Spark AQE 导致的 Driver OOM问题

本文主要是介绍Spark AQE 导致的 Driver OOM问题，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

背景

最近在做Spark 3.1 升级 Spark 3.5的过程中，遇到了一批SQL在运行的过程中 Driver OOM的情况，排查到是AQE开启导致的问题，再次分析记录一下，顺便了解一下Spark中指标的事件处理情况

结论

SQLAppStatusListener 类在内存中存放着 一个整个SQL查询链的所有stage以及stage的指标信息，在AQE中 一个job会被拆分成很多job，甚至几百上千的job，这个时候 stageMetrics的数据就会成百上倍的被存储在内存中，从而导致Driver OOM。
解决方法：

1. 关闭AQE spark.sql.adaptive.enabled false
2. 合并对应的PR-SPARK-45439

分析

背景知识：对于一个完整链接的sql语句来说(比如说从 读取数据源，到 数据处理操作，再到插入hive表),这可以称其为一个最小的SQL执行单元，这最小的数据执行单元在Spark内部是可以跟踪的,也就是用executionId来进行跟踪的。
对于一个sql，举例来说 :

insert into  TableA select * from TableB;

在生成 物理计划的过程中会调用 QueryExecution.assertOptimized 方法，该方法会触发eagerlyExecuteCommands调用，最终会到SQLExecution.withNewExecutionId方法：

  def assertOptimized(): Unit = optimizedPlan...lazy val commandExecuted: LogicalPlan = mode match {case CommandExecutionMode.NON_ROOT => analyzed.mapChildren(eagerlyExecuteCommands)case CommandExecutionMode.ALL => eagerlyExecuteCommands(analyzed)case CommandExecutionMode.SKIP => analyzed}...lazy val optimizedPlan: LogicalPlan = {// We need to materialize the commandExecuted here because optimizedPlan is also tracked under// the optimizing phaseassertCommandExecuted()executePhase(QueryPlanningTracker.OPTIMIZATION) {// clone the plan to avoid sharing the plan instance between different stages like analyzing,// optimizing and planning.val plan =sparkSession.sessionState.optimizer.executeAndTrack(withCachedData.clone(), tracker)// We do not want optimized plans to be re-analyzed as literals that have been constant// folded and such can cause issues during analysis. While `clone` should maintain the// `analyzed` state of the LogicalPlan, we set the plan as analyzed here as well out of// paranoia.plan.setAnalyzed()plan}def assertCommandExecuted(): Unit = commandExecuted...private def eagerlyExecuteCommands(p: LogicalPlan) = p transformDown {case c: Command =>// Since Command execution will eagerly take place here,// and in most cases be the bulk of time and effort,// with the rest of processing of the root plan being just outputting command results,// for eagerly executed commands we mark this place as beginning of execution.tracker.setReadyForExecution()val qe = sparkSession.sessionState.executePlan(c, CommandExecutionMode.NON_ROOT)val name = commandExecutionName(c)val result = QueryExecution.withInternalError(s"Eagerly executed $name failed.") {SQLExecution.withNewExecutionId(qe, Some(name)) {qe.executedPlan.executeCollect()}}  

而SQLExecution.withNewExecutionId主要的作用是设置当前计划的所属的executionId:

    val executionId = SQLExecution.nextExecutionIdsc.setLocalProperty(EXECUTION_ID_KEY, executionId.toString)

该EXECUTION_ID_KEY的值会在JobStart的时候传递给Event，以便记录跟踪整个执行过程中的指标信息。
同时我们在方法中eagerlyExecuteCommands看到qe.executedPlan.executeCollect()这是具体的执行方法，针对于insert into 操作来说，物理计划就是
InsertIntoHadoopFsRelationCommand，这里的run方法最终会流转到DAGScheduler.submitJob方法：

    eventProcessLoop.post(JobSubmitted(jobId, rdd, func2, partitions.toArray, callSite, waiter,JobArtifactSet.getActiveOrDefault(sc),Utils.cloneProperties(properties)))

最终会被DAGScheduler.handleJobSubmitted处理，其中会发送SparkListenerJobStart事件：

    listenerBus.post(SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos,Utils.cloneProperties(properties)))

该事件会被SQLAppStatusListener捕获，从而转到onJobStart处理，这里有会涉及到指标信息的存储，这里我们截图出dump的内存占用情况：

可以看到 SQLAppStatusListener 的 LiveStageMetrics 占用很大，也就是 accumIdsToMetricType占用很大

那在AQE中是怎么回事呢？
我们知道再AQE中，任务会从source节点按照shuffle进行分割，从而形成单独的job，从而生成对应的shuffle指标，具体的分割以及执行代码在AdaptiveSparkPlanExec.getFinalPhysicalPlan中，如下:

      var result = createQueryStages(currentPhysicalPlan)val events = new LinkedBlockingQueue[StageMaterializationEvent]()val errors = new mutable.ArrayBuffer[Throwable]()var stagesToReplace = Seq.empty[QueryStageExec]while (!result.allChildStagesMaterialized) {currentPhysicalPlan = result.newPlanif (result.newStages.nonEmpty) {stagesToReplace = result.newStages ++ stagesToReplaceexecutionId.foreach(onUpdatePlan(_, result.newStages.map(_.plan)))// SPARK-33933: we should submit tasks of broadcast stages first, to avoid waiting// for tasks to be scheduled and leading to broadcast timeout.// This partial fix only guarantees the start of materialization for BroadcastQueryStage// is prior to others, but because the submission of collect job for broadcasting is// running in another thread, the issue is not completely resolved.val reorderedNewStages = result.newStages.sortWith {case (_: BroadcastQueryStageExec, _: BroadcastQueryStageExec) => falsecase (_: BroadcastQueryStageExec, _) => truecase _ => false}// Start materialization of all new stages and fail fast if any stages failed eagerlyreorderedNewStages.foreach { stage =>try {stage.materialize().onComplete { res =>if (res.isSuccess) {events.offer(StageSuccess(stage, res.get))} else {events.offer(StageFailure(stage, res.failed.get))}// explicitly clean up the resources in this stagestage.cleanupResources()}(AdaptiveSparkPlanExec.executionContext)

这里就是得看stage.materialize()这个方法，这两个stage只有两类：BroadcastQueryStageExec 和 ShuffleQueryStageExec，
这两个物理计划稍微分析一下如下:

• BroadcastQueryStageExec
  数据流如下：

  broadcast.submitBroadcastJob||\/
  promise.future||\/
  relationFuture||\/
  child.executeCollectIterator()

  其中 promise的设置在relationFuture方法中，而relationFuture 会被doPrepare调用,而submitBroadcastJob会调用executeQuery,从而调用doPrepare,executeCollectIterator()最终也会发送JobSubmitted事件，分析和上面的一样
• ShuffleQueryStageExec

   shuffle.submitShuffleJob||\/sparkContext.submitMapStage(shuffleDependency)||\/dagScheduler.submitMapStage

该submitMapStage会发送MapStageSubmitted事件:

    eventProcessLoop.post(MapStageSubmitted(jobId, dependency, callSite, waiter, JobArtifactSet.getActiveOrDefault(sc),Utils.cloneProperties(properties)))

最终会被DAGScheduler.handleMapStageSubmitted处理，其中会发送SparkListenerJobStart事件：

    listenerBus.post(SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos,Utils.cloneProperties(properties)))

该事件会被SQLAppStatusListener捕获，从而转到onJobStart处理:

  private val liveExecutions = new ConcurrentHashMap[Long, LiveExecutionData]()private val stageMetrics = new ConcurrentHashMap[Int, LiveStageMetrics]()...override def onJobStart(event: SparkListenerJobStart): Unit = {val executionIdString = event.properties.getProperty(SQLExecution.EXECUTION_ID_KEY)if (executionIdString == null) {// This is not a job created by SQLreturn}val executionId = executionIdString.toLongval jobId = event.jobIdval exec = Option(liveExecutions.get(executionId))

该方法会获取事件中的executionId,在AQE中，同一个执行单元的executionId是一样的，所以stageMetrics内存占用会越来越大。
而这里指标的更新是在AdaptiveSparkPlanExec.onUpdatePlan等方法中。

这样整个事件的数据流以及问题的产生原因就应该很清楚了。

其他

为啥AQE以后多个Job还是共享一个executionId呢？因为原则上来说，如果没有开启AQE之前，一个SQL执行单元的是属于同一个Job的，开启了AQE之后，因为AQE的原因，一个Job被拆成了了多个Job，但是从逻辑上来说，还是属于同一个SQL处理单元的所以还是得归属到一次执行中。

这篇关于Spark AQE 导致的 Driver OOM问题的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！

---