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Posted to issues@spark.apache.org by "Angus Gerry (JIRA)" <ji...@apache.org> on 2016/07/26 00:54:21 UTC
[jira] [Comment Edited] (SPARK-16702) Driver hangs after executors
are lost
[ https://issues.apache.org/jira/browse/SPARK-16702?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15392938#comment-15392938 ]
Angus Gerry edited comment on SPARK-16702 at 7/26/16 12:54 AM:
---------------------------------------------------------------
I'm not so sure about SPARK-12419.
SPARK-16533 however definitely looks the same. The logs in my scenario are similar to what's described there. Effectively it's just repetitions of:
{noformat}
WARN ExecutorAllocationManager: Uncaught exception in thread spark-dynamic-executor-allocation
org.apache.spark.SparkException: Error sending message [message = RequestExecutors(...)]
WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(383,Container container_e12_1466755357617_0813_01_002077 on host: ... was preempted.)] in 3 attempts
WARN NettyRpcEndpointRef: Error sending message [message = KillExecutors(List(450))] in 1 attempts
{noformat}
was (Author: angolon@gmail.com):
I'm not so sure about SPARK-12419.
SPARK-16355 however definitely looks the same. The logs in my scenario are similar to what's described there. Effectively it's just repetitions of:
{noformat}
WARN ExecutorAllocationManager: Uncaught exception in thread spark-dynamic-executor-allocation
org.apache.spark.SparkException: Error sending message [message = RequestExecutors(...)]
WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(383,Container container_e12_1466755357617_0813_01_002077 on host: ... was preempted.)] in 3 attempts
WARN NettyRpcEndpointRef: Error sending message [message = KillExecutors(List(450))] in 1 attempts
{noformat}
> Driver hangs after executors are lost
> -------------------------------------
>
> Key: SPARK-16702
> URL: https://issues.apache.org/jira/browse/SPARK-16702
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.6.1, 1.6.2, 2.0.0
> Reporter: Angus Gerry
> Attachments: SparkThreadsBlocked.txt
>
>
> It's my first time, please be kind.
> I'm still trying to debug this error locally - at this stage I'm pretty convinced that it's a weird deadlock/livelock problem due to the use of {{scheduleAtFixedRate}} within {{ExecutorAllocationManager}}. This problem is possibly tangentially related to the issues discussed in SPARK-1560 around the use of blocking calls within locks.
> h4. Observed Behavior
> When running a spark job, and executors are lost, the job occassionally goes into a state where it makes no progress with tasks. Most commonly it seems that the issue occurs when executors are preempted by yarn, but I'm not confident enough to state that it's restricted to just this scenario.
> Upon inspecting a thread dump from the driver, the following stack traces seem noteworthy (a full thread dump is attached):
> {noformat:title=Thread 178: spark-dynamic-executor-allocation (TIMED_WAITING)}
> sun.misc.Unsafe.park(Native Method)
> java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:226)
> java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedNanos(AbstractQueuedSynchronizer.java:1033)
> java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1326)
> scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208)
> scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
> scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
> scala.concurrent.Await$$anonfun$result$1.apply(package.scala:190)
> scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
> scala.concurrent.Await$.result(package.scala:190)
> org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
> org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:101)
> org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
> org.apache.spark.scheduler.cluster.YarnSchedulerBackend.doRequestTotalExecutors(YarnSchedulerBackend.scala:59)
> org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.requestTotalExecutors(CoarseGrainedSchedulerBackend.scala:447)
> org.apache.spark.SparkContext.requestTotalExecutors(SparkContext.scala:1423)
> org.apache.spark.ExecutorAllocationManager.addExecutors(ExecutorAllocationManager.scala:359)
> org.apache.spark.ExecutorAllocationManager.updateAndSyncNumExecutorsTarget(ExecutorAllocationManager.scala:310)
> org.apache.spark.ExecutorAllocationManager.org$apache$spark$ExecutorAllocationManager$$schedule(ExecutorAllocationManager.scala:264)
> org.apache.spark.ExecutorAllocationManager$$anon$2.run(ExecutorAllocationManager.scala:223)
> java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
> java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304)
> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178)
> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> java.lang.Thread.run(Thread.java:745)
> {noformat}
> {noformat:title=Thread 22: dispatcher-event-loop-10 (BLOCKED)}
> org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint.disableExecutor(CoarseGrainedSchedulerBackend.scala:289)
> org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnDriverEndpoint$$anonfun$onDisconnected$1.apply(YarnSchedulerBackend.scala:121)
> org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnDriverEndpoint$$anonfun$onDisconnected$1.apply(YarnSchedulerBackend.scala:120)
> scala.Option.foreach(Option.scala:257)
> org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnDriverEndpoint.onDisconnected(YarnSchedulerBackend.scala:120)
> org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:142)
> org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
> org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
> org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> java.lang.Thread.run(Thread.java:745)
> {noformat}
> {noformat:title=Thread 640: kill-executor-thread (BLOCKED)}
> org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.killExecutors(CoarseGrainedSchedulerBackend.scala:488)
> org.apache.spark.SparkContext.killAndReplaceExecutor(SparkContext.scala:1499)
> org.apache.spark.HeartbeatReceiver$$anonfun$org$apache$spark$HeartbeatReceiver$$expireDeadHosts$3$$anon$3$$anonfun$run$3.apply$mcV$sp(HeartbeatReceiver.scala:206)
> org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1219)
> org.apache.spark.HeartbeatReceiver$$anonfun$org$apache$spark$HeartbeatReceiver$$expireDeadHosts$3$$anon$3.run(HeartbeatReceiver.scala:203)
> java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
> java.util.concurrent.FutureTask.run(FutureTask.java:262)
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> java.lang.Thread.run(Thread.java:745)
> {noformat}
> {noformat:title=Thread 21: dispatcher-event-loop-9 (TIMED_WAITING)}
> sun.misc.Unsafe.park(Native Method)
> java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:226)
> java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedNanos(AbstractQueuedSynchronizer.java:1033)
> java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1326)
> scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208)
> scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
> scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
> scala.concurrent.Await$$anonfun$result$1.apply(package.scala:190)
> scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
> scala.concurrent.Await$.result(package.scala:190)
> org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
> org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:101)
> org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
> org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.removeExecutor(CoarseGrainedSchedulerBackend.scala:370)
> org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnSchedulerEndpoint$$anonfun$receive$1.applyOrElse(YarnSchedulerBackend.scala:176)
> org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:116)
> org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
> org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
> org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> java.lang.Thread.run(Thread.java:745)
> {noformat}
> My theory is that the following components attempting to call and/or message eachother simultaneously are causing a deadlock/livelock scenario.
> * {{CoarseGrainedSchedulerBackend (RequestExecutors) -> YarnSchedulerEndpoint}}
> * {{YarnSchedulerEndpoint (RemoveExecutor) --> DriverEndpoint}}
> * {{DriverEndpoint (disableExecutor) --> CoarseGrainedSchedulerBackend}}
> This is where the use of {{scheduleAtFixedRate}} comes into play: the deadlock should presumably be released when the blocking call to {{YarnSchedulerEndpoint}} times out, however as soon as lock contention causes a single execution of {{ExecutorAllocationManager.schedule}} to take longer than the hard coded 100 milliseconds, then the possibility exists for that thread to release and then immediately reaquire the lock on {{CoarseGrainedSchedulerBackend}}
> h4. Proposed Solution
> A simple solution would be to have {{YarnSchedulerEndpoint.doRequestTotalExecutors}} not make blocking calls, similar to SPARK-15606. However I think it would also be wise to refactor {{ExecutorAllocationManager}} to not use {{scheduleAtFixedRate}} and rather to sleep for some interval of time.
> That's all I've got, I hope that it's been helpful. I plan on starting to work on my proposed solution, and so would welcome any feedback on the direction I've suggested.
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