Merge branch 'master' of https://github.com/apache/spark into text-plan-size

# Conflicts:
#	sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

Author: DaveDeCaprio

R/pkg/NAMESPACE

@@ -351,6 +351,8 @@ exportMethods("%<=>%",
               "row_number",
               "rpad",
               "rtrim",
+              "schema_of_csv",
+              "schema_of_json",
               "second",
               "sha1",
               "sha2",

R/pkg/R/DataFrame.R

@@ -766,7 +766,6 @@ setMethod("repartition",
 #'  \item{2.} {Return a new SparkDataFrame range partitioned by the given column(s),
 #'                      using \code{spark.sql.shuffle.partitions} as number of partitions.}
 #'}
-#'
 #' At least one partition-by expression must be specified.
 #' When no explicit sort order is specified, "ascending nulls first" is assumed.
 #'
@@ -828,7 +827,6 @@ setMethod("repartitionByRange",
 #' toJSON
 #'
 #' Converts a SparkDataFrame into a SparkDataFrame of JSON string.
-#'
 #' Each row is turned into a JSON document with columns as different fields.
 #' The returned SparkDataFrame has a single character column with the name \code{value}
 #'
@@ -2732,13 +2730,25 @@ setMethod("union",
             dataFrame(unioned)
           })
 
-#' Return a new SparkDataFrame containing the union of rows
+#' Return a new SparkDataFrame containing the union of rows.
 #'
-#' This is an alias for `union`.
+#' This is an alias for \code{union}.
 #'
-#' @rdname union
-#' @name unionAll
+#' @param x a SparkDataFrame.
+#' @param y a SparkDataFrame.
+#' @return A SparkDataFrame containing the result of the unionAll operation.
+#' @family SparkDataFrame functions
 #' @aliases unionAll,SparkDataFrame,SparkDataFrame-method
+#' @rdname unionAll
+#' @name unionAll
+#' @seealso \link{union}
+#' @examples
+#'\dontrun{
+#' sparkR.session()
+#' df1 <- read.json(path)
+#' df2 <- read.json(path2)
+#' unionAllDF <- unionAll(df1, df2)
+#' }
 #' @note unionAll since 1.4.0
 setMethod("unionAll",
           signature(x = "SparkDataFrame", y = "SparkDataFrame"),

R/pkg/R/functions.R

@@ -205,11 +205,18 @@ NULL
 #'              also supported for the schema.
 #'          \item \code{from_csv}: a DDL-formatted string
 #'          }
-#' @param ... additional argument(s). In \code{to_json}, \code{to_csv} and \code{from_json},
-#'            this contains additional named properties to control how it is converted, accepts
-#'            the same options as the JSON/CSV data source. Additionally \code{to_json} supports
-#'            the "pretty" option which enables pretty JSON generation. In \code{arrays_zip},
-#'            this contains additional Columns of arrays to be merged.
+#' @param ... additional argument(s).
+#'          \itemize{
+#'          \item \code{to_json}, \code{from_json} and \code{schema_of_json}: this contains
+#'              additional named properties to control how it is converted and accepts the
+#'              same options as the JSON data source.
+#'          \item \code{to_json}: it supports the "pretty" option which enables pretty
+#'              JSON generation.
+#'          \item \code{to_csv}, \code{from_csv} and \code{schema_of_csv}: this contains
+#'              additional named properties to control how it is converted and accepts the
+#'              same options as the CSV data source.
+#'          \item \code{arrays_zip}, this contains additional Columns of arrays to be merged.
+#'          }
 #' @name column_collection_functions
 #' @rdname column_collection_functions
 #' @family collection functions
@@ -1771,12 +1778,16 @@ setMethod("to_date",
 #' df2 <- mutate(df2, people_json = to_json(df2$people))
 #'
 #' # Converts a map into a JSON object
-#' df2 <- sql("SELECT map('name', 'Bob')) as people")
+#' df2 <- sql("SELECT map('name', 'Bob') as people")
 #' df2 <- mutate(df2, people_json = to_json(df2$people))
 #'
 #' # Converts an array of maps into a JSON array
 #' df2 <- sql("SELECT array(map('name', 'Bob'), map('name', 'Alice')) as people")
-#' df2 <- mutate(df2, people_json = to_json(df2$people))}
+#' df2 <- mutate(df2, people_json = to_json(df2$people))
+#'
+#' # Converts a map into a pretty JSON object
+#' df2 <- sql("SELECT map('name', 'Bob') as people")
+#' df2 <- mutate(df2, people_json = to_json(df2$people, pretty = TRUE))}
 #' @note to_json since 2.2.0
 setMethod("to_json", signature(x = "Column"),
           function(x, ...) {
@@ -2285,6 +2296,32 @@ setMethod("from_json", signature(x = "Column", schema = "characterOrstructType")
             column(jc)
           })
 
+#' @details
+#' \code{schema_of_json}: Parses a JSON string and infers its schema in DDL format.
+#'
+#' @rdname column_collection_functions
+#' @aliases schema_of_json schema_of_json,characterOrColumn-method
+#' @examples
+#'
+#' \dontrun{
+#' json <- "{\"name\":\"Bob\"}"
+#' df <- sql("SELECT * FROM range(1)")
+#' head(select(df, schema_of_json(json)))}
+#' @note schema_of_json since 3.0.0
+setMethod("schema_of_json", signature(x = "characterOrColumn"),
+          function(x, ...) {
+            if (class(x) == "character") {
+              col <- callJStatic("org.apache.spark.sql.functions", "lit", x)
+            } else {
+              col <- x@jc
+            }
+            options <- varargsToStrEnv(...)
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "schema_of_json",
+                              col, options)
+            column(jc)
+          })
+
 #' @details
 #' \code{from_csv}: Parses a column containing a CSV string into a Column of \code{structType}
 #' with the specified \code{schema}.
@@ -2315,6 +2352,32 @@ setMethod("from_csv", signature(x = "Column", schema = "characterOrColumn"),
             column(jc)
           })
 
+#' @details
+#' \code{schema_of_csv}: Parses a CSV string and infers its schema in DDL format.
+#'
+#' @rdname column_collection_functions
+#' @aliases schema_of_csv schema_of_csv,characterOrColumn-method
+#' @examples
+#'
+#' \dontrun{
+#' csv <- "Amsterdam,2018"
+#' df <- sql("SELECT * FROM range(1)")
+#' head(select(df, schema_of_csv(csv)))}
+#' @note schema_of_csv since 3.0.0
+setMethod("schema_of_csv", signature(x = "characterOrColumn"),
+          function(x, ...) {
+            if (class(x) == "character") {
+              col <- callJStatic("org.apache.spark.sql.functions", "lit", x)
+            } else {
+              col <- x@jc
+            }
+            options <- varargsToStrEnv(...)
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "schema_of_csv",
+                              col, options)
+            column(jc)
+          })
+
 #' @details
 #' \code{from_utc_timestamp}: This is a common function for databases supporting TIMESTAMP WITHOUT
 #' TIMEZONE. This function takes a timestamp which is timezone-agnostic, and interprets it as a

R/pkg/R/generics.R

@@ -631,7 +631,7 @@ setGeneric("toRDD", function(x) { standardGeneric("toRDD") })
 #' @rdname union
 setGeneric("union", function(x, y) { standardGeneric("union") })
 
-#' @rdname union
+#' @rdname unionAll
 setGeneric("unionAll", function(x, y) { standardGeneric("unionAll") })
 
 #' @rdname unionByName
@@ -1206,6 +1206,14 @@ setGeneric("rpad", function(x, len, pad) { standardGeneric("rpad") })
 #' @name NULL
 setGeneric("rtrim", function(x, trimString) { standardGeneric("rtrim") })
 
+#' @rdname column_collection_functions
+#' @name NULL
+setGeneric("schema_of_csv", function(x, ...) { standardGeneric("schema_of_csv") })
+
+#' @rdname column_collection_functions
+#' @name NULL
+setGeneric("schema_of_json", function(x, ...) { standardGeneric("schema_of_json") })
+
 #' @rdname column_aggregate_functions
 #' @name NULL
 setGeneric("sd", function(x, na.rm = FALSE) { standardGeneric("sd") })

R/pkg/tests/fulltests/test_sparkSQL.R

@@ -1620,14 +1620,20 @@ test_that("column functions", {
   expect_equal(collect(select(df, bround(df$x, 0)))[[1]][1], 2)
   expect_equal(collect(select(df, bround(df$x, 0)))[[1]][2], 4)
 
-  # Test from_csv()
+  # Test from_csv(), schema_of_csv()
   df <- as.DataFrame(list(list("col" = "1")))
   c <- collect(select(df, alias(from_csv(df$col, "a INT"), "csv")))
   expect_equal(c[[1]][[1]]$a, 1)
   c <- collect(select(df, alias(from_csv(df$col, lit("a INT")), "csv")))
   expect_equal(c[[1]][[1]]$a, 1)
 
-  # Test to_json(), from_json()
+  df <- as.DataFrame(list(list("col" = "1")))
+  c <- collect(select(df, schema_of_csv("Amsterdam,2018")))
+  expect_equal(c[[1]], "struct<_c0:string,_c1:int>")
+  c <- collect(select(df, schema_of_csv(lit("Amsterdam,2018"))))
+  expect_equal(c[[1]], "struct<_c0:string,_c1:int>")
+
+  # Test to_json(), from_json(), schema_of_json()
   df <- sql("SELECT array(named_struct('name', 'Bob'), named_struct('name', 'Alice')) as people")
   j <- collect(select(df, alias(to_json(df$people), "json")))
   expect_equal(j[order(j$json), ][1], "[{\"name\":\"Bob\"},{\"name\":\"Alice\"}]")
@@ -1654,6 +1660,12 @@ test_that("column functions", {
     expect_true(any(apply(s, 1, function(x) { x[[1]]$age == 16 })))
   }
 
+  df <- as.DataFrame(list(list("col" = "1")))
+  c <- collect(select(df, schema_of_json('{"name":"Bob"}')))
+  expect_equal(c[[1]], "struct<name:string>")
+  c <- collect(select(df, schema_of_json(lit('{"name":"Bob"}'))))
+  expect_equal(c[[1]], "struct<name:string>")
+
   # Test to_json() supports arrays of primitive types and arrays
   df <- sql("SELECT array(19, 42, 70) as age")
   j <- collect(select(df, alias(to_json(df$age), "json")))

bin/docker-image-tool.sh

@@ -146,6 +146,12 @@ function build {
   fi
 
   local BUILD_ARGS=(${BUILD_PARAMS})
+
+  # If a custom SPARK_UID was set add it to build arguments
+  if [ -n "$SPARK_UID" ]; then
+    BUILD_ARGS+=(--build-arg spark_uid=$SPARK_UID)
+  fi
+
   local BINDING_BUILD_ARGS=(
     ${BUILD_PARAMS}
     --build-arg
@@ -207,8 +213,10 @@ Options:
   -t tag                Tag to apply to the built image, or to identify the image to be pushed.
   -m                    Use minikube's Docker daemon.
   -n                    Build docker image with --no-cache
-  -b arg      Build arg to build or push the image. For multiple build args, this option needs to
-              be used separately for each build arg.
+  -u uid                UID to use in the USER directive to set the user the main Spark process runs as inside the
+                        resulting container
+  -b arg                Build arg to build or push the image. For multiple build args, this option needs to
+                        be used separately for each build arg.
 
 Using minikube when building images will do so directly into minikube's Docker daemon.
 There is no need to push the images into minikube in that case, they'll be automatically
@@ -243,7 +251,8 @@ PYDOCKERFILE=
 RDOCKERFILE=
 NOCACHEARG=
 BUILD_PARAMS=
-while getopts f:p:R:mr:t:nb: option
+SPARK_UID=
+while getopts f:p:R:mr:t:nb:u: option
 do
  case "${option}"
  in
@@ -263,6 +272,7 @@ do
    fi
    eval $(minikube docker-env)
    ;;
+  u) SPARK_UID=${OPTARG};;
  esac
 done
 

common/unsafe/src/test/java/org/apache/spark/unsafe/PlatformUtilSuite.java

@@ -165,10 +165,14 @@ public void writeMinusZeroIsReplacedWithZero() {
     byte[] floatBytes = new byte[Float.BYTES];
     Platform.putDouble(doubleBytes, Platform.BYTE_ARRAY_OFFSET, -0.0d);
     Platform.putFloat(floatBytes, Platform.BYTE_ARRAY_OFFSET, -0.0f);
-    double doubleFromPlatform = Platform.getDouble(doubleBytes, Platform.BYTE_ARRAY_OFFSET);
-    float floatFromPlatform = Platform.getFloat(floatBytes, Platform.BYTE_ARRAY_OFFSET);
 
-    Assert.assertEquals(Double.doubleToLongBits(0.0d), Double.doubleToLongBits(doubleFromPlatform));
-    Assert.assertEquals(Float.floatToIntBits(0.0f), Float.floatToIntBits(floatFromPlatform));
+    byte[] doubleBytes2 = new byte[Double.BYTES];
+    byte[] floatBytes2 = new byte[Float.BYTES];
+    Platform.putDouble(doubleBytes, Platform.BYTE_ARRAY_OFFSET, 0.0d);
+    Platform.putFloat(floatBytes, Platform.BYTE_ARRAY_OFFSET, 0.0f);
+
+    // Make sure the bytes we write from 0.0 and -0.0 are same.
+    Assert.assertArrayEquals(doubleBytes, doubleBytes2);
+    Assert.assertArrayEquals(floatBytes, floatBytes2);
   }
 }

core/pom.xml

@@ -408,6 +408,19 @@
       <scope>provided</scope>
     </dependency>
 
+    <!--
+     The following kafka dependency used to obtain delegation token.
+     In order to prevent spark-core from depending on kafka, these deps have been placed in the
+     "provided" scope, rather than the "compile" scope, and NoClassDefFoundError exceptions are
+     handled when the user explicitly use neither spark-streaming-kafka nor spark-sql-kafka modules.
+    -->
+    <dependency>
+      <groupId>org.apache.kafka</groupId>
+      <artifactId>kafka-clients</artifactId>
+      <version>${kafka.version}</version>
+      <scope>provided</scope>
+    </dependency>
+
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>

core/src/main/resources/org/apache/spark/ui/static/stagepage.js

@@ -221,7 +221,10 @@ function createDataTableForTaskSummaryMetricsTable(taskSummaryMetricsTable) {
             "searching": false,
             "order": [[0, "asc"]],
             "bSort": false,
-            "bAutoWidth": false
+            "bAutoWidth": false,
+            "oLanguage": {
+                "sEmptyTable": "No tasks have reported metrics yet"
+            }
         };
         taskSummaryMetricsDataTable = $(taskMetricsTable).DataTable(taskConf);
     }
@@ -426,7 +429,10 @@ $(document).ready(function () {
                         }
                     ],
                     "order": [[0, "asc"]],
-                    "bAutoWidth": false
+                    "bAutoWidth": false,
+                    "oLanguage": {
+                        "sEmptyTable": "No data to show yet"
+                    }
                 }
                 var executorSummaryTableSelector =
                     $("#summary-executor-table").DataTable(executorSummaryConf);

core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala

@@ -660,6 +660,7 @@ private[spark] class PythonBroadcast(@transient var path: String) extends Serial
     with Logging {
 
   private var encryptionServer: PythonServer[Unit] = null
+  private var decryptionServer: PythonServer[Unit] = null
 
   /**
    * Read data from disks, then copy it to `out`
@@ -708,16 +709,36 @@ private[spark] class PythonBroadcast(@transient var path: String) extends Serial
       override def handleConnection(sock: Socket): Unit = {
         val env = SparkEnv.get
         val in = sock.getInputStream()
-        val dir = new File(Utils.getLocalDir(env.conf))
-        val file = File.createTempFile("broadcast", "", dir)
-        path = file.getAbsolutePath
-        val out = env.serializerManager.wrapForEncryption(new FileOutputStream(path))
+        val abspath = new File(path).getAbsolutePath
+        val out = env.serializerManager.wrapForEncryption(new FileOutputStream(abspath))
         DechunkedInputStream.dechunkAndCopyToOutput(in, out)
       }
     }
     Array(encryptionServer.port, encryptionServer.secret)
   }
 
+  def setupDecryptionServer(): Array[Any] = {
+    decryptionServer = new PythonServer[Unit]("broadcast-decrypt-server-for-driver") {
+      override def handleConnection(sock: Socket): Unit = {
+        val out = new DataOutputStream(new BufferedOutputStream(sock.getOutputStream()))
+        Utils.tryWithSafeFinally {
+          val in = SparkEnv.get.serializerManager.wrapForEncryption(new FileInputStream(path))
+          Utils.tryWithSafeFinally {
+            Utils.copyStream(in, out, false)
+          } {
+            in.close()
+          }
+          out.flush()
+        } {
+          JavaUtils.closeQuietly(out)
+        }
+      }
+    }
+    Array(decryptionServer.port, decryptionServer.secret)
+  }
+
+  def waitTillBroadcastDataSent(): Unit = decryptionServer.getResult()
+
   def waitTillDataReceived(): Unit = encryptionServer.getResult()
 }
 // scalastyle:on no.finalize