[SPARK-14557][SQL]: Resolving merge conflicts

R/pkg/NAMESPACE

@@ -24,6 +24,9 @@ export("setJobGroup",
        "clearJobGroup",
        "cancelJobGroup")
 
+# Export Utility methods
+export("setLogLevel")
+
 exportClasses("DataFrame")
 
 exportMethods("arrange",
@@ -101,6 +104,7 @@ exportMethods("arrange",
               "withColumn",
               "withColumnRenamed",
               "write.df",
+              "write.jdbc",
               "write.json",
               "write.parquet",
               "write.text")
@@ -125,6 +129,7 @@ exportMethods("%in%",
               "between",
               "bin",
               "bitwiseNOT",
+              "bround",
               "cast",
               "cbrt",
               "ceil",
@@ -284,6 +289,7 @@ export("as.DataFrame",
        "loadDF",
        "parquetFile",
        "read.df",
+       "read.jdbc",
        "read.json",
        "read.parquet",
        "read.text",
@@ -292,7 +298,8 @@ export("as.DataFrame",
        "tableToDF",
        "tableNames",
        "tables",
-       "uncacheTable")
+       "uncacheTable",
+       "print.summary.GeneralizedLinearRegressionModel")
 
 export("structField",
        "structField.jobj",

R/pkg/R/DataFrame.R

@@ -2296,12 +2296,8 @@ setMethod("fillna",
 #' }
 setMethod("as.data.frame",
           signature(x = "DataFrame"),
-          function(x, ...) {
-            # Check if additional parameters have been passed
-            if (length(list(...)) > 0) {
-              stop(paste("Unused argument(s): ", paste(list(...), collapse = ", ")))
-            }
-            collect(x)
+          function(x, row.names = NULL, optional = FALSE, ...) {
+            as.data.frame(collect(x), row.names, optional, ...)
           })
 
 #' The specified DataFrame is attached to the R search path. This means that
@@ -2363,7 +2359,7 @@ setMethod("with",
 #' @examples \dontrun{
 #' # Create a DataFrame from the Iris dataset
 #' irisDF <- createDataFrame(sqlContext, iris)
-#' 
+#'
 #' # Show the structure of the DataFrame
 #' str(irisDF)
 #' }
@@ -2468,3 +2464,40 @@ setMethod("drop",
           function(x) {
             base::drop(x)
           })
+
+#' Saves the content of the DataFrame to an external database table via JDBC
+#'
+#' Additional JDBC database connection properties can be set (...)
+#'
+#' Also, mode is used to specify the behavior of the save operation when
+#' data already exists in the data source. There are four modes: \cr
+#'  append: Contents of this DataFrame are expected to be appended to existing data. \cr
+#'  overwrite: Existing data is expected to be overwritten by the contents of this DataFrame. \cr
+#'  error: An exception is expected to be thrown. \cr
+#'  ignore: The save operation is expected to not save the contents of the DataFrame
+#'     and to not change the existing data. \cr
+#'
+#' @param x A SparkSQL DataFrame
+#' @param url JDBC database url of the form `jdbc:subprotocol:subname`
+#' @param tableName The name of the table in the external database
+#' @param mode One of 'append', 'overwrite', 'error', 'ignore' save mode (it is 'error' by default)
+#' @family DataFrame functions
+#' @rdname write.jdbc
+#' @name write.jdbc
+#' @export
+#' @examples
+#'\dontrun{
+#' sc <- sparkR.init()
+#' sqlContext <- sparkRSQL.init(sc)
+#' jdbcUrl <- "jdbc:mysql://localhost:3306/databasename"
+#' write.jdbc(df, jdbcUrl, "table", user = "username", password = "password")
+#' }
+setMethod("write.jdbc",
+          signature(x = "DataFrame", url = "character", tableName = "character"),
+          function(x, url, tableName, mode = "error", ...){
+            jmode <- convertToJSaveMode(mode)
+            jprops <- varargsToJProperties(...)
+            write <- callJMethod(x@sdf, "write")
+            write <- callJMethod(write, "mode", jmode)
+            invisible(callJMethod(write, "jdbc", url, tableName, jprops))
+          })

R/pkg/R/SQLContext.R

@@ -583,3 +583,61 @@ createExternalTable <- function(sqlContext, tableName, path = NULL, source = NUL
   sdf <- callJMethod(sqlContext, "createExternalTable", tableName, source, options)
   dataFrame(sdf)
 }
+
+#' Create a DataFrame representing the database table accessible via JDBC URL
+#'
+#' Additional JDBC database connection properties can be set (...)
+#'
+#' Only one of partitionColumn or predicates should be set. Partitions of the table will be
+#' retrieved in parallel based on the `numPartitions` or by the predicates.
+#'
+#' Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash
+#' your external database systems.
+#'
+#' @param sqlContext SQLContext to use
+#' @param url JDBC database url of the form `jdbc:subprotocol:subname`
+#' @param tableName the name of the table in the external database
+#' @param partitionColumn the name of a column of integral type that will be used for partitioning
+#' @param lowerBound the minimum value of `partitionColumn` used to decide partition stride
+#' @param upperBound the maximum value of `partitionColumn` used to decide partition stride
+#' @param numPartitions the number of partitions, This, along with `lowerBound` (inclusive),
+#'                      `upperBound` (exclusive), form partition strides for generated WHERE
+#'                      clause expressions used to split the column `partitionColumn` evenly.
+#'                      This defaults to SparkContext.defaultParallelism when unset.
+#' @param predicates a list of conditions in the where clause; each one defines one partition
+#' @return DataFrame
+#' @rdname read.jdbc
+#' @name read.jdbc
+#' @export
+#' @examples
+#'\dontrun{
+#' sc <- sparkR.init()
+#' sqlContext <- sparkRSQL.init(sc)
+#' jdbcUrl <- "jdbc:mysql://localhost:3306/databasename"
+#' df <- read.jdbc(sqlContext, jdbcUrl, "table", predicates = list("field<=123"), user = "username")
+#' df2 <- read.jdbc(sqlContext, jdbcUrl, "table2", partitionColumn = "index", lowerBound = 0,
+#'                  upperBound = 10000, user = "username", password = "password")
+#' }
+
+read.jdbc <- function(sqlContext, url, tableName,
+                      partitionColumn = NULL, lowerBound = NULL, upperBound = NULL,
+                      numPartitions = 0L, predicates = list(), ...) {
+  jprops <- varargsToJProperties(...)
+
+  read <- callJMethod(sqlContext, "read")
+  if (!is.null(partitionColumn)) {
+    if (is.null(numPartitions) || numPartitions == 0) {
+      sc <- callJMethod(sqlContext, "sparkContext")
+      numPartitions <- callJMethod(sc, "defaultParallelism")
+    } else {
+      numPartitions <- numToInt(numPartitions)
+    }
+    sdf <- callJMethod(read, "jdbc", url, tableName, as.character(partitionColumn),
+                       numToInt(lowerBound), numToInt(upperBound), numPartitions, jprops)
+  } else if (length(predicates) > 0) {
+    sdf <- callJMethod(read, "jdbc", url, tableName, as.list(as.character(predicates)), jprops)
+  } else {
+    sdf <- callJMethod(read, "jdbc", url, tableName, jprops)
+  }
+  dataFrame(sdf)
+}

R/pkg/R/context.R

@@ -225,3 +225,20 @@ broadcast <- function(sc, object) {
 setCheckpointDir <- function(sc, dirName) {
   invisible(callJMethod(sc, "setCheckpointDir", suppressWarnings(normalizePath(dirName))))
 }
+
+#' Set new log level
+#'
+#' Set new log level: "ALL", "DEBUG", "ERROR", "FATAL", "INFO", "OFF", "TRACE", "WARN"
+#'
+#' @rdname setLogLevel
+#' @param sc Spark Context to use
+#' @param level New log level
+#' @export
+#' @examples
+#'\dontrun{
+#' setLogLevel(sc, "ERROR")
+#'}
+
+setLogLevel <- function(sc, level) {
+  callJMethod(sc, "setLogLevel", level)
+}

R/pkg/R/functions.R

@@ -994,7 +994,7 @@ setMethod("rint",
 
 #' round
 #'
-#' Returns the value of the column `e` rounded to 0 decimal places.
+#' Returns the value of the column `e` rounded to 0 decimal places using HALF_UP rounding mode.
 #'
 #' @rdname round
 #' @name round
@@ -1008,6 +1008,26 @@ setMethod("round",
             column(jc)
           })
 
+#' bround
+#'
+#' Returns the value of the column `e` rounded to `scale` decimal places using HALF_EVEN rounding
+#' mode if `scale` >= 0 or at integral part when `scale` < 0.
+#' Also known as Gaussian rounding or bankers' rounding that rounds to the nearest even number.
+#' bround(2.5, 0) = 2, bround(3.5, 0) = 4.
+#'
+#' @rdname bround
+#' @name bround
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{bround(df$c, 0)}
+setMethod("bround",
+          signature(x = "Column"),
+          function(x, scale = 0) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "bround", x@jc, as.integer(scale))
+            column(jc)
+          })
+
+
 #' rtrim
 #'
 #' Trim the spaces from right end for the specified string value.

R/pkg/R/generics.R

@@ -397,7 +397,10 @@ setGeneric("arrange", function(x, col, ...) { standardGeneric("arrange") })
 
 #' @rdname as.data.frame
 #' @export
-setGeneric("as.data.frame")
+setGeneric("as.data.frame",
+           function(x, row.names = NULL, optional = FALSE, ...) {
+             standardGeneric("as.data.frame")
+           })
 
 #' @rdname attach
 #' @export
@@ -577,6 +580,12 @@ setGeneric("saveDF", function(df, path, source = NULL, mode = "error", ...) {
   standardGeneric("saveDF")
 })
 
+#' @rdname write.jdbc
+#' @export
+setGeneric("write.jdbc", function(x, url, tableName, mode = "error", ...) {
+  standardGeneric("write.jdbc")
+})
+
 #' @rdname write.json
 #' @export
 setGeneric("write.json", function(x, path) { standardGeneric("write.json") })
@@ -751,6 +760,10 @@ setGeneric("bin", function(x) { standardGeneric("bin") })
 #' @export
 setGeneric("bitwiseNOT", function(x) { standardGeneric("bitwiseNOT") })
 
+#' @rdname bround
+#' @export
+setGeneric("bround", function(x, ...) { standardGeneric("bround") })
+
 #' @rdname cbrt
 #' @export
 setGeneric("cbrt", function(x) { standardGeneric("cbrt") })