spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Ajith S (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-23626) Spark DAGScheduler scheduling performance hindered on JobSubmitted Event
Date Tue, 21 May 2019 07:15:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-23626?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Ajith S updated SPARK-23626:
----------------------------
    Affects Version/s: 3.0.0

> Spark DAGScheduler scheduling performance hindered on JobSubmitted Event
> ------------------------------------------------------------------------
>
>                 Key: SPARK-23626
>                 URL: https://issues.apache.org/jira/browse/SPARK-23626
>             Project: Spark
>          Issue Type: Bug
>          Components: Scheduler
>    Affects Versions: 2.2.1, 2.3.3, 3.0.0
>            Reporter: Ajith S
>            Priority: Major
>              Labels: bulk-closed
>
> DAGScheduler becomes a bottleneck in cluster when multiple JobSubmitted events has to
be processed as DAGSchedulerEventProcessLoop is single threaded and it will block other tasks
in queue like TaskCompletion.
> The JobSubmitted event is time consuming depending on the nature of the job (Example:
calculating parent stage dependencies, shuffle dependencies, partitions) and thus it blocks
all the events to be processed.
>  
> I see multiple JIRA referring to this behavior
> https://issues.apache.org/jira/browse/SPARK-2647
> https://issues.apache.org/jira/browse/SPARK-4961
>  
> Similarly in my cluster some jobs partition calculation is time consuming (Similar to
stack at SPARK-2647) hence it slows down the spark DAGSchedulerEventProcessLoop which results
in user jobs to slowdown, even if its tasks are finished within seconds, as TaskCompletion
Events are processed at a slower rate due to blockage.



--
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


Mime
View raw message