not be (None) + + // remove broadcast 1 block from both the stores asynchronously + // and verify all broadcast 1 blocks have been removed + master.removeBroadcast(1, removeFromMaster = true, blocking = false) + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + driverStore.getLocal(broadcast1BlockId) should be (None) + executorStore.getLocal(broadcast1BlockId) should be (None) + } + + // remove broadcast 2 from both the stores asynchronously + // and verify all broadcast 2 blocks have been removed + master.removeBroadcast(2, removeFromMaster = true, blocking = false) + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + driverStore.getLocal(broadcast2BlockId) should be (None) + driverStore.getLocal(broadcast2BlockId2) should be (None) + executorStore.getLocal(broadcast2BlockId) should be (None) + executorStore.getLocal(broadcast2BlockId2) should be (None) + } + executorStore.stop() + driverStore.stop() + store = null + } + test("reregistration on heart beat") { val heartBeat = PrivateMethod[Unit]('heartBeat) - store = new BlockManager("", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) @@ -275,7 +353,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("reregistration on block update") { - store = new BlockManager("", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) @@ -294,7 +373,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("reregistration doesn't dead lock") { val heartBeat = PrivateMethod[Unit]('heartBeat) - store = new BlockManager("", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = List(new Array[Byte](400)) @@ -331,7 +411,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU storage") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -350,7 +431,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU storage with serialization") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -369,7 +451,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU for partitions of same RDD") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -388,7 +471,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU for partitions of multiple RDDs") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) store.putSingle(rdd(0, 1), new Array[Byte](400), StorageLevel.MEMORY_ONLY) store.putSingle(rdd(0, 2), new Array[Byte](400), StorageLevel.MEMORY_ONLY) store.putSingle(rdd(1, 1), new Array[Byte](400), StorageLevel.MEMORY_ONLY) @@ -414,7 +498,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT // 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) if (tachyonUnitTestEnabled) { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -430,7 +515,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("on-disk storage") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -443,7 +529,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -458,7 +545,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage with getLocalBytes") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -473,7 +561,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage with serialization") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -488,7 +577,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage with serialization and getLocalBytes") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -503,7 +593,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("LRU with mixed storage levels") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -525,7 +616,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU with streams") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val list1 = List(new Array[Byte](200), new Array[Byte](200)) val list2 = List(new Array[Byte](200), new Array[Byte](200)) val list3 = List(new Array[Byte](200), new Array[Byte](200)) @@ -549,7 +641,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("LRU with mixed storage levels and streams") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val list1 = List(new Array[Byte](200), new Array[Byte](200)) val list2 = List(new Array[Byte](200), new Array[Byte](200)) val list3 = List(new Array[Byte](200), new Array[Byte](200)) @@ -595,7 +688,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("overly large block") { - store = new BlockManager("", actorSystem, master, serializer, 500, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 500, conf, + securityMgr, mapOutputTracker) store.putSingle("a1", new Array[Byte](1000), StorageLevel.MEMORY_ONLY) assert(store.getSingle("a1") === None, "a1 was in store") store.putSingle("a2", new Array[Byte](1000), StorageLevel.MEMORY_AND_DISK) @@ -606,7 +700,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("block compression") { try { conf.set("spark.shuffle.compress", "true") - store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) store.putSingle(ShuffleBlockId(0, 0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(ShuffleBlockId(0, 0, 0)) <= 100, "shuffle_0_0_0 was not compressed") @@ -614,7 +709,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT store = null conf.set("spark.shuffle.compress", "false") - store = new BlockManager("exec2", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("exec2", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) store.putSingle(ShuffleBlockId(0, 0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(ShuffleBlockId(0, 0, 0)) >= 1000, "shuffle_0_0_0 was compressed") @@ -622,7 +718,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT store = null conf.set("spark.broadcast.compress", "true") - store = new BlockManager("exec3", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("exec3", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) store.putSingle(BroadcastBlockId(0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(BroadcastBlockId(0)) <= 100, "broadcast_0 was not compressed") @@ -630,28 +727,32 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT store = null conf.set("spark.broadcast.compress", "false") - store = new BlockManager("exec4", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("exec4", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) store.putSingle(BroadcastBlockId(0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(BroadcastBlockId(0)) >= 1000, "broadcast_0 was compressed") store.stop() store = null conf.set("spark.rdd.compress", "true") - store = new BlockManager("exec5", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("exec5", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) store.putSingle(rdd(0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(rdd(0, 0)) <= 100, "rdd_0_0 was not compressed") store.stop() store = null conf.set("spark.rdd.compress", "false") - store = new BlockManager("exec6", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("exec6", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) store.putSingle(rdd(0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(rdd(0, 0)) >= 1000, "rdd_0_0 was compressed") store.stop() store = null // Check that any other block types are also kept uncompressed - store = new BlockManager("exec7", actorSystem, master, serializer, 2000, conf, securityMgr) + store = new BlockManager("exec7", actorSystem, master, serializer, 2000, conf, + securityMgr, mapOutputTracker) store.putSingle("other_block", new Array[Byte](1000), StorageLevel.MEMORY_ONLY) assert(store.memoryStore.getSize("other_block") >= 1000, "other_block was compressed") store.stop() @@ -666,7 +767,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("block store put failure") { // Use Java serializer so we can create an unserializable error. store = new BlockManager("", actorSystem, master, new JavaSerializer(conf), 1200, conf, - securityMgr) + securityMgr, mapOutputTracker) // The put should fail since a1 is not serializable. class UnserializableClass @@ -682,7 +783,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("updated block statuses") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) val list = List.fill(2)(new Array[Byte](200)) val bigList = List.fill(8)(new Array[Byte](200)) @@ -735,8 +837,83 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT assert(!store.get("list5").isDefined, "list5 was in store") } + test("query block statuses") { + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) + val list = List.fill(2)(new Array[Byte](200)) + + // Tell master. By LRU, only list2 and list3 remains. + store.put("list1", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + store.put("list2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true) + store.put("list3", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + + // getLocations and getBlockStatus should yield the same locations + assert(store.master.getLocations("list1").size === 0) + assert(store.master.getLocations("list2").size === 1) + assert(store.master.getLocations("list3").size === 1) + assert(store.master.getBlockStatus("list1", askSlaves = false).size === 0) + assert(store.master.getBlockStatus("list2", askSlaves = false).size === 1) + assert(store.master.getBlockStatus("list3", askSlaves = false).size === 1) + assert(store.master.getBlockStatus("list1", askSlaves = true).size === 0) + assert(store.master.getBlockStatus("list2", askSlaves = true).size === 1) + assert(store.master.getBlockStatus("list3", askSlaves = true).size === 1) + + // This time don't tell master and see what happens. By LRU, only list5 and list6 remains. + store.put("list4", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = false) + store.put("list5", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false) + store.put("list6", list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = false) + + // getLocations should return nothing because the master is not informed + // getBlockStatus without asking slaves should have the same result + // getBlockStatus with asking slaves, however, should return the actual block statuses + assert(store.master.getLocations("list4").size === 0) + assert(store.master.getLocations("list5").size === 0) + assert(store.master.getLocations("list6").size === 0) + assert(store.master.getBlockStatus("list4", askSlaves = false).size === 0) + assert(store.master.getBlockStatus("list5", askSlaves = false).size === 0) + assert(store.master.getBlockStatus("list6", askSlaves = false).size === 0) + assert(store.master.getBlockStatus("list4", askSlaves = true).size === 0) + assert(store.master.getBlockStatus("list5", askSlaves = true).size === 1) + assert(store.master.getBlockStatus("list6", askSlaves = true).size === 1) + } + + test("get matching blocks") { + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) + val list = List.fill(2)(new Array[Byte](10)) + + // insert some blocks + store.put("list1", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true) + store.put("list2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true) + store.put("list3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true) + + // getLocations and getBlockStatus should yield the same locations + assert(store.master.getMatchingBlockIds(_.toString.contains("list"), askSlaves = false).size === 3) + assert(store.master.getMatchingBlockIds(_.toString.contains("list1"), askSlaves = false).size === 1) + + // insert some more blocks + store.put("newlist1", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true) + store.put("newlist2", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false) + store.put("newlist3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false) + + // getLocations and getBlockStatus should yield the same locations + assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = false).size === 1) + assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = true).size === 3) + + val blockIds = Seq(RDDBlockId(1, 0), RDDBlockId(1, 1), RDDBlockId(2, 0)) + blockIds.foreach { blockId => + store.put(blockId, list.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + } + val matchedBlockIds = store.master.getMatchingBlockIds(_ match { + case RDDBlockId(1, _) => true + case _ => false + }, askSlaves = true) + assert(matchedBlockIds.toSet === Set(RDDBlockId(1, 0), RDDBlockId(1, 1))) + } + test("SPARK-1194 regression: fix the same-RDD rule for cache replacement") { - store = new BlockManager("", actorSystem, master, serializer, 1200, conf, securityMgr) + store = new BlockManager("", actorSystem, master, serializer, 1200, conf, + securityMgr, mapOutputTracker) store.putSingle(rdd(0, 0), new Array[Byte](400), StorageLevel.MEMORY_ONLY) store.putSingle(rdd(1, 0), new Array[Byte](400), StorageLevel.MEMORY_ONLY) // Access rdd_1_0 to ensure it's not least recently used. diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala index 62f9b3cc7b2c1..808ddfdcf45d8 100644 --- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala @@ -59,8 +59,16 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach { val newFile = diskBlockManager.getFile(blockId) writeToFile(newFile, 10) assertSegmentEquals(blockId, blockId.name, 0, 10) - + assert(diskBlockManager.containsBlock(blockId)) newFile.delete() + assert(!diskBlockManager.containsBlock(blockId)) + } + + test("enumerating blocks") { + val ids = (1 to 100).map(i => TestBlockId("test_" + i)) + val files = ids.map(id => diskBlockManager.getFile(id)) + files.foreach(file => writeToFile(file, 10)) + assert(diskBlockManager.getAllBlocks.toSet === ids.toSet) } test("block appending") { 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 054eb01a64c11..7bab7da8fed68 100644 --- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala @@ -108,8 +108,7 @@ class JsonProtocolSuite extends FunSuite { // BlockId testBlockId(RDDBlockId(1, 2)) testBlockId(ShuffleBlockId(1, 2, 3)) - testBlockId(BroadcastBlockId(1L)) - testBlockId(BroadcastHelperBlockId(BroadcastBlockId(2L), "Spark")) + testBlockId(BroadcastBlockId(1L, "insert_words_of_wisdom_here")) testBlockId(TaskResultBlockId(1L)) testBlockId(StreamBlockId(1, 2L)) } @@ -555,4 +554,4 @@ class JsonProtocolSuite extends FunSuite { {"Event":"SparkListenerUnpersistRDD","RDD ID":12345} """ - } +} diff --git a/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala new file mode 100644 index 0000000000000..6a5653ed2fb54 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.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.util + +import java.lang.ref.WeakReference + +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.util.Random + +import org.scalatest.FunSuite + +class TimeStampedHashMapSuite extends FunSuite { + + // Test the testMap function - a Scala HashMap should obviously pass + testMap(new mutable.HashMap[String, String]()) + + // Test TimeStampedHashMap basic functionality + testMap(new TimeStampedHashMap[String, String]()) + testMapThreadSafety(new TimeStampedHashMap[String, String]()) + + // Test TimeStampedWeakValueHashMap basic functionality + testMap(new TimeStampedWeakValueHashMap[String, String]()) + testMapThreadSafety(new TimeStampedWeakValueHashMap[String, String]()) + + test("TimeStampedHashMap - clearing by timestamp") { + // clearing by insertion time + val map = new TimeStampedHashMap[String, String](updateTimeStampOnGet = false) + map("k1") = "v1" + assert(map("k1") === "v1") + Thread.sleep(10) + val threshTime = System.currentTimeMillis + assert(map.getTimestamp("k1").isDefined) + assert(map.getTimestamp("k1").get < threshTime) + map.clearOldValues(threshTime) + assert(map.get("k1") === None) + + // clearing by modification time + val map1 = new TimeStampedHashMap[String, String](updateTimeStampOnGet = true) + map1("k1") = "v1" + map1("k2") = "v2" + assert(map1("k1") === "v1") + Thread.sleep(10) + val threshTime1 = System.currentTimeMillis + Thread.sleep(10) + assert(map1("k2") === "v2") // access k2 to update its access time to > threshTime + assert(map1.getTimestamp("k1").isDefined) + assert(map1.getTimestamp("k1").get < threshTime1) + assert(map1.getTimestamp("k2").isDefined) + assert(map1.getTimestamp("k2").get >= threshTime1) + map1.clearOldValues(threshTime1) //should only clear k1 + assert(map1.get("k1") === None) + assert(map1.get("k2").isDefined) + } + + test("TimeStampedWeakValueHashMap - clearing by timestamp") { + // clearing by insertion time + val map = new TimeStampedWeakValueHashMap[String, String](updateTimeStampOnGet = false) + map("k1") = "v1" + assert(map("k1") === "v1") + Thread.sleep(10) + val threshTime = System.currentTimeMillis + assert(map.getTimestamp("k1").isDefined) + assert(map.getTimestamp("k1").get < threshTime) + map.clearOldValues(threshTime) + assert(map.get("k1") === None) + + // clearing by modification time + val map1 = new TimeStampedWeakValueHashMap[String, String](updateTimeStampOnGet = true) + map1("k1") = "v1" + map1("k2") = "v2" + assert(map1("k1") === "v1") + Thread.sleep(10) + val threshTime1 = System.currentTimeMillis + Thread.sleep(10) + assert(map1("k2") === "v2") // access k2 to update its access time to > threshTime + assert(map1.getTimestamp("k1").isDefined) + assert(map1.getTimestamp("k1").get < threshTime1) + assert(map1.getTimestamp("k2").isDefined) + assert(map1.getTimestamp("k2").get >= threshTime1) + map1.clearOldValues(threshTime1) //should only clear k1 + assert(map1.get("k1") === None) + assert(map1.get("k2").isDefined) + } + + test("TimeStampedWeakValueHashMap - clearing weak references") { + var strongRef = new Object + val weakRef = new WeakReference(strongRef) + val map = new TimeStampedWeakValueHashMap[String, Object] + map("k1") = strongRef + map("k2") = "v2" + map("k3") = "v3" + assert(map("k1") === strongRef) + + // clear strong reference to "k1" + strongRef = null + val startTime = System.currentTimeMillis + System.gc() // Make a best effort to run the garbage collection. It *usually* runs GC. + System.runFinalization() // Make a best effort to call finalizer on all cleaned objects. + while(System.currentTimeMillis - startTime < 10000 && weakRef.get != null) { + System.gc() + System.runFinalization() + Thread.sleep(100) + } + assert(map.getReference("k1").isDefined) + val ref = map.getReference("k1").get + assert(ref.get === null) + assert(map.get("k1") === None) + + // operations should only display non-null entries + assert(map.iterator.forall { case (k, v) => k != "k1" }) + assert(map.filter { case (k, v) => k != "k2" }.size === 1) + assert(map.filter { case (k, v) => k != "k2" }.head._1 === "k3") + assert(map.toMap.size === 2) + assert(map.toMap.forall { case (k, v) => k != "k1" }) + val buffer = new ArrayBuffer[String] + map.foreach { case (k, v) => buffer += v.toString } + assert(buffer.size === 2) + assert(buffer.forall(_ != "k1")) + val plusMap = map + (("k4", "v4")) + assert(plusMap.size === 3) + assert(plusMap.forall { case (k, v) => k != "k1" }) + val minusMap = map - "k2" + assert(minusMap.size === 1) + assert(minusMap.head._1 == "k3") + + // clear null values - should only clear k1 + map.clearNullValues() + assert(map.getReference("k1") === None) + assert(map.get("k1") === None) + assert(map.get("k2").isDefined) + assert(map.get("k2").get === "v2") + } + + /** Test basic operations of a Scala mutable Map. */ + def testMap(hashMapConstructor: => mutable.Map[String, String]) { + def newMap() = hashMapConstructor + val testMap1 = newMap() + val testMap2 = newMap() + val name = testMap1.getClass.getSimpleName + + test(name + " - basic test") { + // put, get, and apply + testMap1 += (("k1", "v1")) + assert(testMap1.get("k1").isDefined) + assert(testMap1.get("k1").get === "v1") + testMap1("k2") = "v2" + assert(testMap1.get("k2").isDefined) + assert(testMap1.get("k2").get === "v2") + assert(testMap1("k2") === "v2") + testMap1.update("k3", "v3") + assert(testMap1.get("k3").isDefined) + assert(testMap1.get("k3").get === "v3") + + // remove + testMap1.remove("k1") + assert(testMap1.get("k1").isEmpty) + testMap1.remove("k2") + intercept[NoSuchElementException] { + testMap1("k2") // Map.apply() causes exception + } + testMap1 -= "k3" + assert(testMap1.get("k3").isEmpty) + + // multi put + val keys = (1 to 100).map(_.toString) + val pairs = keys.map(x => (x, x * 2)) + assert((testMap2 ++ pairs).iterator.toSet === pairs.toSet) + testMap2 ++= pairs + + // iterator + assert(testMap2.iterator.toSet === pairs.toSet) + + // filter + val filtered = testMap2.filter { case (_, v) => v.toInt % 2 == 0 } + val evenPairs = pairs.filter { case (_, v) => v.toInt % 2 == 0 } + assert(filtered.iterator.toSet === evenPairs.toSet) + + // foreach + val buffer = new ArrayBuffer[(String, String)] + testMap2.foreach(x => buffer += x) + assert(testMap2.toSet === buffer.toSet) + + // multi remove + testMap2("k1") = "v1" + testMap2 --= keys + assert(testMap2.size === 1) + assert(testMap2.iterator.toSeq.head === ("k1", "v1")) + + // + + val testMap3 = testMap2 + (("k0", "v0")) + assert(testMap3.size === 2) + assert(testMap3.get("k1").isDefined) + assert(testMap3.get("k1").get === "v1") + assert(testMap3.get("k0").isDefined) + assert(testMap3.get("k0").get === "v0") + + // - + val testMap4 = testMap3 - "k0" + assert(testMap4.size === 1) + assert(testMap4.get("k1").isDefined) + assert(testMap4.get("k1").get === "v1") + } + } + + /** Test thread safety of a Scala mutable map. */ + def testMapThreadSafety(hashMapConstructor: => mutable.Map[String, String]) { + def newMap() = hashMapConstructor + val name = newMap().getClass.getSimpleName + val testMap = newMap() + @volatile var error = false + + def getRandomKey(m: mutable.Map[String, String]): Option[String] = { + val keys = testMap.keysIterator.toSeq + if (keys.nonEmpty) { + Some(keys(Random.nextInt(keys.size))) + } else { + None + } + } + + val threads = (1 to 25).map(i => new Thread() { + override def run() { + try { + for (j <- 1 to 1000) { + Random.nextInt(3) match { + case 0 => + testMap(Random.nextString(10)) = Random.nextDouble().toString // put + case 1 => + getRandomKey(testMap).map(testMap.get) // get + case 2 => + getRandomKey(testMap).map(testMap.remove) // remove + } + } + } catch { + case t: Throwable => + error = true + throw t + } + } + }) + + test(name + " - threading safety test") { + threads.map(_.start) + threads.map(_.join) + assert(!error) + } + } +} 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 d48b51aa69565..d043200f71a0b 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 @@ -341,9 +341,11 @@ abstract class DStream[T: ClassTag] ( */ private[streaming] def clearMetadata(time: Time) { val oldRDDs = generatedRDDs.filter(_._1 <= (time - rememberDuration)) + logDebug("Clearing references to old RDDs: [" + + oldRDDs.map(x => s"${x._1} -> ${x._2.id}").mkString(", ") + "]") generatedRDDs --= oldRDDs.keys if (ssc.conf.getBoolean("spark.streaming.unpersist", false)) { - logDebug("Unpersisting old RDDs: " + oldRDDs.keys.mkString(", ")) + logDebug("Unpersisting old RDDs: " + oldRDDs.values.map(_.id).mkString(", ")) oldRDDs.values.foreach(_.unpersist(false)) } logDebug("Cleared " + oldRDDs.size + " RDDs that were older than " + From 83ac9a4bbf272028d0c4639cbd1e12022b9ae77a Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Tue, 8 Apr 2014 00:00:17 -0700 Subject: [PATCH 68/78] [SPARK-1331] Added graceful shutdown to Spark Streaming Current version of StreamingContext.stop() directly kills all the data receivers (NetworkReceiver) without waiting for the data already received to be persisted and processed. This PR provides the fix. Now, when the StreamingContext.stop() is called, the following sequence of steps will happen. 1. The driver will send a stop signal to all the active receivers. 2. Each receiver, when it gets a stop signal from the driver, first stop receiving more data, then waits for the thread that persists data blocks to BlockManager to finish persisting all receive data, and finally quits. 3. After all the receivers have stopped, the driver will wait for the Job Generator and Job Scheduler to finish processing all the received data. It also fixes the semantics of StreamingContext.start and stop. It will throw appropriate errors and warnings if stop() is called before start(), stop() is called twice, etc. Author: Tathagata Das Closes #247 from tdas/graceful-shutdown and squashes the following commits: 61c0016 [Tathagata Das] Updated MIMA binary check excludes. ae1d39b [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into graceful-shutdown 6b59cfc [Tathagata Das] Minor changes based on Andrew's comment on PR. d0b8d65 [Tathagata Das] Reduced time taken by graceful shutdown unit test. f55bc67 [Tathagata Das] Fix scalastyle c69b3a7 [Tathagata Das] Updates based on Patrick's comments. c43b8ae [Tathagata Das] Added graceful shutdown to Spark Streaming. --- project/MimaBuild.scala | 24 +-- .../apache/spark/streaming/Checkpoint.scala | 14 +- .../spark/streaming/StreamingContext.scala | 48 +++++- .../api/java/JavaStreamingContext.scala | 12 +- .../dstream/NetworkInputDStream.scala | 151 +++++++++++------ .../dstream/SocketInputDStream.scala | 1 - .../streaming/receivers/ActorReceiver.scala | 2 +- .../streaming/scheduler/JobGenerator.scala | 124 ++++++++++---- .../streaming/scheduler/JobScheduler.scala | 56 ++++--- .../scheduler/NetworkInputTracker.scala | 154 ++++++++++-------- .../apache/spark/streaming/util/Clock.scala | 5 +- .../spark/streaming/util/RecurringTimer.scala | 62 +++++-- .../streaming/BasicOperationsSuite.scala | 4 +- .../streaming/StreamingContextSuite.scala | 108 ++++++++++-- .../spark/streaming/TestSuiteBase.scala | 2 +- 15 files changed, 552 insertions(+), 215 deletions(-) diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala index e7c9c47c960fa..5ea4817bfde18 100644 --- a/project/MimaBuild.scala +++ b/project/MimaBuild.scala @@ -58,17 +58,19 @@ object MimaBuild { SparkBuild.SPARK_VERSION match { case v if v.startsWith("1.0") => Seq( - excludePackage("org.apache.spark.api.java"), - excludePackage("org.apache.spark.streaming.api.java"), - excludePackage("org.apache.spark.mllib") - ) ++ - excludeSparkClass("rdd.ClassTags") ++ - excludeSparkClass("util.XORShiftRandom") ++ - excludeSparkClass("mllib.recommendation.MFDataGenerator") ++ - excludeSparkClass("mllib.optimization.SquaredGradient") ++ - excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++ - excludeSparkClass("mllib.regression.LassoWithSGD") ++ - excludeSparkClass("mllib.regression.LinearRegressionWithSGD") + excludePackage("org.apache.spark.api.java"), + excludePackage("org.apache.spark.streaming.api.java"), + excludePackage("org.apache.spark.mllib") + ) ++ + excludeSparkClass("rdd.ClassTags") ++ + excludeSparkClass("util.XORShiftRandom") ++ + excludeSparkClass("mllib.recommendation.MFDataGenerator") ++ + excludeSparkClass("mllib.optimization.SquaredGradient") ++ + excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++ + excludeSparkClass("mllib.regression.LassoWithSGD") ++ + excludeSparkClass("mllib.regression.LinearRegressionWithSGD") ++ + excludeSparkClass("streaming.dstream.NetworkReceiver") ++ + excludeSparkClass("streaming.dstream.NetworkReceiver#NetworkReceiverActor") case _ => Seq() } 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 baf80fe2a91b7..93023e8dced57 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -194,19 +194,19 @@ class CheckpointWriter( } } - def stop() { - synchronized { - if (stopped) { - return - } - stopped = true - } + def stop(): Unit = synchronized { + if (stopped) return + executor.shutdown() val startTime = System.currentTimeMillis() val terminated = executor.awaitTermination(10, java.util.concurrent.TimeUnit.SECONDS) + if (!terminated) { + executor.shutdownNow() + } val endTime = System.currentTimeMillis() logInfo("CheckpointWriter executor terminated ? " + terminated + ", waited for " + (endTime - startTime) + " ms.") + stopped = true } private def fs = 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 e198c69470c1f..a4e236c65ff86 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -158,6 +158,15 @@ class StreamingContext private[streaming] ( private[streaming] val waiter = new ContextWaiter + /** Enumeration to identify current state of the StreamingContext */ + private[streaming] object StreamingContextState extends Enumeration { + type CheckpointState = Value + val Initialized, Started, Stopped = Value + } + + import StreamingContextState._ + private[streaming] var state = Initialized + /** * Return the associated Spark context */ @@ -405,9 +414,18 @@ class StreamingContext private[streaming] ( /** * Start the execution of the streams. */ - def start() = synchronized { + def start(): Unit = synchronized { + // Throw exception if the context has already been started once + // or if a stopped context is being started again + if (state == Started) { + throw new SparkException("StreamingContext has already been started") + } + if (state == Stopped) { + throw new SparkException("StreamingContext has already been stopped") + } validate() scheduler.start() + state = Started } /** @@ -428,14 +446,38 @@ class StreamingContext private[streaming] ( } /** - * Stop the execution of the streams. + * Stop the execution of the streams immediately (does not wait for all received data + * to be processed). * @param stopSparkContext Stop the associated SparkContext or not + * */ def stop(stopSparkContext: Boolean = true): Unit = synchronized { - scheduler.stop() + stop(stopSparkContext, false) + } + + /** + * Stop the execution of the streams, with option of ensuring all received data + * has been processed. + * @param stopSparkContext Stop the associated SparkContext or not + * @param stopGracefully Stop gracefully by waiting for the processing of all + * received data to be completed + */ + def stop(stopSparkContext: Boolean, stopGracefully: Boolean): Unit = synchronized { + // Warn (but not fail) if context is stopped twice, + // or context is stopped before starting + if (state == Initialized) { + logWarning("StreamingContext has not been started yet") + return + } + if (state == Stopped) { + logWarning("StreamingContext has already been stopped") + return + } // no need to throw an exception as its okay to stop twice + scheduler.stop(stopGracefully) logInfo("StreamingContext stopped successfully") waiter.notifyStop() if (stopSparkContext) sc.stop() + state = Stopped } } 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 b705d2ec9a58e..c800602d0959b 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 @@ -509,8 +509,16 @@ class JavaStreamingContext(val ssc: StreamingContext) { * Stop the execution of the streams. * @param stopSparkContext Stop the associated SparkContext or not */ - def stop(stopSparkContext: Boolean): Unit = { - ssc.stop(stopSparkContext) + def stop(stopSparkContext: Boolean) = ssc.stop(stopSparkContext) + + /** + * Stop the execution of the streams. + * @param stopSparkContext Stop the associated SparkContext or not + * @param stopGracefully Stop gracefully by waiting for the processing of all + * received data to be completed + */ + def stop(stopSparkContext: Boolean, stopGracefully: Boolean) = { + ssc.stop(stopSparkContext, stopGracefully) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala index 72ad0bae75bfb..d19a635fe8eca 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala @@ -17,7 +17,7 @@ package org.apache.spark.streaming.dstream -import java.util.concurrent.ArrayBlockingQueue +import java.util.concurrent.{TimeUnit, ArrayBlockingQueue} import java.nio.ByteBuffer import scala.collection.mutable.ArrayBuffer @@ -34,6 +34,7 @@ import org.apache.spark.{Logging, SparkEnv} import org.apache.spark.rdd.{RDD, BlockRDD} import org.apache.spark.storage.{BlockId, StorageLevel, StreamBlockId} import org.apache.spark.streaming.scheduler.{DeregisterReceiver, AddBlocks, RegisterReceiver} +import org.apache.spark.util.AkkaUtils /** * Abstract class for defining any [[org.apache.spark.streaming.dstream.InputDStream]] @@ -69,7 +70,7 @@ abstract class NetworkInputDStream[T: ClassTag](@transient ssc_ : StreamingConte // then this returns an empty RDD. This may happen when recovering from a // master failure if (validTime >= graph.startTime) { - val blockIds = ssc.scheduler.networkInputTracker.getBlockIds(id, validTime) + val blockIds = ssc.scheduler.networkInputTracker.getBlocks(id, validTime) Some(new BlockRDD[T](ssc.sc, blockIds)) } else { Some(new BlockRDD[T](ssc.sc, Array[BlockId]())) @@ -79,7 +80,7 @@ abstract class NetworkInputDStream[T: ClassTag](@transient ssc_ : StreamingConte private[streaming] sealed trait NetworkReceiverMessage -private[streaming] case class StopReceiver(msg: String) extends NetworkReceiverMessage +private[streaming] case class StopReceiver() extends NetworkReceiverMessage private[streaming] case class ReportBlock(blockId: BlockId, metadata: Any) extends NetworkReceiverMessage private[streaming] case class ReportError(msg: String) extends NetworkReceiverMessage @@ -90,13 +91,31 @@ private[streaming] case class ReportError(msg: String) extends NetworkReceiverMe */ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging { + /** Local SparkEnv */ lazy protected val env = SparkEnv.get + /** Remote Akka actor for the NetworkInputTracker */ + lazy protected val trackerActor = { + val ip = env.conf.get("spark.driver.host", "localhost") + val port = env.conf.getInt("spark.driver.port", 7077) + val url = "akka.tcp://spark@%s:%s/user/NetworkInputTracker".format(ip, port) + env.actorSystem.actorSelection(url) + } + + /** Akka actor for receiving messages from the NetworkInputTracker in the driver */ lazy protected val actor = env.actorSystem.actorOf( Props(new NetworkReceiverActor()), "NetworkReceiver-" + streamId) + /** Timeout for Akka actor messages */ + lazy protected val askTimeout = AkkaUtils.askTimeout(env.conf) + + /** Thread that starts the receiver and stays blocked while data is being received */ lazy protected val receivingThread = Thread.currentThread() + /** Exceptions that occurs while receiving data */ + protected lazy val exceptions = new ArrayBuffer[Exception] + + /** Identifier of the stream this receiver is associated with */ protected var streamId: Int = -1 /** @@ -112,7 +131,7 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging def getLocationPreference() : Option[String] = None /** - * Starts the receiver. First is accesses all the lazy members to + * Start the receiver. First is accesses all the lazy members to * materialize them. Then it calls the user-defined onStart() method to start * other threads, etc required to receiver the data. */ @@ -124,83 +143,107 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging receivingThread // Call user-defined onStart() + logInfo("Starting receiver") onStart() + + // Wait until interrupt is called on this thread + while(true) Thread.sleep(100000) } catch { case ie: InterruptedException => - logInfo("Receiving thread interrupted") + logInfo("Receiving thread has been interrupted, receiver " + streamId + " stopped") case e: Exception => - stopOnError(e) + logError("Error receiving data in receiver " + streamId, e) + exceptions += e + } + + // Call user-defined onStop() + logInfo("Stopping receiver") + try { + onStop() + } catch { + case e: Exception => + logError("Error stopping receiver " + streamId, e) + exceptions += e + } + + val message = if (exceptions.isEmpty) { + null + } else if (exceptions.size == 1) { + val e = exceptions.head + "Exception in receiver " + streamId + ": " + e.getMessage + "\n" + e.getStackTraceString + } else { + "Multiple exceptions in receiver " + streamId + "(" + exceptions.size + "):\n" + exceptions.zipWithIndex.map { + case (e, i) => "Exception " + i + ": " + e.getMessage + "\n" + e.getStackTraceString + }.mkString("\n") } + logInfo("Deregistering receiver " + streamId) + val future = trackerActor.ask(DeregisterReceiver(streamId, message))(askTimeout) + Await.result(future, askTimeout) + logInfo("Deregistered receiver " + streamId) + env.actorSystem.stop(actor) + logInfo("Stopped receiver " + streamId) } /** - * Stops the receiver. First it interrupts the main receiving thread, - * that is, the thread that called receiver.start(). Then it calls the user-defined - * onStop() method to stop other threads and/or do cleanup. + * Stop the receiver. First it interrupts the main receiving thread, + * that is, the thread that called receiver.start(). */ def stop() { + // Stop receiving by interrupting the receiving thread receivingThread.interrupt() - onStop() - // TODO: terminate the actor + logInfo("Interrupted receiving thread " + receivingThread + " for stopping") } /** - * Stops the receiver and reports exception to the tracker. + * Stop the receiver and reports exception to the tracker. * This should be called whenever an exception is to be handled on any thread * of the receiver. */ protected def stopOnError(e: Exception) { logError("Error receiving data", e) + exceptions += e stop() - actor ! ReportError(e.toString) } - /** - * Pushes a block (as an ArrayBuffer filled with data) into the block manager. + * Push a block (as an ArrayBuffer filled with data) into the block manager. */ def pushBlock(blockId: BlockId, arrayBuffer: ArrayBuffer[T], metadata: Any, level: StorageLevel) { env.blockManager.put(blockId, arrayBuffer.asInstanceOf[ArrayBuffer[Any]], level) - actor ! ReportBlock(blockId, metadata) + trackerActor ! AddBlocks(streamId, Array(blockId), metadata) + logDebug("Pushed block " + blockId) } /** - * Pushes a block (as bytes) into the block manager. + * Push a block (as bytes) into the block manager. */ def pushBlock(blockId: BlockId, bytes: ByteBuffer, metadata: Any, level: StorageLevel) { env.blockManager.putBytes(blockId, bytes, level) - actor ! ReportBlock(blockId, metadata) + trackerActor ! AddBlocks(streamId, Array(blockId), metadata) + } + + /** Set the ID of the DStream that this receiver is associated with */ + protected[streaming] def setStreamId(id: Int) { + streamId = id } /** A helper actor that communicates with the NetworkInputTracker */ private class NetworkReceiverActor extends Actor { - logInfo("Attempting to register with tracker") - val ip = env.conf.get("spark.driver.host", "localhost") - val port = env.conf.getInt("spark.driver.port", 7077) - val url = "akka.tcp://spark@%s:%s/user/NetworkInputTracker".format(ip, port) - val tracker = env.actorSystem.actorSelection(url) - val timeout = 5.seconds override def preStart() { - val future = tracker.ask(RegisterReceiver(streamId, self))(timeout) - Await.result(future, timeout) + logInfo("Registered receiver " + streamId) + val future = trackerActor.ask(RegisterReceiver(streamId, self))(askTimeout) + Await.result(future, askTimeout) } override def receive() = { - case ReportBlock(blockId, metadata) => - tracker ! AddBlocks(streamId, Array(blockId), metadata) - case ReportError(msg) => - tracker ! DeregisterReceiver(streamId, msg) - case StopReceiver(msg) => + case StopReceiver => + logInfo("Received stop signal") stop() - tracker ! DeregisterReceiver(streamId, msg) } } - protected[streaming] def setStreamId(id: Int) { - streamId = id - } - /** * Batches objects created by a [[org.apache.spark.streaming.dstream.NetworkReceiver]] and puts * them into appropriately named blocks at regular intervals. This class starts two threads, @@ -214,23 +257,26 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging val clock = new SystemClock() val blockInterval = env.conf.getLong("spark.streaming.blockInterval", 200) - val blockIntervalTimer = new RecurringTimer(clock, blockInterval, updateCurrentBuffer) + val blockIntervalTimer = new RecurringTimer(clock, blockInterval, updateCurrentBuffer, + "BlockGenerator") val blockStorageLevel = storageLevel val blocksForPushing = new ArrayBlockingQueue[Block](1000) val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } } var currentBuffer = new ArrayBuffer[T] + var stopped = false def start() { blockIntervalTimer.start() blockPushingThread.start() - logInfo("Data handler started") + logInfo("Started BlockGenerator") } def stop() { - blockIntervalTimer.stop() - blockPushingThread.interrupt() - logInfo("Data handler stopped") + blockIntervalTimer.stop(false) + stopped = true + blockPushingThread.join() + logInfo("Stopped BlockGenerator") } def += (obj: T): Unit = synchronized { @@ -248,24 +294,35 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging } } catch { case ie: InterruptedException => - logInfo("Block interval timer thread interrupted") + logInfo("Block updating timer thread was interrupted") case e: Exception => - NetworkReceiver.this.stop() + NetworkReceiver.this.stopOnError(e) } } private def keepPushingBlocks() { - logInfo("Block pushing thread started") + logInfo("Started block pushing thread") try { - while(true) { + while(!stopped) { + Option(blocksForPushing.poll(100, TimeUnit.MILLISECONDS)) match { + case Some(block) => + NetworkReceiver.this.pushBlock(block.id, block.buffer, block.metadata, storageLevel) + case None => + } + } + // Push out the blocks that are still left + logInfo("Pushing out the last " + blocksForPushing.size() + " blocks") + while (!blocksForPushing.isEmpty) { val block = blocksForPushing.take() NetworkReceiver.this.pushBlock(block.id, block.buffer, block.metadata, storageLevel) + logInfo("Blocks left to push " + blocksForPushing.size()) } + logInfo("Stopped blocks pushing thread") } catch { case ie: InterruptedException => - logInfo("Block pushing thread interrupted") + logInfo("Block pushing thread was interrupted") case e: Exception => - NetworkReceiver.this.stop() + NetworkReceiver.this.stopOnError(e) } } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala index 2cdd13f205313..63d94d1cc670a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala @@ -67,7 +67,6 @@ class SocketReceiver[T: ClassTag]( protected def onStop() { blockGenerator.stop() } - } private[streaming] diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ActorReceiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receivers/ActorReceiver.scala index bd78bae8a5c51..44eb2750c6c7a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ActorReceiver.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receivers/ActorReceiver.scala @@ -174,10 +174,10 @@ private[streaming] class ActorReceiver[T: ClassTag]( blocksGenerator.start() supervisor logInfo("Supervision tree for receivers initialized at:" + supervisor.path) + } protected def onStop() = { supervisor ! PoisonPill } - } 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 c7306248b1950..92d885c4bc5a5 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 @@ -39,16 +39,22 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { private val ssc = jobScheduler.ssc private val graph = ssc.graph + val clock = { val clockClass = ssc.sc.conf.get( "spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock") Class.forName(clockClass).newInstance().asInstanceOf[Clock] } + private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds, - longTime => eventActor ! GenerateJobs(new Time(longTime))) - private lazy val checkpointWriter = - if (ssc.checkpointDuration != null && ssc.checkpointDir != null) { - new CheckpointWriter(this, ssc.conf, ssc.checkpointDir, ssc.sparkContext.hadoopConfiguration) + longTime => eventActor ! GenerateJobs(new Time(longTime)), "JobGenerator") + + // This is marked lazy so that this is initialized after checkpoint duration has been set + // in the context and the generator has been started. + private lazy val shouldCheckpoint = ssc.checkpointDuration != null && ssc.checkpointDir != null + + private lazy val checkpointWriter = if (shouldCheckpoint) { + new CheckpointWriter(this, ssc.conf, ssc.checkpointDir, ssc.sparkContext.hadoopConfiguration) } else { null } @@ -57,17 +63,16 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // This not being null means the scheduler has been started and not stopped private var eventActor: ActorRef = null + // last batch whose completion,checkpointing and metadata cleanup has been completed + private var lastProcessedBatch: Time = null + /** Start generation of jobs */ - def start() = synchronized { - if (eventActor != null) { - throw new SparkException("JobGenerator already started") - } + def start(): Unit = synchronized { + if (eventActor != null) return // generator has already been started eventActor = ssc.env.actorSystem.actorOf(Props(new Actor { def receive = { - case event: JobGeneratorEvent => - logDebug("Got event of type " + event.getClass.getName) - processEvent(event) + case event: JobGeneratorEvent => processEvent(event) } }), "JobGenerator") if (ssc.isCheckpointPresent) { @@ -77,30 +82,79 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { } } - /** Stop generation of jobs */ - def stop() = synchronized { - if (eventActor != null) { - timer.stop() - ssc.env.actorSystem.stop(eventActor) - if (checkpointWriter != null) checkpointWriter.stop() - ssc.graph.stop() - logInfo("JobGenerator stopped") + /** + * Stop generation of jobs. processReceivedData = true makes this wait until jobs + * of current ongoing time interval has been generated, processed and corresponding + * checkpoints written. + */ + def stop(processReceivedData: Boolean): Unit = synchronized { + if (eventActor == null) return // generator has already been stopped + + if (processReceivedData) { + logInfo("Stopping JobGenerator gracefully") + val timeWhenStopStarted = System.currentTimeMillis() + val stopTimeout = 10 * ssc.graph.batchDuration.milliseconds + val pollTime = 100 + + // To prevent graceful stop to get stuck permanently + def hasTimedOut = { + val timedOut = System.currentTimeMillis() - timeWhenStopStarted > stopTimeout + if (timedOut) logWarning("Timed out while stopping the job generator") + timedOut + } + + // Wait until all the received blocks in the network input tracker has + // been consumed by network input DStreams, and jobs have been generated with them + logInfo("Waiting for all received blocks to be consumed for job generation") + while(!hasTimedOut && jobScheduler.networkInputTracker.hasMoreReceivedBlockIds) { + Thread.sleep(pollTime) + } + logInfo("Waited for all received blocks to be consumed for job generation") + + // Stop generating jobs + val stopTime = timer.stop(false) + graph.stop() + logInfo("Stopped generation timer") + + // Wait for the jobs to complete and checkpoints to be written + def haveAllBatchesBeenProcessed = { + lastProcessedBatch != null && lastProcessedBatch.milliseconds == stopTime + } + logInfo("Waiting for jobs to be processed and checkpoints to be written") + while (!hasTimedOut && !haveAllBatchesBeenProcessed) { + Thread.sleep(pollTime) + } + logInfo("Waited for jobs to be processed and checkpoints to be written") + } else { + logInfo("Stopping JobGenerator immediately") + // Stop timer and graph immediately, ignore unprocessed data and pending jobs + timer.stop(true) + graph.stop() } + + // Stop the actor and checkpoint writer + if (shouldCheckpoint) checkpointWriter.stop() + ssc.env.actorSystem.stop(eventActor) + logInfo("Stopped JobGenerator") } /** - * On batch completion, clear old metadata and checkpoint computation. + * Callback called when a batch has been completely processed. */ def onBatchCompletion(time: Time) { eventActor ! ClearMetadata(time) } - + + /** + * Callback called when the checkpoint of a batch has been written. + */ def onCheckpointCompletion(time: Time) { eventActor ! ClearCheckpointData(time) } /** Processes all events */ private def processEvent(event: JobGeneratorEvent) { + logDebug("Got event " + event) event match { case GenerateJobs(time) => generateJobs(time) case ClearMetadata(time) => clearMetadata(time) @@ -114,7 +168,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { val startTime = new Time(timer.getStartTime()) graph.start(startTime - graph.batchDuration) timer.start(startTime.milliseconds) - logInfo("JobGenerator started at " + startTime) + logInfo("Started JobGenerator at " + startTime) } /** Restarts the generator based on the information in checkpoint */ @@ -152,15 +206,17 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // Restart the timer timer.start(restartTime.milliseconds) - logInfo("JobGenerator restarted at " + restartTime) + logInfo("Restarted JobGenerator at " + restartTime) } /** Generate jobs and perform checkpoint for the given `time`. */ private def generateJobs(time: Time) { SparkEnv.set(ssc.env) Try(graph.generateJobs(time)) match { - case Success(jobs) => jobScheduler.runJobs(time, jobs) - case Failure(e) => jobScheduler.reportError("Error generating jobs for time " + time, e) + case Success(jobs) => + jobScheduler.runJobs(time, jobs) + case Failure(e) => + jobScheduler.reportError("Error generating jobs for time " + time, e) } eventActor ! DoCheckpoint(time) } @@ -168,20 +224,32 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { /** Clear DStream metadata for the given `time`. */ private def clearMetadata(time: Time) { ssc.graph.clearMetadata(time) - eventActor ! DoCheckpoint(time) + + // If checkpointing is enabled, then checkpoint, + // else mark batch to be fully processed + if (shouldCheckpoint) { + eventActor ! DoCheckpoint(time) + } else { + markBatchFullyProcessed(time) + } } /** Clear DStream checkpoint data for the given `time`. */ private def clearCheckpointData(time: Time) { ssc.graph.clearCheckpointData(time) + markBatchFullyProcessed(time) } /** Perform checkpoint for the give `time`. */ - private def doCheckpoint(time: Time) = synchronized { - if (checkpointWriter != null && (time - graph.zeroTime).isMultipleOf(ssc.checkpointDuration)) { + private def doCheckpoint(time: Time) { + if (shouldCheckpoint && (time - graph.zeroTime).isMultipleOf(ssc.checkpointDuration)) { logInfo("Checkpointing graph for time " + time) ssc.graph.updateCheckpointData(time) checkpointWriter.write(new Checkpoint(ssc, time)) } } + + private def markBatchFullyProcessed(time: Time) { + lastProcessedBatch = 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 de675d3c7fb94..04e0a6a283cfb 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 @@ -39,7 +39,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { private val jobSets = new ConcurrentHashMap[Time, JobSet] private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1) - private val executor = Executors.newFixedThreadPool(numConcurrentJobs) + private val jobExecutor = Executors.newFixedThreadPool(numConcurrentJobs) private val jobGenerator = new JobGenerator(this) val clock = jobGenerator.clock val listenerBus = new StreamingListenerBus() @@ -50,36 +50,54 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { private var eventActor: ActorRef = null - def start() = synchronized { - if (eventActor != null) { - throw new SparkException("JobScheduler already started") - } + def start(): Unit = synchronized { + if (eventActor != null) return // scheduler has already been started + logDebug("Starting JobScheduler") eventActor = ssc.env.actorSystem.actorOf(Props(new Actor { def receive = { case event: JobSchedulerEvent => processEvent(event) } }), "JobScheduler") + listenerBus.start() networkInputTracker = new NetworkInputTracker(ssc) networkInputTracker.start() - Thread.sleep(1000) jobGenerator.start() - logInfo("JobScheduler started") + logInfo("Started JobScheduler") } - def stop() = synchronized { - if (eventActor != null) { - jobGenerator.stop() - networkInputTracker.stop() - executor.shutdown() - if (!executor.awaitTermination(2, TimeUnit.SECONDS)) { - executor.shutdownNow() - } - listenerBus.stop() - ssc.env.actorSystem.stop(eventActor) - logInfo("JobScheduler stopped") + def stop(processAllReceivedData: Boolean): Unit = synchronized { + if (eventActor == null) return // scheduler has already been stopped + logDebug("Stopping JobScheduler") + + // First, stop receiving + networkInputTracker.stop() + + // Second, stop generating jobs. If it has to process all received data, + // then this will wait for all the processing through JobScheduler to be over. + jobGenerator.stop(processAllReceivedData) + + // Stop the executor for receiving new jobs + logDebug("Stopping job executor") + jobExecutor.shutdown() + + // Wait for the queued jobs to complete if indicated + val terminated = if (processAllReceivedData) { + jobExecutor.awaitTermination(1, TimeUnit.HOURS) // just a very large period of time + } else { + jobExecutor.awaitTermination(2, TimeUnit.SECONDS) } + if (!terminated) { + jobExecutor.shutdownNow() + } + logDebug("Stopped job executor") + + // Stop everything else + listenerBus.stop() + ssc.env.actorSystem.stop(eventActor) + eventActor = null + logInfo("Stopped JobScheduler") } def runJobs(time: Time, jobs: Seq[Job]) { @@ -88,7 +106,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { } else { val jobSet = new JobSet(time, jobs) jobSets.put(time, jobSet) - jobSet.jobs.foreach(job => executor.execute(new JobHandler(job))) + jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job))) logInfo("Added jobs for time " + time) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala index cad68e248ab29..067e804202236 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala @@ -17,20 +17,14 @@ package org.apache.spark.streaming.scheduler -import org.apache.spark.streaming.dstream.{NetworkInputDStream, NetworkReceiver} -import org.apache.spark.streaming.dstream.{StopReceiver, ReportBlock, ReportError} -import org.apache.spark.{SparkException, Logging, SparkEnv} -import org.apache.spark.SparkContext._ - -import scala.collection.mutable.HashMap -import scala.collection.mutable.Queue -import scala.concurrent.duration._ +import scala.collection.mutable.{HashMap, Queue, SynchronizedMap} import akka.actor._ -import akka.pattern.ask -import akka.dispatch._ +import org.apache.spark.{Logging, SparkEnv, SparkException} +import org.apache.spark.SparkContext._ import org.apache.spark.storage.BlockId -import org.apache.spark.streaming.{Time, StreamingContext} +import org.apache.spark.streaming.{StreamingContext, Time} +import org.apache.spark.streaming.dstream.{NetworkReceiver, StopReceiver} import org.apache.spark.util.AkkaUtils private[streaming] sealed trait NetworkInputTrackerMessage @@ -52,8 +46,8 @@ class NetworkInputTracker(ssc: StreamingContext) extends Logging { val networkInputStreams = ssc.graph.getNetworkInputStreams() val networkInputStreamMap = Map(networkInputStreams.map(x => (x.id, x)): _*) val receiverExecutor = new ReceiverExecutor() - val receiverInfo = new HashMap[Int, ActorRef] - val receivedBlockIds = new HashMap[Int, Queue[BlockId]] + val receiverInfo = new HashMap[Int, ActorRef] with SynchronizedMap[Int, ActorRef] + val receivedBlockIds = new HashMap[Int, Queue[BlockId]] with SynchronizedMap[Int, Queue[BlockId]] val timeout = AkkaUtils.askTimeout(ssc.conf) @@ -63,7 +57,7 @@ class NetworkInputTracker(ssc: StreamingContext) extends Logging { var currentTime: Time = null /** Start the actor and receiver execution thread. */ - def start() { + def start() = synchronized { if (actor != null) { throw new SparkException("NetworkInputTracker already started") } @@ -77,72 +71,99 @@ class NetworkInputTracker(ssc: StreamingContext) extends Logging { } /** Stop the receiver execution thread. */ - def stop() { + def stop() = synchronized { if (!networkInputStreams.isEmpty && actor != null) { - receiverExecutor.interrupt() - receiverExecutor.stopReceivers() + // First, stop the receivers + receiverExecutor.stop() + + // Finally, stop the actor ssc.env.actorSystem.stop(actor) + actor = null logInfo("NetworkInputTracker stopped") } } - /** Return all the blocks received from a receiver. */ - def getBlockIds(receiverId: Int, time: Time): Array[BlockId] = synchronized { - val queue = receivedBlockIds.synchronized { - receivedBlockIds.getOrElse(receiverId, new Queue[BlockId]()) + /** Register a receiver */ + def registerReceiver(streamId: Int, receiverActor: ActorRef, sender: ActorRef) { + if (!networkInputStreamMap.contains(streamId)) { + throw new Exception("Register received for unexpected id " + streamId) } - val result = queue.synchronized { - queue.dequeueAll(x => true) - } - logInfo("Stream " + receiverId + " received " + result.size + " blocks") - result.toArray + receiverInfo += ((streamId, receiverActor)) + logInfo("Registered receiver for network stream " + streamId + " from " + sender.path.address) + } + + /** Deregister a receiver */ + def deregisterReceiver(streamId: Int, message: String) { + receiverInfo -= streamId + logError("Deregistered receiver for network stream " + streamId + " with message:\n" + message) + } + + /** Get all the received blocks for the given stream. */ + def getBlocks(streamId: Int, time: Time): Array[BlockId] = { + val queue = receivedBlockIds.getOrElseUpdate(streamId, new Queue[BlockId]()) + val result = queue.dequeueAll(x => true).toArray + logInfo("Stream " + streamId + " received " + result.size + " blocks") + result + } + + /** Add new blocks for the given stream */ + def addBlocks(streamId: Int, blockIds: Seq[BlockId], metadata: Any) = { + val queue = receivedBlockIds.getOrElseUpdate(streamId, new Queue[BlockId]) + queue ++= blockIds + networkInputStreamMap(streamId).addMetadata(metadata) + logDebug("Stream " + streamId + " received new blocks: " + blockIds.mkString("[", ", ", "]")) + } + + /** Check if any blocks are left to be processed */ + def hasMoreReceivedBlockIds: Boolean = { + !receivedBlockIds.forall(_._2.isEmpty) } /** Actor to receive messages from the receivers. */ private class NetworkInputTrackerActor extends Actor { def receive = { - case RegisterReceiver(streamId, receiverActor) => { - if (!networkInputStreamMap.contains(streamId)) { - throw new Exception("Register received for unexpected id " + streamId) - } - receiverInfo += ((streamId, receiverActor)) - logInfo("Registered receiver for network stream " + streamId + " from " - + sender.path.address) + case RegisterReceiver(streamId, receiverActor) => + registerReceiver(streamId, receiverActor, sender) + sender ! true + case AddBlocks(streamId, blockIds, metadata) => + addBlocks(streamId, blockIds, metadata) + case DeregisterReceiver(streamId, message) => + deregisterReceiver(streamId, message) sender ! true - } - case AddBlocks(streamId, blockIds, metadata) => { - val tmp = receivedBlockIds.synchronized { - if (!receivedBlockIds.contains(streamId)) { - receivedBlockIds += ((streamId, new Queue[BlockId])) - } - receivedBlockIds(streamId) - } - tmp.synchronized { - tmp ++= blockIds - } - networkInputStreamMap(streamId).addMetadata(metadata) - } - case DeregisterReceiver(streamId, msg) => { - receiverInfo -= streamId - logError("De-registered receiver for network stream " + streamId - + " with message " + msg) - // TODO: Do something about the corresponding NetworkInputDStream - } } } /** This thread class runs all the receivers on the cluster. */ - class ReceiverExecutor extends Thread { - val env = ssc.env - - override def run() { - try { - SparkEnv.set(env) - startReceivers() - } catch { - case ie: InterruptedException => logInfo("ReceiverExecutor interrupted") - } finally { - stopReceivers() + class ReceiverExecutor { + @transient val env = ssc.env + @transient val thread = new Thread() { + override def run() { + try { + SparkEnv.set(env) + startReceivers() + } catch { + case ie: InterruptedException => logInfo("ReceiverExecutor interrupted") + } + } + } + + def start() { + thread.start() + } + + def stop() { + // Send the stop signal to all the receivers + stopReceivers() + + // Wait for the Spark job that runs the receivers to be over + // That is, for the receivers to quit gracefully. + thread.join(10000) + + // Check if all the receivers have been deregistered or not + if (!receiverInfo.isEmpty) { + logWarning("All of the receivers have not deregistered, " + receiverInfo) + } else { + logInfo("All of the receivers have deregistered successfully") } } @@ -150,7 +171,7 @@ class NetworkInputTracker(ssc: StreamingContext) extends Logging { * Get the receivers from the NetworkInputDStreams, distributes them to the * worker nodes as a parallel collection, and runs them. */ - def startReceivers() { + private def startReceivers() { val receivers = networkInputStreams.map(nis => { val rcvr = nis.getReceiver() rcvr.setStreamId(nis.id) @@ -186,13 +207,16 @@ class NetworkInputTracker(ssc: StreamingContext) extends Logging { } // Distribute the receivers and start them + logInfo("Starting " + receivers.length + " receivers") ssc.sparkContext.runJob(tempRDD, startReceiver) + logInfo("All of the receivers have been terminated") } /** Stops the receivers. */ - def stopReceivers() { + private def stopReceivers() { // Signal the receivers to stop receiverInfo.values.foreach(_ ! StopReceiver) + logInfo("Sent stop signal to all " + receiverInfo.size + " receivers") } } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/Clock.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/Clock.scala index c3a849d2769a7..c5ef2cc8c390d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/Clock.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/Clock.scala @@ -48,14 +48,11 @@ class SystemClock() extends Clock { minPollTime } } - - + while (true) { currentTime = System.currentTimeMillis() waitTime = targetTime - currentTime - if (waitTime <= 0) { - return currentTime } val sleepTime = diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala index 559c2473851b3..f71938ac55ccb 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RecurringTimer.scala @@ -17,44 +17,84 @@ package org.apache.spark.streaming.util +import org.apache.spark.Logging + private[streaming] -class RecurringTimer(val clock: Clock, val period: Long, val callback: (Long) => Unit) { +class RecurringTimer(clock: Clock, period: Long, callback: (Long) => Unit, name: String) + extends Logging { - private val thread = new Thread("RecurringTimer") { + private val thread = new Thread("RecurringTimer - " + name) { + setDaemon(true) override def run() { loop } } - - private var nextTime = 0L + @volatile private var prevTime = -1L + @volatile private var nextTime = -1L + @volatile private var stopped = false + + /** + * Get the time when this timer will fire if it is started right now. + * The time will be a multiple of this timer's period and more than + * current system time. + */ def getStartTime(): Long = { (math.floor(clock.currentTime.toDouble / period) + 1).toLong * period } + /** + * Get the time when the timer will fire if it is restarted right now. + * This time depends on when the timer was started the first time, and was stopped + * for whatever reason. The time must be a multiple of this timer's period and + * more than current time. + */ def getRestartTime(originalStartTime: Long): Long = { val gap = clock.currentTime - originalStartTime (math.floor(gap.toDouble / period).toLong + 1) * period + originalStartTime } - def start(startTime: Long): Long = { + /** + * Start at the given start time. + */ + def start(startTime: Long): Long = synchronized { nextTime = startTime thread.start() + logInfo("Started timer for " + name + " at time " + nextTime) nextTime } + /** + * Start at the earliest time it can start based on the period. + */ def start(): Long = { start(getStartTime()) } - def stop() { - thread.interrupt() + /** + * Stop the timer, and return the last time the callback was made. + * interruptTimer = true will interrupt the callback + * if it is in progress (not guaranteed to give correct time in this case). + */ + def stop(interruptTimer: Boolean): Long = synchronized { + if (!stopped) { + stopped = true + if (interruptTimer) thread.interrupt() + thread.join() + logInfo("Stopped timer for " + name + " after time " + prevTime) + } + prevTime } - + + /** + * Repeatedly call the callback every interval. + */ private def loop() { try { - while (true) { + while (!stopped) { clock.waitTillTime(nextTime) callback(nextTime) + prevTime = nextTime nextTime += period + logDebug("Callback for " + name + " called at time " + prevTime) } } catch { case e: InterruptedException => @@ -74,10 +114,10 @@ object RecurringTimer { println("" + currentTime + ": " + (currentTime - lastRecurTime)) lastRecurTime = currentTime } - val timer = new RecurringTimer(new SystemClock(), period, onRecur) + val timer = new RecurringTimer(new SystemClock(), period, onRecur, "Test") timer.start() Thread.sleep(30 * 1000) - timer.stop() + timer.stop(true) } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala index bcb0c28bf07a0..bb73dbf29b649 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala @@ -324,7 +324,7 @@ class BasicOperationsSuite extends TestSuiteBase { val updateStateOperation = (s: DStream[String]) => { val updateFunc = (values: Seq[Int], state: Option[Int]) => { - Some(values.foldLeft(0)(_ + _) + state.getOrElse(0)) + Some(values.sum + state.getOrElse(0)) } s.map(x => (x, 1)).updateStateByKey[Int](updateFunc) } @@ -359,7 +359,7 @@ class BasicOperationsSuite extends TestSuiteBase { // updateFunc clears a state when a StateObject is seen without new values twice in a row val updateFunc = (values: Seq[Int], state: Option[StateObject]) => { val stateObj = state.getOrElse(new StateObject) - values.foldLeft(0)(_ + _) match { + values.sum match { case 0 => stateObj.expireCounter += 1 // no new values case n => { // has new values, increment and reset expireCounter stateObj.counter += n 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 717da8e00462b..9cc27ef7f03b5 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -17,19 +17,22 @@ package org.apache.spark.streaming -import org.scalatest.{FunSuite, BeforeAndAfter} -import org.scalatest.exceptions.TestFailedDueToTimeoutException +import java.util.concurrent.atomic.AtomicInteger + +import org.apache.spark.{Logging, SparkConf, SparkContext, SparkException} +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.dstream.{DStream, NetworkReceiver} +import org.apache.spark.util.{MetadataCleaner, Utils} +import org.scalatest.{BeforeAndAfter, FunSuite} import org.scalatest.concurrent.Timeouts +import org.scalatest.exceptions.TestFailedDueToTimeoutException import org.scalatest.time.SpanSugar._ -import org.apache.spark.{SparkException, SparkConf, SparkContext} -import org.apache.spark.util.{Utils, MetadataCleaner} -import org.apache.spark.streaming.dstream.DStream -class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts { +class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts with Logging { val master = "local[2]" val appName = this.getClass.getSimpleName - val batchDuration = Seconds(1) + val batchDuration = Milliseconds(500) val sparkHome = "someDir" val envPair = "key" -> "value" val ttl = StreamingContext.DEFAULT_CLEANER_TTL + 100 @@ -108,19 +111,31 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts { val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName) myConf.set("spark.cleaner.ttl", ttl.toString) val ssc1 = new StreamingContext(myConf, batchDuration) + addInputStream(ssc1).register + ssc1.start() val cp = new Checkpoint(ssc1, Time(1000)) assert(MetadataCleaner.getDelaySeconds(cp.sparkConf) === ttl) ssc1.stop() val newCp = Utils.deserialize[Checkpoint](Utils.serialize(cp)) assert(MetadataCleaner.getDelaySeconds(newCp.sparkConf) === ttl) - ssc = new StreamingContext(null, cp, null) + ssc = new StreamingContext(null, newCp, null) assert(MetadataCleaner.getDelaySeconds(ssc.conf) === ttl) } - test("start multiple times") { + test("start and stop state check") { ssc = new StreamingContext(master, appName, batchDuration) addInputStream(ssc).register + assert(ssc.state === ssc.StreamingContextState.Initialized) + ssc.start() + assert(ssc.state === ssc.StreamingContextState.Started) + ssc.stop() + assert(ssc.state === ssc.StreamingContextState.Stopped) + } + + test("start multiple times") { + ssc = new StreamingContext(master, appName, batchDuration) + addInputStream(ssc).register ssc.start() intercept[SparkException] { ssc.start() @@ -133,18 +148,61 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts { ssc.start() ssc.stop() ssc.stop() - ssc = null } + test("stop before start and start after stop") { + ssc = new StreamingContext(master, appName, batchDuration) + addInputStream(ssc).register + ssc.stop() // stop before start should not throw exception + ssc.start() + ssc.stop() + intercept[SparkException] { + ssc.start() // start after stop should throw exception + } + } + + test("stop only streaming context") { ssc = new StreamingContext(master, appName, batchDuration) sc = ssc.sparkContext addInputStream(ssc).register ssc.start() ssc.stop(false) - ssc = null assert(sc.makeRDD(1 to 100).collect().size === 100) ssc = new StreamingContext(sc, batchDuration) + addInputStream(ssc).register + ssc.start() + ssc.stop() + } + + test("stop gracefully") { + val conf = new SparkConf().setMaster(master).setAppName(appName) + conf.set("spark.cleaner.ttl", "3600") + sc = new SparkContext(conf) + for (i <- 1 to 4) { + logInfo("==================================") + ssc = new StreamingContext(sc, batchDuration) + var runningCount = 0 + TestReceiver.counter.set(1) + val input = ssc.networkStream(new TestReceiver) + input.count.foreachRDD(rdd => { + val count = rdd.first() + logInfo("Count = " + count) + runningCount += count.toInt + }) + ssc.start() + ssc.awaitTermination(500) + ssc.stop(stopSparkContext = false, stopGracefully = true) + logInfo("Running count = " + runningCount) + logInfo("TestReceiver.counter = " + TestReceiver.counter.get()) + assert(runningCount > 0) + assert( + (TestReceiver.counter.get() == runningCount + 1) || + (TestReceiver.counter.get() == runningCount + 2), + "Received records = " + TestReceiver.counter.get() + ", " + + "processed records = " + runningCount + ) + } } test("awaitTermination") { @@ -199,7 +257,6 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts { test("awaitTermination with error in job generation") { ssc = new StreamingContext(master, appName, batchDuration) val inputStream = addInputStream(ssc) - inputStream.transform(rdd => { throw new TestException("error in transform"); rdd }).register val exception = intercept[TestException] { ssc.start() @@ -215,4 +272,29 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts { } } -class TestException(msg: String) extends Exception(msg) \ No newline at end of file +class TestException(msg: String) extends Exception(msg) + +/** Custom receiver for testing whether all data received by a receiver gets processed or not */ +class TestReceiver extends NetworkReceiver[Int] { + protected lazy val blockGenerator = new BlockGenerator(StorageLevel.MEMORY_ONLY) + protected def onStart() { + blockGenerator.start() + logInfo("BlockGenerator started on thread " + receivingThread) + try { + while(true) { + blockGenerator += TestReceiver.counter.getAndIncrement + Thread.sleep(0) + } + } finally { + logInfo("Receiving stopped at count value of " + TestReceiver.counter.get()) + } + } + + protected def onStop() { + blockGenerator.stop() + } +} + +object TestReceiver { + val counter = new AtomicInteger(1) +} 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 201630672ab4c..aa2d5c2fc2454 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala @@ -277,7 +277,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { assert(timeTaken < maxWaitTimeMillis, "Operation timed out after " + timeTaken + " ms") assert(output.size === numExpectedOutput, "Unexpected number of outputs generated") - Thread.sleep(500) // Give some time for the forgetting old RDDs to complete + Thread.sleep(100) // Give some time for the forgetting old RDDs to complete } catch { case e: Exception => {e.printStackTrace(); throw e} } finally { From 6dc5f5849c0e0378abc6648c919412827d831641 Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Tue, 8 Apr 2014 01:03:33 -0700 Subject: [PATCH 69/78] [SPARK-1396] Properly cleanup DAGScheduler on job cancellation. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Previously, when jobs were cancelled, not all of the state in the DAGScheduler was cleaned up, leading to a slow memory leak in the DAGScheduler. As we expose easier ways to cancel jobs, it's more important to fix these issues. This commit also fixes a second and less serious problem, which is that previously, when a stage failed, not all of the appropriate stages were cancelled. See the "failure of stage used by two jobs" test for an example of this. This just meant that extra work was done, and is not a correctness problem. This commit adds 3 tests. “run shuffle with map stage failure” is a new test to more thoroughly test this functionality, and passes on both the old and new versions of the code. “trivial job cancellation” fails on the old code because all state wasn’t cleaned up correctly when jobs were cancelled (we didn’t remove the job from resultStageToJob). “failure of stage used by two jobs” fails on the old code because taskScheduler.cancelTasks wasn’t called for one of the stages (see test comments). This should be checked in before #246, which makes it easier to cancel stages / jobs. Author: Kay Ousterhout Closes #305 from kayousterhout/incremental_abort_fix and squashes the following commits: f33d844 [Kay Ousterhout] Mark review comments 9217080 [Kay Ousterhout] Properly cleanup DAGScheduler on job cancellation. --- .../apache/spark/scheduler/DAGScheduler.scala | 44 +++++---- .../spark/scheduler/DAGSchedulerSuite.scala | 92 ++++++++++++++++++- 2 files changed, 115 insertions(+), 21 deletions(-) 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 6368665f249ee..c96d7435a7ed4 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -982,15 +982,7 @@ class DAGScheduler( if (!jobIdToStageIds.contains(jobId)) { logDebug("Trying to cancel unregistered job " + jobId) } else { - val independentStages = removeJobAndIndependentStages(jobId) - independentStages.foreach(taskScheduler.cancelTasks) - val error = new SparkException("Job %d cancelled".format(jobId)) - val job = jobIdToActiveJob(jobId) - job.listener.jobFailed(error) - jobIdToStageIds -= jobId - activeJobs -= job - jobIdToActiveJob -= jobId - listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error, job.finalStage.id))) + failJobAndIndependentStages(jobIdToActiveJob(jobId), s"Job $jobId cancelled") } } @@ -1007,19 +999,39 @@ class DAGScheduler( stageToInfos(failedStage).completionTime = Some(System.currentTimeMillis()) for (resultStage <- dependentStages) { val job = resultStageToJob(resultStage) - val error = new SparkException("Job aborted: " + reason) - job.listener.jobFailed(error) - jobIdToStageIdsRemove(job.jobId) - jobIdToActiveJob -= resultStage.jobId - activeJobs -= job - resultStageToJob -= resultStage - listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error, failedStage.id))) + failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason") } if (dependentStages.isEmpty) { logInfo("Ignoring failure of " + failedStage + " because all jobs depending on it are done") } } + /** + * Fails a job and all stages that are only used by that job, and cleans up relevant state. + */ + private def failJobAndIndependentStages(job: ActiveJob, failureReason: String) { + val error = new SparkException(failureReason) + job.listener.jobFailed(error) + + // Cancel all tasks in independent stages. + val independentStages = removeJobAndIndependentStages(job.jobId) + independentStages.foreach(taskScheduler.cancelTasks) + + // Clean up remaining state we store for the job. + jobIdToActiveJob -= job.jobId + activeJobs -= job + jobIdToStageIds -= job.jobId + val resultStagesForJob = resultStageToJob.keySet.filter( + stage => resultStageToJob(stage).jobId == job.jobId) + if (resultStagesForJob.size != 1) { + logWarning( + s"${resultStagesForJob.size} result stages for job ${job.jobId} (expect exactly 1)") + } + resultStageToJob --= resultStagesForJob + + listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error, job.finalStage.id))) + } + /** * Return true if one of stage's ancestors is target. */ 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 ce567b0cde85d..2e3026bffba2f 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -18,7 +18,7 @@ package org.apache.spark.scheduler import scala.Tuple2 -import scala.collection.mutable.{HashMap, Map} +import scala.collection.mutable.{HashSet, HashMap, Map} import org.scalatest.{BeforeAndAfter, FunSuite} @@ -43,6 +43,10 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont val conf = new SparkConf /** Set of TaskSets the DAGScheduler has requested executed. */ val taskSets = scala.collection.mutable.Buffer[TaskSet]() + + /** Stages for which the DAGScheduler has called TaskScheduler.cancelTasks(). */ + val cancelledStages = new HashSet[Int]() + val taskScheduler = new TaskScheduler() { override def rootPool: Pool = null override def schedulingMode: SchedulingMode = SchedulingMode.NONE @@ -53,7 +57,9 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont taskSet.tasks.foreach(_.epoch = mapOutputTracker.getEpoch) taskSets += taskSet } - override def cancelTasks(stageId: Int) {} + override def cancelTasks(stageId: Int) { + cancelledStages += stageId + } override def setDAGScheduler(dagScheduler: DAGScheduler) = {} override def defaultParallelism() = 2 } @@ -91,6 +97,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont before { sc = new SparkContext("local", "DAGSchedulerSuite") taskSets.clear() + cancelledStages.clear() cacheLocations.clear() results.clear() mapOutputTracker = new MapOutputTrackerMaster(conf) @@ -174,15 +181,16 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont } } - /** Sends the rdd to the scheduler for scheduling. */ + /** Sends the rdd to the scheduler for scheduling and returns the job id. */ private def submit( rdd: RDD[_], partitions: Array[Int], func: (TaskContext, Iterator[_]) => _ = jobComputeFunc, allowLocal: Boolean = false, - listener: JobListener = listener) { + listener: JobListener = listener): Int = { val jobId = scheduler.nextJobId.getAndIncrement() runEvent(JobSubmitted(jobId, rdd, func, partitions, allowLocal, null, listener)) + return jobId } /** Sends TaskSetFailed to the scheduler. */ @@ -190,6 +198,11 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont runEvent(TaskSetFailed(taskSet, message)) } + /** Sends JobCancelled to the DAG scheduler. */ + private def cancel(jobId: Int) { + runEvent(JobCancelled(jobId)) + } + test("zero split job") { val rdd = makeRdd(0, Nil) var numResults = 0 @@ -248,7 +261,15 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont test("trivial job failure") { submit(makeRdd(1, Nil), Array(0)) failed(taskSets(0), "some failure") - assert(failure.getMessage === "Job aborted: some failure") + assert(failure.getMessage === "Job aborted due to stage failure: some failure") + assertDataStructuresEmpty + } + + test("trivial job cancellation") { + val rdd = makeRdd(1, Nil) + val jobId = submit(rdd, Array(0)) + cancel(jobId) + assert(failure.getMessage === s"Job $jobId cancelled") assertDataStructuresEmpty } @@ -323,6 +344,67 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont assertDataStructuresEmpty } + test("run shuffle with map stage failure") { + val shuffleMapRdd = makeRdd(2, Nil) + val shuffleDep = new ShuffleDependency(shuffleMapRdd, null) + val reduceRdd = makeRdd(2, List(shuffleDep)) + submit(reduceRdd, Array(0, 1)) + + // Fail the map stage. This should cause the entire job to fail. + val stageFailureMessage = "Exception failure in map stage" + failed(taskSets(0), stageFailureMessage) + assert(failure.getMessage === s"Job aborted due to stage failure: $stageFailureMessage") + assertDataStructuresEmpty + } + + /** + * Makes sure that failures of stage used by multiple jobs are correctly handled. + * + * This test creates the following dependency graph: + * + * shuffleMapRdd1 shuffleMapRDD2 + * | \ | + * | \ | + * | \ | + * | \ | + * reduceRdd1 reduceRdd2 + * + * We start both shuffleMapRdds and then fail shuffleMapRdd1. As a result, the job listeners for + * reduceRdd1 and reduceRdd2 should both be informed that the job failed. shuffleMapRDD2 should + * also be cancelled, because it is only used by reduceRdd2 and reduceRdd2 cannot complete + * without shuffleMapRdd1. + */ + test("failure of stage used by two jobs") { + val shuffleMapRdd1 = makeRdd(2, Nil) + val shuffleDep1 = new ShuffleDependency(shuffleMapRdd1, null) + val shuffleMapRdd2 = makeRdd(2, Nil) + val shuffleDep2 = new ShuffleDependency(shuffleMapRdd2, null) + + val reduceRdd1 = makeRdd(2, List(shuffleDep1)) + val reduceRdd2 = makeRdd(2, List(shuffleDep1, shuffleDep2)) + + // We need to make our own listeners for this test, since by default submit uses the same + // listener for all jobs, and here we want to capture the failure for each job separately. + class FailureRecordingJobListener() extends JobListener { + var failureMessage: String = _ + override def taskSucceeded(index: Int, result: Any) {} + override def jobFailed(exception: Exception) = { failureMessage = exception.getMessage } + } + val listener1 = new FailureRecordingJobListener() + val listener2 = new FailureRecordingJobListener() + + submit(reduceRdd1, Array(0, 1), listener=listener1) + submit(reduceRdd2, Array(0, 1), listener=listener2) + + val stageFailureMessage = "Exception failure in map stage" + failed(taskSets(0), stageFailureMessage) + + assert(cancelledStages.contains(1)) + assert(listener1.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage") + assert(listener2.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage") + assertDataStructuresEmpty + } + test("run trivial shuffle with out-of-band failure and retry") { val shuffleMapRdd = makeRdd(2, Nil) val shuffleDep = new ShuffleDependency(shuffleMapRdd, null) From 3bc054893bf2decdafa97a1e149e489ad154f066 Mon Sep 17 00:00:00 2001 From: Henry Saputra Date: Tue, 8 Apr 2014 14:23:16 -0700 Subject: [PATCH 70/78] Remove extra semicolon in import statement and unused import in ApplicationMaster Small nit cleanup to remove extra semicolon and unused import in Yarn's stable ApplicationMaster (it bothers me every time I saw it) Author: Henry Saputra Closes #358 from hsaputra/nitcleanup_removesemicolon_import_applicationmaster and squashes the following commits: bffb685 [Henry Saputra] Remove extra semicolon in import statement and unused import in ApplicationMaster.scala --- .../scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 30735cbfdf26e..c8a4d2e647cbd 100644 --- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -18,7 +18,6 @@ package org.apache.spark.deploy.yarn import java.io.IOException -import java.net.Socket import java.util.concurrent.CopyOnWriteArrayList import java.util.concurrent.atomic.{AtomicInteger, AtomicReference} @@ -36,7 +35,7 @@ import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC import org.apache.hadoop.yarn.util.{ConverterUtils, Records} -import org.apache.hadoop.yarn.webapp.util.WebAppUtils; +import org.apache.hadoop.yarn.webapp.util.WebAppUtils import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext} import org.apache.spark.deploy.SparkHadoopUtil From a8d86b080ae26c96b078ba14dc60f3b528c07787 Mon Sep 17 00:00:00 2001 From: Kan Zhang Date: Tue, 8 Apr 2014 14:30:24 -0700 Subject: [PATCH 71/78] SPARK-1348 binding Master, Worker, and App Web UI to all interfaces Author: Kan Zhang Closes #318 from kanzhang/SPARK-1348 and squashes the following commits: e625a5f [Kan Zhang] reverting the changes to startJettyServer() 7a8084e [Kan Zhang] SPARK-1348 binding Master, Worker, and App Web UI to all interfaces --- .../scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala | 2 +- .../scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala | 2 +- core/src/main/scala/org/apache/spark/ui/SparkUI.scala | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) 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 bd75b2dfd0e07..01d9f52f4b7b4 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 @@ -59,7 +59,7 @@ class MasterWebUI(val master: Master, requestedPort: Int) extends Logging { def bind() { try { - serverInfo = Some(startJettyServer(host, port, handlers, master.conf)) + serverInfo = Some(startJettyServer("0.0.0.0", port, handlers, master.conf)) logInfo("Started Master web UI at http://%s:%d".format(host, boundPort)) } catch { case e: Exception => diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala index de76a5d5eb7bc..650f3da5ce3ff 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala @@ -60,7 +60,7 @@ class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[I def bind() { try { - serverInfo = Some(JettyUtils.startJettyServer(host, port, handlers, worker.conf)) + serverInfo = Some(JettyUtils.startJettyServer("0.0.0.0", port, handlers, worker.conf)) logInfo("Started Worker web UI at http://%s:%d".format(host, boundPort)) } catch { case e: Exception => 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 ef1ad872c8ef7..f53df7fbedf39 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -80,7 +80,7 @@ private[spark] class SparkUI( /** Bind the HTTP server which backs this web interface */ def bind() { try { - serverInfo = Some(startJettyServer(bindHost, port, handlers, sc.conf)) + serverInfo = Some(startJettyServer("0.0.0.0", port, handlers, sc.conf)) logInfo("Started Spark Web UI at http://%s:%d".format(publicHost, boundPort)) } catch { case e: Exception => From e25b593447a2e0aab9e5066f755e41be9068ecdc Mon Sep 17 00:00:00 2001 From: Aaron Davidson Date: Tue, 8 Apr 2014 14:40:20 -0700 Subject: [PATCH 72/78] SPARK-1445: compute-classpath should not print error if lib_managed not found This was added to the check for the assembly jar, forgot it for the datanucleus jars. Author: Aaron Davidson Closes #361 from aarondav/cc and squashes the following commits: 8facc16 [Aaron Davidson] SPARK-1445: compute-classpath should not print error if lib_managed not found --- bin/compute-classpath.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh index be37102dc069a..2a2bb376fd71f 100755 --- a/bin/compute-classpath.sh +++ b/bin/compute-classpath.sh @@ -63,7 +63,7 @@ fi # built with Hive, so first check if the datanucleus jars exist, and then ensure the current Spark # assembly is built for Hive, before actually populating the CLASSPATH with the jars. # Note that this check order is faster (by up to half a second) in the case where Hive is not used. -num_datanucleus_jars=$(ls "$FWDIR"/lib_managed/jars/ | grep "datanucleus-.*\\.jar" | wc -l) +num_datanucleus_jars=$(ls "$FWDIR"/lib_managed/jars/ 2>/dev/null | grep "datanucleus-.*\\.jar" | wc -l) if [ $num_datanucleus_jars -gt 0 ]; then AN_ASSEMBLY_JAR=${ASSEMBLY_JAR:-$DEPS_ASSEMBLY_JAR} num_hive_files=$(jar tvf "$AN_ASSEMBLY_JAR" org/apache/hadoop/hive/ql/exec 2>/dev/null | wc -l) From fac6085cd774a4dba73ad1618537ef1817b2bcf3 Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Tue, 8 Apr 2014 14:42:02 -0700 Subject: [PATCH 73/78] [SPARK-1397] Notify SparkListeners when stages fail or are cancelled. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit [I wanted to post this for folks to comment but it depends on (and thus includes the changes in) a currently outstanding PR, #305. You can look at just the second commit: https://github.com/kayousterhout/spark-1/commit/93f08baf731b9eaf5c9792a5373560526e2bccac to see just the changes relevant to this PR] Previously, when stages fail or get cancelled, the SparkListener is only notified indirectly through the SparkListenerJobEnd, where we sometimes pass in a single stage that failed. This worked before job cancellation, because jobs would only fail due to a single stage failure. However, with job cancellation, multiple running stages can fail when a job gets cancelled. Right now, this is not handled correctly, which results in stages that get stuck in the “Running Stages” window in the UI even though they’re dead. This PR changes the SparkListenerStageCompleted event to a SparkListenerStageEnded event, and uses this event to tell SparkListeners when stages fail in addition to when they complete successfully. This change is NOT publicly backward compatible for two reasons. First, it changes the SparkListener interface. We could alternately add a new event, SparkListenerStageFailed, and keep the existing SparkListenerStageCompleted. However, this is less consistent with the listener events for tasks / jobs ending, and will result in some code duplication for listeners (because failed and completed stages are handled in similar ways). Note that I haven’t finished updating the JSON code to correctly handle the new event because I’m waiting for feedback on whether this is a good or bad idea (hence the “WIP”). It is also not backwards compatible because it changes the publicly visible JobWaiter.jobFailed() method to no longer include a stage that caused the failure. I think this change should definitely stay, because with cancellation (as described above), a failure isn’t necessarily caused by a single stage. Author: Kay Ousterhout Closes #309 from kayousterhout/stage_cancellation and squashes the following commits: 5533ecd [Kay Ousterhout] Fixes in response to Mark's review 320c7c7 [Kay Ousterhout] Notify SparkListeners when stages fail or are cancelled. --- .../scala/org/apache/spark/FutureAction.scala | 2 +- .../apache/spark/scheduler/DAGScheduler.scala | 121 +++++++++++------- .../apache/spark/scheduler/JobLogger.scala | 8 +- .../apache/spark/scheduler/JobResult.scala | 3 +- .../apache/spark/scheduler/JobWaiter.scala | 2 +- .../spark/scheduler/SparkListener.scala | 2 +- .../apache/spark/scheduler/StageInfo.scala | 9 ++ .../spark/ui/jobs/JobProgressListener.scala | 23 +--- .../org/apache/spark/util/JsonProtocol.scala | 11 +- .../spark/scheduler/DAGSchedulerSuite.scala | 45 ++++++- .../apache/spark/util/JsonProtocolSuite.scala | 3 +- 11 files changed, 151 insertions(+), 78 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/FutureAction.scala b/core/src/main/scala/org/apache/spark/FutureAction.scala index f2decd14ef6d9..2eec09cd1c795 100644 --- a/core/src/main/scala/org/apache/spark/FutureAction.scala +++ b/core/src/main/scala/org/apache/spark/FutureAction.scala @@ -141,7 +141,7 @@ class SimpleFutureAction[T] private[spark](jobWaiter: JobWaiter[_], resultFunc: private def awaitResult(): Try[T] = { jobWaiter.awaitResult() match { case JobSucceeded => scala.util.Success(resultFunc) - case JobFailed(e: Exception, _) => scala.util.Failure(e) + case JobFailed(e: Exception) => scala.util.Failure(e) } } } 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 c96d7435a7ed4..c41d6d75a1d49 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -342,22 +342,24 @@ class DAGScheduler( } /** - * Removes job and any stages that are not needed by any other job. Returns the set of ids for - * stages that were removed. The associated tasks for those stages need to be cancelled if we - * got here via job cancellation. + * Removes state for job and any stages that are not needed by any other job. Does not + * handle cancelling tasks or notifying the SparkListener about finished jobs/stages/tasks. + * + * @param job The job whose state to cleanup. + * @param resultStage Specifies the result stage for the job; if set to None, this method + * searches resultStagesToJob to find and cleanup the appropriate result stage. */ - private def removeJobAndIndependentStages(jobId: Int): Set[Int] = { - val registeredStages = jobIdToStageIds(jobId) - val independentStages = new HashSet[Int]() - if (registeredStages.isEmpty) { - logError("No stages registered for job " + jobId) + private def cleanupStateForJobAndIndependentStages(job: ActiveJob, resultStage: Option[Stage]) { + val registeredStages = jobIdToStageIds.get(job.jobId) + if (registeredStages.isEmpty || registeredStages.get.isEmpty) { + logError("No stages registered for job " + job.jobId) } else { - stageIdToJobIds.filterKeys(stageId => registeredStages.contains(stageId)).foreach { + stageIdToJobIds.filterKeys(stageId => registeredStages.get.contains(stageId)).foreach { case (stageId, jobSet) => - if (!jobSet.contains(jobId)) { + if (!jobSet.contains(job.jobId)) { logError( "Job %d not registered for stage %d even though that stage was registered for the job" - .format(jobId, stageId)) + .format(job.jobId, stageId)) } else { def removeStage(stageId: Int) { // data structures based on Stage @@ -394,23 +396,28 @@ class DAGScheduler( .format(stageId, stageIdToStage.size)) } - jobSet -= jobId + jobSet -= job.jobId if (jobSet.isEmpty) { // no other job needs this stage - independentStages += stageId removeStage(stageId) } } } } - independentStages.toSet - } + jobIdToStageIds -= job.jobId + jobIdToActiveJob -= job.jobId + activeJobs -= job - private def jobIdToStageIdsRemove(jobId: Int) { - if (!jobIdToStageIds.contains(jobId)) { - logDebug("Trying to remove unregistered job " + jobId) + if (resultStage.isEmpty) { + // Clean up result stages. + val resultStagesForJob = resultStageToJob.keySet.filter( + stage => resultStageToJob(stage).jobId == job.jobId) + if (resultStagesForJob.size != 1) { + logWarning( + s"${resultStagesForJob.size} result stages for job ${job.jobId} (expect exactly 1)") + } + resultStageToJob --= resultStagesForJob } else { - removeJobAndIndependentStages(jobId) - jobIdToStageIds -= jobId + resultStageToJob -= resultStage.get } } @@ -460,7 +467,7 @@ class DAGScheduler( val waiter = submitJob(rdd, func, partitions, callSite, allowLocal, resultHandler, properties) waiter.awaitResult() match { case JobSucceeded => {} - case JobFailed(exception: Exception, _) => + case JobFailed(exception: Exception) => logInfo("Failed to run " + callSite) throw exception } @@ -606,7 +613,16 @@ class DAGScheduler( for (job <- activeJobs) { val error = new SparkException("Job cancelled because SparkContext was shut down") job.listener.jobFailed(error) - listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error, -1))) + // Tell the listeners that all of the running stages have ended. Don't bother + // cancelling the stages because if the DAG scheduler is stopped, the entire application + // is in the process of getting stopped. + val stageFailedMessage = "Stage cancelled because SparkContext was shut down" + runningStages.foreach { stage => + val info = stageToInfos(stage) + info.stageFailed(stageFailedMessage) + listenerBus.post(SparkListenerStageCompleted(info)) + } + listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error))) } return true } @@ -676,7 +692,7 @@ class DAGScheduler( } } catch { case e: Exception => - jobResult = JobFailed(e, job.finalStage.id) + jobResult = JobFailed(e) job.listener.jobFailed(e) } finally { val s = job.finalStage @@ -826,11 +842,8 @@ class DAGScheduler( job.numFinished += 1 // If the whole job has finished, remove it if (job.numFinished == job.numPartitions) { - jobIdToActiveJob -= stage.jobId - activeJobs -= job - resultStageToJob -= stage markStageAsFinished(stage) - jobIdToStageIdsRemove(job.jobId) + cleanupStateForJobAndIndependentStages(job, Some(stage)) listenerBus.post(SparkListenerJobEnd(job.jobId, JobSucceeded)) } job.listener.taskSucceeded(rt.outputId, event.result) @@ -982,7 +995,7 @@ class DAGScheduler( if (!jobIdToStageIds.contains(jobId)) { logDebug("Trying to cancel unregistered job " + jobId) } else { - failJobAndIndependentStages(jobIdToActiveJob(jobId), s"Job $jobId cancelled") + failJobAndIndependentStages(jobIdToActiveJob(jobId), s"Job $jobId cancelled", None) } } @@ -999,7 +1012,8 @@ class DAGScheduler( stageToInfos(failedStage).completionTime = Some(System.currentTimeMillis()) for (resultStage <- dependentStages) { val job = resultStageToJob(resultStage) - failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason") + failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason", + Some(resultStage)) } if (dependentStages.isEmpty) { logInfo("Ignoring failure of " + failedStage + " because all jobs depending on it are done") @@ -1008,28 +1022,45 @@ class DAGScheduler( /** * Fails a job and all stages that are only used by that job, and cleans up relevant state. + * + * @param resultStage The result stage for the job, if known. Used to cleanup state for the job + * slightly more efficiently than when not specified. */ - private def failJobAndIndependentStages(job: ActiveJob, failureReason: String) { + private def failJobAndIndependentStages(job: ActiveJob, failureReason: String, + resultStage: Option[Stage]) { val error = new SparkException(failureReason) job.listener.jobFailed(error) - // Cancel all tasks in independent stages. - val independentStages = removeJobAndIndependentStages(job.jobId) - independentStages.foreach(taskScheduler.cancelTasks) - - // Clean up remaining state we store for the job. - jobIdToActiveJob -= job.jobId - activeJobs -= job - jobIdToStageIds -= job.jobId - val resultStagesForJob = resultStageToJob.keySet.filter( - stage => resultStageToJob(stage).jobId == job.jobId) - if (resultStagesForJob.size != 1) { - logWarning( - s"${resultStagesForJob.size} result stages for job ${job.jobId} (expect exactly 1)") + // Cancel all independent, running stages. + val stages = jobIdToStageIds(job.jobId) + if (stages.isEmpty) { + logError("No stages registered for job " + job.jobId) } - resultStageToJob --= resultStagesForJob + stages.foreach { stageId => + val jobsForStage = stageIdToJobIds.get(stageId) + if (jobsForStage.isEmpty || !jobsForStage.get.contains(job.jobId)) { + logError( + "Job %d not registered for stage %d even though that stage was registered for the job" + .format(job.jobId, stageId)) + } else if (jobsForStage.get.size == 1) { + if (!stageIdToStage.contains(stageId)) { + logError("Missing Stage for stage with id $stageId") + } else { + // This is the only job that uses this stage, so fail the stage if it is running. + val stage = stageIdToStage(stageId) + if (runningStages.contains(stage)) { + taskScheduler.cancelTasks(stageId) + val stageInfo = stageToInfos(stage) + stageInfo.stageFailed(failureReason) + listenerBus.post(SparkListenerStageCompleted(stageToInfos(stage))) + } + } + } + } + + cleanupStateForJobAndIndependentStages(job, resultStage) - listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error, job.finalStage.id))) + listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error))) } /** diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala index 5cecf9416b32c..7c5053998f1d6 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala @@ -191,7 +191,11 @@ class JobLogger(val user: String, val logDirName: String) extends SparkListener */ override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) { val stageId = stageCompleted.stageInfo.stageId - stageLogInfo(stageId, "STAGE_ID=%d STATUS=COMPLETED".format(stageId)) + if (stageCompleted.stageInfo.failureReason.isEmpty) { + stageLogInfo(stageId, s"STAGE_ID=$stageId STATUS=COMPLETED") + } else { + stageLogInfo(stageId, s"STAGE_ID=$stageId STATUS=FAILED") + } } /** @@ -227,7 +231,7 @@ class JobLogger(val user: String, val logDirName: String) extends SparkListener var info = "JOB_ID=" + jobId jobEnd.jobResult match { case JobSucceeded => info += " STATUS=SUCCESS" - case JobFailed(exception, _) => + case JobFailed(exception) => info += " STATUS=FAILED REASON=" exception.getMessage.split("\\s+").foreach(info += _ + "_") case _ => diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobResult.scala b/core/src/main/scala/org/apache/spark/scheduler/JobResult.scala index 3cf4e3077e4a4..047bd27056120 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/JobResult.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/JobResult.scala @@ -24,5 +24,4 @@ private[spark] sealed trait JobResult private[spark] case object JobSucceeded extends JobResult -// A failed stage ID of -1 means there is not a particular stage that caused the failure -private[spark] case class JobFailed(exception: Exception, failedStageId: Int) extends JobResult +private[spark] case class JobFailed(exception: Exception) extends JobResult diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala index 8007b5418741e..e9bfee2248e5b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala @@ -64,7 +64,7 @@ private[spark] class JobWaiter[T]( override def jobFailed(exception: Exception): Unit = synchronized { _jobFinished = true - jobResult = JobFailed(exception, -1) + jobResult = JobFailed(exception) this.notifyAll() } 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 d4eb0ac88d8e8..d42e67742a4f7 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala @@ -71,7 +71,7 @@ private[spark] case object SparkListenerShutdown extends SparkListenerEvent */ trait SparkListener { /** - * Called when a stage is completed, with information on the completed stage + * Called when a stage completes successfully or fails, with information on the completed stage. */ def onStageCompleted(stageCompleted: SparkListenerStageCompleted) { } 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 8115a7ed7896d..eec409b182ac6 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala @@ -26,8 +26,17 @@ private[spark] class StageInfo(val stageId: Int, val name: String, val numTasks: Int, val rddInfo: RDDInfo) { /** When this stage was submitted from the DAGScheduler to a TaskScheduler. */ var submissionTime: Option[Long] = None + /** Time when all tasks in the stage completed or when the stage was cancelled. */ var completionTime: Option[Long] = None + /** If the stage failed, the reason why. */ + var failureReason: Option[String] = None + var emittedTaskSizeWarning = false + + def stageFailed(reason: String) { + failureReason = Some(reason) + completionTime = Some(System.currentTimeMillis) + } } private[spark] 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 048f671c8788f..5167e20ea3d7d 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 @@ -74,8 +74,13 @@ private[ui] class JobProgressListener(conf: SparkConf) extends SparkListener { // Remove by stageId, rather than by StageInfo, in case the StageInfo is from storage poolToActiveStages(stageIdToPool(stageId)).remove(stageId) activeStages.remove(stageId) - completedStages += stage - trimIfNecessary(completedStages) + if (stage.failureReason.isEmpty) { + completedStages += stage + trimIfNecessary(completedStages) + } else { + failedStages += stage + trimIfNecessary(failedStages) + } } /** If stages is too large, remove and garbage collect old stages */ @@ -215,20 +220,6 @@ private[ui] class JobProgressListener(conf: SparkConf) extends SparkListener { } } - override def onJobEnd(jobEnd: SparkListenerJobEnd) = synchronized { - jobEnd.jobResult match { - case JobFailed(_, stageId) => - activeStages.get(stageId).foreach { s => - // Remove by stageId, rather than by StageInfo, in case the StageInfo is from storage - activeStages.remove(s.stageId) - poolToActiveStages(stageIdToPool(stageId)).remove(s.stageId) - failedStages += s - trimIfNecessary(failedStages) - } - case _ => - } - } - override def onEnvironmentUpdate(environmentUpdate: SparkListenerEnvironmentUpdate) { synchronized { val schedulingModeName = 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 2155a8888c85c..19654892bf661 100644 --- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala @@ -166,12 +166,14 @@ private[spark] object JsonProtocol { val rddInfo = rddInfoToJson(stageInfo.rddInfo) val submissionTime = stageInfo.submissionTime.map(JInt(_)).getOrElse(JNothing) val completionTime = stageInfo.completionTime.map(JInt(_)).getOrElse(JNothing) + val failureReason = stageInfo.failureReason.map(JString(_)).getOrElse(JNothing) ("Stage ID" -> stageInfo.stageId) ~ ("Stage Name" -> stageInfo.name) ~ ("Number of Tasks" -> stageInfo.numTasks) ~ ("RDD Info" -> rddInfo) ~ ("Submission Time" -> submissionTime) ~ ("Completion Time" -> completionTime) ~ + ("Failure Reason" -> failureReason) ~ ("Emitted Task Size Warning" -> stageInfo.emittedTaskSizeWarning) } @@ -259,9 +261,7 @@ private[spark] object JsonProtocol { val json = jobResult match { case JobSucceeded => Utils.emptyJson case jobFailed: JobFailed => - val exception = exceptionToJson(jobFailed.exception) - ("Exception" -> exception) ~ - ("Failed Stage ID" -> jobFailed.failedStageId) + JObject("Exception" -> exceptionToJson(jobFailed.exception)) } ("Result" -> result) ~ json } @@ -442,11 +442,13 @@ private[spark] object JsonProtocol { val rddInfo = rddInfoFromJson(json \ "RDD Info") val submissionTime = Utils.jsonOption(json \ "Submission Time").map(_.extract[Long]) val completionTime = Utils.jsonOption(json \ "Completion Time").map(_.extract[Long]) + val failureReason = Utils.jsonOption(json \ "Failure Reason").map(_.extract[String]) val emittedTaskSizeWarning = (json \ "Emitted Task Size Warning").extract[Boolean] val stageInfo = new StageInfo(stageId, stageName, numTasks, rddInfo) stageInfo.submissionTime = submissionTime stageInfo.completionTime = completionTime + stageInfo.failureReason = failureReason stageInfo.emittedTaskSizeWarning = emittedTaskSizeWarning stageInfo } @@ -561,8 +563,7 @@ private[spark] object JsonProtocol { case `jobSucceeded` => JobSucceeded case `jobFailed` => val exception = exceptionFromJson(json \ "Exception") - val failedStageId = (json \ "Failed Stage ID").extract[Int] - new JobFailed(exception, failedStageId) + new JobFailed(exception) } } 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 2e3026bffba2f..a74724d785ad3 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -64,6 +64,21 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont override def defaultParallelism() = 2 } + /** Length of time to wait while draining listener events. */ + val WAIT_TIMEOUT_MILLIS = 10000 + val sparkListener = new SparkListener() { + val successfulStages = new HashSet[Int]() + val failedStages = new HashSet[Int]() + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) { + val stageInfo = stageCompleted.stageInfo + if (stageInfo.failureReason.isEmpty) { + successfulStages += stageInfo.stageId + } else { + failedStages += stageInfo.stageId + } + } + } + var mapOutputTracker: MapOutputTrackerMaster = null var scheduler: DAGScheduler = null @@ -89,13 +104,16 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont /** The list of results that DAGScheduler has collected. */ val results = new HashMap[Int, Any]() var failure: Exception = _ - val listener = new JobListener() { + val jobListener = new JobListener() { override def taskSucceeded(index: Int, result: Any) = results.put(index, result) override def jobFailed(exception: Exception) = { failure = exception } } before { sc = new SparkContext("local", "DAGSchedulerSuite") + sparkListener.successfulStages.clear() + sparkListener.failedStages.clear() + sc.addSparkListener(sparkListener) taskSets.clear() cancelledStages.clear() cacheLocations.clear() @@ -187,7 +205,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont partitions: Array[Int], func: (TaskContext, Iterator[_]) => _ = jobComputeFunc, allowLocal: Boolean = false, - listener: JobListener = listener): Int = { + listener: JobListener = jobListener): Int = { val jobId = scheduler.nextJobId.getAndIncrement() runEvent(JobSubmitted(jobId, rdd, func, partitions, allowLocal, null, listener)) return jobId @@ -231,7 +249,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont override def toString = "DAGSchedulerSuite Local RDD" } val jobId = scheduler.nextJobId.getAndIncrement() - runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, null, listener)) + runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, null, jobListener)) assert(results === Map(0 -> 42)) assertDataStructuresEmpty } @@ -262,6 +280,9 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont submit(makeRdd(1, Nil), Array(0)) failed(taskSets(0), "some failure") assert(failure.getMessage === "Job aborted due to stage failure: some failure") + assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + assert(sparkListener.failedStages.contains(0)) + assert(sparkListener.failedStages.size === 1) assertDataStructuresEmpty } @@ -270,6 +291,9 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont val jobId = submit(rdd, Array(0)) cancel(jobId) assert(failure.getMessage === s"Job $jobId cancelled") + assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + assert(sparkListener.failedStages.contains(0)) + assert(sparkListener.failedStages.size === 1) assertDataStructuresEmpty } @@ -354,6 +378,13 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont val stageFailureMessage = "Exception failure in map stage" failed(taskSets(0), stageFailureMessage) assert(failure.getMessage === s"Job aborted due to stage failure: $stageFailureMessage") + + // Listener bus should get told about the map stage failing, but not the reduce stage + // (since the reduce stage hasn't been started yet). + assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + assert(sparkListener.failedStages.contains(1)) + assert(sparkListener.failedStages.size === 1) + assertDataStructuresEmpty } @@ -400,6 +431,14 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont failed(taskSets(0), stageFailureMessage) assert(cancelledStages.contains(1)) + + // Make sure the listeners got told about both failed stages. + assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + assert(sparkListener.successfulStages.isEmpty) + assert(sparkListener.failedStages.contains(1)) + assert(sparkListener.failedStages.contains(3)) + assert(sparkListener.failedStages.size === 2) + assert(listener1.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage") assert(listener2.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage") assertDataStructuresEmpty 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 7bab7da8fed68..0342a8aff3c28 100644 --- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala @@ -89,7 +89,7 @@ class JsonProtocolSuite extends FunSuite { // JobResult val exception = new Exception("Out of Memory! Please restock film.") exception.setStackTrace(stackTrace) - val jobFailed = JobFailed(exception, 2) + val jobFailed = JobFailed(exception) testJobResult(JobSucceeded) testJobResult(jobFailed) @@ -294,7 +294,6 @@ class JsonProtocolSuite extends FunSuite { (result1, result2) match { case (JobSucceeded, JobSucceeded) => case (r1: JobFailed, r2: JobFailed) => - assert(r1.failedStageId === r2.failedStageId) assertEquals(r1.exception, r2.exception) case _ => fail("Job results don't match in types!") } From 12c077d5aa0b76a808a55db625c9677a52bd43f9 Mon Sep 17 00:00:00 2001 From: Sandeep Date: Tue, 8 Apr 2014 16:19:22 -0700 Subject: [PATCH 74/78] SPARK-1433: Upgrade Mesos dependency to 0.17.0 Mesos 0.13.0 was released 6 months ago. Upgrade Mesos dependency to 0.17.0 Author: Sandeep Closes #355 from techaddict/mesos_update and squashes the following commits: f1abeee [Sandeep] SPARK-1433: Upgrade Mesos dependency to 0.17.0 Mesos 0.13.0 was released 6 months ago. Upgrade Mesos dependency to 0.17.0 --- .../cluster/mesos/CoarseMesosSchedulerBackend.scala | 6 ++++-- .../scheduler/cluster/mesos/MesosSchedulerBackend.scala | 2 +- docs/_config.yml | 2 +- pom.xml | 6 +++--- project/SparkBuild.scala | 2 +- 5 files changed, 10 insertions(+), 8 deletions(-) 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 06b041e1fd9a9..c478e685641d7 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 @@ -194,10 +194,12 @@ private[spark] class CoarseMesosSchedulerBackend( .addResources(createResource("cpus", cpusToUse)) .addResources(createResource("mem", sc.executorMemory)) .build() - d.launchTasks(offer.getId, Collections.singletonList(task), filters) + d.launchTasks(Collections.singletonList(offer.getId), + Collections.singletonList(task), + filters) } else { // Filter it out - d.launchTasks(offer.getId, Collections.emptyList[MesosTaskInfo](), filters) + d.declineOffer(offer.getId, filters) } } } 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 dfdcafe19fb93..f878ae338fc95 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 @@ -223,7 +223,7 @@ private[spark] class MesosSchedulerBackend( // Reply to the offers val filters = Filters.newBuilder().setRefuseSeconds(1).build() // TODO: lower timeout? for (i <- 0 until offers.size) { - d.launchTasks(offers(i).getId, mesosTasks(i), filters) + d.launchTasks(Collections.singletonList(offers(i).getId), mesosTasks(i), filters) } } } finally { diff --git a/docs/_config.yml b/docs/_config.yml index d585b8c5ea763..bd5ed6c9220d2 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -7,6 +7,6 @@ SPARK_VERSION: 1.0.0-SNAPSHOT SPARK_VERSION_SHORT: 1.0.0 SCALA_BINARY_VERSION: "2.10" SCALA_VERSION: "2.10.4" -MESOS_VERSION: 0.13.0 +MESOS_VERSION: 0.17.0 SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net SPARK_GITHUB_URL: https://github.com/apache/spark diff --git a/pom.xml b/pom.xml index c03bb35c99442..11511bcb9da52 100644 --- a/pom.xml +++ b/pom.xml @@ -112,7 +112,7 @@ 2.10.4 2.10 - 0.13.0 + 0.17.0 org.spark-project.akka 2.2.3-shaded-protobuf 1.7.5 @@ -848,7 +848,7 @@ - + hadoop-provided @@ -893,6 +893,6 @@ - + diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 6b8740d9f21a1..08667aac2cd2d 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -332,7 +332,7 @@ object SparkBuild extends Build { "org.json4s" %% "json4s-jackson" % "3.2.6" excludeAll(excludeScalap), "it.unimi.dsi" % "fastutil" % "6.4.4", "colt" % "colt" % "1.2.0", - "org.apache.mesos" % "mesos" % "0.13.0", + "org.apache.mesos" % "mesos" % "0.17.0", "commons-net" % "commons-net" % "2.2", "net.java.dev.jets3t" % "jets3t" % "0.7.1" excludeAll(excludeCommonsLogging), "org.apache.derby" % "derby" % "10.4.2.0" % "test", From ce8ec5456169682f27f846e7b8d51e6c4bcf75e3 Mon Sep 17 00:00:00 2001 From: Holden Karau Date: Tue, 8 Apr 2014 18:15:52 -0700 Subject: [PATCH 75/78] Spark 1271: Co-Group and Group-By should pass Iterable[X] Author: Holden Karau Closes #242 from holdenk/spark-1320-cogroupandgroupshouldpassiterator and squashes the following commits: f289536 [Holden Karau] Fix bad merge, should have been Iterable rather than Iterator 77048f8 [Holden Karau] Fix merge up to master d3fe909 [Holden Karau] use toSeq instead 7a092a3 [Holden Karau] switch resultitr to resultiterable eb06216 [Holden Karau] maybe I should have had a coffee first. use correct import for guava iterables c5075aa [Holden Karau] If guava 14 had iterables 2d06e10 [Holden Karau] Fix Java 8 cogroup tests for the new API 11e730c [Holden Karau] Fix streaming tests 66b583d [Holden Karau] Fix the core test suite to compile 4ed579b [Holden Karau] Refactor from iterator to iterable d052c07 [Holden Karau] Python tests now pass with iterator pandas 3bcd81d [Holden Karau] Revert "Try and make pickling list iterators work" cd1e81c [Holden Karau] Try and make pickling list iterators work c60233a [Holden Karau] Start investigating moving to iterators for python API like the Java/Scala one. tl;dr: We will have to write our own iterator since the default one doesn't pickle well 88a5cef [Holden Karau] Fix cogroup test in JavaAPISuite for streaming a5ee714 [Holden Karau] oops, was checking wrong iterator e687f21 [Holden Karau] Fix groupbykey test in JavaAPISuite of streaming ec8cc3e [Holden Karau] Fix test issues\! 4b0eeb9 [Holden Karau] Switch cast in PairDStreamFunctions fa395c9 [Holden Karau] Revert "Add a join based on the problem in SVD" ec99e32 [Holden Karau] Revert "Revert this but for now put things in list pandas" b692868 [Holden Karau] Revert 7e533f7 [Holden Karau] Fix the bug 8a5153a [Holden Karau] Revert me, but we have some stuff to debug b4e86a9 [Holden Karau] Add a join based on the problem in SVD c4510e2 [Holden Karau] Revert this but for now put things in list pandas b4e0b1d [Holden Karau] Fix style issues 71e8b9f [Holden Karau] I really need to stop calling size on iterators, it is the path of sadness. b1ae51a [Holden Karau] Fix some of the types in the streaming JavaAPI suite. Probably still needs more work 37888ec [Holden Karau] core/tests now pass 249abde [Holden Karau] org.apache.spark.rdd.PairRDDFunctionsSuite passes 6698186 [Holden Karau] Revert "I think this might be a bad rabbit hole. Started work to make CoGroupedRDD use iterator and then went crazy" fe992fe [Holden Karau] hmmm try and fix up basic operation suite 172705c [Holden Karau] Fix Java API suite caafa63 [Holden Karau] I think this might be a bad rabbit hole. Started work to make CoGroupedRDD use iterator and then went crazy 88b3329 [Holden Karau] Fix groupbykey to actually give back an iterator 4991af6 [Holden Karau] Fix some tests be50246 [Holden Karau] Calling size on an iterator is not so good if we want to use it after 687ffbc [Holden Karau] This is the it compiles point of replacing Seq with Iterator and JList with JIterator in the groupby and cogroup signatures --- .../scala/org/apache/spark/bagel/Bagel.scala | 20 ++++--- .../apache/spark/api/java/JavaPairRDD.scala | 36 ++++++------ .../apache/spark/api/java/JavaRDDLike.scala | 6 +- .../apache/spark/rdd/PairRDDFunctions.scala | 39 +++++++------ .../main/scala/org/apache/spark/rdd/RDD.scala | 6 +- .../java/org/apache/spark/JavaAPISuite.java | 20 ++++--- .../scala/org/apache/spark/FailureSuite.scala | 4 +- .../org/apache/spark/PipedRDDSuite.scala | 2 +- .../spark/rdd/PairRDDFunctionsSuite.scala | 12 ++-- .../ExternalAppendOnlyMapSuite.scala | 4 +- .../apache/spark/examples/JavaPageRank.java | 21 ++++--- .../bagel/WikipediaPageRankStandalone.scala | 14 +++-- .../java/org/apache/spark/Java8APISuite.java | 11 ++-- .../org/apache/spark/mllib/linalg/SVD.scala | 6 +- .../spark/mllib/recommendation/ALS.scala | 4 +- .../org/apache/spark/mllib/util/LAUtils.scala | 6 +- python/pyspark/join.py | 5 +- python/pyspark/rdd.py | 10 ++-- python/pyspark/resultiterable.py | 33 +++++++++++ .../streaming/api/java/JavaPairDStream.scala | 42 +++++++------- .../dstream/PairDStreamFunctions.scala | 29 +++++----- .../streaming/dstream/StateDStream.scala | 13 +++-- .../apache/spark/streaming/JavaAPISuite.java | 58 ++++++++++++++++--- .../streaming/BasicOperationsSuite.scala | 4 +- 24 files changed, 252 insertions(+), 153 deletions(-) create mode 100644 python/pyspark/resultiterable.py diff --git a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala index 70c7474a936dc..70a99b33d753c 100644 --- a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala +++ b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala @@ -220,20 +220,23 @@ object Bagel extends Logging { */ private def comp[K: Manifest, V <: Vertex, M <: Message[K], C]( sc: SparkContext, - grouped: RDD[(K, (Seq[C], Seq[V]))], + grouped: RDD[(K, (Iterable[C], Iterable[V]))], compute: (V, Option[C]) => (V, Array[M]), storageLevel: StorageLevel ): (RDD[(K, (V, Array[M]))], Int, Int) = { var numMsgs = sc.accumulator(0) var numActiveVerts = sc.accumulator(0) - val processed = grouped.flatMapValues { - case (_, vs) if vs.size == 0 => None - case (c, vs) => + val processed = grouped.mapValues(x => (x._1.iterator, x._2.iterator)) + .flatMapValues { + case (_, vs) if !vs.hasNext => None + case (c, vs) => { val (newVert, newMsgs) = - compute(vs(0), c match { - case Seq(comb) => Some(comb) - case Seq() => None - }) + compute(vs.next, + c.hasNext match { + case true => Some(c.next) + case false => None + } + ) numMsgs += newMsgs.size if (newVert.active) { @@ -241,6 +244,7 @@ object Bagel extends Logging { } Some((newVert, newMsgs)) + } }.persist(storageLevel) // Force evaluation of processed RDD for accurate performance measurements diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index 9596dbaf75488..e6c5d85917678 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -18,6 +18,7 @@ package org.apache.spark.api.java import java.util.{Comparator, List => JList} +import java.lang.{Iterable => JIterable} import scala.collection.JavaConversions._ import scala.reflect.ClassTag @@ -250,14 +251,14 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * Group the values for each key in the RDD into a single sequence. Allows controlling the * partitioning of the resulting key-value pair RDD by passing a Partitioner. */ - def groupByKey(partitioner: Partitioner): JavaPairRDD[K, JList[V]] = + def groupByKey(partitioner: Partitioner): JavaPairRDD[K, JIterable[V]] = fromRDD(groupByResultToJava(rdd.groupByKey(partitioner))) /** * Group the values for each key in the RDD into a single sequence. Hash-partitions the * resulting RDD with into `numPartitions` partitions. */ - def groupByKey(numPartitions: Int): JavaPairRDD[K, JList[V]] = + def groupByKey(numPartitions: Int): JavaPairRDD[K, JIterable[V]] = fromRDD(groupByResultToJava(rdd.groupByKey(numPartitions))) /** @@ -367,7 +368,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * Group the values for each key in the RDD into a single sequence. Hash-partitions the * resulting RDD with the existing partitioner/parallelism level. */ - def groupByKey(): JavaPairRDD[K, JList[V]] = + def groupByKey(): JavaPairRDD[K, JIterable[V]] = fromRDD(groupByResultToJava(rdd.groupByKey())) /** @@ -462,7 +463,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * list of values for that key in `this` as well as `other`. */ def cogroup[W](other: JavaPairRDD[K, W], partitioner: Partitioner) - : JavaPairRDD[K, (JList[V], JList[W])] = + : JavaPairRDD[K, (JIterable[V], JIterable[W])] = fromRDD(cogroupResultToJava(rdd.cogroup(other, partitioner))) /** @@ -470,14 +471,14 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], - partitioner: Partitioner): JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] = + partitioner: Partitioner): JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] = fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, partitioner))) /** * 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: JavaPairRDD[K, W]): JavaPairRDD[K, (JList[V], JList[W])] = + def cogroup[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JIterable[V], JIterable[W])] = fromRDD(cogroupResultToJava(rdd.cogroup(other))) /** @@ -485,7 +486,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2]) - : JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] = + : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] = fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2))) /** @@ -493,7 +494,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * list of values for that key in `this` as well as `other`. */ def cogroup[W](other: JavaPairRDD[K, W], numPartitions: Int) - : JavaPairRDD[K, (JList[V], JList[W])] = + : JavaPairRDD[K, (JIterable[V], JIterable[W])] = fromRDD(cogroupResultToJava(rdd.cogroup(other, numPartitions))) /** @@ -501,16 +502,16 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2], numPartitions: Int) - : JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] = + : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] = fromRDD(cogroupResult2ToJava(rdd.cogroup(other1, other2, numPartitions))) /** Alias for cogroup. */ - def groupWith[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JList[V], JList[W])] = + def groupWith[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (JIterable[V], JIterable[W])] = fromRDD(cogroupResultToJava(rdd.groupWith(other))) /** Alias for cogroup. */ def groupWith[W1, W2](other1: JavaPairRDD[K, W1], other2: JavaPairRDD[K, W2]) - : JavaPairRDD[K, (JList[V], JList[W1], JList[W2])] = + : JavaPairRDD[K, (JIterable[V], JIterable[W1], JIterable[W2])] = fromRDD(cogroupResult2ToJava(rdd.groupWith(other1, other2))) /** @@ -695,21 +696,22 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) object JavaPairRDD { private[spark] - def groupByResultToJava[K: ClassTag, T](rdd: RDD[(K, Seq[T])]): RDD[(K, JList[T])] = { - rddToPairRDDFunctions(rdd).mapValues(seqAsJavaList) + def groupByResultToJava[K: ClassTag, T](rdd: RDD[(K, Iterable[T])]): RDD[(K, JIterable[T])] = { + rddToPairRDDFunctions(rdd).mapValues(asJavaIterable) } private[spark] def cogroupResultToJava[K: ClassTag, V, W]( - rdd: RDD[(K, (Seq[V], Seq[W]))]): RDD[(K, (JList[V], JList[W]))] = { - rddToPairRDDFunctions(rdd).mapValues(x => (seqAsJavaList(x._1), seqAsJavaList(x._2))) + rdd: RDD[(K, (Iterable[V], Iterable[W]))]): RDD[(K, (JIterable[V], JIterable[W]))] = { + rddToPairRDDFunctions(rdd).mapValues(x => (asJavaIterable(x._1), asJavaIterable(x._2))) } private[spark] def cogroupResult2ToJava[K: ClassTag, V, W1, W2]( - rdd: RDD[(K, (Seq[V], Seq[W1], Seq[W2]))]): RDD[(K, (JList[V], JList[W1], JList[W2]))] = { + rdd: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]) + : RDD[(K, (JIterable[V], JIterable[W1], JIterable[W2]))] = { rddToPairRDDFunctions(rdd) - .mapValues(x => (seqAsJavaList(x._1), seqAsJavaList(x._2), seqAsJavaList(x._3))) + .mapValues(x => (asJavaIterable(x._1), asJavaIterable(x._2), asJavaIterable(x._3))) } def fromRDD[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]): JavaPairRDD[K, V] = { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index 6e8ec8e0c7629..ae577b500ccb4 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -17,7 +17,7 @@ package org.apache.spark.api.java -import java.util.{Comparator, Iterator => JIterator, List => JList} +import java.util.{Comparator, List => JList, Iterator => JIterator} import java.lang.{Iterable => JIterable} import scala.collection.JavaConversions._ @@ -204,7 +204,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements * mapping to that key. */ - def groupBy[K](f: JFunction[T, K]): JavaPairRDD[K, JList[T]] = { + def groupBy[K](f: JFunction[T, K]): JavaPairRDD[K, JIterable[T]] = { implicit val ctagK: ClassTag[K] = fakeClassTag implicit val ctagV: ClassTag[JList[T]] = fakeClassTag JavaPairRDD.fromRDD(groupByResultToJava(rdd.groupBy(f)(fakeClassTag))) @@ -214,7 +214,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements * mapping to that key. */ - def groupBy[K](f: JFunction[T, K], numPartitions: Int): JavaPairRDD[K, JList[T]] = { + def groupBy[K](f: JFunction[T, K], numPartitions: Int): JavaPairRDD[K, JIterable[T]] = { implicit val ctagK: ClassTag[K] = fakeClassTag implicit val ctagV: ClassTag[JList[T]] = fakeClassTag JavaPairRDD.fromRDD(groupByResultToJava(rdd.groupBy(f, numPartitions)(fakeClassTag[K]))) 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 14386ff5b9127..a92a84b5342d1 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -261,7 +261,7 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) * Group the values for each key in the RDD into a single sequence. Allows controlling the * partitioning of the resulting key-value pair RDD by passing a Partitioner. */ - def groupByKey(partitioner: Partitioner): RDD[(K, Seq[V])] = { + def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = { // 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. @@ -270,14 +270,14 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) def mergeCombiners(c1: ArrayBuffer[V], c2: ArrayBuffer[V]) = c1 ++ c2 val bufs = combineByKey[ArrayBuffer[V]]( createCombiner _, mergeValue _, mergeCombiners _, partitioner, mapSideCombine=false) - bufs.asInstanceOf[RDD[(K, Seq[V])]] + bufs.mapValues(_.toIterable) } /** * Group the values for each key in the RDD into a single sequence. Hash-partitions the * resulting RDD with into `numPartitions` partitions. */ - def groupByKey(numPartitions: Int): RDD[(K, Seq[V])] = { + def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = { groupByKey(new HashPartitioner(numPartitions)) } @@ -298,7 +298,7 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) */ def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = { this.cogroup(other, partitioner).flatMapValues { case (vs, ws) => - for (v <- vs.iterator; w <- ws.iterator) yield (v, w) + for (v <- vs; w <- ws) yield (v, w) } } @@ -311,9 +311,9 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = { this.cogroup(other, partitioner).flatMapValues { case (vs, ws) => if (ws.isEmpty) { - vs.iterator.map(v => (v, None)) + vs.map(v => (v, None)) } else { - for (v <- vs.iterator; w <- ws.iterator) yield (v, Some(w)) + for (v <- vs; w <- ws) yield (v, Some(w)) } } } @@ -328,9 +328,9 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) : RDD[(K, (Option[V], W))] = { this.cogroup(other, partitioner).flatMapValues { case (vs, ws) => if (vs.isEmpty) { - ws.iterator.map(w => (None, w)) + ws.map(w => (None, w)) } else { - for (v <- vs.iterator; w <- ws.iterator) yield (Some(v), w) + for (v <- vs; w <- ws) yield (Some(v), w) } } } @@ -358,7 +358,7 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) * Group the values for each key in the RDD into a single sequence. Hash-partitions the * resulting RDD with the existing partitioner/parallelism level. */ - def groupByKey(): RDD[(K, Seq[V])] = { + def groupByKey(): RDD[(K, Iterable[V])] = { groupByKey(defaultPartitioner(self)) } @@ -453,7 +453,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](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)], partitioner: Partitioner): RDD[(K, (Seq[V], Seq[W]))] = { + def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner) + : RDD[(K, (Iterable[V], Iterable[W]))] = { if (partitioner.isInstanceOf[HashPartitioner] && getKeyClass().isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -468,13 +469,15 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](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, (Seq[V], Seq[W1], Seq[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { if (partitioner.isInstanceOf[HashPartitioner] && getKeyClass().isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } val cg = new CoGroupedRDD[K](Seq(self, other1, other2), partitioner) cg.mapValues { case Seq(vs, w1s, w2s) => - (vs.asInstanceOf[Seq[V]], w1s.asInstanceOf[Seq[W1]], w2s.asInstanceOf[Seq[W2]]) + (vs.asInstanceOf[Seq[V]], + w1s.asInstanceOf[Seq[W1]], + w2s.asInstanceOf[Seq[W2]]) } } @@ -482,7 +485,7 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](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, (Seq[V], Seq[W]))] = { + def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = { cogroup(other, defaultPartitioner(self, other)) } @@ -491,7 +494,7 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](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, (Seq[V], Seq[W1], Seq[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { cogroup(other1, other2, defaultPartitioner(self, other1, other2)) } @@ -499,7 +502,7 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](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, (Seq[V], Seq[W]))] = { + def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = { cogroup(other, new HashPartitioner(numPartitions)) } @@ -508,18 +511,18 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](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, (Seq[V], Seq[W1], Seq[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { cogroup(other1, other2, new HashPartitioner(numPartitions)) } /** Alias for cogroup. */ - def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Seq[V], Seq[W]))] = { + def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = { cogroup(other, defaultPartitioner(self, other)) } /** Alias for cogroup. */ def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]) - : RDD[(K, (Seq[V], Seq[W1], Seq[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { cogroup(other1, other2, defaultPartitioner(self, other1, other2)) } 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 bf3c57ad41eb2..74fa2a4fcd401 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -438,20 +438,20 @@ abstract class RDD[T: ClassTag]( /** * Return an RDD of grouped items. */ - def groupBy[K: ClassTag](f: T => K): RDD[(K, Seq[T])] = + def groupBy[K: ClassTag](f: T => K): RDD[(K, Iterable[T])] = groupBy[K](f, defaultPartitioner(this)) /** * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements * mapping to that key. */ - def groupBy[K: ClassTag](f: T => K, numPartitions: Int): RDD[(K, Seq[T])] = + def groupBy[K: ClassTag](f: T => K, numPartitions: Int): RDD[(K, Iterable[T])] = groupBy(f, new HashPartitioner(numPartitions)) /** * Return an RDD of grouped items. */ - def groupBy[K: ClassTag](f: T => K, p: Partitioner): RDD[(K, Seq[T])] = { + def groupBy[K: ClassTag](f: T => K, p: Partitioner): RDD[(K, Iterable[T])] = { val cleanF = sc.clean(f) this.map(t => (cleanF(t), t)).groupByKey(p) } diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java index 762405be2a8f9..ab2fdac553349 100644 --- a/core/src/test/java/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java @@ -18,10 +18,12 @@ package org.apache.spark; import java.io.*; +import java.lang.StringBuilder; import java.util.*; import scala.Tuple2; +import com.google.common.collect.Iterables; import com.google.common.collect.Lists; import com.google.common.base.Optional; import com.google.common.base.Charsets; @@ -197,7 +199,7 @@ public void lookup() { new Tuple2("Oranges", "Citrus") )); Assert.assertEquals(2, categories.lookup("Oranges").size()); - Assert.assertEquals(2, categories.groupByKey().lookup("Oranges").get(0).size()); + Assert.assertEquals(2, Iterables.size(categories.groupByKey().lookup("Oranges").get(0))); } @Test @@ -209,15 +211,15 @@ public Boolean call(Integer x) { return x % 2 == 0; } }; - JavaPairRDD> oddsAndEvens = rdd.groupBy(isOdd); + JavaPairRDD> oddsAndEvens = rdd.groupBy(isOdd); Assert.assertEquals(2, oddsAndEvens.count()); - Assert.assertEquals(2, oddsAndEvens.lookup(true).get(0).size()); // Evens - Assert.assertEquals(5, oddsAndEvens.lookup(false).get(0).size()); // Odds + Assert.assertEquals(2, Iterables.size(oddsAndEvens.lookup(true).get(0))); // Evens + Assert.assertEquals(5, Iterables.size(oddsAndEvens.lookup(false).get(0))); // Odds oddsAndEvens = rdd.groupBy(isOdd, 1); Assert.assertEquals(2, oddsAndEvens.count()); - Assert.assertEquals(2, oddsAndEvens.lookup(true).get(0).size()); // Evens - Assert.assertEquals(5, oddsAndEvens.lookup(false).get(0).size()); // Odds + Assert.assertEquals(2, Iterables.size(oddsAndEvens.lookup(true).get(0))); // Evens + Assert.assertEquals(5, Iterables.size(oddsAndEvens.lookup(false).get(0))); // Odds } @SuppressWarnings("unchecked") @@ -232,9 +234,9 @@ public void cogroup() { new Tuple2("Oranges", 2), new Tuple2("Apples", 3) )); - JavaPairRDD, List>> cogrouped = categories.cogroup(prices); - Assert.assertEquals("[Fruit, Citrus]", cogrouped.lookup("Oranges").get(0)._1().toString()); - Assert.assertEquals("[2]", cogrouped.lookup("Oranges").get(0)._2().toString()); + JavaPairRDD, Iterable>> cogrouped = categories.cogroup(prices); + Assert.assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1())); + Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2())); cogrouped.collect(); } diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala index f3fb64d87a2fd..12dbebcb28644 100644 --- a/core/src/test/scala/org/apache/spark/FailureSuite.scala +++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala @@ -72,7 +72,7 @@ class FailureSuite extends FunSuite with LocalSparkContext { throw new Exception("Intentional task failure") } } - (k, v(0) * v(0)) + (k, v.head * v.head) }.collect() FailureSuiteState.synchronized { assert(FailureSuiteState.tasksRun === 4) @@ -137,5 +137,3 @@ class FailureSuite extends FunSuite with LocalSparkContext { // TODO: Need to add tests with shuffle fetch failures. } - - diff --git a/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala index 627e9b5cd9060..867b28cc0d971 100644 --- a/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala @@ -85,7 +85,7 @@ class PipedRDDSuite extends FunSuite with SharedSparkContext { (f: String => Unit) => { bl.value.map(f(_)); f("\u0001") }, - (i: Tuple2[String, Seq[String]], f: String => Unit) => { + (i: Tuple2[String, Iterable[String]], f: String => Unit) => { for (e <- i._2) { f(e + "_") } diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala index f9e994b13dfbc..8f3e6bd21b752 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala @@ -225,11 +225,12 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) val joined = rdd1.groupWith(rdd2).collect() assert(joined.size === 4) - assert(joined.toSet === Set( - (1, (ArrayBuffer(1, 2), ArrayBuffer('x'))), - (2, (ArrayBuffer(1), ArrayBuffer('y', 'z'))), - (3, (ArrayBuffer(1), ArrayBuffer())), - (4, (ArrayBuffer(), ArrayBuffer('w'))) + val joinedSet = joined.map(x => (x._1, (x._2._1.toList, x._2._2.toList))).toSet + assert(joinedSet === Set( + (1, (List(1, 2), List('x'))), + (2, (List(1), List('y', 'z'))), + (3, (List(1), List())), + (4, (List(), List('w'))) )) } @@ -447,4 +448,3 @@ class ConfigTestFormat() extends FakeFormat() with Configurable { super.getRecordWriter(p1) } } - diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala index fce1184d46364..cdebefb67510c 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala @@ -174,9 +174,9 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext { assert(result1.toSet == Set[(Int, Int)]((0, 5), (1, 5))) // groupByKey - val result2 = rdd.groupByKey().collect() + val result2 = rdd.groupByKey().collect().map(x => (x._1, x._2.toList)).toSet assert(result2.toSet == Set[(Int, Seq[Int])] - ((0, ArrayBuffer[Int](1, 1, 1, 1, 1)), (1, ArrayBuffer[Int](1, 1, 1, 1, 1)))) + ((0, List[Int](1, 1, 1, 1, 1)), (1, List[Int](1, 1, 1, 1, 1)))) } test("simple cogroup") { diff --git a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java index eb70fb547564c..8513ba07e7705 100644 --- a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java +++ b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java @@ -17,7 +17,10 @@ package org.apache.spark.examples; + import scala.Tuple2; + +import com.google.common.collect.Iterables; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; @@ -26,8 +29,9 @@ import org.apache.spark.api.java.function.PairFlatMapFunction; import org.apache.spark.api.java.function.PairFunction; -import java.util.List; import java.util.ArrayList; +import java.util.List; +import java.util.Iterator; import java.util.regex.Pattern; /** @@ -66,7 +70,7 @@ public static void main(String[] args) throws Exception { JavaRDD lines = ctx.textFile(args[1], 1); // Loads all URLs from input file and initialize their neighbors. - JavaPairRDD> links = lines.mapToPair(new PairFunction() { + JavaPairRDD> links = lines.mapToPair(new PairFunction() { @Override public Tuple2 call(String s) { String[] parts = SPACES.split(s); @@ -75,9 +79,9 @@ public Tuple2 call(String s) { }).distinct().groupByKey().cache(); // Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one. - JavaPairRDD ranks = links.mapValues(new Function, Double>() { + JavaPairRDD ranks = links.mapValues(new Function, Double>() { @Override - public Double call(List rs) { + public Double call(Iterable rs) { return 1.0; } }); @@ -86,12 +90,13 @@ public Double call(List rs) { for (int current = 0; current < Integer.parseInt(args[2]); current++) { // Calculates URL contributions to the rank of other URLs. JavaPairRDD contribs = links.join(ranks).values() - .flatMapToPair(new PairFlatMapFunction, Double>, String, Double>() { + .flatMapToPair(new PairFlatMapFunction, Double>, String, Double>() { @Override - public Iterable> call(Tuple2, Double> s) { + public Iterable> call(Tuple2, Double> s) { + int urlCount = Iterables.size(s._1); List> results = new ArrayList>(); - for (String n : s._1()) { - results.add(new Tuple2(n, s._2() / s._1().size())); + for (String n : s._1) { + results.add(new Tuple2(n, s._2() / urlCount)); } return results; } diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala index 27afa6b642758..7aac6a13597e6 100644 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala +++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala @@ -115,12 +115,16 @@ object WikipediaPageRankStandalone { var ranks = links.mapValues { edges => defaultRank } for (i <- 1 to numIterations) { val contribs = links.groupWith(ranks).flatMap { - case (id, (linksWrapper, rankWrapper)) => - if (linksWrapper.length > 0) { - if (rankWrapper.length > 0) { - linksWrapper(0).map(dest => (dest, rankWrapper(0) / linksWrapper(0).size)) + case (id, (linksWrapperIterable, rankWrapperIterable)) => + val linksWrapper = linksWrapperIterable.iterator + val rankWrapper = rankWrapperIterable.iterator + if (linksWrapper.hasNext) { + val linksWrapperHead = linksWrapper.next + if (rankWrapper.hasNext) { + val rankWrapperHead = rankWrapper.next + linksWrapperHead.map(dest => (dest, rankWrapperHead / linksWrapperHead.size)) } else { - linksWrapper(0).map(dest => (dest, defaultRank / linksWrapper(0).size)) + linksWrapperHead.map(dest => (dest, defaultRank / linksWrapperHead.size)) } } else { Array[(String, Double)]() diff --git a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java index f67251217ed4a..7eb8b45fc3cf0 100644 --- a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java +++ b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java @@ -23,6 +23,7 @@ import scala.Tuple2; +import com.google.common.collections.Iterables; import com.google.common.base.Optional; import com.google.common.io.Files; import org.apache.hadoop.io.IntWritable; @@ -85,15 +86,15 @@ public void foreach() { public void groupBy() { JavaRDD rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13)); Function isOdd = x -> x % 2 == 0; - JavaPairRDD> oddsAndEvens = rdd.groupBy(isOdd); + JavaPairRDD> oddsAndEvens = rdd.groupBy(isOdd); Assert.assertEquals(2, oddsAndEvens.count()); - Assert.assertEquals(2, oddsAndEvens.lookup(true).get(0).size()); // Evens - Assert.assertEquals(5, oddsAndEvens.lookup(false).get(0).size()); // Odds + Assert.assertEquals(2, Iterables.size(oddsAndEvens.lookup(true).get(0))); // Evens + Assert.assertEquals(5, Iterables.size(oddsAndEvens.lookup(false).get(0))); // Odds oddsAndEvens = rdd.groupBy(isOdd, 1); Assert.assertEquals(2, oddsAndEvens.count()); - Assert.assertEquals(2, oddsAndEvens.lookup(true).get(0).size()); // Evens - Assert.assertEquals(5, oddsAndEvens.lookup(false).get(0).size()); // Odds + Assert.assertEquals(2, Iterables.size(oddsAndEvens.lookup(true).get(0))); // Evens + Assert.assertEquals(5, Iterables.size(oddsAndEvens.lookup(false).get(0))); // Odds } @Test diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala index 3e7cc648d1d37..0d97b7d92f155 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala @@ -69,11 +69,11 @@ class SVD { /** * Compute SVD using the current set parameters - * Returns (U, S, V) such that A = USV^T + * Returns (U, S, V) such that A = USV^T * U is a row-by-row dense matrix * S is a simple double array of singular values * V is a 2d array matrix - * See [[denseSVD]] for more documentation + * See [[denseSVD]] for more documentation */ def compute(matrix: RDD[Array[Double]]): (RDD[Array[Double]], Array[Double], Array[Array[Double]]) = { @@ -393,5 +393,3 @@ object SVD { System.exit(0) } } - - diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index 0cc9f48769f83..3124fac326d22 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -421,12 +421,12 @@ class ALS private ( * Compute the new feature vectors for a block of the users matrix given the list of factors * it received from each product and its InLinkBlock. */ - private def updateBlock(messages: Seq[(Int, Array[Array[Double]])], inLinkBlock: InLinkBlock, + private def updateBlock(messages: Iterable[(Int, Array[Array[Double]])], inLinkBlock: InLinkBlock, rank: Int, lambda: Double, alpha: Double, YtY: Option[Broadcast[DoubleMatrix]]) : Array[Array[Double]] = { // Sort the incoming block factor messages by block ID and make them an array - val blockFactors = messages.sortBy(_._1).map(_._2).toArray // Array[Array[Double]] + val blockFactors = messages.toSeq.sortBy(_._1).map(_._2).toArray // Array[Array[Double]] val numBlocks = blockFactors.length val numUsers = inLinkBlock.elementIds.length diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LAUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LAUtils.scala index afe081295bfae..87aac347579c7 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/LAUtils.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LAUtils.scala @@ -38,8 +38,10 @@ object LAUtils { case (i, cols) => val rowArray = Array.ofDim[Double](n) var j = 0 - while (j < cols.size) { - rowArray(cols(j)._1) = cols(j)._2 + val colsItr = cols.iterator + while (colsItr.hasNext) { + val element = colsItr.next + rowArray(element._1) = element._2 j += 1 } MatrixRow(i, rowArray) diff --git a/python/pyspark/join.py b/python/pyspark/join.py index 5f4294fb1b777..6f94d26ef86a9 100644 --- a/python/pyspark/join.py +++ b/python/pyspark/join.py @@ -31,11 +31,12 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ +from pyspark.resultiterable import ResultIterable def _do_python_join(rdd, other, numPartitions, dispatch): vs = rdd.map(lambda (k, v): (k, (1, v))) ws = other.map(lambda (k, v): (k, (2, v))) - return vs.union(ws).groupByKey(numPartitions).flatMapValues(dispatch) + return vs.union(ws).groupByKey(numPartitions).flatMapValues(lambda x : dispatch(x.__iter__())) def python_join(rdd, other, numPartitions): @@ -88,5 +89,5 @@ def dispatch(seq): vbuf.append(v) elif n == 2: wbuf.append(v) - return (vbuf, wbuf) + return (ResultIterable(vbuf), ResultIterable(wbuf)) return vs.union(ws).groupByKey(numPartitions).mapValues(dispatch) diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index fb27863e07f55..91fc7e637e2c6 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -38,6 +38,7 @@ from pyspark.statcounter import StatCounter from pyspark.rddsampler import RDDSampler from pyspark.storagelevel import StorageLevel +from pyspark.resultiterable import ResultIterable from py4j.java_collections import ListConverter, MapConverter @@ -1118,7 +1119,7 @@ def groupByKey(self, numPartitions=None): Hash-partitions the resulting RDD with into numPartitions partitions. >>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) - >>> sorted(x.groupByKey().collect()) + >>> map((lambda (x,y): (x, list(y))), sorted(x.groupByKey().collect())) [('a', [1, 1]), ('b', [1])] """ @@ -1133,7 +1134,7 @@ def mergeCombiners(a, b): return a + b return self.combineByKey(createCombiner, mergeValue, mergeCombiners, - numPartitions) + numPartitions).mapValues(lambda x: ResultIterable(x)) # TODO: add tests def flatMapValues(self, f): @@ -1180,7 +1181,7 @@ def cogroup(self, other, numPartitions=None): >>> x = sc.parallelize([("a", 1), ("b", 4)]) >>> y = sc.parallelize([("a", 2)]) - >>> sorted(x.cogroup(y).collect()) + >>> map((lambda (x,y): (x, (list(y[0]), list(y[1])))), sorted(list(x.cogroup(y).collect()))) [('a', ([1], [2])), ('b', ([4], []))] """ return python_cogroup(self, other, numPartitions) @@ -1217,7 +1218,7 @@ def keyBy(self, f): >>> x = sc.parallelize(range(0,3)).keyBy(lambda x: x*x) >>> y = sc.parallelize(zip(range(0,5), range(0,5))) - >>> sorted(x.cogroup(y).collect()) + >>> map((lambda (x,y): (x, (list(y[0]), (list(y[1]))))), sorted(x.cogroup(y).collect())) [(0, ([0], [0])), (1, ([1], [1])), (2, ([], [2])), (3, ([], [3])), (4, ([2], [4]))] """ return self.map(lambda x: (f(x), x)) @@ -1317,7 +1318,6 @@ def getStorageLevel(self): # keys in the pairs. This could be an expensive operation, since those # hashes aren't retained. - class PipelinedRDD(RDD): """ Pipelined maps: diff --git a/python/pyspark/resultiterable.py b/python/pyspark/resultiterable.py new file mode 100644 index 0000000000000..7f418f8d2e29a --- /dev/null +++ b/python/pyspark/resultiterable.py @@ -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. +# + +__all__ = ["ResultIterable"] + +import collections + +class ResultIterable(collections.Iterable): + """ + A special result iterable. This is used because the standard iterator can not be pickled + """ + def __init__(self, data): + self.data = data + self.index = 0 + self.maxindex = len(data) + def __iter__(self): + return iter(self.data) + def __len__(self): + return len(self.data) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala index ac451d1913aaa..2ac943d7bf781 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala @@ -17,7 +17,7 @@ package org.apache.spark.streaming.api.java -import java.lang.{Long => JLong} +import java.lang.{Long => JLong, Iterable => JIterable} import java.util.{List => JList} import scala.collection.JavaConversions._ @@ -115,15 +115,15 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * Return a new DStream by applying `groupByKey` to each RDD. Hash partitioning is used to * generate the RDDs with Spark's default number of partitions. */ - def groupByKey(): JavaPairDStream[K, JList[V]] = - dstream.groupByKey().mapValues(seqAsJavaList _) + def groupByKey(): JavaPairDStream[K, JIterable[V]] = + dstream.groupByKey().mapValues(asJavaIterable _) /** * Return a new DStream by applying `groupByKey` to each RDD. Hash partitioning is used to * generate the RDDs with `numPartitions` partitions. */ - def groupByKey(numPartitions: Int): JavaPairDStream[K, JList[V]] = - dstream.groupByKey(numPartitions).mapValues(seqAsJavaList _) + def groupByKey(numPartitions: Int): JavaPairDStream[K, JIterable[V]] = + dstream.groupByKey(numPartitions).mapValues(asJavaIterable _) /** * Return a new DStream by applying `groupByKey` on each RDD of `this` DStream. @@ -131,8 +131,8 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * single sequence to generate the RDDs of the new DStream. org.apache.spark.Partitioner * is used to control the partitioning of each RDD. */ - def groupByKey(partitioner: Partitioner): JavaPairDStream[K, JList[V]] = - dstream.groupByKey(partitioner).mapValues(seqAsJavaList _) + def groupByKey(partitioner: Partitioner): JavaPairDStream[K, JIterable[V]] = + dstream.groupByKey(partitioner).mapValues(asJavaIterable _) /** * Return a new DStream by applying `reduceByKey` to each RDD. The values for each key are @@ -196,8 +196,8 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * @param windowDuration width of the window; must be a multiple of this DStream's * batching interval */ - def groupByKeyAndWindow(windowDuration: Duration): JavaPairDStream[K, JList[V]] = { - dstream.groupByKeyAndWindow(windowDuration).mapValues(seqAsJavaList _) + def groupByKeyAndWindow(windowDuration: Duration): JavaPairDStream[K, JIterable[V]] = { + dstream.groupByKeyAndWindow(windowDuration).mapValues(asJavaIterable _) } /** @@ -211,8 +211,8 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * DStream's batching interval */ def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration) - : JavaPairDStream[K, JList[V]] = { - dstream.groupByKeyAndWindow(windowDuration, slideDuration).mapValues(seqAsJavaList _) + : JavaPairDStream[K, JIterable[V]] = { + dstream.groupByKeyAndWindow(windowDuration, slideDuration).mapValues(asJavaIterable _) } /** @@ -227,9 +227,9 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * @param numPartitions Number of partitions of each RDD in the new DStream. */ def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int) - :JavaPairDStream[K, JList[V]] = { + :JavaPairDStream[K, JIterable[V]] = { dstream.groupByKeyAndWindow(windowDuration, slideDuration, numPartitions) - .mapValues(seqAsJavaList _) + .mapValues(asJavaIterable _) } /** @@ -247,9 +247,9 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner - ):JavaPairDStream[K, JList[V]] = { + ):JavaPairDStream[K, JIterable[V]] = { dstream.groupByKeyAndWindow(windowDuration, slideDuration, partitioner) - .mapValues(seqAsJavaList _) + .mapValues(asJavaIterable _) } /** @@ -518,9 +518,9 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( * Hash partitioning is used to generate the RDDs with Spark's default number * of partitions. */ - def cogroup[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (JList[V], JList[W])] = { + def cogroup[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (JIterable[V], JIterable[W])] = { implicit val cm: ClassTag[W] = fakeClassTag - dstream.cogroup(other.dstream).mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) + dstream.cogroup(other.dstream).mapValues(t => (asJavaIterable(t._1), asJavaIterable((t._2)))) } /** @@ -530,10 +530,10 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( def cogroup[W]( other: JavaPairDStream[K, W], numPartitions: Int - ): JavaPairDStream[K, (JList[V], JList[W])] = { + ): JavaPairDStream[K, (JIterable[V], JIterable[W])] = { implicit val cm: ClassTag[W] = fakeClassTag dstream.cogroup(other.dstream, numPartitions) - .mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) + .mapValues(t => (asJavaIterable(t._1), asJavaIterable((t._2)))) } /** @@ -543,10 +543,10 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( def cogroup[W]( other: JavaPairDStream[K, W], partitioner: Partitioner - ): JavaPairDStream[K, (JList[V], JList[W])] = { + ): JavaPairDStream[K, (JIterable[V], JIterable[W])] = { implicit val cm: ClassTag[W] = fakeClassTag dstream.cogroup(other.dstream, partitioner) - .mapValues(t => (seqAsJavaList(t._1), seqAsJavaList((t._2)))) + .mapValues(t => (asJavaIterable(t._1), asJavaIterable((t._2)))) } /** diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala index 2473496949360..354bc132dcdc0 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala @@ -51,7 +51,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) * Return a new DStream by applying `groupByKey` to each RDD. Hash partitioning is used to * generate the RDDs with Spark's default number of partitions. */ - def groupByKey(): DStream[(K, Seq[V])] = { + def groupByKey(): DStream[(K, Iterable[V])] = { groupByKey(defaultPartitioner()) } @@ -59,7 +59,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) * Return a new DStream by applying `groupByKey` to each RDD. Hash partitioning is used to * generate the RDDs with `numPartitions` partitions. */ - def groupByKey(numPartitions: Int): DStream[(K, Seq[V])] = { + def groupByKey(numPartitions: Int): DStream[(K, Iterable[V])] = { groupByKey(defaultPartitioner(numPartitions)) } @@ -67,12 +67,12 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) * Return a new DStream by applying `groupByKey` on each RDD. The supplied * org.apache.spark.Partitioner is used to control the partitioning of each RDD. */ - def groupByKey(partitioner: Partitioner): DStream[(K, Seq[V])] = { + def groupByKey(partitioner: Partitioner): DStream[(K, Iterable[V])] = { val createCombiner = (v: V) => ArrayBuffer[V](v) val mergeValue = (c: ArrayBuffer[V], v: V) => (c += v) val mergeCombiner = (c1: ArrayBuffer[V], c2: ArrayBuffer[V]) => (c1 ++ c2) combineByKey(createCombiner, mergeValue, mergeCombiner, partitioner) - .asInstanceOf[DStream[(K, Seq[V])]] + .asInstanceOf[DStream[(K, Iterable[V])]] } /** @@ -126,7 +126,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) * @param windowDuration width of the window; must be a multiple of this DStream's * batching interval */ - def groupByKeyAndWindow(windowDuration: Duration): DStream[(K, Seq[V])] = { + def groupByKeyAndWindow(windowDuration: Duration): DStream[(K, Iterable[V])] = { groupByKeyAndWindow(windowDuration, self.slideDuration, defaultPartitioner()) } @@ -140,7 +140,8 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) * the new DStream will generate RDDs); must be a multiple of this * DStream's batching interval */ - def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration): DStream[(K, Seq[V])] = + def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration) + : DStream[(K, Iterable[V])] = { groupByKeyAndWindow(windowDuration, slideDuration, defaultPartitioner()) } @@ -161,7 +162,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) windowDuration: Duration, slideDuration: Duration, numPartitions: Int - ): DStream[(K, Seq[V])] = { + ): DStream[(K, Iterable[V])] = { groupByKeyAndWindow(windowDuration, slideDuration, defaultPartitioner(numPartitions)) } @@ -180,14 +181,14 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) windowDuration: Duration, slideDuration: Duration, partitioner: Partitioner - ): DStream[(K, Seq[V])] = { - val createCombiner = (v: Seq[V]) => new ArrayBuffer[V] ++= v - val mergeValue = (buf: ArrayBuffer[V], v: Seq[V]) => buf ++= v + ): DStream[(K, Iterable[V])] = { + val createCombiner = (v: Iterable[V]) => new ArrayBuffer[V] ++= v + val mergeValue = (buf: ArrayBuffer[V], v: Iterable[V]) => buf ++= v val mergeCombiner = (buf1: ArrayBuffer[V], buf2: ArrayBuffer[V]) => buf1 ++= buf2 self.groupByKey(partitioner) .window(windowDuration, slideDuration) .combineByKey[ArrayBuffer[V]](createCombiner, mergeValue, mergeCombiner, partitioner) - .asInstanceOf[DStream[(K, Seq[V])]] + .asInstanceOf[DStream[(K, Iterable[V])]] } /** @@ -438,7 +439,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) * Hash partitioning is used to generate the RDDs with Spark's default number * of partitions. */ - def cogroup[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (Seq[V], Seq[W]))] = { + def cogroup[W: ClassTag](other: DStream[(K, W)]): DStream[(K, (Iterable[V], Iterable[W]))] = { cogroup(other, defaultPartitioner()) } @@ -447,7 +448,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) * Hash partitioning is used to generate the RDDs with `numPartitions` partitions. */ def cogroup[W: ClassTag](other: DStream[(K, W)], numPartitions: Int) - : DStream[(K, (Seq[V], Seq[W]))] = { + : DStream[(K, (Iterable[V], Iterable[W]))] = { cogroup(other, defaultPartitioner(numPartitions)) } @@ -458,7 +459,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)]) def cogroup[W: ClassTag]( other: DStream[(K, W)], partitioner: Partitioner - ): DStream[(K, (Seq[V], Seq[W]))] = { + ): DStream[(K, (Iterable[V], Iterable[W]))] = { self.transformWith( other, (rdd1: RDD[(K, V)], rdd2: RDD[(K, W)]) => rdd1.cogroup(rdd2, partitioner) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala index 5f7d3ba26c656..7e22268767de7 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala @@ -56,9 +56,14 @@ class StateDStream[K: ClassTag, V: ClassTag, S: ClassTag]( // first map the cogrouped tuple to tuples of required type, // and then apply the update function val updateFuncLocal = updateFunc - val finalFunc = (iterator: Iterator[(K, (Seq[V], Seq[S]))]) => { + val finalFunc = (iterator: Iterator[(K, (Iterable[V], Iterable[S]))]) => { val i = iterator.map(t => { - (t._1, t._2._1, t._2._2.headOption) + val itr = t._2._2.iterator + val headOption = itr.hasNext match { + case true => Some(itr.next()) + case false => None + } + (t._1, t._2._1.toSeq, headOption) }) updateFuncLocal(i) } @@ -90,8 +95,8 @@ class StateDStream[K: ClassTag, V: ClassTag, S: ClassTag]( // first map the grouped tuple to tuples of required type, // and then apply the update function val updateFuncLocal = updateFunc - val finalFunc = (iterator: Iterator[(K, Seq[V])]) => { - updateFuncLocal(iterator.map(tuple => (tuple._1, tuple._2, None))) + val finalFunc = (iterator: Iterator[(K, Iterable[V])]) => { + updateFuncLocal(iterator.map(tuple => (tuple._1, tuple._2.toSeq, None))) } val groupedRDD = parentRDD.groupByKey(partitioner) 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 e93bf18b6d0b9..13fa64894b773 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -23,6 +23,7 @@ import org.junit.Test; import java.io.*; import java.util.*; +import java.lang.Iterable; import com.google.common.base.Optional; import com.google.common.collect.Lists; @@ -45,6 +46,18 @@ // see http://stackoverflow.com/questions/758570/. public class JavaAPISuite extends LocalJavaStreamingContext implements Serializable { + public void equalIterator(Iterator a, Iterator b) { + while (a.hasNext() && b.hasNext()) { + Assert.assertEquals(a.next(), b.next()); + } + Assert.assertEquals(a.hasNext(), b.hasNext()); + } + + public void equalIterable(Iterable a, Iterable b) { + equalIterator(a.iterator(), b.iterator()); + } + + @SuppressWarnings("unchecked") @Test public void testCount() { @@ -1016,11 +1029,24 @@ public void testPairGroupByKey() { JavaDStream> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream pairStream = JavaPairDStream.fromJavaDStream(stream); - JavaPairDStream> grouped = pairStream.groupByKey(); + JavaPairDStream> grouped = pairStream.groupByKey(); JavaTestUtils.attachTestOutputStream(grouped); - List>>> result = JavaTestUtils.runStreams(ssc, 2, 2); - - Assert.assertEquals(expected, result); + List>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + + Assert.assertEquals(expected.size(), result.size()); + Iterator>>> resultItr = result.iterator(); + Iterator>>> expectedItr = expected.iterator(); + while (resultItr.hasNext() && expectedItr.hasNext()) { + Iterator>> resultElements = resultItr.next().iterator(); + Iterator>> expectedElements = expectedItr.next().iterator(); + while (resultElements.hasNext() && expectedElements.hasNext()) { + Tuple2> resultElement = resultElements.next(); + Tuple2> expectedElement = expectedElements.next(); + Assert.assertEquals(expectedElement._1(), resultElement._1()); + equalIterable(expectedElement._2(), resultElement._2()); + } + Assert.assertEquals(resultElements.hasNext(), expectedElements.hasNext()); + } } @SuppressWarnings("unchecked") @@ -1128,7 +1154,7 @@ public void testGroupByKeyAndWindow() { JavaDStream> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream pairStream = JavaPairDStream.fromJavaDStream(stream); - JavaPairDStream> groupWindowed = + JavaPairDStream> groupWindowed = pairStream.groupByKeyAndWindow(new Duration(2000), new Duration(1000)); JavaTestUtils.attachTestOutputStream(groupWindowed); List>>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1471,11 +1497,25 @@ public void testCoGroup() { ssc, stringStringKVStream2, 1); JavaPairDStream pairStream2 = JavaPairDStream.fromJavaDStream(stream2); - JavaPairDStream, List>> grouped = pairStream1.cogroup(pairStream2); + JavaPairDStream, Iterable>> grouped = pairStream1.cogroup(pairStream2); JavaTestUtils.attachTestOutputStream(grouped); - List, List>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); - - Assert.assertEquals(expected, result); + List, Iterable>>>> result = JavaTestUtils.runStreams(ssc, 2, 2); + + Assert.assertEquals(expected.size(), result.size()); + Iterator, Iterable>>>> resultItr = result.iterator(); + Iterator, List>>>> expectedItr = expected.iterator(); + while (resultItr.hasNext() && expectedItr.hasNext()) { + Iterator, Iterable>>> resultElements = resultItr.next().iterator(); + Iterator, List>>> expectedElements = expectedItr.next().iterator(); + while (resultElements.hasNext() && expectedElements.hasNext()) { + Tuple2, Iterable>> resultElement = resultElements.next(); + Tuple2, List>> expectedElement = expectedElements.next(); + Assert.assertEquals(expectedElement._1(), resultElement._1()); + equalIterable(expectedElement._2()._1(), resultElement._2()._1()); + equalIterable(expectedElement._2()._2(), resultElement._2()._2()); + } + Assert.assertEquals(resultElements.hasNext(), expectedElements.hasNext()); + } } @SuppressWarnings("unchecked") diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala index bb73dbf29b649..8aec27e39478a 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala @@ -117,7 +117,7 @@ class BasicOperationsSuite extends TestSuiteBase { test("groupByKey") { testOperation( Seq( Seq("a", "a", "b"), Seq("", ""), Seq() ), - (s: DStream[String]) => s.map(x => (x, 1)).groupByKey(), + (s: DStream[String]) => s.map(x => (x, 1)).groupByKey().mapValues(_.toSeq), Seq( Seq(("a", Seq(1, 1)), ("b", Seq(1))), Seq(("", Seq(1, 1))), Seq() ), true ) @@ -251,7 +251,7 @@ class BasicOperationsSuite extends TestSuiteBase { Seq( ) ) val operation = (s1: DStream[String], s2: DStream[String]) => { - s1.map(x => (x,1)).cogroup(s2.map(x => (x, "x"))) + s1.map(x => (x,1)).cogroup(s2.map(x => (x, "x"))).mapValues(x => (x._1.toSeq, x._2.toSeq)) } testOperation(inputData1, inputData2, operation, outputData, true) } From b9e0c937dfa1ca93b63d0b39d5f156b16c2fdc0a Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 8 Apr 2014 20:37:01 -0700 Subject: [PATCH 76/78] [SPARK-1434] [MLLIB] change labelParser from anonymous function to trait This is a patch to address @mateiz 's comment in https://github.com/apache/spark/pull/245 MLUtils#loadLibSVMData uses an anonymous function for the label parser. Java users won't like it. So I make a trait for LabelParser and provide two implementations: binary and multiclass. Author: Xiangrui Meng Closes #345 from mengxr/label-parser and squashes the following commits: ac44409 [Xiangrui Meng] use singleton objects for label parsers 3b1a7c6 [Xiangrui Meng] add tests for label parsers c2e571c [Xiangrui Meng] rename LabelParser.apply to LabelParser.parse use extends for singleton 11c94e0 [Xiangrui Meng] add return types 7f8eb36 [Xiangrui Meng] change labelParser from annoymous function to trait --- .../spark/mllib/util/LabelParsers.scala | 49 +++++++++++++++++++ .../org/apache/spark/mllib/util/MLUtils.scala | 28 ++--------- .../spark/mllib/util/LabelParsersSuite.scala | 41 ++++++++++++++++ .../spark/mllib/util/MLUtilsSuite.scala | 4 +- 4 files changed, 97 insertions(+), 25 deletions(-) create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/util/LabelParsers.scala create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/util/LabelParsersSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LabelParsers.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LabelParsers.scala new file mode 100644 index 0000000000000..f7966d3ebb613 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LabelParsers.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.util + +/** Trait for label parsers. */ +trait LabelParser extends Serializable { + /** Parses a string label into a double label. */ + def parse(labelString: String): Double +} + +/** + * Label parser for binary labels, which outputs 1.0 (positive) if the value is greater than 0.5, + * or 0.0 (negative) otherwise. So it works with +1/-1 labeling and +1/0 labeling. + */ +object BinaryLabelParser extends LabelParser { + /** Gets the default instance of BinaryLabelParser. */ + def getInstance(): LabelParser = this + + /** + * Parses the input label into positive (1.0) if the value is greater than 0.5, + * or negative (0.0) otherwise. + */ + override def parse(labelString: String): Double = if (labelString.toDouble > 0.5) 1.0 else 0.0 +} + +/** + * Label parser for multiclass labels, which converts the input label to double. + */ +object MulticlassLabelParser extends LabelParser { + /** Gets the default instance of MulticlassLabelParser. */ + def getInstance(): LabelParser = this + + override def parse(labelString: String): Double = labelString.toDouble +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala index cb85e433bfc73..83d1bd3fd57fe 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala @@ -38,17 +38,6 @@ object MLUtils { eps } - /** - * Multiclass label parser, which parses a string into double. - */ - val multiclassLabelParser: String => Double = _.toDouble - - /** - * Binary label parser, which outputs 1.0 (positive) if the value is greater than 0.5, - * or 0.0 (negative) otherwise. - */ - val binaryLabelParser: String => Double = label => if (label.toDouble > 0.5) 1.0 else 0.0 - /** * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint]. * The LIBSVM format is a text-based format used by LIBSVM and LIBLINEAR. @@ -69,7 +58,7 @@ object MLUtils { def loadLibSVMData( sc: SparkContext, path: String, - labelParser: String => Double, + labelParser: LabelParser, numFeatures: Int, minSplits: Int): RDD[LabeledPoint] = { val parsed = sc.textFile(path, minSplits) @@ -89,7 +78,7 @@ object MLUtils { }.reduce(math.max) } parsed.map { items => - val label = labelParser(items.head) + val label = labelParser.parse(items.head) val (indices, values) = items.tail.map { item => val indexAndValue = item.split(':') val index = indexAndValue(0).toInt - 1 @@ -107,14 +96,7 @@ object MLUtils { * with number of features determined automatically and the default number of partitions. */ def loadLibSVMData(sc: SparkContext, path: String): RDD[LabeledPoint] = - loadLibSVMData(sc, path, binaryLabelParser, -1, sc.defaultMinSplits) - - /** - * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], - * with number of features specified explicitly and the default number of partitions. - */ - def loadLibSVMData(sc: SparkContext, path: String, numFeatures: Int): RDD[LabeledPoint] = - loadLibSVMData(sc, path, binaryLabelParser, numFeatures, sc.defaultMinSplits) + loadLibSVMData(sc, path, BinaryLabelParser, -1, sc.defaultMinSplits) /** * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], @@ -124,7 +106,7 @@ object MLUtils { def loadLibSVMData( sc: SparkContext, path: String, - labelParser: String => Double): RDD[LabeledPoint] = + labelParser: LabelParser): RDD[LabeledPoint] = loadLibSVMData(sc, path, labelParser, -1, sc.defaultMinSplits) /** @@ -135,7 +117,7 @@ object MLUtils { def loadLibSVMData( sc: SparkContext, path: String, - labelParser: String => Double, + labelParser: LabelParser, numFeatures: Int): RDD[LabeledPoint] = loadLibSVMData(sc, path, labelParser, numFeatures, sc.defaultMinSplits) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/LabelParsersSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/LabelParsersSuite.scala new file mode 100644 index 0000000000000..ac85677f2f014 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/LabelParsersSuite.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.mllib.util + +import org.scalatest.FunSuite + +class LabelParsersSuite extends FunSuite { + test("binary label parser") { + for (parser <- Seq(BinaryLabelParser, BinaryLabelParser.getInstance())) { + assert(parser.parse("+1") === 1.0) + assert(parser.parse("1") === 1.0) + assert(parser.parse("0") === 0.0) + assert(parser.parse("-1") === 0.0) + } + } + + test("multiclass label parser") { + for (parser <- Seq(MulticlassLabelParser, MulticlassLabelParser.getInstance())) { + assert(parser.parse("0") == 0.0) + assert(parser.parse("+1") === 1.0) + assert(parser.parse("1") === 1.0) + assert(parser.parse("2") === 2.0) + assert(parser.parse("3") === 3.0) + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala index 27d41c7869aa0..e451c350b8d88 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala @@ -80,7 +80,7 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext { Files.write(lines, file, Charsets.US_ASCII) val path = tempDir.toURI.toString - val pointsWithNumFeatures = MLUtils.loadLibSVMData(sc, path, 6).collect() + val pointsWithNumFeatures = MLUtils.loadLibSVMData(sc, path, BinaryLabelParser, 6).collect() val pointsWithoutNumFeatures = MLUtils.loadLibSVMData(sc, path).collect() for (points <- Seq(pointsWithNumFeatures, pointsWithoutNumFeatures)) { @@ -93,7 +93,7 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext { assert(points(2).features === Vectors.sparse(6, Seq((1, 4.0), (3, 5.0), (5, 6.0)))) } - val multiclassPoints = MLUtils.loadLibSVMData(sc, path, MLUtils.multiclassLabelParser).collect() + val multiclassPoints = MLUtils.loadLibSVMData(sc, path, MulticlassLabelParser).collect() assert(multiclassPoints.length === 3) assert(multiclassPoints(0).label === 1.0) assert(multiclassPoints(1).label === -1.0) From fa0524fd02eedd0bbf1edc750dc3997a86ea25f5 Mon Sep 17 00:00:00 2001 From: Holden Karau Date: Tue, 8 Apr 2014 22:29:21 -0700 Subject: [PATCH 77/78] Spark-939: allow user jars to take precedence over spark jars I still need to do a small bit of re-factoring [mostly the one Java file I'll switch it back to a Scala file and use it in both the close loaders], but comments on other things I should do would be great. Author: Holden Karau Closes #217 from holdenk/spark-939-allow-user-jars-to-take-precedence-over-spark-jars and squashes the following commits: cf0cac9 [Holden Karau] Fix the executorclassloader 1955232 [Holden Karau] Fix long line in TestUtils 8f89965 [Holden Karau] Fix tests for new class name 7546549 [Holden Karau] CR feedback, merge some of the testutils methods down, rename the classloader 644719f [Holden Karau] User the class generator for the repl class loader tests too f0b7114 [Holden Karau] Fix the core/src/test/scala/org/apache/spark/executor/ExecutorURLClassLoaderSuite.scala tests 204b199 [Holden Karau] Fix the generated classes 9f68f10 [Holden Karau] Start rewriting the ExecutorURLClassLoaderSuite to not use the hard coded classes 858aba2 [Holden Karau] Remove a bunch of test junk 261aaee [Holden Karau] simplify executorurlclassloader a bit 7a7bf5f [Holden Karau] CR feedback d4ae848 [Holden Karau] rewrite component into scala aa95083 [Holden Karau] CR feedback 7752594 [Holden Karau] re-add https comment a0ef85a [Holden Karau] Fix style issues 125ea7f [Holden Karau] Easier to just remove those files, we don't need them bb8d179 [Holden Karau] Fix issues with the repl class loader 241b03d [Holden Karau] fix my rat excludes a343350 [Holden Karau] Update rat-excludes and remove a useless file d90d217 [Holden Karau] Fix fall back with custom class loader and add a test for it 4919bf9 [Holden Karau] Fix parent calling class loader issue 8a67302 [Holden Karau] Test are good 9e2d236 [Holden Karau] It works comrade 691ee00 [Holden Karau] It works ish dc4fe44 [Holden Karau] Does not depend on being in my home directory 47046ff [Holden Karau] Remove bad import' 22d83cb [Holden Karau] Add a test suite for the executor url class loader suite 7ef4628 [Holden Karau] Clean up 792d961 [Holden Karau] Almost works 16aecd1 [Holden Karau] Doesn't quite work 8d2241e [Holden Karau] Adda FakeClass for testing ClassLoader precedence options 648b559 [Holden Karau] Both class loaders compile. Now for testing e1d9f71 [Holden Karau] One loader workers. --- .rat-excludes | 2 +- .../scala/org/apache/spark/TestUtils.scala | 20 +++-- .../org/apache/spark/executor/Executor.scala | 17 +++-- .../executor/ExecutorURLClassLoader.scala | 45 ++++++++++- .../apache/spark/util/ParentClassLoader.scala | 32 ++++++++ .../ExecutorURLClassLoaderSuite.scala | 67 ++++++++++++++++ docs/configuration.md | 9 +++ project/SparkBuild.scala | 1 + .../spark/repl/ExecutorClassLoader.scala | 39 +++++++--- .../spark/repl/ExecutorClassLoaderSuite.scala | 76 +++++++++++++++++++ 10 files changed, 287 insertions(+), 21 deletions(-) rename core/src/{test => main}/scala/org/apache/spark/TestUtils.scala (84%) create mode 100644 core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala create mode 100644 core/src/test/scala/org/apache/spark/executor/ExecutorURLClassLoaderSuite.scala create mode 100644 repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala diff --git a/.rat-excludes b/.rat-excludes index 85bfad60fcadc..a2b5665a0be26 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -39,4 +39,4 @@ work .*\.q golden test.out/* -.*iml +.*iml \ No newline at end of file diff --git a/core/src/test/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala similarity index 84% rename from core/src/test/scala/org/apache/spark/TestUtils.scala rename to core/src/main/scala/org/apache/spark/TestUtils.scala index 1611d09652d40..4597595a838e3 100644 --- a/core/src/test/scala/org/apache/spark/TestUtils.scala +++ b/core/src/main/scala/org/apache/spark/TestUtils.scala @@ -26,7 +26,14 @@ import scala.collection.JavaConversions._ import javax.tools.{JavaFileObject, SimpleJavaFileObject, ToolProvider} import com.google.common.io.Files -object TestUtils { +/** + * Utilities for tests. Included in main codebase since it's used by multiple + * projects. + * + * TODO: See if we can move this to the test codebase by specifying + * test dependencies between projects. + */ +private[spark] object TestUtils { /** * Create a jar that defines classes with the given names. @@ -34,13 +41,14 @@ object TestUtils { * Note: if this is used during class loader tests, class names should be unique * in order to avoid interference between tests. */ - def createJarWithClasses(classNames: Seq[String]): URL = { + def createJarWithClasses(classNames: Seq[String], value: String = ""): URL = { val tempDir = Files.createTempDir() - val files = for (name <- classNames) yield createCompiledClass(name, tempDir) + val files = for (name <- classNames) yield createCompiledClass(name, tempDir, value) val jarFile = new File(tempDir, "testJar-%s.jar".format(System.currentTimeMillis())) createJar(files, jarFile) } + /** * Create a jar file that contains this set of files. All files will be located at the root * of the jar. @@ -80,9 +88,11 @@ object TestUtils { } /** Creates a compiled class with the given name. Class file will be placed in destDir. */ - def createCompiledClass(className: String, destDir: File): File = { + def createCompiledClass(className: String, destDir: File, value: String = ""): File = { val compiler = ToolProvider.getSystemJavaCompiler - val sourceFile = new JavaSourceFromString(className, s"public class $className {}") + val sourceFile = new JavaSourceFromString(className, + "public class " + className + " { @Override public String toString() { " + + "return \"" + value + "\";}}") // Calling this outputs a class file in pwd. It's easier to just rename the file than // build a custom FileManager that controls the output location. 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 aecb069e4202b..c12bd922d40e4 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -291,15 +291,19 @@ private[spark] class Executor( * Create a ClassLoader for use in tasks, adding any JARs specified by the user or any classes * created by the interpreter to the search path */ - private def createClassLoader(): ExecutorURLClassLoader = { - val loader = Thread.currentThread().getContextClassLoader + private def createClassLoader(): MutableURLClassLoader = { + val loader = this.getClass.getClassLoader // For each of the jars in the jarSet, add them to the class loader. // We assume each of the files has already been fetched. val urls = currentJars.keySet.map { uri => new File(uri.split("/").last).toURI.toURL }.toArray - new ExecutorURLClassLoader(urls, loader) + val userClassPathFirst = conf.getBoolean("spark.files.userClassPathFirst", false) + userClassPathFirst match { + case true => new ChildExecutorURLClassLoader(urls, loader) + case false => new ExecutorURLClassLoader(urls, loader) + } } /** @@ -310,11 +314,14 @@ private[spark] class Executor( val classUri = conf.get("spark.repl.class.uri", null) if (classUri != null) { logInfo("Using REPL class URI: " + classUri) + val userClassPathFirst: java.lang.Boolean = + conf.getBoolean("spark.files.userClassPathFirst", false) try { val klass = Class.forName("org.apache.spark.repl.ExecutorClassLoader") .asInstanceOf[Class[_ <: ClassLoader]] - val constructor = klass.getConstructor(classOf[String], classOf[ClassLoader]) - constructor.newInstance(classUri, parent) + val constructor = klass.getConstructor(classOf[String], classOf[ClassLoader], + classOf[Boolean]) + constructor.newInstance(classUri, parent, userClassPathFirst) } catch { case _: ClassNotFoundException => logError("Could not find org.apache.spark.repl.ExecutorClassLoader on classpath!") diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorURLClassLoader.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorURLClassLoader.scala index f9bfe8ed2f5ba..208e77073fd03 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorURLClassLoader.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorURLClassLoader.scala @@ -19,13 +19,56 @@ package org.apache.spark.executor import java.net.{URLClassLoader, URL} +import org.apache.spark.util.ParentClassLoader + /** * The addURL method in URLClassLoader is protected. We subclass it to make this accessible. + * We also make changes so user classes can come before the default classes. */ + +private[spark] trait MutableURLClassLoader extends ClassLoader { + def addURL(url: URL) + def getURLs: Array[URL] +} + +private[spark] class ChildExecutorURLClassLoader(urls: Array[URL], parent: ClassLoader) + extends MutableURLClassLoader { + + private object userClassLoader extends URLClassLoader(urls, null){ + override def addURL(url: URL) { + super.addURL(url) + } + override def findClass(name: String): Class[_] = { + super.findClass(name) + } + } + + private val parentClassLoader = new ParentClassLoader(parent) + + override def findClass(name: String): Class[_] = { + try { + userClassLoader.findClass(name) + } catch { + case e: ClassNotFoundException => { + parentClassLoader.loadClass(name) + } + } + } + + def addURL(url: URL) { + userClassLoader.addURL(url) + } + + def getURLs() = { + userClassLoader.getURLs() + } +} + private[spark] class ExecutorURLClassLoader(urls: Array[URL], parent: ClassLoader) - extends URLClassLoader(urls, parent) { + extends URLClassLoader(urls, parent) with MutableURLClassLoader { override def addURL(url: URL) { super.addURL(url) } } + diff --git a/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala b/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala new file mode 100644 index 0000000000000..3abc12681fe9a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/ParentClassLoader.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.util + +/** + * A class loader which makes findClass accesible to the child + */ +private[spark] class ParentClassLoader(parent: ClassLoader) extends ClassLoader(parent) { + + override def findClass(name: String) = { + super.findClass(name) + } + + override def loadClass(name: String): Class[_] = { + super.loadClass(name) + } +} diff --git a/core/src/test/scala/org/apache/spark/executor/ExecutorURLClassLoaderSuite.scala b/core/src/test/scala/org/apache/spark/executor/ExecutorURLClassLoaderSuite.scala new file mode 100644 index 0000000000000..c40cfc0696fce --- /dev/null +++ b/core/src/test/scala/org/apache/spark/executor/ExecutorURLClassLoaderSuite.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.executor + +import java.io.File +import java.net.URLClassLoader + +import org.scalatest.FunSuite + +import org.apache.spark.TestUtils + +class ExecutorURLClassLoaderSuite extends FunSuite { + + val childClassNames = List("FakeClass1", "FakeClass2") + val parentClassNames = List("FakeClass1", "FakeClass2", "FakeClass3") + val urls = List(TestUtils.createJarWithClasses(childClassNames, "1")).toArray + val urls2 = List(TestUtils.createJarWithClasses(parentClassNames, "2")).toArray + + test("child first") { + val parentLoader = new URLClassLoader(urls2, null) + val classLoader = new ChildExecutorURLClassLoader(urls, parentLoader) + val fakeClass = classLoader.loadClass("FakeClass2").newInstance() + val fakeClassVersion = fakeClass.toString + assert(fakeClassVersion === "1") + } + + test("parent first") { + val parentLoader = new URLClassLoader(urls2, null) + val classLoader = new ExecutorURLClassLoader(urls, parentLoader) + val fakeClass = classLoader.loadClass("FakeClass1").newInstance() + val fakeClassVersion = fakeClass.toString + assert(fakeClassVersion === "2") + } + + test("child first can fall back") { + val parentLoader = new URLClassLoader(urls2, null) + val classLoader = new ChildExecutorURLClassLoader(urls, parentLoader) + val fakeClass = classLoader.loadClass("FakeClass3").newInstance() + val fakeClassVersion = fakeClass.toString + assert(fakeClassVersion === "2") + } + + test("child first can fail") { + val parentLoader = new URLClassLoader(urls2, null) + val classLoader = new ChildExecutorURLClassLoader(urls, parentLoader) + intercept[java.lang.ClassNotFoundException] { + classLoader.loadClass("FakeClassDoesNotExist").newInstance() + } + } + + +} diff --git a/docs/configuration.md b/docs/configuration.md index 57bda20edcdf1..9c602402f0635 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -596,6 +596,15 @@ Apart from these, the following properties are also available, and may be useful the driver. + + spark.files.userClassPathFirst + false + + (Experimental) Whether to give user-added jars precedence over Spark's own jars when + loading classes in Executors. This feature can be used to mitigate conflicts between + Spark's dependencies and user dependencies. It is currently an experimental feature. + + spark.authenticate false diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 08667aac2cd2d..694f90a83ab67 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -195,6 +195,7 @@ object SparkBuild extends Build { concurrentRestrictions in Global += Tags.limit(Tags.Test, 1), resolvers ++= Seq( + // HTTPS is unavailable for Maven Central "Maven Repository" at "http://repo.maven.apache.org/maven2", "Apache Repository" at "https://repository.apache.org/content/repositories/releases", "JBoss Repository" at "https://repository.jboss.org/nexus/content/repositories/releases/", diff --git a/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala b/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala index bf73800388ebf..a30dcfdcecf27 100644 --- a/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala +++ b/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala @@ -26,21 +26,23 @@ import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.spark.SparkEnv import org.apache.spark.util.Utils - +import org.apache.spark.util.ParentClassLoader import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm._ import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._ - /** * A ClassLoader that reads classes from a Hadoop FileSystem or HTTP URI, - * used to load classes defined by the interpreter when the REPL is used - */ -class ExecutorClassLoader(classUri: String, parent: ClassLoader) -extends ClassLoader(parent) { + * used to load classes defined by the interpreter when the REPL is used. + * Allows the user to specify if user class path should be first + */ +class ExecutorClassLoader(classUri: String, parent: ClassLoader, + userClassPathFirst: Boolean) extends ClassLoader { val uri = new URI(classUri) val directory = uri.getPath + val parentLoader = new ParentClassLoader(parent) + // Hadoop FileSystem object for our URI, if it isn't using HTTP var fileSystem: FileSystem = { if (uri.getScheme() == "http") { @@ -49,8 +51,27 @@ extends ClassLoader(parent) { FileSystem.get(uri, new Configuration()) } } - + override def findClass(name: String): Class[_] = { + userClassPathFirst match { + case true => findClassLocally(name).getOrElse(parentLoader.loadClass(name)) + case false => { + try { + parentLoader.loadClass(name) + } catch { + case e: ClassNotFoundException => { + val classOption = findClassLocally(name) + classOption match { + case None => throw new ClassNotFoundException(name, e) + case Some(a) => a + } + } + } + } + } + } + + def findClassLocally(name: String): Option[Class[_]] = { try { val pathInDirectory = name.replace('.', '/') + ".class" val inputStream = { @@ -68,9 +89,9 @@ extends ClassLoader(parent) { } val bytes = readAndTransformClass(name, inputStream) inputStream.close() - return defineClass(name, bytes, 0, bytes.length) + Some(defineClass(name, bytes, 0, bytes.length)) } catch { - case e: Exception => throw new ClassNotFoundException(name, e) + case e: Exception => None } } diff --git a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala new file mode 100644 index 0000000000000..336df988a1b7f --- /dev/null +++ b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala @@ -0,0 +1,76 @@ +/* + * 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.repl + +import java.io.File +import java.net.URLClassLoader + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.FunSuite + +import com.google.common.io.Files + +import org.apache.spark.TestUtils + +class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { + + val childClassNames = List("ReplFakeClass1", "ReplFakeClass2") + val parentClassNames = List("ReplFakeClass1", "ReplFakeClass2", "ReplFakeClass3") + val tempDir1 = Files.createTempDir() + val tempDir2 = Files.createTempDir() + val url1 = "file://" + tempDir1 + val urls2 = List(tempDir2.toURI.toURL).toArray + + override def beforeAll() { + childClassNames.foreach(TestUtils.createCompiledClass(_, tempDir1, "1")) + parentClassNames.foreach(TestUtils.createCompiledClass(_, tempDir2, "2")) + } + + test("child first") { + val parentLoader = new URLClassLoader(urls2, null) + val classLoader = new ExecutorClassLoader(url1, parentLoader, true) + val fakeClass = classLoader.loadClass("ReplFakeClass2").newInstance() + val fakeClassVersion = fakeClass.toString + assert(fakeClassVersion === "1") + } + + test("parent first") { + val parentLoader = new URLClassLoader(urls2, null) + val classLoader = new ExecutorClassLoader(url1, parentLoader, false) + val fakeClass = classLoader.loadClass("ReplFakeClass1").newInstance() + val fakeClassVersion = fakeClass.toString + assert(fakeClassVersion === "2") + } + + test("child first can fall back") { + val parentLoader = new URLClassLoader(urls2, null) + val classLoader = new ExecutorClassLoader(url1, parentLoader, true) + val fakeClass = classLoader.loadClass("ReplFakeClass3").newInstance() + val fakeClassVersion = fakeClass.toString + assert(fakeClassVersion === "2") + } + + test("child first can fail") { + val parentLoader = new URLClassLoader(urls2, null) + val classLoader = new ExecutorClassLoader(url1, parentLoader, true) + intercept[java.lang.ClassNotFoundException] { + classLoader.loadClass("ReplFakeClassDoesNotExist").newInstance() + } + } + +} From 9689b663a2a4947ad60795321c770052f3c637f1 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 8 Apr 2014 23:01:15 -0700 Subject: [PATCH 78/78] [SPARK-1390] Refactoring of matrices backed by RDDs This is to refactor interfaces for matrices backed by RDDs. It would be better if we have a clear separation of local matrices and those backed by RDDs. Right now, we have 1. `org.apache.spark.mllib.linalg.SparseMatrix`, which is a wrapper over an RDD of matrix entries, i.e., coordinate list format. 2. `org.apache.spark.mllib.linalg.TallSkinnyDenseMatrix`, which is a wrapper over RDD[Array[Double]], i.e. row-oriented format. We will see naming collision when we introduce local `SparseMatrix`, and the name `TallSkinnyDenseMatrix` is not exact if we switch to `RDD[Vector]` from `RDD[Array[Double]]`. It would be better to have "RDD" in the class name to suggest that operations may trigger jobs. The proposed names are (all under `org.apache.spark.mllib.linalg.rdd`): 1. `RDDMatrix`: trait for matrices backed by one or more RDDs 2. `CoordinateRDDMatrix`: wrapper of `RDD[(Long, Long, Double)]` 3. `RowRDDMatrix`: wrapper of `RDD[Vector]` whose rows do not have special ordering 4. `IndexedRowRDDMatrix`: wrapper of `RDD[(Long, Vector)]` whose rows are associated with indices The current code also introduces local matrices. Author: Xiangrui Meng Closes #296 from mengxr/mat and squashes the following commits: 24d8294 [Xiangrui Meng] fix for groupBy returning Iterable bfc2b26 [Xiangrui Meng] merge master 8e4f1f5 [Xiangrui Meng] Merge branch 'master' into mat 0135193 [Xiangrui Meng] address Reza's comments 03cd7e1 [Xiangrui Meng] add pca/gram to IndexedRowMatrix add toBreeze to DistributedMatrix for test simplify tests b177ff1 [Xiangrui Meng] address Matei's comments be119fe [Xiangrui Meng] rename m/n to numRows/numCols for local matrix add tests for matrices b881506 [Xiangrui Meng] rename SparkPCA/SVD to TallSkinnyPCA/SVD e7d0d4a [Xiangrui Meng] move IndexedRDDMatrixRow to IndexedRowRDDMatrix 0d1491c [Xiangrui Meng] fix test errors a85262a [Xiangrui Meng] rename RDDMatrixRow to IndexedRDDMatrixRow b8b6ac3 [Xiangrui Meng] Remove old code 4cf679c [Xiangrui Meng] port pca to RowRDDMatrix, and add multiply and covariance 7836e2f [Xiangrui Meng] initial refactoring of matrices backed by RDDs --- .../spark/examples/mllib/SparkPCA.scala | 51 --- .../spark/examples/mllib/SparkSVD.scala | 59 --- .../spark/examples/mllib/TallSkinnyPCA.scala | 64 +++ .../spark/examples/mllib/TallSkinnySVD.scala | 64 +++ .../apache/spark/mllib/linalg/Matrices.scala | 101 +++++ .../apache/spark/mllib/linalg/MatrixSVD.scala | 29 -- .../org/apache/spark/mllib/linalg/PCA.scala | 120 ------ .../org/apache/spark/mllib/linalg/SVD.scala | 395 ------------------ ...scala => SingularValueDecomposition.scala} | 9 +- .../mllib/linalg/TallSkinnyMatrixSVD.scala | 31 -- .../linalg/distributed/CoordinateMatrix.scala | 112 +++++ .../DistributedMatrix.scala} | 23 +- .../linalg/distributed/IndexedRowMatrix.scala | 148 +++++++ .../mllib/linalg/distributed/RowMatrix.scala | 344 +++++++++++++++ .../org/apache/spark/mllib/util/LAUtils.scala | 67 --- .../linalg/BreezeMatrixConversionSuite.scala} | 29 +- .../spark/mllib/linalg/MatricesSuite.scala} | 27 +- .../apache/spark/mllib/linalg/PCASuite.scala | 124 ------ .../apache/spark/mllib/linalg/SVDSuite.scala | 194 --------- .../distributed/CoordinateMatrixSuite.scala | 98 +++++ .../distributed/IndexedRowMatrixSuite.scala | 120 ++++++ .../linalg/distributed/RowMatrixSuite.scala | 173 ++++++++ 22 files changed, 1280 insertions(+), 1102 deletions(-) delete mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/SparkPCA.scala delete mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/SparkSVD.scala create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala delete mode 100644 mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixSVD.scala delete mode 100644 mllib/src/main/scala/org/apache/spark/mllib/linalg/PCA.scala delete mode 100644 mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala rename mllib/src/main/scala/org/apache/spark/mllib/linalg/{MatrixRow.scala => SingularValueDecomposition.scala} (81%) delete mode 100644 mllib/src/main/scala/org/apache/spark/mllib/linalg/TallSkinnyMatrixSVD.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala rename mllib/src/main/scala/org/apache/spark/mllib/linalg/{SparseMatrix.scala => distributed/DistributedMatrix.scala} (60%) create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala delete mode 100644 mllib/src/main/scala/org/apache/spark/mllib/util/LAUtils.scala rename mllib/src/{main/scala/org/apache/spark/mllib/linalg/MatrixEntry.scala => test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala} (51%) rename mllib/src/{main/scala/org/apache/spark/mllib/linalg/TallSkinnyDenseMatrix.scala => test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala} (58%) delete mode 100644 mllib/src/test/scala/org/apache/spark/mllib/linalg/PCASuite.scala delete mode 100644 mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrixSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SparkPCA.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SparkPCA.scala deleted file mode 100644 index d4e08c5e12d81..0000000000000 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/SparkPCA.scala +++ /dev/null @@ -1,51 +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.examples.mllib - -import org.apache.spark.SparkContext -import org.apache.spark.mllib.linalg.PCA -import org.apache.spark.mllib.linalg.MatrixEntry -import org.apache.spark.mllib.linalg.SparseMatrix -import org.apache.spark.mllib.util._ - - -/** - * Compute PCA of an example matrix. - */ -object SparkPCA { - def main(args: Array[String]) { - if (args.length != 3) { - System.err.println("Usage: SparkPCA m n") - System.exit(1) - } - val sc = new SparkContext(args(0), "PCA", - System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass)) - - val m = args(2).toInt - val n = args(3).toInt - - // Make example matrix - val data = Array.tabulate(m, n) { (a, b) => - (a + 2).toDouble * (b + 1) / (1 + a + b) } - - // recover top principal component - val coeffs = new PCA().setK(1).compute(sc.makeRDD(data)) - - println("top principal component = " + coeffs.mkString(", ")) - } -} diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SparkSVD.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SparkSVD.scala deleted file mode 100644 index 2933cec497b37..0000000000000 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/SparkSVD.scala +++ /dev/null @@ -1,59 +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.examples.mllib - -import org.apache.spark.SparkContext -import org.apache.spark.mllib.linalg.SVD -import org.apache.spark.mllib.linalg.MatrixEntry -import org.apache.spark.mllib.linalg.SparseMatrix - -/** - * Compute SVD of an example matrix - * Input file should be comma separated, 1 indexed of the form - * i,j,value - * Where i is the column, j the row, and value is the matrix entry - * - * For example input file, see: - * mllib/data/als/test.data (example is 4 x 4) - */ -object SparkSVD { - def main(args: Array[String]) { - if (args.length != 4) { - System.err.println("Usage: SparkSVD m n") - System.exit(1) - } - val sc = new SparkContext(args(0), "SVD", - System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass)) - - // Load and parse the data file - val data = sc.textFile(args(1)).map { line => - val parts = line.split(',') - MatrixEntry(parts(0).toInt - 1, parts(1).toInt - 1, parts(2).toDouble) - } - val m = args(2).toInt - val n = args(3).toInt - - // recover largest singular vector - val decomposed = new SVD().setK(1).compute(SparseMatrix(data, m, n)) - val u = decomposed.U.data - val s = decomposed.S.data - val v = decomposed.V.data - - println("singular values = " + s.collect().mkString) - } -} diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.scala new file mode 100644 index 0000000000000..a177435e606ab --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnyPCA.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.examples.mllib + +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.mllib.linalg.distributed.RowMatrix +import org.apache.spark.mllib.linalg.Vectors + +/** + * Compute the principal components of a tall-and-skinny matrix, whose rows are observations. + * + * The input matrix must be stored in row-oriented dense format, one line per row with its entries + * separated by space. For example, + * {{{ + * 0.5 1.0 + * 2.0 3.0 + * 4.0 5.0 + * }}} + * represents a 3-by-2 matrix, whose first row is (0.5, 1.0). + */ +object TallSkinnyPCA { + def main(args: Array[String]) { + if (args.length != 2) { + System.err.println("Usage: TallSkinnyPCA ") + System.exit(1) + } + + val conf = new SparkConf() + .setMaster(args(0)) + .setAppName("TallSkinnyPCA") + .setSparkHome(System.getenv("SPARK_HOME")) + .setJars(SparkContext.jarOfClass(this.getClass)) + val sc = new SparkContext(conf) + + // Load and parse the data file. + val rows = sc.textFile(args(1)).map { line => + val values = line.split(' ').map(_.toDouble) + Vectors.dense(values) + } + val mat = new RowMatrix(rows) + + // Compute principal components. + val pc = mat.computePrincipalComponents(mat.numCols().toInt) + + println("Principal components are:\n" + pc) + + sc.stop() + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala new file mode 100644 index 0000000000000..49d09692c8e4a --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.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.examples.mllib + +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.mllib.linalg.distributed.RowMatrix +import org.apache.spark.mllib.linalg.Vectors + +/** + * Compute the singular value decomposition (SVD) of a tall-and-skinny matrix. + * + * The input matrix must be stored in row-oriented dense format, one line per row with its entries + * separated by space. For example, + * {{{ + * 0.5 1.0 + * 2.0 3.0 + * 4.0 5.0 + * }}} + * represents a 3-by-2 matrix, whose first row is (0.5, 1.0). + */ +object TallSkinnySVD { + def main(args: Array[String]) { + if (args.length != 2) { + System.err.println("Usage: TallSkinnySVD ") + System.exit(1) + } + + val conf = new SparkConf() + .setMaster(args(0)) + .setAppName("TallSkinnySVD") + .setSparkHome(System.getenv("SPARK_HOME")) + .setJars(SparkContext.jarOfClass(this.getClass)) + val sc = new SparkContext(conf) + + // Load and parse the data file. + val rows = sc.textFile(args(1)).map { line => + val values = line.split(' ').map(_.toDouble) + Vectors.dense(values) + } + val mat = new RowMatrix(rows) + + // Compute SVD. + val svd = mat.computeSVD(mat.numCols().toInt) + + println("Singular values are " + svd.s) + + sc.stop() + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala new file mode 100644 index 0000000000000..b11ba5d30fbd3 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.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.mllib.linalg + +import breeze.linalg.{Matrix => BM, DenseMatrix => BDM} + +/** + * Trait for a local matrix. + */ +trait Matrix extends Serializable { + + /** Number of rows. */ + def numRows: Int + + /** Number of columns. */ + def numCols: Int + + /** Converts to a dense array in column major. */ + def toArray: Array[Double] + + /** Converts to a breeze matrix. */ + private[mllib] def toBreeze: BM[Double] + + /** Gets the (i, j)-th element. */ + private[mllib] def apply(i: Int, j: Int): Double = toBreeze(i, j) + + override def toString: String = toBreeze.toString() +} + +/** + * Column-majored dense matrix. + * The entry values are stored in a single array of doubles with columns listed in sequence. + * For example, the following matrix + * {{{ + * 1.0 2.0 + * 3.0 4.0 + * 5.0 6.0 + * }}} + * is stored as `[1.0, 3.0, 5.0, 2.0, 4.0, 6.0]`. + * + * @param numRows number of rows + * @param numCols number of columns + * @param values matrix entries in column major + */ +class DenseMatrix(val numRows: Int, val numCols: Int, val values: Array[Double]) extends Matrix { + + require(values.length == numRows * numCols) + + override def toArray: Array[Double] = values + + private[mllib] override def toBreeze: BM[Double] = new BDM[Double](numRows, numCols, values) +} + +/** + * Factory methods for [[org.apache.spark.mllib.linalg.Matrix]]. + */ +object Matrices { + + /** + * Creates a column-majored dense matrix. + * + * @param numRows number of rows + * @param numCols number of columns + * @param values matrix entries in column major + */ + def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix = { + new DenseMatrix(numRows, numCols, values) + } + + /** + * Creates a Matrix instance from a breeze matrix. + * @param breeze a breeze matrix + * @return a Matrix instance + */ + private[mllib] def fromBreeze(breeze: BM[Double]): Matrix = { + breeze match { + case dm: BDM[Double] => + require(dm.majorStride == dm.rows, + "Do not support stride size different from the number of rows.") + new DenseMatrix(dm.rows, dm.cols, dm.data) + case _ => + throw new UnsupportedOperationException( + s"Do not support conversion from type ${breeze.getClass.getName}.") + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixSVD.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixSVD.scala deleted file mode 100644 index 319f82b449096..0000000000000 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixSVD.scala +++ /dev/null @@ -1,29 +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.mllib.linalg - -/** - * Class that represents the SV decomposition of a matrix - * - * @param U such that A = USV^T - * @param S such that A = USV^T - * @param V such that A = USV^T - */ -case class MatrixSVD(val U: SparseMatrix, - val S: SparseMatrix, - val V: SparseMatrix) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/PCA.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/PCA.scala deleted file mode 100644 index fe5b3f6c7e463..0000000000000 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/PCA.scala +++ /dev/null @@ -1,120 +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.mllib.linalg - -import org.apache.spark.rdd.RDD - - -import org.jblas.DoubleMatrix - - -/** - * Class used to obtain principal components - */ -class PCA { - private var k = 1 - - /** - * Set the number of top-k principle components to return - */ - def setK(k: Int): PCA = { - this.k = k - this - } - - /** - * Compute PCA using the current set parameters - */ - def compute(matrix: TallSkinnyDenseMatrix): Array[Array[Double]] = { - computePCA(matrix) - } - - /** - * Compute PCA using the parameters currently set - * See computePCA() for more details - */ - def compute(matrix: RDD[Array[Double]]): Array[Array[Double]] = { - computePCA(matrix) - } - - /** - * Computes the top k principal component coefficients for the m-by-n data matrix X. - * Rows of X correspond to observations and columns correspond to variables. - * The coefficient matrix is n-by-k. Each column of coeff contains coefficients - * for one principal component, and the columns are in descending - * order of component variance. - * This function centers the data and uses the - * singular value decomposition (SVD) algorithm. - * - * @param matrix dense matrix to perform PCA on - * @return An nxk matrix with principal components in columns. Columns are inner arrays - */ - private def computePCA(matrix: TallSkinnyDenseMatrix): Array[Array[Double]] = { - val m = matrix.m - val n = matrix.n - - if (m <= 0 || n <= 0) { - throw new IllegalArgumentException("Expecting a well-formed matrix: m=$m n=$n") - } - - computePCA(matrix.rows.map(_.data)) - } - - /** - * Computes the top k principal component coefficients for the m-by-n data matrix X. - * Rows of X correspond to observations and columns correspond to variables. - * The coefficient matrix is n-by-k. Each column of coeff contains coefficients - * for one principal component, and the columns are in descending - * order of component variance. - * This function centers the data and uses the - * singular value decomposition (SVD) algorithm. - * - * @param matrix dense matrix to perform pca on - * @return An nxk matrix of principal components - */ - private def computePCA(matrix: RDD[Array[Double]]): Array[Array[Double]] = { - val n = matrix.first.size - - // compute column sums and normalize matrix - val colSumsTemp = matrix.map((_, 1)).fold((Array.ofDim[Double](n), 0)) { - (a, b) => - val am = new DoubleMatrix(a._1) - val bm = new DoubleMatrix(b._1) - am.addi(bm) - (a._1, a._2 + b._2) - } - - val m = colSumsTemp._2 - val colSums = colSumsTemp._1.map(x => x / m) - - val data = matrix.map { - x => - val row = Array.ofDim[Double](n) - var i = 0 - while (i < n) { - row(i) = x(i) - colSums(i) - i += 1 - } - row - } - - val (u, s, v) = new SVD().setK(k).compute(data) - v - } -} - diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala deleted file mode 100644 index 0d97b7d92f155..0000000000000 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SVD.scala +++ /dev/null @@ -1,395 +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.mllib.linalg - -import org.apache.spark.SparkContext -import org.apache.spark.SparkContext._ -import org.apache.spark.rdd.RDD - -import org.jblas.{DoubleMatrix, Singular, MatrixFunctions} - -/** - * Class used to obtain singular value decompositions - */ -class SVD { - private var k = 1 - private var computeU = true - - // All singular values smaller than rCond * sigma(0) - // are treated as zero, where sigma(0) is the largest singular value. - private var rCond = 1e-9 - - /** - * Set the number of top-k singular vectors to return - */ - def setK(k: Int): SVD = { - this.k = k - this - } - - /** - * Sets the reciprocal condition number (rCond). All singular values - * smaller than rCond * sigma(0) are treated as zero, - * where sigma(0) is the largest singular value. - */ - def setReciprocalConditionNumber(smallS: Double): SVD = { - this.rCond = smallS - this - } - - /** - * Should U be computed? - */ - def setComputeU(compU: Boolean): SVD = { - this.computeU = compU - this - } - - /** - * Compute SVD using the current set parameters - */ - def compute(matrix: TallSkinnyDenseMatrix): TallSkinnyMatrixSVD = { - denseSVD(matrix) - } - - /** - * Compute SVD using the current set parameters - * Returns (U, S, V) such that A = USV^T - * U is a row-by-row dense matrix - * S is a simple double array of singular values - * V is a 2d array matrix - * See [[denseSVD]] for more documentation - */ - def compute(matrix: RDD[Array[Double]]): - (RDD[Array[Double]], Array[Double], Array[Array[Double]]) = { - denseSVD(matrix) - } - - /** - * See full paramter definition of sparseSVD for more description. - * - * @param matrix sparse matrix to factorize - * @return Three sparse matrices: U, S, V such that A = USV^T - */ - def compute(matrix: SparseMatrix): MatrixSVD = { - sparseSVD(matrix) - } - - /** - * Singular Value Decomposition for Tall and Skinny matrices. - * Given an m x n matrix A, this will compute matrices U, S, V such that - * A = U * S * V' - * - * There is no restriction on m, but we require n^2 doubles to fit in memory. - * Further, n should be less than m. - * - * The decomposition is computed by first computing A'A = V S^2 V', - * computing svd locally on that (since n x n is small), - * from which we recover S and V. - * Then we compute U via easy matrix multiplication - * as U = A * V * S^-1 - * - * Only the k largest singular values and associated vectors are found. - * If there are k such values, then the dimensions of the return will be: - * - * S is k x k and diagonal, holding the singular values on diagonal - * U is m x k and satisfies U'U = eye(k) - * V is n x k and satisfies V'V = eye(k) - * - * @param matrix dense matrix to factorize - * @return See [[TallSkinnyMatrixSVD]] for the output matrices and arrays - */ - private def denseSVD(matrix: TallSkinnyDenseMatrix): TallSkinnyMatrixSVD = { - val m = matrix.m - val n = matrix.n - - if (m < n || m <= 0 || n <= 0) { - throw new IllegalArgumentException("Expecting a tall and skinny matrix m=$m n=$n") - } - - if (k < 1 || k > n) { - throw new IllegalArgumentException("Request up to n singular values n=$n k=$k") - } - - val rowIndices = matrix.rows.map(_.i) - - // compute SVD - val (u, sigma, v) = denseSVD(matrix.rows.map(_.data)) - - if (computeU) { - // prep u for returning - val retU = TallSkinnyDenseMatrix( - u.zip(rowIndices).map { - case (row, i) => MatrixRow(i, row) - }, - m, - k) - - TallSkinnyMatrixSVD(retU, sigma, v) - } else { - TallSkinnyMatrixSVD(null, sigma, v) - } - } - - /** - * Singular Value Decomposition for Tall and Skinny matrices. - * Given an m x n matrix A, this will compute matrices U, S, V such that - * A = U * S * V' - * - * There is no restriction on m, but we require n^2 doubles to fit in memory. - * Further, n should be less than m. - * - * The decomposition is computed by first computing A'A = V S^2 V', - * computing svd locally on that (since n x n is small), - * from which we recover S and V. - * Then we compute U via easy matrix multiplication - * as U = A * V * S^-1 - * - * Only the k largest singular values and associated vectors are found. - * If there are k such values, then the dimensions of the return will be: - * - * S is k x k and diagonal, holding the singular values on diagonal - * U is m x k and satisfies U'U = eye(k) - * V is n x k and satisfies V'V = eye(k) - * - * The return values are as lean as possible: an RDD of rows for U, - * a simple array for sigma, and a dense 2d matrix array for V - * - * @param matrix dense matrix to factorize - * @return Three matrices: U, S, V such that A = USV^T - */ - private def denseSVD(matrix: RDD[Array[Double]]): - (RDD[Array[Double]], Array[Double], Array[Array[Double]]) = { - val n = matrix.first.size - - if (k < 1 || k > n) { - throw new IllegalArgumentException( - "Request up to n singular values k=$k n=$n") - } - - // Compute A^T A - val fullata = matrix.mapPartitions { - iter => - val localATA = Array.ofDim[Double](n, n) - while (iter.hasNext) { - val row = iter.next() - var i = 0 - while (i < n) { - var j = 0 - while (j < n) { - localATA(i)(j) += row(i) * row(j) - j += 1 - } - i += 1 - } - } - Iterator(localATA) - }.fold(Array.ofDim[Double](n, n)) { - (a, b) => - var i = 0 - while (i < n) { - var j = 0 - while (j < n) { - a(i)(j) += b(i)(j) - j += 1 - } - i += 1 - } - a - } - - // Construct jblas A^T A locally - val ata = new DoubleMatrix(fullata) - - // Since A^T A is small, we can compute its SVD directly - val svd = Singular.sparseSVD(ata) - val V = svd(0) - val sigmas = MatrixFunctions.sqrt(svd(1)).toArray.filter(x => x / svd(1).get(0) > rCond) - - val sk = Math.min(k, sigmas.size) - val sigma = sigmas.take(sk) - - // prepare V for returning - val retV = Array.tabulate(n, sk)((i, j) => V.get(i, j)) - - if (computeU) { - // Compute U as U = A V S^-1 - // Compute VS^-1 - val vsinv = new DoubleMatrix(Array.tabulate(n, sk)((i, j) => V.get(i, j) / sigma(j))) - val retU = matrix.map { - x => - val v = new DoubleMatrix(Array(x)) - v.mmul(vsinv).data - } - (retU, sigma, retV) - } else { - (null, sigma, retV) - } - } - - /** - * Singular Value Decomposition for Tall and Skinny sparse matrices. - * Given an m x n matrix A, this will compute matrices U, S, V such that - * A = U * S * V' - * - * There is no restriction on m, but we require O(n^2) doubles to fit in memory. - * Further, n should be less than m. - * - * The decomposition is computed by first computing A'A = V S^2 V', - * computing svd locally on that (since n x n is small), - * from which we recover S and V. - * Then we compute U via easy matrix multiplication - * as U = A * V * S^-1 - * - * Only the k largest singular values and associated vectors are found. - * If there are k such values, then the dimensions of the return will be: - * - * S is k x k and diagonal, holding the singular values on diagonal - * U is m x k and satisfies U'U = eye(k) - * V is n x k and satisfies V'V = eye(k) - * - * All input and output is expected in sparse matrix format, 0-indexed - * as tuples of the form ((i,j),value) all in RDDs using the - * SparseMatrix class - * - * @param matrix sparse matrix to factorize - * @return Three sparse matrices: U, S, V such that A = USV^T - */ - private def sparseSVD(matrix: SparseMatrix): MatrixSVD = { - val data = matrix.data - val m = matrix.m - val n = matrix.n - - if (m < n || m <= 0 || n <= 0) { - throw new IllegalArgumentException("Expecting a tall and skinny matrix") - } - - if (k < 1 || k > n) { - throw new IllegalArgumentException("Must request up to n singular values") - } - - // Compute A^T A, assuming rows are sparse enough to fit in memory - val rows = data.map(entry => - (entry.i, (entry.j, entry.mval))).groupByKey() - val emits = rows.flatMap { - case (rowind, cols) => - cols.flatMap { - case (colind1, mval1) => - cols.map { - case (colind2, mval2) => - ((colind1, colind2), mval1 * mval2) - } - } - }.reduceByKey(_ + _) - - // Construct jblas A^T A locally - val ata = DoubleMatrix.zeros(n, n) - for (entry <- emits.collect()) { - ata.put(entry._1._1, entry._1._2, entry._2) - } - - // Since A^T A is small, we can compute its SVD directly - val svd = Singular.sparseSVD(ata) - val V = svd(0) - // This will be updated to rcond - val sigmas = MatrixFunctions.sqrt(svd(1)).toArray.filter(x => x > 1e-9) - - if (sigmas.size < k) { - throw new Exception("Not enough singular values to return k=" + k + " s=" + sigmas.size) - } - - val sigma = sigmas.take(k) - - val sc = data.sparkContext - - // prepare V for returning - val retVdata = sc.makeRDD( - Array.tabulate(V.rows, sigma.length) { - (i, j) => - MatrixEntry(i, j, V.get(i, j)) - }.flatten) - val retV = SparseMatrix(retVdata, V.rows, sigma.length) - - val retSdata = sc.makeRDD(Array.tabulate(sigma.length) { - x => MatrixEntry(x, x, sigma(x)) - }) - - val retS = SparseMatrix(retSdata, sigma.length, sigma.length) - - // Compute U as U = A V S^-1 - // turn V S^-1 into an RDD as a sparse matrix - val vsirdd = sc.makeRDD(Array.tabulate(V.rows, sigma.length) { - (i, j) => ((i, j), V.get(i, j) / sigma(j)) - }.flatten) - - if (computeU) { - // Multiply A by VS^-1 - val aCols = data.map(entry => (entry.j, (entry.i, entry.mval))) - val bRows = vsirdd.map(entry => (entry._1._1, (entry._1._2, entry._2))) - val retUdata = aCols.join(bRows).map { - case (key, ((rowInd, rowVal), (colInd, colVal))) => - ((rowInd, colInd), rowVal * colVal) - }.reduceByKey(_ + _).map { - case ((row, col), mval) => MatrixEntry(row, col, mval) - } - - val retU = SparseMatrix(retUdata, m, sigma.length) - MatrixSVD(retU, retS, retV) - } else { - MatrixSVD(null, retS, retV) - } - } -} - -/** - * Top-level methods for calling sparse Singular Value Decomposition - * NOTE: All matrices are 0-indexed - */ -object SVD { - def main(args: Array[String]) { - if (args.length < 8) { - println("Usage: SVD " + - " ") - System.exit(1) - } - - val (master, inputFile, m, n, k, output_u, output_s, output_v) = - (args(0), args(1), args(2).toInt, args(3).toInt, - args(4).toInt, args(5), args(6), args(7)) - - val sc = new SparkContext(master, "SVD") - - val rawData = sc.textFile(inputFile) - val data = rawData.map { - line => - val parts = line.split(',') - MatrixEntry(parts(0).toInt, parts(1).toInt, parts(2).toDouble) - } - - val decomposed = new SVD().setK(k).compute(SparseMatrix(data, m, n)) - val u = decomposed.U.data - val s = decomposed.S.data - val v = decomposed.V.data - - println("Computed " + s.collect().length + " singular values and vectors") - u.saveAsTextFile(output_u) - s.saveAsTextFile(output_s) - v.saveAsTextFile(output_v) - System.exit(0) - } -} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixRow.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala similarity index 81% rename from mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixRow.scala rename to mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala index 2608a67bfe260..46b105457430c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixRow.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala @@ -17,10 +17,5 @@ package org.apache.spark.mllib.linalg -/** - * Class that represents a row of a dense matrix - * - * @param i row index (0 indexing used) - * @param data entries of the row - */ -case class MatrixRow(val i: Int, val data: Array[Double]) +/** Represents singular value decomposition (SVD) factors. */ +case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/TallSkinnyMatrixSVD.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/TallSkinnyMatrixSVD.scala deleted file mode 100644 index b3a450e92394e..0000000000000 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/TallSkinnyMatrixSVD.scala +++ /dev/null @@ -1,31 +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.mllib.linalg - -/** - * Class that represents the singular value decomposition of a matrix - * - * @param U such that A = USV^T is a TallSkinnyDenseMatrix - * @param S such that A = USV^T is a simple double array - * @param V such that A = USV^T, V is a 2d array matrix that holds - * singular vectors in columns. Columns are inner arrays - * i.e. V(i)(j) is standard math notation V_{ij} - */ -case class TallSkinnyMatrixSVD(val U: TallSkinnyDenseMatrix, - val S: Array[Double], - val V: Array[Array[Double]]) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala new file mode 100644 index 0000000000000..9194f657494b2 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala @@ -0,0 +1,112 @@ +/* + * 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.linalg.distributed + +import breeze.linalg.{DenseMatrix => BDM} + +import org.apache.spark.rdd.RDD +import org.apache.spark.SparkContext._ +import org.apache.spark.mllib.linalg.Vectors + +/** + * Represents an entry in an distributed matrix. + * @param i row index + * @param j column index + * @param value value of the entry + */ +case class MatrixEntry(i: Long, j: Long, value: Double) + +/** + * Represents a matrix in coordinate format. + * + * @param entries matrix entries + * @param nRows number of rows. A non-positive value means unknown, and then the number of rows will + * be determined by the max row index plus one. + * @param nCols number of columns. A non-positive value means unknown, and then the number of + * columns will be determined by the max column index plus one. + */ +class CoordinateMatrix( + val entries: RDD[MatrixEntry], + private var nRows: Long, + private var nCols: Long) extends DistributedMatrix { + + /** Alternative constructor leaving matrix dimensions to be determined automatically. */ + def this(entries: RDD[MatrixEntry]) = this(entries, 0L, 0L) + + /** Gets or computes the number of columns. */ + override def numCols(): Long = { + if (nCols <= 0L) { + computeSize() + } + nCols + } + + /** Gets or computes the number of rows. */ + override def numRows(): Long = { + if (nRows <= 0L) { + computeSize() + } + nRows + } + + /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */ + def toIndexedRowMatrix(): IndexedRowMatrix = { + val nl = numCols() + if (nl > Int.MaxValue) { + sys.error(s"Cannot convert to a row-oriented format because the number of columns $nl is " + + "too large.") + } + val n = nl.toInt + val indexedRows = entries.map(entry => (entry.i, (entry.j.toInt, entry.value))) + .groupByKey() + .map { case (i, vectorEntries) => + IndexedRow(i, Vectors.sparse(n, vectorEntries.toSeq)) + } + new IndexedRowMatrix(indexedRows, numRows(), n) + } + + /** + * Converts to RowMatrix, dropping row indices after grouping by row index. + * The number of columns must be within the integer range. + */ + def toRowMatrix(): RowMatrix = { + toIndexedRowMatrix().toRowMatrix() + } + + /** Determines the size by computing the max row/column index. */ + private def computeSize() { + // Reduce will throw an exception if `entries` is empty. + val (m1, n1) = entries.map(entry => (entry.i, entry.j)).reduce { case ((i1, j1), (i2, j2)) => + (math.max(i1, i2), math.max(j1, j2)) + } + // There may be empty columns at the very right and empty rows at the very bottom. + nRows = math.max(nRows, m1 + 1L) + nCols = math.max(nCols, n1 + 1L) + } + + /** Collects data and assembles a local matrix. */ + private[mllib] override def toBreeze(): BDM[Double] = { + val m = numRows().toInt + val n = numCols().toInt + val mat = BDM.zeros[Double](m, n) + entries.collect().foreach { case MatrixEntry(i, j, value) => + mat(i.toInt, j.toInt) = value + } + mat + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SparseMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala similarity index 60% rename from mllib/src/main/scala/org/apache/spark/mllib/linalg/SparseMatrix.scala rename to mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala index cbd1a2a5a4bd8..13f72a3c724ef 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SparseMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala @@ -15,16 +15,23 @@ * limitations under the License. */ -package org.apache.spark.mllib.linalg +package org.apache.spark.mllib.linalg.distributed -import org.apache.spark.rdd.RDD +import breeze.linalg.{DenseMatrix => BDM} +import org.apache.spark.mllib.linalg.Matrix /** - * Class that represents a sparse matrix - * - * @param data RDD of nonzero entries - * @param m number of rows - * @param n numner of columns + * Represents a distributively stored matrix backed by one or more RDDs. */ -case class SparseMatrix(val data: RDD[MatrixEntry], val m: Int, val n: Int) +trait DistributedMatrix extends Serializable { + + /** Gets or computes the number of rows. */ + def numRows(): Long + + /** Gets or computes the number of columns. */ + def numCols(): Long + + /** Collects data and assembles a local dense breeze matrix (for test only). */ + private[mllib] def toBreeze(): BDM[Double] +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala new file mode 100644 index 0000000000000..e110f070bd7c1 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.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.mllib.linalg.distributed + +import breeze.linalg.{DenseMatrix => BDM} + +import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg._ +import org.apache.spark.mllib.linalg.SingularValueDecomposition + +/** Represents a row of [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]]. */ +case class IndexedRow(index: Long, vector: Vector) + +/** + * Represents a row-oriented [[org.apache.spark.mllib.linalg.distributed.DistributedMatrix]] with + * indexed rows. + * + * @param rows indexed rows of this matrix + * @param nRows number of rows. A non-positive value means unknown, and then the number of rows will + * be determined by the max row index plus one. + * @param nCols number of columns. A non-positive value means unknown, and then the number of + * columns will be determined by the size of the first row. + */ +class IndexedRowMatrix( + val rows: RDD[IndexedRow], + private var nRows: Long, + private var nCols: Int) extends DistributedMatrix { + + /** Alternative constructor leaving matrix dimensions to be determined automatically. */ + def this(rows: RDD[IndexedRow]) = this(rows, 0L, 0) + + override def numCols(): Long = { + if (nCols <= 0) { + // Calling `first` will throw an exception if `rows` is empty. + nCols = rows.first().vector.size + } + nCols + } + + override def numRows(): Long = { + if (nRows <= 0L) { + // Reduce will throw an exception if `rows` is empty. + nRows = rows.map(_.index).reduce(math.max) + 1L + } + nRows + } + + /** + * Drops row indices and converts this matrix to a + * [[org.apache.spark.mllib.linalg.distributed.RowMatrix]]. + */ + def toRowMatrix(): RowMatrix = { + new RowMatrix(rows.map(_.vector), 0L, nCols) + } + + /** + * Computes the singular value decomposition of this matrix. + * Denote this matrix by A (m x n), this will compute matrices U, S, V such that A = U * S * V'. + * + * There is no restriction on m, but we require `n^2` doubles to fit in memory. + * Further, n should be less than m. + + * The decomposition is computed by first computing A'A = V S^2 V', + * computing svd locally on that (since n x n is small), from which we recover S and V. + * Then we compute U via easy matrix multiplication as U = A * (V * S^-1). + * Note that this approach requires `O(n^3)` time on the master node. + * + * At most k largest non-zero singular values and associated vectors are returned. + * If there are k such values, then the dimensions of the return will be: + * + * U is an [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]] of size m x k that + * satisfies U'U = eye(k), + * s is a Vector of size k, holding the singular values in descending order, + * and V is a local Matrix of size n x k that satisfies V'V = eye(k). + * + * @param k number of singular values to keep. We might return less than k if there are + * numerically zero singular values. See rCond. + * @param computeU whether to compute U + * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0) + * are treated as zero, where sigma(0) is the largest singular value. + * @return SingularValueDecomposition(U, s, V) + */ + def computeSVD( + k: Int, + computeU: Boolean = false, + rCond: Double = 1e-9): SingularValueDecomposition[IndexedRowMatrix, Matrix] = { + val indices = rows.map(_.index) + val svd = toRowMatrix().computeSVD(k, computeU, rCond) + val U = if (computeU) { + val indexedRows = indices.zip(svd.U.rows).map { case (i, v) => + IndexedRow(i, v) + } + new IndexedRowMatrix(indexedRows, nRows, nCols) + } else { + null + } + SingularValueDecomposition(U, svd.s, svd.V) + } + + /** + * Multiply this matrix by a local matrix on the right. + * + * @param B a local matrix whose number of rows must match the number of columns of this matrix + * @return an IndexedRowMatrix representing the product, which preserves partitioning + */ + def multiply(B: Matrix): IndexedRowMatrix = { + val mat = toRowMatrix().multiply(B) + val indexedRows = rows.map(_.index).zip(mat.rows).map { case (i, v) => + IndexedRow(i, v) + } + new IndexedRowMatrix(indexedRows, nRows, nCols) + } + + /** + * Computes the Gramian matrix `A^T A`. + */ + def computeGramianMatrix(): Matrix = { + toRowMatrix().computeGramianMatrix() + } + + private[mllib] override def toBreeze(): BDM[Double] = { + val m = numRows().toInt + val n = numCols().toInt + val mat = BDM.zeros[Double](m, n) + rows.collect().foreach { case IndexedRow(rowIndex, vector) => + val i = rowIndex.toInt + vector.toBreeze.activeIterator.foreach { case (j, v) => + mat(i, j) = v + } + } + mat + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala new file mode 100644 index 0000000000000..f59811f18a68f --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala @@ -0,0 +1,344 @@ +/* + * 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.linalg.distributed + +import java.util + +import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, svd => brzSvd} +import breeze.numerics.{sqrt => brzSqrt} +import com.github.fommil.netlib.BLAS.{getInstance => blas} + +import org.apache.spark.mllib.linalg._ +import org.apache.spark.rdd.RDD +import org.apache.spark.Logging + +/** + * Represents a row-oriented distributed Matrix with no meaningful row indices. + * + * @param rows rows stored as an RDD[Vector] + * @param nRows number of rows. A non-positive value means unknown, and then the number of rows will + * be determined by the number of records in the RDD `rows`. + * @param nCols number of columns. A non-positive value means unknown, and then the number of + * columns will be determined by the size of the first row. + */ +class RowMatrix( + val rows: RDD[Vector], + private var nRows: Long, + private var nCols: Int) extends DistributedMatrix with Logging { + + /** Alternative constructor leaving matrix dimensions to be determined automatically. */ + def this(rows: RDD[Vector]) = this(rows, 0L, 0) + + /** Gets or computes the number of columns. */ + override def numCols(): Long = { + if (nCols <= 0) { + // Calling `first` will throw an exception if `rows` is empty. + nCols = rows.first().size + } + nCols + } + + /** Gets or computes the number of rows. */ + override def numRows(): Long = { + if (nRows <= 0L) { + nRows = rows.count() + if (nRows == 0L) { + sys.error("Cannot determine the number of rows because it is not specified in the " + + "constructor and the rows RDD is empty.") + } + } + nRows + } + + /** + * Computes the Gramian matrix `A^T A`. + */ + def computeGramianMatrix(): Matrix = { + val n = numCols().toInt + val nt: Int = n * (n + 1) / 2 + + // Compute the upper triangular part of the gram matrix. + val GU = rows.aggregate(new BDV[Double](new Array[Double](nt)))( + seqOp = (U, v) => { + RowMatrix.dspr(1.0, v, U.data) + U + }, + combOp = (U1, U2) => U1 += U2 + ) + + RowMatrix.triuToFull(n, GU.data) + } + + /** + * Computes the singular value decomposition of this matrix. + * Denote this matrix by A (m x n), this will compute matrices U, S, V such that A = U * S * V'. + * + * There is no restriction on m, but we require `n^2` doubles to fit in memory. + * Further, n should be less than m. + + * The decomposition is computed by first computing A'A = V S^2 V', + * computing svd locally on that (since n x n is small), from which we recover S and V. + * Then we compute U via easy matrix multiplication as U = A * (V * S^-1). + * Note that this approach requires `O(n^3)` time on the master node. + * + * At most k largest non-zero singular values and associated vectors are returned. + * If there are k such values, then the dimensions of the return will be: + * + * U is a RowMatrix of size m x k that satisfies U'U = eye(k), + * s is a Vector of size k, holding the singular values in descending order, + * and V is a Matrix of size n x k that satisfies V'V = eye(k). + * + * @param k number of singular values to keep. We might return less than k if there are + * numerically zero singular values. See rCond. + * @param computeU whether to compute U + * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0) + * are treated as zero, where sigma(0) is the largest singular value. + * @return SingularValueDecomposition(U, s, V) + */ + def computeSVD( + k: Int, + computeU: Boolean = false, + rCond: Double = 1e-9): SingularValueDecomposition[RowMatrix, Matrix] = { + val n = numCols().toInt + require(k > 0 && k <= n, s"Request up to n singular values k=$k n=$n.") + + val G = computeGramianMatrix() + + // TODO: Use sparse SVD instead. + val (u: BDM[Double], sigmaSquares: BDV[Double], v: BDM[Double]) = + brzSvd(G.toBreeze.asInstanceOf[BDM[Double]]) + val sigmas: BDV[Double] = brzSqrt(sigmaSquares) + + // Determine effective rank. + val sigma0 = sigmas(0) + val threshold = rCond * sigma0 + var i = 0 + while (i < k && sigmas(i) >= threshold) { + i += 1 + } + val sk = i + + if (sk < k) { + logWarning(s"Requested $k singular values but only found $sk nonzeros.") + } + + val s = Vectors.dense(util.Arrays.copyOfRange(sigmas.data, 0, sk)) + val V = Matrices.dense(n, sk, util.Arrays.copyOfRange(u.data, 0, n * sk)) + + if (computeU) { + // N = Vk * Sk^{-1} + val N = new BDM[Double](n, sk, util.Arrays.copyOfRange(u.data, 0, n * sk)) + var i = 0 + var j = 0 + while (j < sk) { + i = 0 + val sigma = sigmas(j) + while (i < n) { + N(i, j) /= sigma + i += 1 + } + j += 1 + } + val U = this.multiply(Matrices.fromBreeze(N)) + SingularValueDecomposition(U, s, V) + } else { + SingularValueDecomposition(null, s, V) + } + } + + /** + * Computes the covariance matrix, treating each row as an observation. + * @return a local dense matrix of size n x n + */ + def computeCovariance(): Matrix = { + val n = numCols().toInt + + if (n > 10000) { + val mem = n * n * java.lang.Double.SIZE / java.lang.Byte.SIZE + logWarning(s"The number of columns $n is greater than 10000! " + + s"We need at least $mem bytes of memory.") + } + + val (m, mean) = rows.aggregate[(Long, BDV[Double])]((0L, BDV.zeros[Double](n)))( + seqOp = (s: (Long, BDV[Double]), v: Vector) => (s._1 + 1L, s._2 += v.toBreeze), + combOp = (s1: (Long, BDV[Double]), s2: (Long, BDV[Double])) => (s1._1 + s2._1, s1._2 += s2._2) + ) + + // Update _m if it is not set, or verify its value. + if (nRows <= 0L) { + nRows = m + } else { + require(nRows == m, + s"The number of rows $m is different from what specified or previously computed: ${nRows}.") + } + + mean :/= m.toDouble + + // We use the formula Cov(X, Y) = E[X * Y] - E[X] E[Y], which is not accurate if E[X * Y] is + // large but Cov(X, Y) is small, but it is good for sparse computation. + // TODO: find a fast and stable way for sparse data. + + val G = computeGramianMatrix().toBreeze.asInstanceOf[BDM[Double]] + + var i = 0 + var j = 0 + val m1 = m - 1.0 + var alpha = 0.0 + while (i < n) { + alpha = m / m1 * mean(i) + j = 0 + while (j < n) { + G(i, j) = G(i, j) / m1 - alpha * mean(j) + j += 1 + } + i += 1 + } + + Matrices.fromBreeze(G) + } + + /** + * Computes the top k principal components. + * Rows correspond to observations and columns correspond to variables. + * The principal components are stored a local matrix of size n-by-k. + * Each column corresponds for one principal component, + * and the columns are in descending order of component variance. + * + * @param k number of top principal components. + * @return a matrix of size n-by-k, whose columns are principal components + */ + def computePrincipalComponents(k: Int): Matrix = { + val n = numCols().toInt + require(k > 0 && k <= n, s"k = $k out of range (0, n = $n]") + + val Cov = computeCovariance().toBreeze.asInstanceOf[BDM[Double]] + + val (u: BDM[Double], _, _) = brzSvd(Cov) + + if (k == n) { + Matrices.dense(n, k, u.data) + } else { + Matrices.dense(n, k, util.Arrays.copyOfRange(u.data, 0, n * k)) + } + } + + /** + * Multiply this matrix by a local matrix on the right. + * + * @param B a local matrix whose number of rows must match the number of columns of this matrix + * @return a [[org.apache.spark.mllib.linalg.distributed.RowMatrix]] representing the product, + * which preserves partitioning + */ + def multiply(B: Matrix): RowMatrix = { + val n = numCols().toInt + require(n == B.numRows, s"Dimension mismatch: $n vs ${B.numRows}") + + require(B.isInstanceOf[DenseMatrix], + s"Only support dense matrix at this time but found ${B.getClass.getName}.") + + val Bb = rows.context.broadcast(B) + val AB = rows.mapPartitions({ iter => + val Bi = Bb.value.toBreeze.asInstanceOf[BDM[Double]] + iter.map(v => Vectors.fromBreeze(Bi.t * v.toBreeze)) + }, preservesPartitioning = true) + + new RowMatrix(AB, nRows, B.numCols) + } + + private[mllib] override def toBreeze(): BDM[Double] = { + val m = numRows().toInt + val n = numCols().toInt + val mat = BDM.zeros[Double](m, n) + var i = 0 + rows.collect().foreach { v => + v.toBreeze.activeIterator.foreach { case (j, v) => + mat(i, j) = v + } + i += 1 + } + mat + } +} + +object RowMatrix { + + /** + * Adds alpha * x * x.t to a matrix in-place. This is the same as BLAS's DSPR. + * + * @param U the upper triangular part of the matrix packed in an array (column major) + */ + private def dspr(alpha: Double, v: Vector, U: Array[Double]): Unit = { + // TODO: Find a better home (breeze?) for this method. + val n = v.size + v match { + case dv: DenseVector => + blas.dspr("U", n, 1.0, dv.values, 1, U) + case sv: SparseVector => + val indices = sv.indices + val values = sv.values + val nnz = indices.length + var colStartIdx = 0 + var prevCol = 0 + var col = 0 + var j = 0 + var i = 0 + var av = 0.0 + while (j < nnz) { + col = indices(j) + // Skip empty columns. + colStartIdx += (col - prevCol) * (col + prevCol + 1) / 2 + col = indices(j) + av = alpha * values(j) + i = 0 + while (i <= j) { + U(colStartIdx + indices(i)) += av * values(i) + i += 1 + } + j += 1 + prevCol = col + } + } + } + + /** + * Fills a full square matrix from its upper triangular part. + */ + private def triuToFull(n: Int, U: Array[Double]): Matrix = { + val G = new BDM[Double](n, n) + + var row = 0 + var col = 0 + var idx = 0 + var value = 0.0 + while (col < n) { + row = 0 + while (row < col) { + value = U(idx) + G(row, col) = value + G(col, row) = value + idx += 1 + row += 1 + } + G(col, col) = U(idx) + idx += 1 + col +=1 + } + + Matrices.dense(n, n, G.data) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LAUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LAUtils.scala deleted file mode 100644 index 87aac347579c7..0000000000000 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/LAUtils.scala +++ /dev/null @@ -1,67 +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.mllib.util - -import org.apache.spark.SparkContext._ - -import org.apache.spark.mllib.linalg._ - -/** - * Helper methods for linear algebra - */ -object LAUtils { - /** - * Convert a SparseMatrix into a TallSkinnyDenseMatrix - * - * @param sp Sparse matrix to be converted - * @return dense version of the input - */ - def sparseToTallSkinnyDense(sp: SparseMatrix): TallSkinnyDenseMatrix = { - val m = sp.m - val n = sp.n - val rows = sp.data.map(x => (x.i, (x.j, x.mval))).groupByKey().map { - case (i, cols) => - val rowArray = Array.ofDim[Double](n) - var j = 0 - val colsItr = cols.iterator - while (colsItr.hasNext) { - val element = colsItr.next - rowArray(element._1) = element._2 - j += 1 - } - MatrixRow(i, rowArray) - } - TallSkinnyDenseMatrix(rows, m, n) - } - - /** - * Convert a TallSkinnyDenseMatrix to a SparseMatrix - * - * @param a matrix to be converted - * @return sparse version of the input - */ - def denseToSparse(a: TallSkinnyDenseMatrix): SparseMatrix = { - val m = a.m - val n = a.n - val data = a.rows.flatMap { - mrow => Array.tabulate(n)(j => MatrixEntry(mrow.i, j, mrow.data(j))) - .filter(x => x.mval != 0) - } - SparseMatrix(data, m, n) - } -} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixEntry.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala similarity index 51% rename from mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixEntry.scala rename to mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala index 416996fcbe760..82d49c76ed02b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/MatrixEntry.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala @@ -17,11 +17,24 @@ package org.apache.spark.mllib.linalg -/** - * Class that represents an entry in a sparse matrix of doubles. - * - * @param i row index (0 indexing used) - * @param j column index (0 indexing used) - * @param mval value of entry in matrix - */ -case class MatrixEntry(val i: Int, val j: Int, val mval: Double) +import org.scalatest.FunSuite + +import breeze.linalg.{DenseMatrix => BDM} + +class BreezeMatrixConversionSuite extends FunSuite { + test("dense matrix to breeze") { + val mat = Matrices.dense(3, 2, Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0)) + val breeze = mat.toBreeze.asInstanceOf[BDM[Double]] + assert(breeze.rows === mat.numRows) + assert(breeze.cols === mat.numCols) + assert(breeze.data.eq(mat.asInstanceOf[DenseMatrix].values), "should not copy data") + } + + test("dense breeze matrix to matrix") { + val breeze = new BDM[Double](3, 2, Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0)) + val mat = Matrices.fromBreeze(breeze).asInstanceOf[DenseMatrix] + assert(mat.numRows === breeze.rows) + assert(mat.numCols === breeze.cols) + assert(mat.values.eq(breeze.data), "should not copy data") + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/TallSkinnyDenseMatrix.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala similarity index 58% rename from mllib/src/main/scala/org/apache/spark/mllib/linalg/TallSkinnyDenseMatrix.scala rename to mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala index e4ef3c58e8680..9c66b4db9f16b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/TallSkinnyDenseMatrix.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala @@ -17,14 +17,23 @@ package org.apache.spark.mllib.linalg -import org.apache.spark.rdd.RDD +import org.scalatest.FunSuite +class MatricesSuite extends FunSuite { + test("dense matrix construction") { + val m = 3 + val n = 2 + val values = Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0) + val mat = Matrices.dense(m, n, values).asInstanceOf[DenseMatrix] + assert(mat.numRows === m) + assert(mat.numCols === n) + assert(mat.values.eq(values), "should not copy data") + assert(mat.toArray.eq(values), "toArray should not copy data") + } -/** - * Class that represents a dense matrix - * - * @param rows RDD of rows - * @param m number of rows - * @param n number of columns - */ -case class TallSkinnyDenseMatrix(val rows: RDD[MatrixRow], val m: Int, val n: Int) + test("dense matrix construction with wrong dimension") { + intercept[RuntimeException] { + Matrices.dense(3, 2, Array(0.0, 1.0, 2.0)) + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/PCASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/PCASuite.scala deleted file mode 100644 index 5e5086b1bf73e..0000000000000 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/PCASuite.scala +++ /dev/null @@ -1,124 +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.mllib.linalg - -import scala.util.Random - -import org.scalatest.BeforeAndAfterAll -import org.scalatest.FunSuite - -import org.apache.spark.SparkContext -import org.apache.spark.SparkContext._ -import org.apache.spark.rdd.RDD - -import org.apache.spark.mllib.util._ - -import org.jblas._ - -class PCASuite extends FunSuite with BeforeAndAfterAll { - @transient private var sc: SparkContext = _ - - override def beforeAll() { - sc = new SparkContext("local", "test") - } - - override def afterAll() { - sc.stop() - System.clearProperty("spark.driver.port") - } - - val EPSILON = 1e-3 - - // Return jblas matrix from sparse matrix RDD - def getDenseMatrix(matrix: SparseMatrix) : DoubleMatrix = { - val data = matrix.data - val ret = DoubleMatrix.zeros(matrix.m, matrix.n) - matrix.data.collect().map(x => ret.put(x.i, x.j, x.mval)) - ret - } - - def assertMatrixApproximatelyEquals(a: DoubleMatrix, b: DoubleMatrix) { - assert(a.rows == b.rows && a.columns == b.columns, - "dimension mismatch: $a.rows vs $b.rows and $a.columns vs $b.columns") - for (i <- 0 until a.columns) { - val aCol = a.getColumn(i) - val bCol = b.getColumn(i) - val diff = Math.min(aCol.sub(bCol).norm1, aCol.add(bCol).norm1) - assert(diff < EPSILON, "matrix mismatch: " + diff) - } - } - - test("full rank matrix pca") { - val m = 5 - val n = 3 - val dataArr = Array.tabulate(m,n){ (a, b) => - MatrixEntry(a, b, Math.sin(a + b + a * b)) }.flatten - val data = sc.makeRDD(dataArr, 3) - val a = LAUtils.sparseToTallSkinnyDense(SparseMatrix(data, m, n)) - - val realPCAArray = Array((0,0,-0.2579), (0,1,-0.6602), (0,2,0.7054), - (1,0,-0.1448), (1,1,0.7483), (1,2,0.6474), - (2,0,0.9553), (2,1,-0.0649), (2,2,0.2886)) - val realPCA = sc.makeRDD(realPCAArray.map(x => MatrixEntry(x._1, x._2, x._3)), 3) - - val coeffs = new DoubleMatrix(new PCA().setK(n).compute(a)) - - assertMatrixApproximatelyEquals(getDenseMatrix(SparseMatrix(realPCA,n,n)), coeffs) - } - - test("sparse matrix full rank matrix pca") { - val m = 5 - val n = 3 - // the entry that gets dropped is zero to test sparse support - val dataArr = Array.tabulate(m,n){ (a, b) => - MatrixEntry(a, b, Math.sin(a + b + a * b)) }.flatten.drop(1) - val data = sc.makeRDD(dataArr, 3) - val a = LAUtils.sparseToTallSkinnyDense(SparseMatrix(data, m, n)) - - val realPCAArray = Array((0,0,-0.2579), (0,1,-0.6602), (0,2,0.7054), - (1,0,-0.1448), (1,1,0.7483), (1,2,0.6474), - (2,0,0.9553), (2,1,-0.0649), (2,2,0.2886)) - val realPCA = sc.makeRDD(realPCAArray.map(x => MatrixEntry(x._1, x._2, x._3))) - - val coeffs = new DoubleMatrix(new PCA().setK(n).compute(a)) - - assertMatrixApproximatelyEquals(getDenseMatrix(SparseMatrix(realPCA,n,n)), coeffs) - } - - test("truncated matrix pca") { - val m = 5 - val n = 3 - val dataArr = Array.tabulate(m,n){ (a, b) => - MatrixEntry(a, b, Math.sin(a + b + a * b)) }.flatten - - val data = sc.makeRDD(dataArr, 3) - val a = LAUtils.sparseToTallSkinnyDense(SparseMatrix(data, m, n)) - - val realPCAArray = Array((0,0,-0.2579), (0,1,-0.6602), - (1,0,-0.1448), (1,1,0.7483), - (2,0,0.9553), (2,1,-0.0649)) - val realPCA = sc.makeRDD(realPCAArray.map(x => MatrixEntry(x._1, x._2, x._3))) - - val k = 2 - val coeffs = new DoubleMatrix(new PCA().setK(k).compute(a)) - - assertMatrixApproximatelyEquals(getDenseMatrix(SparseMatrix(realPCA,n,k)), coeffs) - } -} - - diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala deleted file mode 100644 index 20e2b0f84be06..0000000000000 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/SVDSuite.scala +++ /dev/null @@ -1,194 +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.mllib.linalg - -import scala.util.Random - -import org.scalatest.BeforeAndAfterAll -import org.scalatest.FunSuite - -import org.jblas.{DoubleMatrix, Singular, MatrixFunctions} - -import org.apache.spark.SparkContext -import org.apache.spark.SparkContext._ -import org.apache.spark.rdd.RDD - -import org.apache.spark.mllib.util._ - -import org.jblas._ - -class SVDSuite extends FunSuite with BeforeAndAfterAll { - @transient private var sc: SparkContext = _ - - override def beforeAll() { - sc = new SparkContext("local", "test") - } - - override def afterAll() { - sc.stop() - System.clearProperty("spark.driver.port") - } - - val EPSILON = 1e-4 - - // Return jblas matrix from sparse matrix RDD - def getDenseMatrix(matrix: SparseMatrix) : DoubleMatrix = { - val data = matrix.data - val m = matrix.m - val n = matrix.n - val ret = DoubleMatrix.zeros(m, n) - matrix.data.collect().map(x => ret.put(x.i, x.j, x.mval)) - ret - } - - def assertMatrixApproximatelyEquals(a: DoubleMatrix, b: DoubleMatrix) { - assert(a.rows == b.rows && a.columns == b.columns, - "dimension mismatch: $a.rows vs $b.rows and $a.columns vs $b.columns") - for (i <- 0 until a.columns) { - val aCol = a.getColumn(i) - val bCol = b.getColumn(i) - val diff = Math.min(aCol.sub(bCol).norm1, aCol.add(bCol).norm1) - assert(diff < EPSILON, "matrix mismatch: " + diff) - } - } - - test("full rank matrix svd") { - val m = 10 - val n = 3 - val datarr = Array.tabulate(m,n){ (a, b) => - MatrixEntry(a, b, (a + 2).toDouble * (b + 1) / (1 + a + b)) }.flatten - val data = sc.makeRDD(datarr, 3) - - val a = SparseMatrix(data, m, n) - - val decomposed = new SVD().setK(n).compute(a) - val u = decomposed.U - val v = decomposed.V - val s = decomposed.S - - val denseA = getDenseMatrix(a) - val svd = Singular.sparseSVD(denseA) - - val retu = getDenseMatrix(u) - val rets = getDenseMatrix(s) - val retv = getDenseMatrix(v) - - - // check individual decomposition - assertMatrixApproximatelyEquals(retu, svd(0)) - assertMatrixApproximatelyEquals(rets, DoubleMatrix.diag(svd(1))) - assertMatrixApproximatelyEquals(retv, svd(2)) - - // check multiplication guarantee - assertMatrixApproximatelyEquals(retu.mmul(rets).mmul(retv.transpose), denseA) - } - - test("dense full rank matrix svd") { - val m = 10 - val n = 3 - val datarr = Array.tabulate(m,n){ (a, b) => - MatrixEntry(a, b, (a + 2).toDouble * (b + 1) / (1 + a + b)) }.flatten - val data = sc.makeRDD(datarr, 3) - - val a = LAUtils.sparseToTallSkinnyDense(SparseMatrix(data, m, n)) - - val decomposed = new SVD().setK(n).setComputeU(true).compute(a) - val u = LAUtils.denseToSparse(decomposed.U) - val v = decomposed.V - val s = decomposed.S - - val denseA = getDenseMatrix(LAUtils.denseToSparse(a)) - val svd = Singular.sparseSVD(denseA) - - val retu = getDenseMatrix(u) - val rets = DoubleMatrix.diag(new DoubleMatrix(s)) - val retv = new DoubleMatrix(v) - - - // check individual decomposition - assertMatrixApproximatelyEquals(retu, svd(0)) - assertMatrixApproximatelyEquals(rets, DoubleMatrix.diag(svd(1))) - assertMatrixApproximatelyEquals(retv, svd(2)) - - // check multiplication guarantee - assertMatrixApproximatelyEquals(retu.mmul(rets).mmul(retv.transpose), denseA) - } - - test("rank one matrix svd") { - val m = 10 - val n = 3 - val data = sc.makeRDD(Array.tabulate(m, n){ (a,b) => - MatrixEntry(a, b, 1.0) }.flatten ) - val k = 1 - - val a = SparseMatrix(data, m, n) - - val decomposed = new SVD().setK(k).compute(a) - val u = decomposed.U - val s = decomposed.S - val v = decomposed.V - val retrank = s.data.collect().length - - assert(retrank == 1, "rank returned not one") - - val denseA = getDenseMatrix(a) - val svd = Singular.sparseSVD(denseA) - - val retu = getDenseMatrix(u) - val rets = getDenseMatrix(s) - val retv = getDenseMatrix(v) - - // check individual decomposition - assertMatrixApproximatelyEquals(retu, svd(0).getColumn(0)) - assertMatrixApproximatelyEquals(rets, DoubleMatrix.diag(svd(1).getRow(0))) - assertMatrixApproximatelyEquals(retv, svd(2).getColumn(0)) - - // check multiplication guarantee - assertMatrixApproximatelyEquals(retu.mmul(rets).mmul(retv.transpose), denseA) - } - - test("truncated with k") { - val m = 10 - val n = 3 - val data = sc.makeRDD(Array.tabulate(m,n){ (a, b) => - MatrixEntry(a, b, (a + 2).toDouble * (b + 1)/(1 + a + b)) }.flatten ) - val a = SparseMatrix(data, m, n) - - val k = 1 // only one svalue above this - - val decomposed = new SVD().setK(k).compute(a) - val u = decomposed.U - val s = decomposed.S - val v = decomposed.V - val retrank = s.data.collect().length - - val denseA = getDenseMatrix(a) - val svd = Singular.sparseSVD(denseA) - - val retu = getDenseMatrix(u) - val rets = getDenseMatrix(s) - val retv = getDenseMatrix(v) - - assert(retrank == 1, "rank returned not one") - - // check individual decomposition - assertMatrixApproximatelyEquals(retu, svd(0).getColumn(0)) - assertMatrixApproximatelyEquals(rets, DoubleMatrix.diag(svd(1).getRow(0))) - assertMatrixApproximatelyEquals(retv, svd(2).getColumn(0)) - } -} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrixSuite.scala new file mode 100644 index 0000000000000..cd45438fb628f --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrixSuite.scala @@ -0,0 +1,98 @@ +/* + * 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.linalg.distributed + +import org.scalatest.FunSuite + +import breeze.linalg.{DenseMatrix => BDM} + +import org.apache.spark.mllib.util.LocalSparkContext +import org.apache.spark.mllib.linalg.Vectors + +class CoordinateMatrixSuite extends FunSuite with LocalSparkContext { + + val m = 5 + val n = 4 + var mat: CoordinateMatrix = _ + + override def beforeAll() { + super.beforeAll() + val entries = sc.parallelize(Seq( + (0, 0, 1.0), + (0, 1, 2.0), + (1, 1, 3.0), + (1, 2, 4.0), + (2, 2, 5.0), + (2, 3, 6.0), + (3, 0, 7.0), + (3, 3, 8.0), + (4, 1, 9.0)), 3).map { case (i, j, value) => + MatrixEntry(i, j, value) + } + mat = new CoordinateMatrix(entries) + } + + test("size") { + assert(mat.numRows() === m) + assert(mat.numCols() === n) + } + + test("empty entries") { + val entries = sc.parallelize(Seq[MatrixEntry](), 1) + val emptyMat = new CoordinateMatrix(entries) + intercept[RuntimeException] { + emptyMat.numCols() + } + intercept[RuntimeException] { + emptyMat.numRows() + } + } + + test("toBreeze") { + val expected = BDM( + (1.0, 2.0, 0.0, 0.0), + (0.0, 3.0, 4.0, 0.0), + (0.0, 0.0, 5.0, 6.0), + (7.0, 0.0, 0.0, 8.0), + (0.0, 9.0, 0.0, 0.0)) + assert(mat.toBreeze() === expected) + } + + test("toIndexedRowMatrix") { + val indexedRowMatrix = mat.toIndexedRowMatrix() + val expected = BDM( + (1.0, 2.0, 0.0, 0.0), + (0.0, 3.0, 4.0, 0.0), + (0.0, 0.0, 5.0, 6.0), + (7.0, 0.0, 0.0, 8.0), + (0.0, 9.0, 0.0, 0.0)) + assert(indexedRowMatrix.toBreeze() === expected) + } + + test("toRowMatrix") { + val rowMatrix = mat.toRowMatrix() + val rows = rowMatrix.rows.collect().toSet + val expected = Set( + Vectors.dense(1.0, 2.0, 0.0, 0.0), + Vectors.dense(0.0, 3.0, 4.0, 0.0), + Vectors.dense(0.0, 0.0, 5.0, 6.0), + Vectors.dense(7.0, 0.0, 0.0, 8.0), + Vectors.dense(0.0, 9.0, 0.0, 0.0)) + assert(rows === expected) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala new file mode 100644 index 0000000000000..f7c46f23b746d --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrixSuite.scala @@ -0,0 +1,120 @@ +/* + * 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.linalg.distributed + +import org.scalatest.FunSuite + +import breeze.linalg.{diag => brzDiag, DenseMatrix => BDM, DenseVector => BDV} + +import org.apache.spark.mllib.util.LocalSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.mllib.linalg.{Matrices, Vectors} + +class IndexedRowMatrixSuite extends FunSuite with LocalSparkContext { + + val m = 4 + val n = 3 + val data = Seq( + (0L, Vectors.dense(0.0, 1.0, 2.0)), + (1L, Vectors.dense(3.0, 4.0, 5.0)), + (3L, Vectors.dense(9.0, 0.0, 1.0)) + ).map(x => IndexedRow(x._1, x._2)) + var indexedRows: RDD[IndexedRow] = _ + + override def beforeAll() { + super.beforeAll() + indexedRows = sc.parallelize(data, 2) + } + + test("size") { + val mat1 = new IndexedRowMatrix(indexedRows) + assert(mat1.numRows() === m) + assert(mat1.numCols() === n) + + val mat2 = new IndexedRowMatrix(indexedRows, 5, 0) + assert(mat2.numRows() === 5) + assert(mat2.numCols() === n) + } + + test("empty rows") { + val rows = sc.parallelize(Seq[IndexedRow](), 1) + val mat = new IndexedRowMatrix(rows) + intercept[RuntimeException] { + mat.numRows() + } + intercept[RuntimeException] { + mat.numCols() + } + } + + test("toBreeze") { + val mat = new IndexedRowMatrix(indexedRows) + val expected = BDM( + (0.0, 1.0, 2.0), + (3.0, 4.0, 5.0), + (0.0, 0.0, 0.0), + (9.0, 0.0, 1.0)) + assert(mat.toBreeze() === expected) + } + + test("toRowMatrix") { + val idxRowMat = new IndexedRowMatrix(indexedRows) + val rowMat = idxRowMat.toRowMatrix() + assert(rowMat.numCols() === n) + assert(rowMat.numRows() === 3, "should drop empty rows") + assert(rowMat.rows.collect().toSeq === data.map(_.vector).toSeq) + } + + test("multiply a local matrix") { + val A = new IndexedRowMatrix(indexedRows) + val B = Matrices.dense(3, 2, Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0)) + val C = A.multiply(B) + val localA = A.toBreeze() + val localC = C.toBreeze() + val expected = localA * B.toBreeze.asInstanceOf[BDM[Double]] + assert(localC === expected) + } + + test("gram") { + val A = new IndexedRowMatrix(indexedRows) + val G = A.computeGramianMatrix() + val expected = BDM( + (90.0, 12.0, 24.0), + (12.0, 17.0, 22.0), + (24.0, 22.0, 30.0)) + assert(G.toBreeze === expected) + } + + test("svd") { + val A = new IndexedRowMatrix(indexedRows) + val svd = A.computeSVD(n, computeU = true) + assert(svd.U.isInstanceOf[IndexedRowMatrix]) + val localA = A.toBreeze() + val U = svd.U.toBreeze() + val s = svd.s.toBreeze.asInstanceOf[BDV[Double]] + val V = svd.V.toBreeze.asInstanceOf[BDM[Double]] + assert(closeToZero(U.t * U - BDM.eye[Double](n))) + assert(closeToZero(V.t * V - BDM.eye[Double](n))) + assert(closeToZero(U * brzDiag(s) * V.t - localA)) + } + + def closeToZero(G: BDM[Double]): Boolean = { + G.valuesIterator.map(math.abs).sum < 1e-6 + } +} + diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala new file mode 100644 index 0000000000000..71ee8e8a4f6fd --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/distributed/RowMatrixSuite.scala @@ -0,0 +1,173 @@ +/* + * 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.linalg.distributed + +import org.scalatest.FunSuite + +import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, norm => brzNorm, svd => brzSvd} + +import org.apache.spark.mllib.util.LocalSparkContext +import org.apache.spark.mllib.linalg.{Matrices, Vectors, Vector} + +class RowMatrixSuite extends FunSuite with LocalSparkContext { + + val m = 4 + val n = 3 + val arr = Array(0.0, 3.0, 6.0, 9.0, 1.0, 4.0, 7.0, 0.0, 2.0, 5.0, 8.0, 1.0) + val denseData = Seq( + Vectors.dense(0.0, 1.0, 2.0), + Vectors.dense(3.0, 4.0, 5.0), + Vectors.dense(6.0, 7.0, 8.0), + Vectors.dense(9.0, 0.0, 1.0) + ) + val sparseData = Seq( + Vectors.sparse(3, Seq((1, 1.0), (2, 2.0))), + Vectors.sparse(3, Seq((0, 3.0), (1, 4.0), (2, 5.0))), + Vectors.sparse(3, Seq((0, 6.0), (1, 7.0), (2, 8.0))), + Vectors.sparse(3, Seq((0, 9.0), (2, 1.0))) + ) + + val principalComponents = BDM( + (0.0, 1.0, 0.0), + (math.sqrt(2.0) / 2.0, 0.0, math.sqrt(2.0) / 2.0), + (math.sqrt(2.0) / 2.0, 0.0, - math.sqrt(2.0) / 2.0)) + + var denseMat: RowMatrix = _ + var sparseMat: RowMatrix = _ + + override def beforeAll() { + super.beforeAll() + denseMat = new RowMatrix(sc.parallelize(denseData, 2)) + sparseMat = new RowMatrix(sc.parallelize(sparseData, 2)) + } + + test("size") { + assert(denseMat.numRows() === m) + assert(denseMat.numCols() === n) + assert(sparseMat.numRows() === m) + assert(sparseMat.numCols() === n) + } + + test("empty rows") { + val rows = sc.parallelize(Seq[Vector](), 1) + val emptyMat = new RowMatrix(rows) + intercept[RuntimeException] { + emptyMat.numCols() + } + intercept[RuntimeException] { + emptyMat.numRows() + } + } + + test("toBreeze") { + val expected = BDM( + (0.0, 1.0, 2.0), + (3.0, 4.0, 5.0), + (6.0, 7.0, 8.0), + (9.0, 0.0, 1.0)) + for (mat <- Seq(denseMat, sparseMat)) { + assert(mat.toBreeze() === expected) + } + } + + test("gram") { + val expected = + Matrices.dense(n, n, Array(126.0, 54.0, 72.0, 54.0, 66.0, 78.0, 72.0, 78.0, 94.0)) + for (mat <- Seq(denseMat, sparseMat)) { + val G = mat.computeGramianMatrix() + assert(G.toBreeze === expected.toBreeze) + } + } + + test("svd of a full-rank matrix") { + for (mat <- Seq(denseMat, sparseMat)) { + val localMat = mat.toBreeze() + val (localU, localSigma, localVt) = brzSvd(localMat) + val localV: BDM[Double] = localVt.t.toDenseMatrix + for (k <- 1 to n) { + val svd = mat.computeSVD(k, computeU = true) + val U = svd.U + val s = svd.s + val V = svd.V + assert(U.numRows() === m) + assert(U.numCols() === k) + assert(s.size === k) + assert(V.numRows === n) + assert(V.numCols === k) + assertColumnEqualUpToSign(U.toBreeze(), localU, k) + assertColumnEqualUpToSign(V.toBreeze.asInstanceOf[BDM[Double]], localV, k) + assert(closeToZero(s.toBreeze.asInstanceOf[BDV[Double]] - localSigma(0 until k))) + } + val svdWithoutU = mat.computeSVD(n) + assert(svdWithoutU.U === null) + } + } + + test("svd of a low-rank matrix") { + val rows = sc.parallelize(Array.fill(4)(Vectors.dense(1.0, 1.0)), 2) + val mat = new RowMatrix(rows, 4, 2) + val svd = mat.computeSVD(2, computeU = true) + assert(svd.s.size === 1, "should not return zero singular values") + assert(svd.U.numRows() === 4) + assert(svd.U.numCols() === 1) + assert(svd.V.numRows === 2) + assert(svd.V.numCols === 1) + } + + def closeToZero(G: BDM[Double]): Boolean = { + G.valuesIterator.map(math.abs).sum < 1e-6 + } + + def closeToZero(v: BDV[Double]): Boolean = { + brzNorm(v, 1.0) < 1e-6 + } + + def assertColumnEqualUpToSign(A: BDM[Double], B: BDM[Double], k: Int) { + assert(A.rows === B.rows) + for (j <- 0 until k) { + val aj = A(::, j) + val bj = B(::, j) + assert(closeToZero(aj - bj) || closeToZero(aj + bj), + s"The $j-th columns mismatch: $aj and $bj") + } + } + + test("pca") { + for (mat <- Seq(denseMat, sparseMat); k <- 1 to n) { + val pc = denseMat.computePrincipalComponents(k) + assert(pc.numRows === n) + assert(pc.numCols === k) + assertColumnEqualUpToSign(pc.toBreeze.asInstanceOf[BDM[Double]], principalComponents, k) + } + } + + test("multiply a local matrix") { + val B = Matrices.dense(n, 2, Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0)) + for (mat <- Seq(denseMat, sparseMat)) { + val AB = mat.multiply(B) + assert(AB.numRows() === m) + assert(AB.numCols() === 2) + assert(AB.rows.collect().toSeq === Seq( + Vectors.dense(5.0, 14.0), + Vectors.dense(14.0, 50.0), + Vectors.dense(23.0, 86.0), + Vectors.dense(2.0, 32.0) + )) + } + } +}