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https://issues.apache.org/jira/browse/SPARK-13669?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-13669:
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Assignee: (was: Apache Spark)
> Job will always fail in the external shuffle service unavailable situation
> --------------------------------------------------------------------------
>
> Key: SPARK-13669
> URL: https://issues.apache.org/jira/browse/SPARK-13669
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, YARN
> Reporter: Saisai Shao
>
> Currently we are running into an issue with Yarn work preserving enabled +
> external shuffle service.
> In the work preserving enabled scenario, the failure of NM will not lead to
> the exit of executors, so executors can still accept and run the tasks. The
> problem here is when NM is failed, external shuffle service is actually
> inaccessible, so reduce tasks will always complain about the “Fetch failure”,
> and the failure of reduce stage will make the parent stage (map stage) rerun.
> The tricky thing here is Spark scheduler is not aware of the unavailability
> of external shuffle service, and will reschedule the map tasks on the
> executor where NM is failed, and again reduce stage will be failed with
> “Fetch failure”, and after 4 retries, the job is failed.
> So here the actual problem is Spark’s scheduler is not aware of the
> unavailability of external shuffle service, and will still assign the tasks
> on to that nodes. The fix is to avoid assigning tasks on to that nodes.
> Currently in the Spark, one related configuration is
> “spark.scheduler.executorTaskBlacklistTime”, but I don’t think it will be
> worked in this scenario. This configuration is used to avoid same reattempt
> task to run on the same executor. Also ways like MapReduce’s blacklist
> mechanism may not handle this scenario, since all the reduce tasks will be
> failed, so counting the failure tasks will equally mark all the executors as
> “bad” one.
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