TasksetManager冲突导致SparkContext异常关闭

背景介绍

当正在悠闲敲着代码的时候，业务方兄弟反馈接收到大量线上运行的spark streaming任务的告警短信，查看应用的web页面信息，发现spark应用已经退出了，第一时间拉起线上的应用，再慢慢的定位故障原因。本文代码基于spark 1.6.1。

问题定位

登陆到线上机器，查看错误日志，发现系统一直报

Cannot
call methods on a stopped SparkContext.

，全部日志如下

[ERROR][JobScheduler][2017-03-08+15:56:00.067][org.apache.spark.streaming.scheduler.JobScheduler]Error running job streaming job 1488959760000 ms.0
java.lang.IllegalStateException: Cannot call methods on a stopped SparkContext.
This stopped SparkContext was created at:

org.apache.spark.SparkContext.<init>(SparkContext.scala:82)
org.apache.spark.streaming.StreamingContext$.createNewSparkContext(StreamingContext.scala:874)
org.apache.spark.streaming.StreamingContext.<init>(StreamingContext.scala:81)
com.xxxx.xxxx.MainApp$.createStreamingContext(MainApp.scala:46)
com.xxxx.xxxx.MainApp$$anonfun$15.apply(MainApp.scala:126)
com.xxxx.xxxx.MainApp$$anonfun$15.apply(MainApp.scala:126)
scala.Option.getOrElse(Option.scala:120)
org.apache.spark.streaming.StreamingContext$.getOrCreate(StreamingContext.scala:864)
com.xxxx.xxxx.MainApp$.main(MainApp.scala:125)
com.xxxx.xxxx.MainApp.main(MainApp.scala)
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
java.lang.reflect.Method.invoke(Method.java:498)
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

看到此处应该很清楚了，是SparkContext已经停止了，接下来我们分析下是什么原因导致了SparkContext的停止，首先找到关闭的日志；分析SparkContext的代码可知，在关闭结束后会打印一个成功关闭的详情日志。

logInfo("Successfully stopped SparkContext")

通过

grep

命令找到相应的日志的位置，如下所示

[INFO][dag-scheduler-event-loop][2017-03-03+22:16:30.841][org.apache.spark.SparkContext]Successfully stopped SparkContext

从日志中可以看出是dag-scheduler-event-loop线程关闭了SparkContext，查看该线程的日志信息，显示如下

java.lang.IllegalStateException: more than one active taskSet for stage 4571114: 4571114.2,4571114.1
at org.apache.spark.scheduler.TaskSchedulerImpl.submitTasks(TaskSchedulerImpl.scala:173)
at org.apache.spark.scheduler.DAGScheduler.submitMissingTasks(DAGScheduler.scala:1052)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:921)
at org.apache.spark.scheduler.DAGScheduler.handleTaskCompletion(DAGScheduler.scala:1214)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1637)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)

上面显示有一个stage同时启动了两个TasksetManager，TaskScheduler.submitTasks的代码如下：

override def submitTasks(taskSet: TaskSet) {
val tasks = taskSet.tasks
logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
this.synchronized {
val manager = createTaskSetManager(taskSet, maxTaskFailures)
val stage = taskSet.stageId
val stageTaskSets =
taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])
stageTaskSets(taskSet.stageAttemptId) = manager
val conflictingTaskSet = stageTaskSets.exists { case (_, ts) =>
ts.taskSet != taskSet && !ts.isZombie
}
if (conflictingTaskSet) {
throw new IllegalStateException(s"more than one active taskSet for stage $stage:" +
s" ${stageTaskSets.toSeq.map{_._2.taskSet.id}.mkString(",")}")
}
.........
}

看到这震惊了，怎么会出现两个呢？继续看之前的日志，发现stage

4571114

被resubmit了；

[INFO][dag-scheduler-event-loop][2017-03-03+22:16:29.547][org.apache.spark.scheduler.DAGScheduler]Resubmitting ShuffleMapStage 4571114 (map at MainApp.scala:73) because some of its tasks had failed: 0
[INFO][dag-scheduler-event-loop][2017-03-03+22:16:29.547][org.apache.spark.scheduler.DAGScheduler]Submitting ShuffleMapStage 4571114 (MapPartitionsRDD[3719544] at map at MainApp.scala:73), which has no missing parents

查看stage重新提交的代码，以下代码截取自DAGScheduler.handleTaskCompletion方法

case smt: ShuffleMapTask =>
val shuffleStage = stage.asInstanceOf[ShuffleMapStage]
updateAccumulators(event)
val status = event.result.asInstanceOf[MapStatus]
val execId = status.location.executorId
logDebug("ShuffleMapTask finished on " + execId)
if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) {
logInfo(s"Ignoring possibly bogus $smt completion from executor $execId")
} else {
shuffleStage.addOutputLoc(smt.partitionId, status)
}

if (runningStages.contains(shuffleStage) && shuffleStage.pendingPartitions.isEmpty) {
markStageAsFinished(shuffleStage)
logInfo("looking for newly runnable stages")
logInfo("running: " + runningStages)
logInfo("waiting: " + waitingStages)
logInfo("failed: " + failedStages)

// We supply true to increment the epoch number here in case this is a
// recomputation of the map outputs. In that case, some nodes may have cached
// locations with holes (from when we detected the error) and will need the
// epoch incremented to refetch them.
// TODO: Only increment the epoch number if this is not the first time
//       we registered these map outputs.
mapOutputTracker.registerMapOutputs(
shuffleStage.shuffleDep.shuffleId,
shuffleStage.outputLocInMapOutputTrackerFormat(),
changeEpoch = true)

clearCacheLocs()

if (!shuffleStage.isAvailable) {
// Some tasks had failed; let's resubmit this shuffleStage
// TODO: Lower-level scheduler should also deal with this
logInfo("Resubmitting " + shuffleStage + " (" + shuffleStage.name +
") because some of its tasks had failed: " +
shuffleStage.findMissingPartitions().mkString(", "))
submitStage(shuffleStage)
} else {
// Mark any map-stage jobs waiting on this stage as finished
if (shuffleStage.mapStageJobs.nonEmpty) {
val stats = mapOutputTracker.getStatistics(shuffleStage.shuffleDep)
for (job <- shuffleStage.mapStageJobs) {
markMapStageJobAsFinished(job, stats)
}
}
}

可以看出只有shuffleStage.pendingPartitions为空同时shuffleStage.isAvailable为false的时候才会触发resubmit，我们来看下这两个变量是什么时候开始，pendingPartitions表示现在正在处理的partition的数量，当task运行结束后会删除，

val stage = stageIdToStage(task.stageId)
event.reason match {
case Success =>
listenerBus.post(SparkListenerTaskEnd(stageId, stage.latestInfo.attemptId, taskType,
event.reason, event.taskInfo, event.taskMetrics))
//从正在处理的partition中移除
stage.pendingPartitions -= task.partitionId

isAvaible判断的是已经告知driver的shuffle数据位置的partition数目是否等于总共的partition数目

def isAvailable: Boolean = _numAvailableOutputs == numPartitions

这个变量也是在ShuffleTask运行结束后进行更新的，不过需要注意的是，只有在Shuffle数据所在的executor还是可用的时候才进行更新，如果运行shuffleTask的executor已经挂了，肯定也无法通过该executor获取磁盘上的shuffle数据

case smt: ShuffleMapTask =>
val shuffleStage = stage.asInstanceOf[ShuffleMapStage]
updateAccumulators(event)
val status = event.result.asInstanceOf[MapStatus]
val execId = status.location.executorId
logDebug("ShuffleMapTask finished on " + execId)
if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) {
logInfo(s"Ignoring possibly bogus $smt completion from executor $execId")
} else {
shuffleStage.addOutputLoc(smt.partitionId, status)
}

唯一的可能造成重新调度的就是该处了，根据关键信息查询下日志信息

[INFO][dag-scheduler-event-loop][2017-03-03+22:16:27.427][org.apache.spark.scheduler.DAGScheduler]Ignoring possibly bogus ShuffleMapTask(4571114, 0) completion from executor 4

但就算此时刚运行完shuffleTask的executor挂掉了，造成了stage的重新调度，也不会导致TasksetManager冲突，因为此时taskset.isZombie状态肯定变了为true，因为TasksetManager.handleSuccessfulTask方法执行在DAGScheduler.handleTaskCompletion之前。

val conflictingTaskSet = stageTaskSets.exists { case (_, ts)
=> ts.taskSet != taskSet && !ts.isZombie

TasksetManager.handleSuccessfulTask

def handleSuccessfulTask(tid: Long, result: DirectTaskResult[_]): Unit = {
val info = taskInfos(tid)
val index = info.index
info.markSuccessful()
removeRunningTask(tid)
// This method is called by "TaskSchedulerImpl.handleSuccessfulTask" which holds the
// "TaskSchedulerImpl" lock until exiting. To avoid the SPARK-7655 issue, we should not
// "deserialize" the value when holding a lock to avoid blocking other threads. So we call
// "result.value()" in "TaskResultGetter.enqueueSuccessfulTask" before reaching here.
// Note: "result.value()" only deserializes the value when it's called at the first time, so
// here "result.value()" just returns the value and won't block other threads.
//最终会提交一个CompletionEvent事件到DAGScheduler的事件队列中等待处理
sched.dagScheduler.taskEnded(
tasks(index), Success, result.value(), result.accumUpdates, info, result.metrics)
if (!successful(index)) {
tasksSuccessful += 1
logInfo("Finished task %s in stage %s (TID %d) in %d ms on %s (%d/%d)".format(
info.id, taskSet.id, info.taskId, info.duration, info.host, tasksSuccessful, numTasks))
// Mark successful and stop if all the tasks have succeeded.
successful(index) = true
if (tasksSuccessful == numTasks) {
isZombie = true
}
} else {
logInfo("Ignoring task-finished event for " + info.id + " in stage " + taskSet.id +
" because task " + index + " has already completed successfully")
}
failedExecutors.remove(index)
maybeFinishTaskSet()
}

可能有的同学已经看出问题来了，为了将问题说的更明白，我画了一个task执行成功的时序图task执行成功时序图结合时序图和代码我们可以看出DAGSchduler.handleCompletion执行发生在了TasksetManager.handleSuccessfulTask方法中isZombie变为true之前，handleSuccessfulTask是在

task-result-getter

线程中执行的,导致isZombie还未变为true，DAGSchduler就触发了stage的重新提交，最终导致TaskManger冲突。以下日志分别是resubmit提交的时间和handleSuccessfuleTask的结束时间，从侧面(由于isZombie变为true并没有马上打印时间)也能够看出resubmit重新提交的时间早于handleSuccessfuleTask。

handleSuccessfuleTask结束时间
[INFO][task-result-getter-2][2017-03-03+22:16:29.999][org.apache.spark.scheduler.TaskSchedulerImpl]Removed TaskSet 4571114.1, whose tasks have all completed, from pool

resubmit stage任务重新提交时间
[INFO][dag-scheduler-event-loop][2017-03-03+22:16:29.549][org.apache.spark.scheduler.TaskSchedulerImpl]Adding task set 4571114.2 with 1 tasks

事件发生的时间轴事件时间

问题修复

该问题修复其实很简单，只需要修改TasksetManager.handleSuccessfulTask的方法，在isZombie=true后再发送CompletionEvent事件即可，代码修复如下

def handleSuccessfulTask(tid: Long, result: DirectTaskResult[_]): Unit = {
val info = taskInfos(tid)
val index = info.index
info.markSuccessful()
removeRunningTask(tid)
// This method is called by "TaskSchedulerImpl.handleSuccessfulTask" which holds the
// "TaskSchedulerImpl" lock until exiting. To avoid the SPARK-7655 issue, we should not
// "deserialize" the value when holding a lock to avoid blocking other threads. So we call
// "result.value()" in "TaskResultGetter.enqueueSuccessfulTask" before reaching here.
// Note: "result.value()" only deserializes the value when it's called at the first time, so
// here "result.value()" just returns the value and won't block other threads.
if (!successful(index)) {
tasksSuccessful += 1
logInfo("Finished task %s in stage %s (TID %d) in %d ms on %s (%d/%d)".format(
info.id, taskSet.id, info.taskId, info.duration, info.host, tasksSuccessful, numTasks))
// Mark successful and stop if all the tasks have succeeded.
successful(index) = true
if (tasksSuccessful == numTasks) {
isZombie = true
}
sched.dagScheduler.taskEnded(
tasks(index), Success, result.value(), result.accumUpdates, info, result.metrics)
} else {
logInfo("Ignoring task-finished event for " + info.id + " in stage " + taskSet.id +
" because task " + index + " has already completed successfully")
}
failedExecutors.remove(index)
maybeFinishTaskSet()
sched.dagScheduler.taskEnded(
tasks(index), Success, result.value(), result.accumUpdates, info, result.metrics)
}