Saisai Shao created SPARK-13669:
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Summary: 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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