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Posted to issues@spark.apache.org by "Karthik Palaniappan (JIRA)" <ji...@apache.org> on 2017/03/17 18:28:41 UTC
[jira] [Comment Edited] (SPARK-19941) Spark should not schedule
tasks on executors on decommissioning YARN nodes
[ https://issues.apache.org/jira/browse/SPARK-19941?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15930433#comment-15930433 ]
Karthik Palaniappan edited comment on SPARK-19941 at 3/17/17 6:28 PM:
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Yeah, I could have been more clear. The application *should* continue, but the driver should drain executors *on decommissioning nodes* similar to how YARN is draining the NMs. All other executors can continue to have tasks scheduled on them.
was (Author: karthik palaniappan):
Yeah, I could have been more clear. The application *should* continue, but the driver should drain executors *on decommissioning nodes* similar to how YARN is draining the NMs. All other executors should continue running.
> Spark should not schedule tasks on executors on decommissioning YARN nodes
> --------------------------------------------------------------------------
>
> Key: SPARK-19941
> URL: https://issues.apache.org/jira/browse/SPARK-19941
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler, YARN
> Affects Versions: 2.1.0
> Environment: Hadoop 2.8.0-rc1
> Reporter: Karthik Palaniappan
>
> Hadoop 2.8 added a mechanism to gracefully decommission Node Managers in YARN: https://issues.apache.org/jira/browse/YARN-914
> Essentially you can mark nodes to be decommissioned, and let them a) finish work in progress and b) finish serving shuffle data. But no new work will be scheduled on the node.
> Spark should respect when NMs are set to decommissioned, and similarly decommission executors on those nodes by not scheduling any more tasks on them.
> It looks like in the future YARN may inform the app master when containers will be killed: https://issues.apache.org/jira/browse/YARN-3784. However, I don't think Spark should schedule based on a timeout. We should gracefully decommission the executor as fast as possible (which is the spirit of YARN-914). The app master can query the RM for NM statuses (if it doesn't already have them) and stop scheduling on executors on NMs that are decommissioning.
> Stretch feature: The timeout may be useful in determining whether running further tasks on the executor is even helpful. Spark may be able to tell that shuffle data will not be consumed by the time the node is decommissioned, so it is not worth computing. The executor can be killed immediately.
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