Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark …

…into numpy

Conflicts:
	python/pyspark/rddsampler.py

bin/pyspark

@@ -132,7 +132,5 @@ if [[ "$1" =~ \.py$ ]]; then
   gatherSparkSubmitOpts "$@"
   exec "$FWDIR"/bin/spark-submit "${SUBMISSION_OPTS[@]}" "$primary" "${APPLICATION_OPTS[@]}"
 else
-  # PySpark shell requires special handling downstream
-  export PYSPARK_SHELL=1
   exec "$PYSPARK_DRIVER_PYTHON" $PYSPARK_DRIVER_PYTHON_OPTS
 fi

bin/pyspark2.cmd

@@ -59,7 +59,6 @@ for /f %%i in ('echo %1^| findstr /R "\.py"') do (
 )
 
 if [%PYTHON_FILE%] == [] (
-  set PYSPARK_SHELL=1
   if [%IPYTHON%] == [1] (
 	ipython %IPYTHON_OPTS%
   ) else (

bin/spark-submit

@@ -22,6 +22,9 @@
 export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
 ORIG_ARGS=("$@")
 
+# Set COLUMNS for progress bar
+export COLUMNS=`tput cols`
+
 while (($#)); do
   if [ "$1" = "--deploy-mode" ]; then
     SPARK_SUBMIT_DEPLOY_MODE=$2

conf/spark-env.sh.template

@@ -28,7 +28,7 @@
 # - SPARK_YARN_DIST_FILES, Comma separated list of files to be distributed with the job.
 # - SPARK_YARN_DIST_ARCHIVES, Comma separated list of archives to be distributed with the job.
 
-# Options for the daemons used in the standalone deploy mode:
+# Options for the daemons used in the standalone deploy mode
 # - SPARK_MASTER_IP, to bind the master to a different IP address or hostname
 # - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master
 # - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y")
@@ -41,3 +41,10 @@
 # - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")
 # - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
 # - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers
+
+# Generic options for the daemons used in the standalone deploy mode
+# - SPARK_CONF_DIR      Alternate conf dir. (Default: ${SPARK_HOME}/conf)
+# - SPARK_LOG_DIR       Where log files are stored.  (Default: ${SPARK_HOME}/logs)
+# - 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)

core/src/main/java/org/apache/spark/SparkStageInfo.java

@@ -26,6 +26,7 @@
 public interface SparkStageInfo {
   int stageId();
   int currentAttemptId();
+  long submissionTime();
   String name();
   int numTasks();
   int numActiveTasks();

core/src/main/java/org/apache/spark/api/java/function/package.scala

@@ -22,4 +22,4 @@ package org.apache.spark.api.java
  * these interfaces to pass functions to various Java API methods for Spark. Please visit Spark's
  * Java programming guide for more details.
  */
-package object function 
+package object function 

core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js

@@ -26,13 +26,6 @@ $(function() {
         // Switch the class of the arrow from open to closed.
         $(this).find('.expand-additional-metrics-arrow').toggleClass('arrow-open');
         $(this).find('.expand-additional-metrics-arrow').toggleClass('arrow-closed');
-
-        // If clicking caused the metrics to expand, automatically check all options for additional
-        // metrics (don't trigger a click when collapsing metrics, because it leads to weird
-        // toggling behavior).
-        if (!$(additionalMetricsDiv).hasClass('collapsed')) {
-            $(this).parent().find('input:checkbox:not(:checked)').trigger('click');
-        }
     });
 
     $("input:checkbox:not(:checked)").each(function() {
@@ -48,6 +41,16 @@ $(function() {
         stripeTables();
     });
 
+    $("#select-all-metrics").click(function() {
+       if (this.checked) {
+          // Toggle all un-checked options.
+          $('input:checkbox:not(:checked)').trigger('click');
+       } else {
+          // Toggle all checked options.
+          $('input:checkbox:checked').trigger('click');
+       }
+    });
+
     // Trigger a click on the checkbox if a user clicks the label next to it.
     $("span.additional-metric-title").click(function() {
         $(this).parent().find('input:checkbox').trigger('click');

core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala

@@ -28,7 +28,9 @@ import org.apache.spark.scheduler._
  * the scheduler queue is not drained in N seconds, then new executors are added. If the queue
  * persists for another M seconds, then more executors are added and so on. The number added
  * in each round increases exponentially from the previous round until an upper bound on the
- * number of executors has been reached.
+ * number of executors has been reached. The upper bound is based both on a configured property
+ * and on the number of tasks pending: the policy will never increase the number of executor
+ * requests past the number needed to handle all pending tasks.
  *
  * The rationale for the exponential increase is twofold: (1) Executors should be added slowly
  * in the beginning in case the number of extra executors needed turns out to be small. Otherwise,
@@ -82,6 +84,12 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
   // During testing, the methods to actually kill and add executors are mocked out
   private val testing = conf.getBoolean("spark.dynamicAllocation.testing", false)
 
+  // TODO: The default value of 1 for spark.executor.cores works right now because dynamic
+  // allocation is only supported for YARN and the default number of cores per executor in YARN is
+  // 1, but it might need to be attained differently for different cluster managers
+  private val tasksPerExecutor =
+    conf.getInt("spark.executor.cores", 1) / conf.getInt("spark.task.cpus", 1)
+
   validateSettings()
 
   // Number of executors to add in the next round
@@ -110,6 +118,9 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
   // Clock used to schedule when executors should be added and removed
   private var clock: Clock = new RealClock
 
+  // Listener for Spark events that impact the allocation policy
+  private val listener = new ExecutorAllocationListener(this)
+
   /**
    * Verify that the settings specified through the config are valid.
    * If not, throw an appropriate exception.
@@ -141,6 +152,9 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
       throw new SparkException("Dynamic allocation of executors requires the external " +
         "shuffle service. You may enable this through spark.shuffle.service.enabled.")
     }
+    if (tasksPerExecutor == 0) {
+      throw new SparkException("spark.executor.cores must not be less than spark.task.cpus.cores")
+    }
   }
 
   /**
@@ -154,7 +168,6 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
    * Register for scheduler callbacks to decide when to add and remove executors.
    */
   def start(): Unit = {
-    val listener = new ExecutorAllocationListener(this)
     sc.addSparkListener(listener)
     startPolling()
   }
@@ -218,13 +231,27 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
       return 0
     }
 
-    // Request executors with respect to the upper bound
-    val actualNumExecutorsToAdd =
-      if (numExistingExecutors + numExecutorsToAdd <= maxNumExecutors) {
-        numExecutorsToAdd
-      } else {
-        maxNumExecutors - numExistingExecutors
-      }
+    // The number of executors needed to satisfy all pending tasks is the number of tasks pending
+    // divided by the number of tasks each executor can fit, rounded up.
+    val maxNumExecutorsPending =
+      (listener.totalPendingTasks() + tasksPerExecutor - 1) / tasksPerExecutor
+    if (numExecutorsPending >= maxNumExecutorsPending) {
+      logDebug(s"Not adding executors because there are already $numExecutorsPending " +
+        s"pending and pending tasks could only fill $maxNumExecutorsPending")
+      numExecutorsToAdd = 1
+      return 0
+    }
+
+    // It's never useful to request more executors than could satisfy all the pending tasks, so
+    // cap request at that amount.
+    // Also cap request with respect to the configured upper bound.
+    val maxNumExecutorsToAdd = math.min(
+      maxNumExecutorsPending - numExecutorsPending,
+      maxNumExecutors - numExistingExecutors)
+    assert(maxNumExecutorsToAdd > 0)
+
+    val actualNumExecutorsToAdd = math.min(numExecutorsToAdd, maxNumExecutorsToAdd)
+
     val newTotalExecutors = numExistingExecutors + actualNumExecutorsToAdd
     val addRequestAcknowledged = testing || sc.requestExecutors(actualNumExecutorsToAdd)
     if (addRequestAcknowledged) {
@@ -445,6 +472,16 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
         blockManagerRemoved: SparkListenerBlockManagerRemoved): Unit = {
       allocationManager.onExecutorRemoved(blockManagerRemoved.blockManagerId.executorId)
     }
+
+    /**
+     * An estimate of the total number of pending tasks remaining for currently running stages. Does
+     * not account for tasks which may have failed and been resubmitted.
+     */
+    def totalPendingTasks(): Int = {
+      stageIdToNumTasks.map { case (stageId, numTasks) =>
+        numTasks - stageIdToTaskIndices.get(stageId).map(_.size).getOrElse(0)
+      }.sum
+    }
   }
 
 }