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https://issues.apache.org/jira/browse/SPARK-2019?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14166555#comment-14166555
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Denis Serduik edited comment on SPARK-2019 at 10/10/14 8:40 AM:
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I have noticed the same problem with workers behavior. My installation: Spark
1.0.2-hadoop2.0.0-mr1-cdh4.2.0 on Mesos 0.13. In my case, workers fail when
there was an error while serialization the closure. Also please note, we run
Spark in coarse-grained mode
was (Author: dmaverick):
I have noticed the same problem with workers behavior. My installation: Spark
1.0.2-hadoop2.0.0-mr1-cdh4.2.0 on Mesos 0.13. In my case, workers fail when
there was an error while serialization the closure. Also please notice that we
run Spark in coarse-grained mode
> Spark workers die/disappear when job fails for nearly any reason
> ----------------------------------------------------------------
>
> Key: SPARK-2019
> URL: https://issues.apache.org/jira/browse/SPARK-2019
> Project: Spark
> Issue Type: Bug
> Affects Versions: 0.9.0
> Reporter: sam
>
> We either have to reboot all the nodes, or run 'sudo service spark-worker
> restart' across our cluster. I don't think this should happen - the job
> failures are often not even that bad. There is a 5 upvoted SO question here:
> http://stackoverflow.com/questions/22031006/spark-0-9-0-worker-keeps-dying-in-standalone-mode-when-job-fails
>
> We shouldn't be giving restart privileges to our devs, and therefore our
> sysadm has to frequently restart the workers. When the sysadm is not around,
> there is nothing our devs can do.
> Many thanks
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