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https://issues.apache.org/jira/browse/SPARK-24309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Marcelo Vanzin resolved SPARK-24309.
------------------------------------
Resolution: Fixed
Fix Version/s: 2.3.1
2.4.0
Issue resolved by pull request 21356
[https://github.com/apache/spark/pull/21356]
> AsyncEventQueue should handle an interrupt from a Listener
> ----------------------------------------------------------
>
> Key: SPARK-24309
> URL: https://issues.apache.org/jira/browse/SPARK-24309
> Project: Spark
> Issue Type: Bug
> Components: Scheduler, Spark Core
> Affects Versions: 2.3.0
> Reporter: Imran Rashid
> Assignee: Imran Rashid
> Priority: Blocker
> Fix For: 2.4.0, 2.3.1
>
>
> AsyncEventQueue does not properly handle an interrupt from a Listener -- the
> spark app won't even stop!
> I observed this on an actual workload as the EventLoggingListener can
> generate an interrupt from the underlying hdfs calls:
> {noformat}
> 18/05/16 17:46:36 WARN hdfs.DFSClient: Error transferring data from
> DatanodeInfoWithStorage[10.17.206.36:20002,DS-3adac910-5d0a-418b-b0f7-6332b35bf6a1,DISK]
> to
> DatanodeInfoWithStorage[10.17.206.42:20002,DS-2e7ed0aa-0e68-441e-b5b2-96ad4a9ce7a5,DISK]:
> 100000 millis timeout while waiting for channel to be ready for read. ch :
> java.nio.channels.SocketChannel[connected local=/10.17.206.35:33950
> remote=/10.17.206.36:20002]
> 18/05/16 17:46:36 WARN hdfs.DFSClient: DataStreamer Exception
> java.net.SocketTimeoutException: 100000 millis timeout while waiting for
> channel to be ready for read. ch : java.nio.channels.SocketChannel[connected
> local=/10.17.206.35:33950 remote=/10.17.206.36:20002]
> at
> org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:164)
> at
> org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:161)
> at
> org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:131)
> at
> org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:118)
> at java.io.FilterInputStream.read(FilterInputStream.java:83)
> at java.io.FilterInputStream.read(FilterInputStream.java:83)
> at
> org.apache.hadoop.hdfs.protocolPB.PBHelper.vintPrefixed(PBHelper.java:2305)
> at
> org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer$StreamerStreams.sendTransferBlock(DFSOutputStream.java:516)
> at
> org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.transfer(DFSOutputStream.java:1450)
> at
> org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.addDatanode2ExistingPipeline(DFSOutputStream.java:1408)
> at
> org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.setupPipelineForAppendOrRecovery(DFSOutputStream.java:1559)
> at
> org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.processDatanodeError(DFSOutputStream.java:1254)
> at
> org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:739)
> 18/05/16 17:46:36 ERROR scheduler.AsyncEventQueue: Listener
> EventLoggingListener threw an exception
> [... a few more of these ...]
> 18/05/16 17:46:36 INFO scheduler.AsyncEventQueue: Stopping listener queue
> eventLog.
> java.lang.InterruptedException
> at
> java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireInterruptibly(AbstractQueuedSynchronizer.java:1220)
> at
> java.util.concurrent.locks.ReentrantLock.lockInterruptibly(ReentrantLock.java:335)
> at
> java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:439)
> at
> org.apache.spark.scheduler.AsyncEventQueue$$anonfun$org$apache$spark$scheduler$AsyncEventQueue$$dispatch$1.apply(AsyncEventQueue.scala:94)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
> at
> org.apache.spark.scheduler.AsyncEventQueue.org$apache$spark$scheduler$AsyncEventQueue$$dispatch(AsyncEventQueue.scala:83)
> at
> org.apache.spark.scheduler.AsyncEventQueue$$anon$1$$anonfun$run$1.apply$mcV$sp(AsyncEventQueue.scala:79)
> at
> org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1319)
> at
> org.apache.spark.scheduler.AsyncEventQueue$$anon$1.run(AsyncEventQueue.scala:78)
> {noformat}
> When this happens, the AsyncEventQueue will continue to pile up events in its
> queue, though its no longer processing them. And then in the call to stop,
> it'll block on {{queue.put(POISON_PILL)}} forever, so the SparkContext won't
> stop.
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