You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Saisai Shao (JIRA)" <ji...@apache.org> on 2019/01/03 02:08:00 UTC
[jira] [Updated] (SPARK-26513) Trigger GC on executor node idle
[ https://issues.apache.org/jira/browse/SPARK-26513?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Saisai Shao updated SPARK-26513:
--------------------------------
Fix Version/s: (was: 3.0.0)
> Trigger GC on executor node idle
> --------------------------------
>
> Key: SPARK-26513
> URL: https://issues.apache.org/jira/browse/SPARK-26513
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.3.0
> Reporter: Sandish Kumar HN
> Priority: Major
>
>
> Correct me if I'm wrong.
> *Stage:*
> On a large cluster, each stage would have some executors. were a few executors would finish a couple of tasks first and wait for whole stage or remaining tasks to finish which are executed by different executors nodes in a cluster. a stage will only be completed when all tasks in a current stage finish its execution. and the next stage execution has to wait till all tasks of the current stage are completed.
>
> why don't we trigger GC, when the executor node is waiting for remaining tasks to finish, or executor Idle? anyways executor has to wait for the remaining tasks to finish which can at least take a couple of seconds. why don't we trigger GC? which will max take <300ms
>
> I have proposed a small code snippet which triggers GC when running tasks are empty and heap usage in current executor node is more than the given threshold.
> This could improve performance for long-running spark job's.
> we referred this paper [https://www.computer.org/csdl/proceedings/hipc/2016/5411/00/07839705.pdf] and we found performance improvements in our long-running spark batch job's.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org