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https://issues.apache.org/jira/browse/SPARK-8881?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14618313#comment-14618313
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Sean Owen commented on SPARK-8881:
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Yes, the punchline is that each worker is asked for 48/4 = 12 cores, but 12 is
less than the 16 cores each executor needs, so for every worker, 0 executors
are allocated. Grabbing cores in chunks of 16 in this case works, as does only
considering 3 workers to allocate 3 executors, since the problem is that it
never makes sense to try allocating N executors over M>N workers.
> Scheduling fails if num_executors < num_workers
> -----------------------------------------------
>
> Key: SPARK-8881
> URL: https://issues.apache.org/jira/browse/SPARK-8881
> Project: Spark
> Issue Type: Bug
> Components: Deploy
> Affects Versions: 1.4.0, 1.5.0
> Reporter: Nishkam Ravi
>
> Current scheduling algorithm (in Master.scala) has two issues:
> 1. cores are allocated one at a time instead of spark.executor.cores at a time
> 2. when spark.cores.max/spark.executor.cores < num_workers, executors are not
> launched and the app hangs (due to 1)
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