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Posted to issues@spark.apache.org by "Zhihui (JIRA)" <ji...@apache.org> on 2014/06/12 11:36:01 UTC
[jira] [Updated] (SPARK-1946) Submit stage after executors have
been registered
[ https://issues.apache.org/jira/browse/SPARK-1946?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Zhihui updated SPARK-1946:
--------------------------
Description:
Because creating TaskSetManager and registering executors are asynchronous, if running job without enough executors, it will lead to some issues
* early stages' tasks run without preferred locality.
* the default parallelism in yarn is based on number of executors,
* the number of intermediate files per node for shuffle (this can bring the node down btw)
* and amount of memory consumed on a node for rdd MEMORY persisted data (making the job fail if disk is not specified : like some of the mllib algos ?)
* and so on ...
A simple solution is sleeping few seconds in application, so that executors have enough time to register.
A better way is to make DAGScheduler submit stage after a few of executors have been registered by configuration properties.
\# submit stage only after successfully registered executors arrived the ratio, default value 0
spark.executor.registeredRatio = 0.8
\# whatever registeredRatio is arrived, submit stage after the maxRegisteredWaitingTime(millisecond), default value 10000
spark.executor.maxRegisteredWaitingTime = 5000
was:
Because creating TaskSetManager and registering executors are asynchronous, in most situation, early stages' tasks run without preferred locality.
A simple solution is sleeping few seconds in application, so that executors have enough time to register.
A better way is to make DAGScheduler submit stage after a few of executors have been registered by configuration properties.
\# submit stage only after successfully registered executors arrived the ratio, default value 0
spark.executor.registeredRatio = 0.8
\# whatever registeredRatio is arrived, submit stage after the maxRegisteredWaitingTime(millisecond), default value 10000
spark.executor.maxRegisteredWaitingTime = 5000
> Submit stage after executors have been registered
> -------------------------------------------------
>
> Key: SPARK-1946
> URL: https://issues.apache.org/jira/browse/SPARK-1946
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 1.0.0
> Reporter: Zhihui
> Attachments: Spark Task Scheduler Optimization Proposal.pptx
>
>
> Because creating TaskSetManager and registering executors are asynchronous, if running job without enough executors, it will lead to some issues
> * early stages' tasks run without preferred locality.
> * the default parallelism in yarn is based on number of executors,
> * the number of intermediate files per node for shuffle (this can bring the node down btw)
> * and amount of memory consumed on a node for rdd MEMORY persisted data (making the job fail if disk is not specified : like some of the mllib algos ?)
> * and so on ...
> A simple solution is sleeping few seconds in application, so that executors have enough time to register.
> A better way is to make DAGScheduler submit stage after a few of executors have been registered by configuration properties.
> \# submit stage only after successfully registered executors arrived the ratio, default value 0
> spark.executor.registeredRatio = 0.8
> \# whatever registeredRatio is arrived, submit stage after the maxRegisteredWaitingTime(millisecond), default value 10000
> spark.executor.maxRegisteredWaitingTime = 5000
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