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Shixiong Zhu commented on SPARK-10638: -------------------------------------- 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} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org