[
https://issues.apache.org/jira/browse/SPARK-33121?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dmitry Tverdokhleb updated SPARK-33121:
---------------------------------------
Description:
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.
was:
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", 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.
> Spark does not shutdown gracefully
> ----------------------------------
>
> 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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