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https://issues.apache.org/jira/browse/SPARK-35046?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17324398#comment-17324398
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Hyukjin Kwon commented on SPARK-35046:
--------------------------------------
[~m.rostami] it sounds more like a question. Let's ask it in the mailing list
first before filing it as an issue. You could have a better answer in the
mailing list.
> Wrong memory allocation on standalone mode cluster
> --------------------------------------------------
>
> Key: SPARK-35046
> URL: https://issues.apache.org/jira/browse/SPARK-35046
> Project: Spark
> Issue Type: Bug
> Components: Scheduler, Spark Core
> Affects Versions: 3.0.1
> Reporter: Mohamadreza Rostami
> Priority: Major
>
> I see a bug in executer memory allocation in the standalone cluster, but I
> can't find which part of the spark code causes this problem. That why's I
> decided to raise this issue here.
> Assume you have 3 workers with 10 CPU cores and 10 Gigabyte memories. Assume
> also you have 2 spark jobs that run on this cluster of workers, and these
> jobs configs set as below:
> -----------------
> job-1:
> executer-memory: 5g
> executer-CPU: 4
> max-cores: 8
> ------------------
> job-2:
> executer-memory: 6g
> executer-CPU: 4
> max-cores: 8
> ------------------
> In this situation, We expect that if we submit both of these jobs, the first
> job that submits get 2 executers which each of them has 4 CPU core and 5g
> memory, and the second job gets only one executer on thirds worker who has 4
> CPU core and 6g memory because worker 1 and worker 2 doesn't have enough
> memory to accept the second job. But surprisingly, we see that one of the
> first or second workers creates an executor for job-2, and the worker's
> consuming memory goes beyond what's allocated to that and gets 11g memory
> from the operating system.
> Is this behavior normal? I think this can cause some undefined behavior
> problem in the cluster.
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