You are viewing a plain text version of this content. The canonical link for it is here.
Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2022/03/29 15:23:50 UTC

[GitHub] [spark] mridulm edited a comment on pull request #35683: [SPARK-30835][CORE][YARN] Add support for YARN decommissioning & pre-emption

mridulm edited a comment on pull request #35683:
URL: https://github.com/apache/spark/pull/35683#issuecomment-1082010575


   > If decommissioning is enabled, shuffle blocks are already migrated to another node before the executor exits. However, this is best effort. So shuffle data might be left if shuffle blocks migration is left incomplete or the executor doesn't exit cleanly. Can you please clarify what are the expectations for shuffle data when a node is put in decommissioning state?
   
   
   I am not sure I follow.
   In k8s, where there is no shuffle service - it makes sense to move shuffle blocks when executor is getting decommissioned.
   In yarn, particularly with dynamic resource allocation (but even without it), it is very common for executors to exit - while shuffle service continues to serve the shuffle blocks generated on that node, which might have no active executors for application on them.
   You can have quite wild swings in executor allocations (ramp up to 1000s of executors in 10s of seconds, and ramp down to very low number equally fast).
   
   We should not be moving shuffle blocks when an executor is exiting - only when the node is actually getting decommissioned (they might be the same for k8s, not for yarn).
   


-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org



---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org