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https://issues.apache.org/jira/browse/SPARK-10638?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15074467#comment-15074467
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Shixiong Zhu commented on SPARK-10638:
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Do you have any stack trace or reproduce codes? Maybe there is some dead-lock.
> spark streaming stop gracefully keeps the spark context
> -------------------------------------------------------
>
> Key: SPARK-10638
> URL: https://issues.apache.org/jira/browse/SPARK-10638
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Affects Versions: 1.4.0
> Reporter: Mamdouh Alramadan
>
> With spark 1.4 on Mesos cluster, I am trying to stop the context with
> graceful shutdown, I have seen this mailing list that [~tdas] addressed
> http://mail-archives.apache.org/mod_mbox/incubator-spark-commits/201505.mbox/%3c176cb228a2704ab996839fb97fa90...@git.apache.org%3E
> which introduces a new config that was not documented, however, even with
> including it, the streaming job still stops correctly but the process doesn't
> die after all e.g. the Spark Context still running. My Mesos UI still sees
> the framework which is still allocating all the cores needed
> the code used for the shutdown hook is:
> {code:title=Start.scala|borderStyle=solid}
> sys.ShutdownHookThread {
> logInfo("Received SIGTERM, calling streaming stop")
> streamingContext.stop(stopSparkContext = true, stopGracefully = true)
> logInfo("Application Stopped")
> }
> {code}
> The logs are for this process are:
> {code:title=SparkLogs|borderStyle=solid}
> ```
> 5/09/16 16:37:51 INFO Start: Received SIGTERM, calling streaming stop
> 15/09/16 16:37:51 INFO JobGenerator: Stopping JobGenerator gracefully
> 15/09/16 16:37:51 INFO JobGenerator: Waiting for all received blocks to be
> consumed for job generation
> 15/09/16 16:37:51 INFO JobGenerator: Waited for all received blocks to be
> consumed for job generation
> 15/09/16 16:37:51 INFO StreamingContext: Invoking stop(stopGracefully=true)
> from shutdown hook
> 15/09/16 16:38:00 INFO RecurringTimer: Stopped timer for JobGenerator after
> time 1442421480000
> 15/09/16 16:38:00 INFO JobScheduler: Starting job streaming job 1442421480000
> ms.0 from job set of time 1442421480000 ms
> 15/09/16 16:38:00 INFO JobGenerator: Stopped generation timer
> 15/09/16 16:38:00 INFO JobGenerator: Waiting for jobs to be processed and
> checkpoints to be written
> 15/09/16 16:38:00 INFO JobScheduler: Added jobs for time 1442421480000 ms
> 15/09/16 16:38:00 INFO JobGenerator: Checkpointing graph for time
> 1442421480000 ms
> 15/09/16 16:38:00 INFO DStreamGraph: Updating checkpoint data for time
> 1442421480000 ms
> 15/09/16 16:38:00 INFO DStreamGraph: Updated checkpoint data for time
> 1442421480000 ms
> 15/09/16 16:38:00 INFO SparkContext: Starting job: foreachRDD at
> StreamDigest.scala:21
> 15/09/16 16:38:00 INFO DAGScheduler: Got job 12 (foreachRDD at
> StreamDigest.scala:21) with 1 output partitions (allowLocal=true)
> 15/09/16 16:38:00 INFO DAGScheduler: Final stage: ResultStage 12(foreachRDD
> at StreamDigest.scala:21)
> 15/09/16 16:38:00 INFO DAGScheduler: Parents of final stage: List()
> 15/09/16 16:38:00 INFO CheckpointWriter: Saving checkpoint for time
> 1442421480000 ms to file
> 'hdfs://EMRURL/sparkStreaming/checkpoint/checkpoint-1442421480000'
> 15/09/16 16:38:00 INFO DAGScheduler: Missing parents: List()
> .
> .
> .
> .
> 15/09/16 16:38:00 INFO JobGenerator: Waited for jobs to be processed and
> checkpoints to be written
> 15/09/16 16:38:00 INFO CheckpointWriter: CheckpointWriter executor terminated
> ? true, waited for 1 ms.
> 15/09/16 16:38:00 INFO JobGenerator: Stopped JobGenerator
> 15/09/16 16:38:00 INFO JobScheduler: Stopped JobScheduler
> ```
> {code}
> And in my spark-defaults.conf I included
> {code:title=spark-defaults.conf|borderStyle=solid}
> spark.streaming.stopGracefullyOnShutdown true
> {code}
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