Spark调度系列-----5.Spark task和Stage的跳过执行(ui显示task和stage skipped)_spark skipped

      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 HashSet[Int]()) += 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