SparkUI中显示stage skipped的原因【源码分析】

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。

哪种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  
    }  
  }

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()  
  }

结论：

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。

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

任务出现skipped是正常的，之所以出现skipped是因为要计算的数据已经缓存到了内存，没有必要再重复计算。出现skipped对结果没有影响