Other minor fixes

Author: pwendell

core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala

@@ -924,9 +924,9 @@ class DAGScheduler(
                 AccumulableInfo(id, name, Some(stringPartialValue), stringValue)
             }
           }
-          listenerBus.post(SparkListenerTaskEnd(stageId, taskType, event.reason, event.taskInfo,
-            event.taskMetrics))
         }
+        listenerBus.post(SparkListenerTaskEnd(stageId, taskType, event.reason, event.taskInfo,
+          event.taskMetrics))
         stage.pendingTasks -= task
         task match {
           case rt: ResultTask[_, _] =>

core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala

@@ -60,7 +60,6 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
   override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized {
     val stage = stageCompleted.stageInfo
     val stageId = stage.stageId
-
     val stageData = stageIdToData.getOrElseUpdate(stageId, {
       logWarning("Stage completed for unknown stage " + stageId)
       new StageUIData

core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala

@@ -215,12 +215,16 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
           Some(UIUtils.listingTable(quantileHeaders, quantileRow, listings, fixedWidth = true))
         }
       val executorTable = new ExecutorTable(stageId, parent)
+
+      val maybeAccumulableTable: Seq[Node] =
+        if (accumulables.size > 0) { <h4>Accumulators</h4> ++ accumulableTable } else Seq()
+
       val content =
         summary ++
         <h4>Summary Metrics for {numCompleted} Completed Tasks</h4> ++
         <div>{summaryTable.getOrElse("No tasks have reported metrics yet.")}</div> ++
         <h4>Aggregated Metrics by Executor</h4> ++ executorTable.toNodeSeq ++
-        <h4>Accumulators</h4> ++ accumulableTable ++
+        maybeAccumulableTable ++
         <h4>Tasks</h4> ++ taskTable
 
       UIUtils.headerSparkPage(content, basePath, appName, "Details for Stage %d".format(stageId),

docs/programming-guide.md

@@ -1174,8 +1174,9 @@ value of the broadcast variable (e.g. if the variable is shipped to a new node l
 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 types, and programmers
-can add support for new types. Accumulator values are displayed in Spark's UI and can be 
-useful for understanding the progress of running stages.
+can add support for new types. If accumulators are created with a name, they will be
+displayed in Spark's UI. This can can be useful for understanding the progress of 
+running stages (NOTE: this is not yet supported in Python).
 
 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 `add` method or the `+=` operator (in Scala and Python).