Add number of bytes spilled to Web UI
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e6447152b3
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@ -32,9 +32,9 @@ case class Aggregator[K, V, C] (
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mergeCombiners: (C, C) => C) {
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private val sparkConf = SparkEnv.get.conf
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private val externalSorting = sparkConf.getBoolean("spark.shuffle.externalSorting", true)
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private val externalSorting = sparkConf.getBoolean("spark.shuffle.externalSorting", false)
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def combineValuesByKey(iter: Iterator[_ <: Product2[K, V]]) : Iterator[(K, C)] = {
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def combineValuesByKey(iter: Iterator[_ <: Product2[K, V]], context: TaskContext) : Iterator[(K, C)] = {
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if (!externalSorting) {
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val combiners = new AppendOnlyMap[K,C]
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var kv: Product2[K, V] = null
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@ -53,11 +53,12 @@ case class Aggregator[K, V, C] (
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val (k, v) = iter.next()
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combiners.insert(k, v)
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}
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combiners.registerBytesSpilled(context.attemptId)
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combiners.iterator
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}
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}
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def combineCombinersByKey(iter: Iterator[(K, C)]) : Iterator[(K, C)] = {
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def combineCombinersByKey(iter: Iterator[(K, C)], context: TaskContext) : Iterator[(K, C)] = {
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if (!externalSorting) {
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val combiners = new AppendOnlyMap[K,C]
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var kc: Product2[K, C] = null
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@ -75,6 +76,7 @@ case class Aggregator[K, V, C] (
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val (k, c) = iter.next()
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combiners.insert(k, c)
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}
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combiners.registerBytesSpilled(context.attemptId)
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combiners.iterator
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}
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}
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@ -60,6 +60,9 @@ class SparkEnv private[spark] (
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// All accesses should be manually synchronized
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val shuffleMemoryMap = mutable.HashMap[Long, Long]()
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// A mapping of task ID to number of bytes spilled by that task. This is mainly for book-keeping.
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val bytesSpilledMap = mutable.HashMap[Long, Long]()
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private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]()
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// A general, soft-reference map for metadata needed during HadoopRDD split computation
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@ -229,6 +229,7 @@ private[spark] class Executor(
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m.executorRunTime = (taskFinish - taskStart).toInt
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m.jvmGCTime = gcTime - startGCTime
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m.resultSerializationTime = (afterSerialization - beforeSerialization).toInt
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m.bytesSpilled = env.bytesSpilledMap.get(taskId).getOrElse(0)
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}
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val accumUpdates = Accumulators.values
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@ -279,11 +280,12 @@ private[spark] class Executor(
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//System.exit(1)
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}
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} finally {
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// TODO: Unregister shuffle memory only for ShuffleMapTask
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// TODO: Unregister shuffle memory only for ResultTask
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val shuffleMemoryMap = env.shuffleMemoryMap
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shuffleMemoryMap.synchronized {
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shuffleMemoryMap.remove(Thread.currentThread().getId)
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}
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env.bytesSpilledMap.remove(taskId)
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runningTasks.remove(taskId)
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}
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}
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@ -48,6 +48,11 @@ class TaskMetrics extends Serializable {
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*/
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var resultSerializationTime: Long = _
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/**
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* The number of bytes spilled to disk by this task
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*/
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var bytesSpilled: Long = _
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/**
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* If this task reads from shuffle output, metrics on getting shuffle data will be collected here
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*/
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@ -106,7 +106,8 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
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override val partitioner = Some(part)
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override def compute(s: Partition, context: TaskContext): Iterator[(K, CoGroupCombiner)] = {
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val externalSorting = sparkConf.getBoolean("spark.shuffle.externalSorting", true)
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val externalSorting = sparkConf.getBoolean("spark.shuffle.externalSorting", false)
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val split = s.asInstanceOf[CoGroupPartition]
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val numRdds = split.deps.size
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@ -150,6 +151,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
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map.insert(kv._1, new CoGroupValue(kv._2, depNum))
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}
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}
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map.registerBytesSpilled(context.attemptId)
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new InterruptibleIterator(context, map.iterator)
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}
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}
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@ -88,20 +88,22 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)])
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val aggregator = new Aggregator[K, V, C](createCombiner, mergeValue, mergeCombiners)
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if (self.partitioner == Some(partitioner)) {
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self.mapPartitionsWithContext((context, iter) => {
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new InterruptibleIterator(context, aggregator.combineValuesByKey(iter))
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new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
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}, preservesPartitioning = true)
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} else if (mapSideCombine) {
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val combined = self.mapPartitions(aggregator.combineValuesByKey, preservesPartitioning = true)
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val combined = self.mapPartitionsWithContext((context, iter) => {
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aggregator.combineValuesByKey(iter, context)
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}, preservesPartitioning = true)
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val partitioned = new ShuffledRDD[K, C, (K, C)](combined, partitioner)
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.setSerializer(serializerClass)
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partitioned.mapPartitionsWithContext((context, iter) => {
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new InterruptibleIterator(context, aggregator.combineCombinersByKey(iter))
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new InterruptibleIterator(context, aggregator.combineCombinersByKey(iter, context))
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}, preservesPartitioning = true)
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} else {
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// Don't apply map-side combiner.
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val values = new ShuffledRDD[K, V, (K, V)](self, partitioner).setSerializer(serializerClass)
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values.mapPartitionsWithContext((context, iter) => {
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new InterruptibleIterator(context, aggregator.combineValuesByKey(iter))
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new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
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}, preservesPartitioning = true)
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}
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}
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@ -24,4 +24,5 @@ private[spark] class ExecutorSummary {
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var succeededTasks : Int = 0
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var shuffleRead : Long = 0
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var shuffleWrite : Long = 0
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var bytesSpilled : Long = 0
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}
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@ -48,6 +48,7 @@ private[spark] class ExecutorTable(val parent: JobProgressUI, val stageId: Int)
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<th>Succeeded Tasks</th>
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<th>Shuffle Read</th>
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<th>Shuffle Write</th>
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<th>Bytes Spilled</th>
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</thead>
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<tbody>
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{createExecutorTable()}
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@ -80,6 +81,7 @@ private[spark] class ExecutorTable(val parent: JobProgressUI, val stageId: Int)
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<td>{v.succeededTasks}</td>
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<td>{Utils.bytesToString(v.shuffleRead)}</td>
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<td>{Utils.bytesToString(v.shuffleWrite)}</td>
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<td>{Utils.bytesToString(v.bytesSpilled)}</td>
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</tr>
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}
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}
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@ -52,6 +52,7 @@ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkList
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val stageIdToTime = HashMap[Int, Long]()
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val stageIdToShuffleRead = HashMap[Int, Long]()
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val stageIdToShuffleWrite = HashMap[Int, Long]()
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val stageIdToBytesSpilled = HashMap[Int, Long]()
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val stageIdToTasksActive = HashMap[Int, HashSet[TaskInfo]]()
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val stageIdToTasksComplete = HashMap[Int, Int]()
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val stageIdToTasksFailed = HashMap[Int, Int]()
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@ -78,6 +79,7 @@ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkList
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stageIdToTime.remove(s.stageId)
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stageIdToShuffleRead.remove(s.stageId)
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stageIdToShuffleWrite.remove(s.stageId)
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stageIdToBytesSpilled.remove(s.stageId)
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stageIdToTasksActive.remove(s.stageId)
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stageIdToTasksComplete.remove(s.stageId)
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stageIdToTasksFailed.remove(s.stageId)
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@ -149,6 +151,7 @@ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkList
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Option(taskEnd.taskMetrics).foreach { taskMetrics =>
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taskMetrics.shuffleReadMetrics.foreach { y.shuffleRead += _.remoteBytesRead }
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taskMetrics.shuffleWriteMetrics.foreach { y.shuffleWrite += _.shuffleBytesWritten }
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y.bytesSpilled += taskMetrics.bytesSpilled
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}
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}
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case _ => {}
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@ -184,6 +187,10 @@ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkList
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stageIdToShuffleWrite(sid) += shuffleWrite
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totalShuffleWrite += shuffleWrite
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stageIdToBytesSpilled.getOrElseUpdate(sid, 0L)
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val bytesSpilled = metrics.map(m => m.bytesSpilled).getOrElse(0L)
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stageIdToBytesSpilled(sid) += bytesSpilled
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val taskList = stageIdToTaskInfos.getOrElse(
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sid, HashSet[(TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])]())
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taskList -= ((taskEnd.taskInfo, None, None))
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@ -56,6 +56,8 @@ private[spark] class StagePage(parent: JobProgressUI) {
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val hasShuffleRead = shuffleReadBytes > 0
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val shuffleWriteBytes = listener.stageIdToShuffleWrite.getOrElse(stageId, 0L)
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val hasShuffleWrite = shuffleWriteBytes > 0
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val bytesSpilled = listener.stageIdToBytesSpilled.getOrElse(stageId, 0L)
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val hasBytesSpilled = bytesSpilled > 0
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var activeTime = 0L
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listener.stageIdToTasksActive(stageId).foreach(activeTime += _.timeRunning(now))
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@ -81,6 +83,12 @@ private[spark] class StagePage(parent: JobProgressUI) {
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{Utils.bytesToString(shuffleWriteBytes)}
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</li>
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}
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{if (hasBytesSpilled)
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<li>
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<strong>Bytes spilled: </strong>
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{Utils.bytesToString(bytesSpilled)}
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</li>
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}
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</ul>
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</div>
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@ -89,9 +97,10 @@ private[spark] class StagePage(parent: JobProgressUI) {
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Seq("Duration", "GC Time", "Result Ser Time") ++
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{if (hasShuffleRead) Seq("Shuffle Read") else Nil} ++
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{if (hasShuffleWrite) Seq("Write Time", "Shuffle Write") else Nil} ++
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{if (hasBytesSpilled) Seq("Bytes Spilled") else Nil} ++
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Seq("Errors")
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val taskTable = listingTable(taskHeaders, taskRow(hasShuffleRead, hasShuffleWrite), tasks)
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val taskTable = listingTable(taskHeaders, taskRow(hasShuffleRead, hasShuffleWrite, hasBytesSpilled), tasks)
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// Excludes tasks which failed and have incomplete metrics
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val validTasks = tasks.filter(t => t._1.status == "SUCCESS" && (t._2.isDefined))
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@ -153,13 +162,20 @@ private[spark] class StagePage(parent: JobProgressUI) {
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}
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val shuffleWriteQuantiles = "Shuffle Write" +: getQuantileCols(shuffleWriteSizes)
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val bytesSpilledSizes = validTasks.map {
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case(info, metrics, exception) =>
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metrics.get.bytesSpilled.toDouble
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}
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val bytesSpilledQuantiles = "Bytes Spilled" +: getQuantileCols(bytesSpilledSizes)
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val listings: Seq[Seq[String]] = Seq(
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serializationQuantiles,
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serviceQuantiles,
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gettingResultQuantiles,
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schedulerDelayQuantiles,
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if (hasShuffleRead) shuffleReadQuantiles else Nil,
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if (hasShuffleWrite) shuffleWriteQuantiles else Nil)
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if (hasShuffleWrite) shuffleWriteQuantiles else Nil,
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if (hasBytesSpilled) bytesSpilledQuantiles else Nil)
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val quantileHeaders = Seq("Metric", "Min", "25th percentile",
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"Median", "75th percentile", "Max")
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@ -178,8 +194,7 @@ private[spark] class StagePage(parent: JobProgressUI) {
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}
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}
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def taskRow(shuffleRead: Boolean, shuffleWrite: Boolean)
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def taskRow(shuffleRead: Boolean, shuffleWrite: Boolean, bytesSpilled: Boolean)
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(taskData: (TaskInfo, Option[TaskMetrics], Option[ExceptionFailure])): Seq[Node] = {
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def fmtStackTrace(trace: Seq[StackTraceElement]): Seq[Node] =
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trace.map(e => <span style="display:block;">{e.toString}</span>)
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@ -205,6 +220,10 @@ private[spark] class StagePage(parent: JobProgressUI) {
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val writeTimeReadable = maybeWriteTime.map{ t => t / (1000 * 1000)}.map{ ms =>
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if (ms == 0) "" else parent.formatDuration(ms)}.getOrElse("")
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val maybeBytesSpilled = metrics.map{m => m.bytesSpilled}
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val bytesSpilledSortable = maybeBytesSpilled.map(_.toString).getOrElse("")
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val bytesSpilledReadable = maybeBytesSpilled.map{Utils.bytesToString(_)}.getOrElse("")
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<tr>
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<td>{info.index}</td>
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<td>{info.taskId}</td>
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@ -234,6 +253,11 @@ private[spark] class StagePage(parent: JobProgressUI) {
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{shuffleWriteReadable}
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</td>
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}}
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{if (bytesSpilled) {
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<td sorttable_customkey={bytesSpilledSortable}>
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{bytesSpilledReadable}
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</td>
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}}
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<td>{exception.map(e =>
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<span>
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{e.className} ({e.description})<br/>
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@ -77,7 +77,7 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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}
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// Number of pairs in the in-memory map
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private var numPairsInMemory = 0
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private var numPairsInMemory = 0L
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// Number of in-memory pairs inserted before tracking the map's shuffle memory usage
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private val trackMemoryThreshold = 1000
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@ -85,6 +85,9 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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// How many times we have spilled so far
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private var spillCount = 0
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// Number of bytes spilled in total
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private var bytesSpilled = 0L
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private val fileBufferSize = sparkConf.getInt("spark.shuffle.file.buffer.kb", 100) * 1024
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private val syncWrites = sparkConf.getBoolean("spark.shuffle.sync", false)
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private val comparator = new KCComparator[K, C]
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@ -161,6 +164,14 @@ private[spark] class ExternalAppendOnlyMap[K, V, C](
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shuffleMemoryMap(Thread.currentThread().getId) = 0
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}
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numPairsInMemory = 0
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bytesSpilled += mapSize
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}
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/**
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* Register the total number of bytes spilled by this task
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*/
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def registerBytesSpilled(taskId: Long) {
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SparkEnv.get.bytesSpilledMap(taskId) = bytesSpilled
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}
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/**
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