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https://issues.apache.org/jira/browse/SPARK-12554?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15074189#comment-15074189
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Andrew Or commented on SPARK-12554:
-----------------------------------
I've downgraded the issue.
I also think the behavior is by design. The semantics of `spark.executor.cores`
is that each executor has exactly N number of cores. Your cluster "hanging"
means it doesn't have enough resources to launch the expected number of
executors, so you should provision more resources.
> Standalone app scheduler will hang when app.coreToAssign < minCoresPerExecutor
> ------------------------------------------------------------------------------
>
> Key: SPARK-12554
> URL: https://issues.apache.org/jira/browse/SPARK-12554
> Project: Spark
> Issue Type: Bug
> Components: Deploy, Scheduler
> Affects Versions: 1.5.2
> Reporter: Lijie Xu
>
> In scheduleExecutorsOnWorker() in Master.scala,
> *val keepScheduling = coresToAssign >= minCoresPerExecutor* should be changed
> to *val keepScheduling = coresToAssign > 0*
> Suppose that an app's requested cores is 10 (i.e., spark.cores.max = 10) and
> app.coresPerExecutor is 4 (i.e., spark.executor.cores = 4).
> After allocating two executors (each has 4 cores) to this app, the
> *app.coresToAssign = 2* and *minCoresPerExecutor = coresPerExecutor = 4*, so
> *keepScheduling = false* and no extra executor will be allocated to this app.
> If *spark.scheduler.minRegisteredResourcesRatio* is set to a large number
> (e.g., > 0.8 in this case), the app will hang and never finish.
> Another case: if a small app's coresPerExecutor is larger than its requested
> cores (e.g., spark.cores.max = 10, spark.executor.cores = 16), *val
> keepScheduling = coresToAssign >= minCoresPerExecutor* is always FALSE. As a
> result, this app will never get an executor to run.
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