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https://issues.apache.org/jira/browse/SPARK-20662?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16035478#comment-16035478
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Marcelo Vanzin commented on SPARK-20662:
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bq. It's probably not a good idea to let one job takes all resources while
starving others.
I'm pretty sure that's why resource managers have queues.
What you want here is a client-controlled, opt-in, application-level "nicety
config" that tells it to not submit more tasks than a limit at a time. That
control already exists - set a maximum number of executors for the app. number
of executors times number of cores = max number of tasks.
> Block jobs that have greater than a configured number of tasks
> --------------------------------------------------------------
>
> Key: SPARK-20662
> URL: https://issues.apache.org/jira/browse/SPARK-20662
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 1.6.0, 2.0.0
> Reporter: Xuefu Zhang
>
> In a shared cluster, it's desirable for an admin to block large Spark jobs.
> While there might not be a single metrics defining the size of a job, the
> number of tasks is usually a good indicator. Thus, it would be useful for
> Spark scheduler to block a job whose number of tasks reaches a configured
> limit. By default, the limit could be just infinite, to retain the existing
> behavior.
> MapReduce has mapreduce.job.max.map and mapreduce.job.max.reduce to be
> configured, which blocks a MR job at job submission time.
> The proposed configuration is spark.job.max.tasks with a default value -1
> (infinite).
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