前段時間工作中踩到SPARK-21444的坑纷闺，這里做個記錄安接。
1 場景描述
我們的一個spark app在正常運(yùn)行幾個月后經(jīng)常出現(xiàn)driver端hang住的情況挽拔，用yarn container -list查看發(fā)現(xiàn)只有driver的container還在矛纹，executors已全部退出抵栈。然后查看driver端的日志，發(fā)現(xiàn)DAGSchedulerEventProcessLoop failed昙读，SparkContext已shut down召调，錯誤棧如下：
19/03/27 13:39:37 ERROR scheduler.DAGSchedulerEventProcessLoop: DAGSchedulerEventProcessLoop failed; shutting down SparkContext
org.apache.spark.SparkException: Exception thrown in awaitResult:
? ? ? ? at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:205)
? ? ? ? at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
? ? ? ? at org.apache.spark.storage.BlockManagerMaster.removeBroadcast(BlockManagerMaster.scala:152)
? ? ? ? at org.apache.spark.broadcast.TorrentBroadcast$.unpersist(TorrentBroadcast.scala:306)
? ? ? ? at org.apache.spark.broadcast.TorrentBroadcast.doDestroy(TorrentBroadcast.scala:197)
? ? ? ? at org.apache.spark.broadcast.Broadcast.destroy(Broadcast.scala:111)
? ? ? ? at org.apache.spark.broadcast.Broadcast.destroy(Broadcast.scala:98)
? ? ? ? at org.apache.spark.ShuffleStatus.invalidateSerializedMapOutputStatusCache(MapOutputTracker.scala:180)
? ? ? ? at org.apache.spark.ShuffleStatus$$anonfun$removeOutputsOnExecutor$1.apply$mcVI$sp(MapOutputTracker.scala:118)
? ? ? ? at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:160)
? ? ? ? at org.apache.spark.ShuffleStatus.removeOutputsOnExecutor(MapOutputTracker.scala:114)
? ? ? ? at org.apache.spark.MapOutputTrackerMaster$$anonfun$removeOutputsOnExecutor$2.apply(MapOutputTracker.scala:424)
? ? ? ? at org.apache.spark.MapOutputTrackerMaster$$anonfun$removeOutputsOnExecutor$2.apply(MapOutputTracker.scala:424)
? ? ? ? at scala.collection.Iterator$class.foreach(Iterator.scala:893)
? ? ? ? at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
? ? ? ? at org.apache.spark.MapOutputTrackerMaster.removeOutputsOnExecutor(MapOutputTracker.scala:424)
? ? ? ? at org.apache.spark.scheduler.DAGScheduler.handleExecutorLost(DAGScheduler.scala:1471)
? ? ? ? at org.apache.spark.scheduler.DAGScheduler.handleTaskCompletion(DAGScheduler.scala:1428)
? ? ? ? at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1787)
? ? ? ? at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1745)
? ? ? ? at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1734)
? ? ? ? at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
Caused by: java.io.IOException: Connection reset by peer
? ? ? ? at sun.nio.ch.FileDispatcherImpl.read0(Native Method)
? ? ? ? at sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:39)
? ? ? ? at sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:223)
? ? ? ? at sun.nio.ch.IOUtil.read(IOUtil.java:192)
? ? ? ? at sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:380)
? ? ? ? at io.netty.buffer.PooledUnsafeDirectByteBuf.setBytes(PooledUnsafeDirectByteBuf.java:221)
? ? ? ? at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:899)
? ? ? ? at io.netty.channel.socket.nio.NioSocketChannel.doReadBytes(NioSocketChannel.java:275)
? ? ? ? at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:119)
? ? ? ? at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:643)
? ? ? ? at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:566)
? ? ? ? at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:480)
? ? ? ? at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:442)
? ? ? ? at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:131)
? ? ? ? at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
? ? ? ? at java.lang.Thread.run(Thread.java:748)
19/03/27 13:39:37 INFO scheduler.DAGScheduler: Executor lost: 388 (epoch 5955)
2 源碼分析
2.1 為何調(diào)用handleExecutorLost
從錯誤棧看到dagscheduler在處理CompletionEvent時(handleTaskCompletion方法中)調(diào)用了handleExecutorLost蛮浑，我們看下handleTaskCompletion的源碼：
/**
? * Responds to a task finishing. This is called inside the event loop so it assumes that it can
? * modify the scheduler's internal state. Use taskEnded() to post a task end event from outside.
? */
? private[scheduler] def handleTaskCompletion(event: CompletionEvent) {
//省略無關(guān)代碼 (下面以。只嚣。沮稚。代替)
? ? event.reason match {
? ? //。册舞。蕴掏。
? ? case FetchFailed(bmAddress, shuffleId, mapId, reduceId, failureMessage) =>
? ? ? //。调鲸。盛杰。
? ? ? ? ? // TODO: mark the executor as failed only if there were lots of fetch failures on it
? ? ? ? ? if (bmAddress != null) {
? ? ? ? ? ? handleExecutorLost(bmAddress.executorId, filesLost = true, Some(task.epoch))
? ? ? ? ? }
? ? ? ? }
? ? //...
? ? }
可以看到，當(dāng)task complete的原因是FetchFailed并且FetchFailed傳回的blockManager地址不為空藐石，則DAGScheduler會認(rèn)為對應(yīng)的executor掛了即供，然后調(diào)用handleExecutorLost進(jìn)行處理。
注意：這里有標(biāo)注 //TODO: mark the executor as failed only if there were lots of fetch failures on it
但是當(dāng)前版本(spark 2.2.3)中于微，只要一個executor上有一次fetchFailed就會調(diào)用handleExecutorLost處理逗嫡。
2.2 為何會有FetchFailed
為什么會有FetchFailed產(chǎn)生呢？我們在driver日志中發(fā)現(xiàn)：
19/03/26 00:42:32 ERROR cluster.YarnClusterScheduler: Lost executor 674 on hostA: Container container_e131_1552474836332_972818_01_000882 on host: hostA was preempted.
還發(fā)現(xiàn)：
org.apache.spark.shuffle.FetchFailedException: Failed to connect to hostA/xxxxxx:xxx
這說明FetchFailed是由于hostA上的executor 674的yarn container被preempted了株依。這是因?yàn)殡S著我們集群用戶增多驱证，hadoop集群負(fù)載逐漸增加，導(dǎo)致出現(xiàn)資源搶占恋腕。
下面我們看看handleExecutorLost做了什么抹锄。
2.3 handleExecutorLost做了什么
? private[scheduler] def handleExecutorLost(
? ? ? execId: String,
? ? ? filesLost: Boolean,
? ? ? maybeEpoch: Option[Long] = None) {
? ? val currentEpoch = maybeEpoch.getOrElse(mapOutputTracker.getEpoch)
? ? if (!failedEpoch.contains(execId) || failedEpoch(execId) < currentEpoch) {
? ? ? failedEpoch(execId) = currentEpoch
? ? ? logInfo("Executor lost: %s (epoch %d)".format(execId, currentEpoch))
? ? ? blockManagerMaster.removeExecutor(execId)
? ? ? if (filesLost || !env.blockManager.externalShuffleServiceEnabled) {
? ? ? ? logInfo("Shuffle files lost for executor: %s (epoch %d)".format(execId, currentEpoch))
? ? ? ? mapOutputTracker.removeOutputsOnExecutor(execId)
? ? ? ? clearCacheLocs()
? ? ? }
? ? } else {
? ? ? logDebug("Additional executor lost message for " + execId +
? ? ? ? ? ? ? "(epoch " + currentEpoch + ")")
? ? }
? }
handleExecutorLost除了調(diào)用blockManagerMaster.removeExecutor移除lost executor外，還調(diào)用了mapOutputTracker.removeOutputsOnExecutor方法 (關(guān)于MapOutputTracker, 讀者可參考[Spark MapOutputTracker淺析](http://www.reibang.com/p/1409dbc78a15))：
? def removeOutputsOnExecutor(execId: String): Unit = {
? ? shuffleStatuses.valuesIterator.foreach { _.removeOutputsOnExecutor(execId) }
? ? incrementEpoch()
? }
shuffleStatuses記錄了所有shuffle的shuffleStatus對象，對每個shuffleStatus調(diào)用其removeOutputsOnExecutor方法移除指定executor上運(yùn)行的所有shuffle map tasks的mapStatus :
? def removeOutputsOnExecutor(execId: String): Unit = synchronized {
? ? for (mapId <- 0 until mapStatuses.length) {
? ? ? if (mapStatuses(mapId) != null && mapStatuses(mapId).location.executorId == execId) {
? ? ? ? _numAvailableOutputs -= 1
? ? ? ? mapStatuses(mapId) = null
? ? ? ? invalidateSerializedMapOutputStatusCache()
? ? ? }
? ? }
? }
2.4 removeOutputsOnExecutor做了什么
shuffleStatus的removeOutputsOnExecutor方法最后會調(diào)用invalidateSerializedMapOutputStatusCache方法伙单，這個方法會將mapStatus的cache清理掉获高，清理cache的步驟之一就是銷毀廣播變量cachedSerializedBroadcast（這個廣播變量包含了序列化后的所有shuffle map tasks的output狀態(tài)信息，即mapStatus對象）车份。
? /**
? * Clears the cached serialized map output statuses.
? */
? def invalidateSerializedMapOutputStatusCache(): Unit = synchronized {
? ? if (cachedSerializedBroadcast != null) {
? ? ? cachedSerializedBroadcast.destroy()
? ? ? cachedSerializedBroadcast = null
? ? }
? ? cachedSerializedMapStatus = null
? }
這里需要簡單介紹一下destroy廣播變量的過程：
銷毀一個broadcast變量其實(shí)就是刪除所有節(jié)點(diǎn)上對應(yīng)的broacast block谋减，大致流程是：
1. 向blockManagerMasterEndpoint發(fā)送RemoveBroadcast消息。
2. blockManagerMasterEndpoint接收到RemoveBroadcast消息后會將此消息轉(zhuǎn)發(fā)給所有executors和driver上的blockManagerSlaveEndpoint.
3. blockManagerSlaveEndpoint接收到RemoveBroadcast消息后會調(diào)用本地blockManager的removeBroadcast方法移除指定broadcast的所有blocks.
2.5 DAGSchedulerEventProcessLoop是如何fail的
通過上面的介紹扫沼，我們知道：
1. preemption導(dǎo)致FetchFailed異常出爹；
2. FetchFailed異常導(dǎo)致dagScheduler.handleExecutorLost方法被調(diào)用；
3. handleExecutorLost方法會調(diào)用shuffleStatus.invalidateSerializedMapOutputStatusCache方法清理map outputs狀態(tài)信息緩存缎除；
4. invalidateSerializedMapOutputStatusCache方法調(diào)用broadcast.destroy方法銷毀map outputs狀態(tài)信息緩存的廣播變量严就；
5. destroy方法最終會通過blockManagerMasterEndpoint向所有的blockManagerSlaveEndpoint發(fā)送RemoveBroadcast消息并等待返回。
現(xiàn)在我們來分析DAGSchedulerEventProcessLoop failed的原因：
FetchFailed是preemption產(chǎn)生的器罐，并且當(dāng)時我們的spark application有大量executor被preempted梢为。在這個時候，當(dāng)blockManagerMasterEndpoint向所有blockManagerSlaveEndpoint發(fā)送RemoveBroadcast消息時轰坊，很大概率會發(fā)向某個已經(jīng)被preempted但還沒來得及在blockManagerMasterEndpoint中被移除的executor铸董，這種情況下就會出現(xiàn)connection exception。然而肴沫，在spark 2.2.3版本中粟害，這樣的connection exception在invalidateSerializedMapOutputStatusCache方法及其外層調(diào)用方法中都沒有捕獲，最終被拋到DAGSchedulerEventProcessLoop中并導(dǎo)致其failed.
3 解決方案
這個bug已經(jīng)在spark 2.3.0中被fix了颤芬，但是為了避免引入新的bug悲幅，我們沒有直接升級到2.3版本，而是在2.2.3版本上加上了2.3.0中針對這個bug的fix站蝠，fix的代碼很簡單汰具，只是修改了一下invalidateSerializedMapOutputStatusCache方法：
? /**
? * Clears the cached serialized map output statuses.
? */
? def invalidateSerializedMapOutputStatusCache(): Unit = synchronized {
? ? if (cachedSerializedBroadcast != null) {
? ? ? // Prevent errors during broadcast cleanup from crashing the DAGScheduler (see SPARK-21444)
? ? ? Utils.tryLogNonFatalError{
? ? ? ? // Use `blocking = false` so that this operation doesn't hang while trying to send cleanup
? ? ? ? // RPCs to dead executors.
? ? ? ? cachedSerializedBroadcast.destroy(blocking = false)
? ? ? }
? ? ? cachedSerializedBroadcast = null
? ? }
? ? cachedSerializedMapStatus = null
? }
4 總結(jié)
本文從報(bào)錯棧開始，通過源碼分析一步步找出DAGSchedulerEventProcessLoop在executor被preempted的情況下fail掉的root cause菱魔，并展示了spark官方的fix方法留荔。
5 說明
本文源碼版本：2.2.3
