Merge remote-tracking branch 'upstream/master' into empstring_fix_spark-15125

Author: sureshthalamati

LICENSE

@@ -263,7 +263,7 @@ The text of each license is also included at licenses/LICENSE-[project].txt.
      (New BSD license) Protocol Buffer Java API (org.spark-project.protobuf:protobuf-java:2.4.1-shaded - http://code.google.com/p/protobuf)
      (The BSD License) Fortran to Java ARPACK (net.sourceforge.f2j:arpack_combined_all:0.1 - http://f2j.sourceforge.net)
      (The BSD License) xmlenc Library (xmlenc:xmlenc:0.52 - http://xmlenc.sourceforge.net)
-     (The New BSD License) Py4J (net.sf.py4j:py4j:0.10.3 - http://py4j.sourceforge.net/)
+     (The New BSD License) Py4J (net.sf.py4j:py4j:0.10.4 - http://py4j.sourceforge.net/)
      (Two-clause BSD-style license) JUnit-Interface (com.novocode:junit-interface:0.10 - http://github.com/szeiger/junit-interface/)
      (BSD licence) sbt and sbt-launch-lib.bash
      (BSD 3 Clause) d3.min.js (https://github.com/mbostock/d3/blob/master/LICENSE)

R/pkg/NAMESPACE

@@ -3,7 +3,7 @@
 importFrom("methods", "setGeneric", "setMethod", "setOldClass")
 importFrom("methods", "is", "new", "signature", "show")
 importFrom("stats", "gaussian", "setNames")
-importFrom("utils", "download.file", "packageVersion", "untar")
+importFrom("utils", "download.file", "object.size", "packageVersion", "untar")
 
 # Disable native libraries till we figure out how to package it
 # See SPARKR-7839
@@ -71,6 +71,7 @@ exportMethods("arrange",
               "covar_samp",
               "covar_pop",
               "createOrReplaceTempView",
+              "crossJoin",
               "crosstab",
               "dapply",
               "dapplyCollect",

R/pkg/R/DataFrame.R

@@ -2271,12 +2271,13 @@ setMethod("dropDuplicates",
 
 #' Join
 #'
-#' Join two SparkDataFrames based on the given join expression.
+#' Joins two SparkDataFrames based on the given join expression.
 #'
 #' @param x A SparkDataFrame
 #' @param y A SparkDataFrame
 #' @param joinExpr (Optional) The expression used to perform the join. joinExpr must be a
-#' Column expression. If joinExpr is omitted, join() will perform a Cartesian join
+#' Column expression. If joinExpr is omitted, the default, inner join is attempted and an error is
+#' thrown if it would be a Cartesian Product. For Cartesian join, use crossJoin instead.
 #' @param joinType The type of join to perform. The following join types are available:
 #' 'inner', 'outer', 'full', 'fullouter', leftouter', 'left_outer', 'left',
 #' 'right_outer', 'rightouter', 'right', and 'leftsemi'. The default joinType is "inner".
@@ -2285,23 +2286,24 @@ setMethod("dropDuplicates",
 #' @aliases join,SparkDataFrame,SparkDataFrame-method
 #' @rdname join
 #' @name join
-#' @seealso \link{merge}
+#' @seealso \link{merge} \link{crossJoin}
 #' @export
 #' @examples
 #'\dontrun{
 #' sparkR.session()
 #' df1 <- read.json(path)
 #' df2 <- read.json(path2)
-#' join(df1, df2) # Performs a Cartesian
 #' join(df1, df2, df1$col1 == df2$col2) # Performs an inner join based on expression
 #' join(df1, df2, df1$col1 == df2$col2, "right_outer")
+#' join(df1, df2) # Attempts an inner join
 #' }
 #' @note join since 1.4.0
 setMethod("join",
           signature(x = "SparkDataFrame", y = "SparkDataFrame"),
           function(x, y, joinExpr = NULL, joinType = NULL) {
             if (is.null(joinExpr)) {
-              sdf <- callJMethod(x@sdf, "crossJoin", y@sdf)
+              # this may not fail until the planner checks for Cartesian join later on.
+              sdf <- callJMethod(x@sdf, "join", y@sdf)
             } else {
               if (class(joinExpr) != "Column") stop("joinExpr must be a Column")
               if (is.null(joinType)) {
@@ -2322,22 +2324,52 @@ setMethod("join",
             dataFrame(sdf)
           })
 
+#' CrossJoin
+#'
+#' Returns Cartesian Product on two SparkDataFrames.
+#'
+#' @param x A SparkDataFrame
+#' @param y A SparkDataFrame
+#' @return A SparkDataFrame containing the result of the join operation.
+#' @family SparkDataFrame functions
+#' @aliases crossJoin,SparkDataFrame,SparkDataFrame-method
+#' @rdname crossJoin
+#' @name crossJoin
+#' @seealso \link{merge} \link{join}
+#' @export
+#' @examples
+#'\dontrun{
+#' sparkR.session()
+#' df1 <- read.json(path)
+#' df2 <- read.json(path2)
+#' crossJoin(df1, df2) # Performs a Cartesian
+#' }
+#' @note crossJoin since 2.1.0
+setMethod("crossJoin",
+          signature(x = "SparkDataFrame", y = "SparkDataFrame"),
+          function(x, y) {
+            sdf <- callJMethod(x@sdf, "crossJoin", y@sdf)
+            dataFrame(sdf)
+          })
+
 #' Merges two data frames
 #'
 #' @name merge
-#' @param x the first data frame to be joined
-#' @param y the second data frame to be joined
+#' @param x the first data frame to be joined.
+#' @param y the second data frame to be joined.
 #' @param by a character vector specifying the join columns. If by is not
 #'   specified, the common column names in \code{x} and \code{y} will be used.
+#'   If by or both by.x and by.y are explicitly set to NULL or of length 0, the Cartesian
+#'   Product of x and y will be returned.
 #' @param by.x a character vector specifying the joining columns for x.
 #' @param by.y a character vector specifying the joining columns for y.
 #' @param all a boolean value setting \code{all.x} and \code{all.y}
 #'            if any of them are unset.
 #' @param all.x a boolean value indicating whether all the rows in x should
-#'              be including in the join
+#'              be including in the join.
 #' @param all.y a boolean value indicating whether all the rows in y should
-#'              be including in the join
-#' @param sort a logical argument indicating whether the resulting columns should be sorted
+#'              be including in the join.
+#' @param sort a logical argument indicating whether the resulting columns should be sorted.
 #' @param suffixes a string vector of length 2 used to make colnames of
 #'                 \code{x} and \code{y} unique.
 #'                 The first element is appended to each colname of \code{x}.
@@ -2351,20 +2383,21 @@ setMethod("join",
 #' @family SparkDataFrame functions
 #' @aliases merge,SparkDataFrame,SparkDataFrame-method
 #' @rdname merge
-#' @seealso \link{join}
+#' @seealso \link{join} \link{crossJoin}
 #' @export
 #' @examples
 #'\dontrun{
 #' sparkR.session()
 #' df1 <- read.json(path)
 #' df2 <- read.json(path2)
-#' merge(df1, df2) # Performs a Cartesian
+#' merge(df1, df2) # Performs an inner join by common columns
 #' merge(df1, df2, by = "col1") # Performs an inner join based on expression
 #' merge(df1, df2, by.x = "col1", by.y = "col2", all.y = TRUE)
 #' merge(df1, df2, by.x = "col1", by.y = "col2", all.x = TRUE)
 #' merge(df1, df2, by.x = "col1", by.y = "col2", all.x = TRUE, all.y = TRUE)
 #' merge(df1, df2, by.x = "col1", by.y = "col2", all = TRUE, sort = FALSE)
 #' merge(df1, df2, by = "col1", all = TRUE, suffixes = c("-X", "-Y"))
+#' merge(df1, df2, by = NULL) # Performs a Cartesian join
 #' }
 #' @note merge since 1.5.0
 setMethod("merge",
@@ -2401,7 +2434,7 @@ setMethod("merge",
               joinY <- by
             } else {
               # if by or both by.x and by.y have length 0, use Cartesian Product
-              joinRes <- join(x, y)
+              joinRes <- crossJoin(x, y)
               return (joinRes)
             }
 

R/pkg/R/generics.R

@@ -468,6 +468,10 @@ setGeneric("createOrReplaceTempView",
              standardGeneric("createOrReplaceTempView")
            })
 
+# @rdname crossJoin
+# @export
+setGeneric("crossJoin", function(x, y) { standardGeneric("crossJoin") })
+
 #' @rdname dapply
 #' @export
 setGeneric("dapply", function(x, func, schema) { standardGeneric("dapply") })

R/pkg/inst/tests/testthat/test_sparkSQL.R

@@ -212,7 +212,7 @@ test_that("createDataFrame uses files for large objects", {
   # To simulate a large file scenario, we set spark.r.maxAllocationLimit to a smaller value
   conf <- callJMethod(sparkSession, "conf")
   callJMethod(conf, "set", "spark.r.maxAllocationLimit", "100")
-  df <- createDataFrame(iris)
+  df <- suppressWarnings(createDataFrame(iris))
 
   # Resetting the conf back to default value
   callJMethod(conf, "set", "spark.r.maxAllocationLimit", toString(.Machine$integer.max / 10))
@@ -390,6 +390,19 @@ test_that("create DataFrame with different data types", {
   expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE))
 })
 
+test_that("SPARK-17811: can create DataFrame containing NA as date and time", {
+  df <- data.frame(
+    id = 1:2,
+    time = c(as.POSIXlt("2016-01-10"), NA),
+    date = c(as.Date("2016-10-01"), NA))
+
+  DF <- collect(createDataFrame(df))
+  expect_true(is.na(DF$date[2]))
+  expect_equal(DF$date[1], as.Date("2016-10-01"))
+  expect_true(is.na(DF$time[2]))
+  expect_equal(DF$time[1], as.POSIXlt("2016-01-10"))
+})
+
 test_that("create DataFrame with complex types", {
   e <- new.env()
   assign("n", 3L, envir = e)
@@ -1572,7 +1585,7 @@ test_that("filter() on a DataFrame", {
   #expect_true(is.ts(filter(1:100, rep(1, 3)))) # nolint
 })
 
-test_that("join() and merge() on a DataFrame", {
+test_that("join(), crossJoin() and merge() on a DataFrame", {
   df <- read.json(jsonPath)
 
   mockLines2 <- c("{\"name\":\"Michael\", \"test\": \"yes\"}",
@@ -1583,7 +1596,14 @@ test_that("join() and merge() on a DataFrame", {
   writeLines(mockLines2, jsonPath2)
   df2 <- read.json(jsonPath2)
 
-  joined <- join(df, df2)
+  # inner join, not cartesian join
+  expect_equal(count(where(join(df, df2), df$name == df2$name)), 3)
+  # cartesian join
+  expect_error(tryCatch(count(join(df, df2)), error = function(e) { stop(e) }),
+               paste0(".*(org.apache.spark.sql.AnalysisException: Detected cartesian product for",
+                      " INNER join between logical plans).*"))
+
+  joined <- crossJoin(df, df2)
   expect_equal(names(joined), c("age", "name", "name", "test"))
   expect_equal(count(joined), 12)
   expect_equal(names(collect(joined)), c("age", "name", "name", "test"))

R/run-tests.sh

@@ -26,6 +26,8 @@ rm -f $LOGFILE
 SPARK_TESTING=1 $FWDIR/../bin/spark-submit --driver-java-options "-Dlog4j.configuration=file:$FWDIR/log4j.properties" --conf spark.hadoop.fs.default.name="file:///" $FWDIR/pkg/tests/run-all.R 2>&1 | tee -a $LOGFILE
 FAILED=$((PIPESTATUS[0]||$FAILED))
 
+NUM_TEST_WARNING="$(grep -c -e 'Warnings ----------------' $LOGFILE)"
+
 # Also run the documentation tests for CRAN
 CRAN_CHECK_LOG_FILE=$FWDIR/cran-check.out
 rm -f $CRAN_CHECK_LOG_FILE
@@ -37,10 +39,10 @@ NUM_CRAN_WARNING="$(grep -c WARNING$ $CRAN_CHECK_LOG_FILE)"
 NUM_CRAN_ERROR="$(grep -c ERROR$ $CRAN_CHECK_LOG_FILE)"
 NUM_CRAN_NOTES="$(grep -c NOTE$ $CRAN_CHECK_LOG_FILE)"
 
-if [[ $FAILED != 0 ]]; then
+if [[ $FAILED != 0 || $NUM_TEST_WARNING != 0 ]]; then
     cat $LOGFILE
     echo -en "\033[31m"  # Red
-    echo "Had test failures; see logs."
+    echo "Had test warnings or failures; see logs."
     echo -en "\033[0m"  # No color
     exit -1
 else

bin/pyspark

@@ -57,7 +57,7 @@ export PYSPARK_PYTHON
 
 # Add the PySpark classes to the Python path:
 export PYTHONPATH="${SPARK_HOME}/python/:$PYTHONPATH"
-export PYTHONPATH="${SPARK_HOME}/python/lib/py4j-0.10.3-src.zip:$PYTHONPATH"
+export PYTHONPATH="${SPARK_HOME}/python/lib/py4j-0.10.4-src.zip:$PYTHONPATH"
 
 # Load the PySpark shell.py script when ./pyspark is used interactively:
 export OLD_PYTHONSTARTUP="$PYTHONSTARTUP"

bin/pyspark2.cmd

@@ -30,7 +30,7 @@ if "x%PYSPARK_DRIVER_PYTHON%"=="x" (
 )
 
 set PYTHONPATH=%SPARK_HOME%\python;%PYTHONPATH%
-set PYTHONPATH=%SPARK_HOME%\python\lib\py4j-0.10.3-src.zip;%PYTHONPATH%
+set PYTHONPATH=%SPARK_HOME%\python\lib\py4j-0.10.4-src.zip;%PYTHONPATH%
 
 set OLD_PYTHONSTARTUP=%PYTHONSTARTUP%
 set PYTHONSTARTUP=%SPARK_HOME%\python\pyspark\shell.py

conf/spark-env.sh.template

@@ -63,3 +63,4 @@
 # - SPARK_PID_DIR       Where the pid file is stored. (Default: /tmp)
 # - SPARK_IDENT_STRING  A string representing this instance of spark. (Default: $USER)
 # - SPARK_NICENESS      The scheduling priority for daemons. (Default: 0)
+# - SPARK_NO_DAEMONIZE  Run the proposed command in the foreground. It will not output a PID file.

core/pom.xml

@@ -331,7 +331,7 @@
     <dependency>
       <groupId>net.sf.py4j</groupId>
       <artifactId>py4j</artifactId>
-      <version>0.10.3</version>
+      <version>0.10.4</version>
     </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>