Spark调度系列-----5
Spark调度系列—–5.Spark task和Stage的跳过执行(ui显示task和stage skipped)
heayin123 2015-12-22 17:46:15 13656 收藏 4
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在spark的首页ui上经常显示任务和Stage被skipped，如以下截图所式：
本文将阐述什么情况下Stage或者Task会显示为skipped，以及stage和task显示为skipped的时候是否spark application执行会出问题？
Spark Job的ResultStage的最后一个Task成功执行之后，DAGScheduler.handleTaskCompletion方法会发送SparkListenerJobEnd事件，源码如下：
private[scheduler] def handleTaskCompletion(event: CompletionEvent) {
val task = event.task
val stageId = task.stageId
val taskType = Utils.getFormattedClassName(task)
outputCommitCoordinator.taskCompleted(stageId, task.partitionId,
event.taskInfo.attempt, event.reason)
// The success case is dealt with separately below, since we need to compute accumulator
// updates before posting.
if (event.reason != Success) {
val attemptId = stageIdToStage.get(task.stageId).map(_.latestInfo.attemptId).getOrElse(-1)
listenerBus.post(SparkListenerTaskEnd(stageId, attemptId, taskType, event.reason,
event.taskInfo, event.taskMetrics))
}
if (!stageIdToStage.contains(task.stageId)) {
// Skip all the actions if the stage has been cancelled.
return
}
val stage = stageIdToStage(task.stageId)
event.reason match {
case Success =>
listenerBus.post(SparkListenerTaskEnd(stageId, stage.latestInfo.attemptId, taskType,
event.reason, event.taskInfo, event.taskMetrics))
stage.pendingTasks -= task
task match {
case rt: ResultTask[_, _] =>
// Cast to ResultStage here because it's part of the ResultTask
// TODO Refactor this out to a function that accepts a ResultStage
val resultStage = stage.asInstanceOf[ResultStage]
resultStage.resultOfJob match {
case Some(job) =>
if (!job.finished(rt.outputId)) {
updateAccumulators(event)
job.finished(rt.outputId) = true
job.numFinished += 1
// If the whole job has finished, remove it
if (job.numFinished == job.numPartitions) {//ResultStage所有任务都执行完毕，发送SparkListenerJobEnd事件
markStageAsFinished(resultStage)
cleanupStateForJobAndIndependentStages(job)
listenerBus.post(
SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobSucceeded))
}
// taskSucceeded runs some user code that might throw an exception. Make sure
// we are resilient against that.
try {
job.listener.taskSucceeded(rt.outputId, event.result)
} catch {
case e: Exception =>
// TODO: Perhaps we want to mark the resultStage as failed?
job.listener.jobFailed(new SparkDriverExecutionException(e))
}
}
case None =>
logInfo("Ignoring result from " + rt + " because its job has finished")
}
JobProgressListener.onJobEnd方法负责处理SparkListenerJobEnd事件，代码如下：
override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = synchronized {
val jobData = activeJobs.remove(jobEnd.jobId).getOrElse {
logWarning(s”Job completed for unknown job ${jobEnd.jobId}”)
new JobUIData(jobId = jobEnd.jobId)
}
jobData.completionTime = Option(jobEnd.time).filter(_ >= 0)
jobData.stageIds.foreach(pendingStages.remove)
jobEnd.jobResult match {
case JobSucceeded =>
completedJobs += jobData
trimJobsIfNecessary(completedJobs)
jobData.status = JobExecutionStatus.SUCCEEDED
numCompletedJobs += 1
case JobFailed(exception) =>
failedJobs += jobData
trimJobsIfNecessary(failedJobs)
jobData.status = JobExecutionStatus.FAILED
numFailedJobs += 1
}
for (stageId <- jobData.stageIds) {
stageIdToActiveJobIds.get(stageId).foreach { jobsUsingStage =>
jobsUsingStage.remove(jobEnd.jobId)
if (jobsUsingStage.isEmpty) {
stageIdToActiveJobIds.remove(stageId)
}
stageIdToInfo.get(stageId).foreach { stageInfo =>
if (stageInfo.submissionTime.isEmpty) {//Job的Stage没有提交执行，则这个Stage和它对应的Task会标记为skipped stage和skipped task进行统计
// if this stage is pending, it won't complete, so mark it as "skipped":
skippedStages += stageInfo
trimStagesIfNecessary(skippedStages)
jobData.numSkippedStages += 1
jobData.numSkippedTasks += stageInfo.numTasks
}
}
}
}
}
StageInfo.submissionTime在Stage被分解成TaskSet，并且TaskSet被提交到TaskSetManager之前进行设置，源码如下：
private def submitMissingTasks(stage: Stage, jobId: Int) {
logDebug(“submitMissingTasks(“ + stage + “)”)
// Get our pending tasks and remember them in our pendingTasks entry
stage.pendingTasks.clear()
// First figure out the indexes of partition ids to compute.
//parititionsToCompute是一个List, 表示一个stage需要compute的所有分区的index
val partitionsToCompute: Seq[Int] = {
stage match {
case stage: ShuffleMapStage =>
(0 until stage.numPartitions).filter(id => stage.outputLocs(id).isEmpty)
case stage: ResultStage =>
val job = stage.resultOfJob.get
(0 until job.numPartitions).filter(id => !job.finished(id))
}
}
val properties = jobIdToActiveJob.get(stage.firstJobId).map(_.properties).orNull
runningStages += stage
// SparkListenerStageSubmitted should be posted before testing whether tasks are
// serializable. If tasks are not serializable, a SparkListenerStageCompleted event
// will be posted, which should always come after a corresponding SparkListenerStageSubmitted
// event.
stage.latestInfo = StageInfo.fromStage(stage, Some(partitionsToCompute.size))
outputCommitCoordinator.stageStart(stage.id)
listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
// TODO: Maybe we can keep the taskBinary in Stage to avoid serializing it multiple times.
// Broadcasted binary for the task, used to dispatch tasks to executors. Note that we broadcast
// the serialized copy of the RDD and for each task we will deserialize it, which means each
// task gets a different copy of the RDD. This provides stronger isolation between tasks that
// might modify state of objects referenced in their closures. This is necessary in Hadoop
// where the JobConf/Configuration object is not thread-safe.
var taskBinary: Broadcast[Array[Byte]] = null
try {
// For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
// For ResultTask, serialize and broadcast (rdd, func).
val taskBinaryBytes: Array[Byte] = stage match {
case stage: ShuffleMapStage =>
closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef).array()
case stage: ResultStage =>
closureSerializer.serialize((stage.rdd, stage.resultOfJob.get.func): AnyRef).array()
}
taskBinary = sc.broadcast(taskBinaryBytes)//将任务信息构造成广播变量，广播到每个Executor
} catch {
// In the case of a failure during serialization, abort the stage.
case e: NotSerializableException =>
abortStage(stage, "Task not serializable: " + e.toString)
runningStages -= stage
// Abort execution
return
case NonFatal(e) =>
abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}")
runningStages -= stage
return
}
//tasks是一个List，它表示一个stage每个task的描述，描述信息为：task所在stage id、task处理的partition、partition所在的主机地址和Executor id
val tasks: Seq[Task[_]] = try {
stage match {
case stage: ShuffleMapStage =>
partitionsToCompute.map { id =>
/*
* 获取task所在的节点，数据所在的节点优先启动任务处理这些数据，在这里用到ShuffleMapStage.
* */
val locs = getPreferredLocs(stage.rdd, id)
val part = stage.rdd.partitions(id)
new ShuffleMapTask(stage.id, taskBinary, part, locs)//taskBinary是广播变量
}
case stage: ResultStage =>
val job = stage.resultOfJob.get
partitionsToCompute.map { id =>
val p: Int = job.partitions(id)
val part = stage.rdd.partitions(p)
val locs = getPreferredLocs(stage.rdd, p)
new ResultTask(stage.id, taskBinary, part, locs, id)
}
}
} catch {
case NonFatal(e) =>
abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}")
runningStages -= stage
return
}
if (tasks.size > 0) {
logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")")
stage.pendingTasks ++= tasks
logDebug("New pending tasks: " + stage.pendingTasks)
taskScheduler.submitTasks(
new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.firstJobId, properties))
stage.latestInfo.submissionTime = Some(clock.getTimeMillis())//设置StageInfo的submissionTime成员，表示这个TaskSet会被执行，不会被skipped
} else
Job的Stage没有分解成TaskSet提交执行，则这个Stage和它对应的Task会标记为skipped stage和skipped task进行统计显示。
那种Stage不会分解成TaskSet分解执行呢？
Spark在提交Job的时候，会发送JobSubmitted事件，DAGScheduler.doOnReceive接收到JobSubmitted事件之后，会调用DAGScheduler.handleJobSubmitted方法处理任务提交。
DAGScheduler.handleJobSubmitted首先调用DAGScheduler.newResultStage方法创建最后一个Stage，DAGScheduler.newResultStage通过以下一系列函数调用最终会调用到DAGScheduler.registerShuffleDependencies，这个方法将这个RDD所有的祖宗Stage加入到DAGScheduler.jobIdToStageIds这个HashMap中。然后获取这个Job的每个Stage对应的StageInfo，转换成一个Seq，发送SparkListenerJobStart事件。
DAGScheduler.newResultStage->
DAGScheduler.getParentStagesAndId->
DAGScheduler.getParentStagesAndId->getParentStages
DAGScheduler.getParentStagesAndId->getShuffleMapStage
DAGScheduler.registerShuffleDependencies
DAGScheduler.registerShuffleDependencies首先调用DAGScheduler.getAncestorShuffleDependencies找到当前rdd所有祖宗的rdd依赖，包括父辈、爷爷辈，以致更高辈分的rdd依赖，然后调用DAGScheduler.newOrUsedShuffleStage创建每个祖宗rdd依赖对应的ShuffleMapStage，
private def registerShuffleDependencies(shuffleDep: ShuffleDependency[_, _, _], firstJobId: Int) {
val parentsWithNoMapStage = getAncestorShuffleDependencies(shuffleDep.rdd)//获取所有祖宗rdd依赖，包括父辈、爷爷辈等
while (parentsWithNoMapStage.nonEmpty) {
val currentShufDep = parentsWithNoMapStage.pop()
//根据ShuffleDependency和jobid生成Stage，由于是从栈里面弹出，所以最先添加的是Root stage，依次类推，最先添加的Stage shuffleId越小
val stage = newOrUsedShuffleStage(currentShufDep, firstJobId)
shuffleToMapStage(currentShufDep.shuffleId) = stage
}
}
private def newOrUsedShuffleStage(
shuffleDep: ShuffleDependency[_, _, ],
firstJobId: Int): ShuffleMapStage = {
val rdd = shuffleDep.rdd
val numTasks = rdd.partitions.size
val stage = newShuffleMapStage(rdd, numTasks, shuffleDep, firstJobId, rdd.creationSite)//创建stage
if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) {
val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId)
val locs = MapOutputTracker.deserializeMapStatuses(serLocs)
for (i <- 0 until locs.size) {
stage.outputLocs(i) = Option(locs(i)).toList // locs(i) will be null if missing
}
stage.numAvailableOutputs = locs.count( != null)
} else {
// Kind of ugly: need to register RDDs with the cache and map output tracker here
// since we can’t do it in the RDD constructor because # of partitions is unknown
logInfo(“Registering RDD “ + rdd.id + “ (“ + rdd.getCreationSite + “)”)
mapOutputTracker.registerShuffle(shuffleDep.shuffleId, rdd.partitions.size)
}
stage
}
DAGScheduler.newOrUsedShuffleStage会调用DAGScheduler.newShuffleMapStage创建stage。
DAGScheduler.newShuffleMapStage方法创建了stage之后，调用DAGScheduler.updateJobIdStageIdMaps方法将新创建的stage.id加入到DAGScheduler.jobIdToStageIds中。源码如下：
private def updateJobIdStageIdMaps(jobId: Int, stage: Stage): Unit = {
def updateJobIdStageIdMapsList(stages: List[Stage]) {
if (stages.nonEmpty) {
val s = stages.head
s.jobIds += jobId
jobIdToStageIds.getOrElseUpdate(jobId, new HashSetInt) += s.id//将stage id加入到jobIdToStageIds中
val parents: List[Stage] = getParentStages(s.rdd, jobId)
val parentsWithoutThisJobId = parents.filter { ! _.jobIds.contains(jobId) }
updateJobIdStageIdMapsList(parentsWithoutThisJobId ++ stages.tail)
}
}
updateJobIdStageIdMapsList(List(stage))
}
DAGScheduler.handleJobSubmitted源码如下：
private[scheduler] def handleJobSubmitted(jobId: Int,
finalRDD: RDD[],
func: (TaskContext, Iterator[]) => ,
partitions: Array[Int],
allowLocal: Boolean,
callSite: CallSite,
listener: JobListener,
properties: Properties) {
var finalStage: ResultStage = null
try {
// New stage creation may throw an exception if, for example, jobs are run on a
// HadoopRDD whose underlying HDFS files have been deleted.
finalStage = newResultStage(finalRDD, partitions.size, jobId, callSite)//创建ResultStage，在这个方法里面会将这个Job执行过程中，需要可能经历的Stage全部放入到
} catch {
case e: Exception =>
logWarning(“Creating new stage failed due to exception - job: “ + jobId, e)
listener.jobFailed(e)
return
}
if (finalStage != null) {
val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
clearCacheLocs()
logInfo(“Got job %s (%s) with %d output partitions (allowLocal=%s)”.format(
job.jobId, callSite.shortForm, partitions.length, allowLocal))
logInfo(“Final stage: “ + finalStage + “(“ + finalStage.name + “)”)
logInfo(“Parents of final stage: “ + finalStage.parents)
logInfo(“Missing parents: “ + getMissingParentStages(finalStage))
val shouldRunLocally =
localExecutionEnabled && allowLocal && finalStage.parents.isEmpty && partitions.length == 1
val jobSubmissionTime = clock.getTimeMillis()
if (shouldRunLocally) {
// Compute very short actions like first() or take() with no parent stages locally.
listenerBus.post(
SparkListenerJobStart(job.jobId, jobSubmissionTime, Seq.empty, properties))
runLocally(job)
} else {
jobIdToActiveJob(jobId) = job
activeJobs += job
finalStage.resultOfJob = Some(job)
val stageIds = jobIdToStageIds(jobId).toArray//获取一个Job对应的所有的Stage id，Job的所有Stage在执行newResultStage的时候会创建，所以在这里能获取成功
val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(.latestInfo))//获取每个Stage对应的StageInfo
listenerBus.post(
SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))//发送Job启动事件SparkListenerJobStart
submitStage(finalStage)
}
}
submitWaitingStages()
}
JobProgressListener.onJobStart负责接收处理SparkListenerJobStart事件。它会把DAGScheduler.handleJobSubmitted方法创建的所有StageInfo信息放到JobProgressListener.stageIdToInfo这个HashMap中。
至此可以得出结论：JobProgressListener.onJobEnd方法中，处理的obProgressListener.stageIdToInfo信息是执行DAGScheduler.handleJobSubmitted产生的。在Job对应的所有Stage分解成Task之前就已经产生了。
根据本人的
Spark storage系列——3.Spark cache数据块之后对后继Job任务调度的影响，以及后继Job Task执行的影响
文章可以知道，在将Stage分解成TaskSet的时候，如果一个RDD已经Cache到了BlockManager，则这个RDD对应的所有祖宗Stage都不会分解成TaskSet进行执行，所以这些祖宗Stage对应的StageInfo.submissionTime.isEmpty就会返回true，所以这些祖宗Stage和它们对应的Task就会在Spark ui上显示为skipped
Stage执行完成之后，会执行JobProgressListener.onStageCompleted将Stage信息保存到JobProgressListener.stageIdToInfo，源码如下：
override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = synchronized {
val stage = stageCompleted.stageInfo
stageIdToInfo(stage.stageId) = stage//保存Stage的信息，便于跟踪显示
val stageData = stageIdToData.getOrElseUpdate((stage.stageId, stage.attemptId), {
logWarning(“Stage completed for unknown stage “ + stage.stageId)
new StageUIData
})
Stage对应的TaskSet中所有任务成功执行后，会将Stage对应的StageInfo反馈到JobProgressListener.stageIdToInfo，这样这些任务就不会显示为skipped了
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版权声明：本文为CSDN博主「heayin123」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/u012684933/article/details/50378725