Angus Gerry created SPARK-16702:
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Summary: 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: 2.0.0, 1.6.2, 1.6.1
Reporter: Angus Gerry
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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