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Posted to issues@spark.apache.org by "Dmitry Tverdokhleb (Jira)" <ji...@apache.org> on 2021/04/22 13:49:00 UTC

[jira] [Updated] (SPARK-33121) Spark Streaming 3.1.1 hangs on shutdown

     [ https://issues.apache.org/jira/browse/SPARK-33121?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dmitry Tverdokhleb updated SPARK-33121:
---------------------------------------
    Summary: Spark Streaming 3.1.1 hangs on shutdown  (was: Spark does not shutdown gracefully)

> Spark Streaming 3.1.1 hangs on shutdown
> ---------------------------------------
>
>                 Key: SPARK-33121
>                 URL: https://issues.apache.org/jira/browse/SPARK-33121
>             Project: Spark
>          Issue Type: Bug
>          Components: DStreams
>    Affects Versions: 3.0.1
>            Reporter: Dmitry Tverdokhleb
>            Priority: Major
>
> Hi. I am trying to migrate from spark 2.4.5 to 3.1.1 and there is a problem in graceful shutdown.
> Config parameter "spark.streaming.stopGracefullyOnShutdown" is set as "true".
> Here is the code:
> {code:java}
> inputStream.foreachRDD {
>   rdd =>
>     rdd.foreachPartition {
>         Thread.sleep(5000)
>     }
> }
> {code}
> I send a SIGTERM signal to stop the spark streaming and after sleeping an exception arises:
> {noformat}
> streaming-agg-tds-data_1  | java.util.concurrent.RejectedExecutionException: Task org.apache.spark.executor.Executor$TaskRunner@7ca7f0b8 rejected from java.util.concurrent.ThreadPoolExecutor@2474219c[Terminated, pool size = 0, active threads = 0, queued tasks = 0, completed tasks = 1]
> streaming-agg-tds-data_1  |     at java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2063)
> streaming-agg-tds-data_1  |     at java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:830)
> streaming-agg-tds-data_1  |     at java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1379)
> streaming-agg-tds-data_1  |     at org.apache.spark.executor.Executor.launchTask(Executor.scala:270)
> streaming-agg-tds-data_1  |     at org.apache.spark.scheduler.local.LocalEndpoint.$anonfun$reviveOffers$1(LocalSchedulerBackend.scala:93)
> streaming-agg-tds-data_1  |     at org.apache.spark.scheduler.local.LocalEndpoint.$anonfun$reviveOffers$1$adapted(LocalSchedulerBackend.scala:91)
> streaming-agg-tds-data_1  |     at scala.collection.Iterator.foreach(Iterator.scala:941)
> streaming-agg-tds-data_1  |     at scala.collection.Iterator.foreach$(Iterator.scala:941)
> streaming-agg-tds-data_1  |     at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
> streaming-agg-tds-data_1  |     at scala.collection.IterableLike.foreach(IterableLike.scala:74)
> streaming-agg-tds-data_1  |     at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
> streaming-agg-tds-data_1  |     at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
> streaming-agg-tds-data_1  |     at org.apache.spark.scheduler.local.LocalEndpoint.reviveOffers(LocalSchedulerBackend.scala:91)
> streaming-agg-tds-data_1  |     at org.apache.spark.scheduler.local.LocalEndpoint$$anonfun$receive$1.applyOrElse(LocalSchedulerBackend.scala:68)
> streaming-agg-tds-data_1  |     at org.apache.spark.rpc.netty.Inbox.$anonfun$process$1(Inbox.scala:115)
> streaming-agg-tds-data_1  |     at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:213)
> streaming-agg-tds-data_1  |     at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
> streaming-agg-tds-data_1  |     at org.apache.spark.rpc.netty.MessageLoop.org$apache$spark$rpc$netty$MessageLoop$$receiveLoop(MessageLoop.scala:75)
> streaming-agg-tds-data_1  |     at org.apache.spark.rpc.netty.MessageLoop$$anon$1.run(MessageLoop.scala:41)
> streaming-agg-tds-data_1  |     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> streaming-agg-tds-data_1  |     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> streaming-agg-tds-data_1  |     at java.lang.Thread.run(Thread.java:748)
> streaming-agg-tds-data_1  | 2021-04-22 13:33:41 WARN  JobGenerator - Timed out while stopping the job generator (timeout = 10000)
> streaming-agg-tds-data_1  | 2021-04-22 13:33:41 INFO  JobGenerator - Waited for jobs to be processed and checkpoints to be written
> streaming-agg-tds-data_1  | 2021-04-22 13:33:41 INFO  JobGenerator - Stopped JobGenerator{noformat}
> After this exception and "JobGenerator - Stopped JobGenerator" log, streaming freezes, and halts by timeout (Config parameter "hadoop.service.shutdown.timeout").
> Besides, there is no problem with the graceful shutdown in spark 2.4.5.



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