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Posted to issues@spark.apache.org by "Harel Ben Attia (JIRA)" <ji...@apache.org> on 2018/02/11 06:24:00 UTC
[jira] [Commented] (SPARK-10912) Improve Spark metrics
executor.filesystem
[ https://issues.apache.org/jira/browse/SPARK-10912?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16359810#comment-16359810 ]
Harel Ben Attia commented on SPARK-10912:
-----------------------------------------
We would really be glad to see this happening as well, without the need to change spark's source code.
Also, externalizing the array to a configuration properly in metrics.properties would be best (or auto-supporting each used FileSystem schema obviously, but this might include bigger changes to the registration logic, so it's not necessary).
> Improve Spark metrics executor.filesystem
> -----------------------------------------
>
> Key: SPARK-10912
> URL: https://issues.apache.org/jira/browse/SPARK-10912
> Project: Spark
> Issue Type: Improvement
> Components: Deploy
> Affects Versions: 1.5.0
> Reporter: Yongjia Wang
> Priority: Minor
> Attachments: s3a_metrics.patch
>
>
> In org.apache.spark.executor.ExecutorSource it has 2 filesystem metrics: "hdfs" and "file". I started using s3 as the persistent storage with Spark standalone cluster in EC2, and s3 read/write metrics do not appear anywhere. The 'file' metric appears to be only for driver reading local file, it would be nice to also report shuffle read/write metrics, so it can help with optimization.
> I think these 2 things (s3 and shuffle) are very useful and cover all the missing information about Spark IO especially for s3 setup.
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