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https://issues.apache.org/jira/browse/SPARK-22148?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17078278#comment-17078278
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Thomas Graves commented on SPARK-22148:
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so off the top of my head, I think the main issue with just requesting more is
that the dynamic allocation manager isn't tied very tightly to the scheduler or
the blacklist tracker, so getting the information required to properly track
why we have more executors then needed took quite a bit more work and code
refactoring. If you are still seeing issues regularly though we could revisit
to see if we could either request more or perhaps kill executors that are
blacklisted that aren't completely idle. But I would have to re-read through
these and think about it more. If you have ideas feel free to propose, though
we should do it under a new Jira and link them
> TaskSetManager.abortIfCompletelyBlacklisted should not abort when all current
> executors are blacklisted but dynamic allocation is enabled
> -----------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-22148
> URL: https://issues.apache.org/jira/browse/SPARK-22148
> Project: Spark
> Issue Type: Bug
> Components: Scheduler, Spark Core
> Affects Versions: 2.2.0
> Reporter: Juan Rodríguez Hortalá
> Assignee: Dhruve Ashar
> Priority: Major
> Fix For: 2.4.1, 3.0.0
>
> Attachments: SPARK-22148_WIP.diff
>
>
> Currently TaskSetManager.abortIfCompletelyBlacklisted aborts the TaskSet and
> the whole Spark job with `task X (partition Y) cannot run anywhere due to
> node and executor blacklist. Blacklisting behavior can be configured via
> spark.blacklist.*.` when all the available executors are blacklisted for a
> pending Task or TaskSet. This makes sense for static allocation, where the
> set of executors is fixed for the duration of the application, but this might
> lead to unnecessary job failures when dynamic allocation is enabled. For
> example, in a Spark application with a single job at a time, when a node
> fails at the end of a stage attempt, all other executors will complete their
> tasks, but the tasks running in the executors of the failing node will be
> pending. Spark will keep waiting for those tasks for 2 minutes by default
> (spark.network.timeout) until the heartbeat timeout is triggered, and then it
> will blacklist those executors for that stage. At that point in time, other
> executors would had been released after being idle for 1 minute by default
> (spark.dynamicAllocation.executorIdleTimeout), because the next stage hasn't
> started yet and so there are no more tasks available (assuming the default of
> spark.speculation = false). So Spark will fail because the only executors
> available are blacklisted for that stage.
> An alternative is requesting more executors to the cluster manager in this
> situation. This could be retried a configurable number of times after a
> configurable wait time between request attempts, so if the cluster manager
> fails to provide a suitable executor then the job is aborted like in the
> previous case.
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