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Posted to yarn-issues@hadoop.apache.org by "mumu (Jira)" <ji...@apache.org> on 2024/04/12 09:13:00 UTC
[jira] [Commented] (YARN-3415) Non-AM containers can be counted towards amResourceUsage of a Fair Scheduler queue
[ https://issues.apache.org/jira/browse/YARN-3415?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17836516#comment-17836516 ]
mumu commented on YARN-3415:
----------------------------
[~zxu] Hello, I am using version 2.7.2 and after merging this patch, I found that the issue did not occur. However, I found that AM Used Resources is greater than AM Max Resources and tasks can still be submitted.
> Non-AM containers can be counted towards amResourceUsage of a Fair Scheduler queue
> ----------------------------------------------------------------------------------
>
> Key: YARN-3415
> URL: https://issues.apache.org/jira/browse/YARN-3415
> Project: Hadoop YARN
> Issue Type: Bug
> Components: fairscheduler
> Affects Versions: 2.6.0
> Reporter: Rohit Agarwal
> Assignee: Zhihai Xu
> Priority: Critical
> Fix For: 2.8.0, 3.0.0-alpha1
>
> Attachments: YARN-3415.000.patch, YARN-3415.001.patch, YARN-3415.002.patch
>
>
> We encountered this problem while running a spark cluster. The amResourceUsage for a queue became artificially high and then the cluster got deadlocked because the maxAMShare constrain kicked in and no new AM got admitted to the cluster.
> I have described the problem in detail here: https://github.com/apache/spark/pull/5233#issuecomment-87160289
> In summary - the condition for adding the container's memory towards amResourceUsage is fragile. It depends on the number of live containers belonging to the app. We saw that the spark AM went down without explicitly releasing its requested containers and then one of those containers memory was counted towards amResource.
> cc - [~sandyr]
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