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Posted to issues@spark.apache.org by "Thomas Graves (JIRA)" <ji...@apache.org> on 2017/07/10 13:44:00 UTC
[jira] [Commented] (SPARK-19659) Fetch big blocks to disk when
shuffle-read
[ https://issues.apache.org/jira/browse/SPARK-19659?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16080340#comment-16080340 ]
Thomas Graves commented on SPARK-19659:
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can you be more specific here, do you mean that if you want to use this feature you have to use the shuffle service from 2.2.0? Is that shuffle service compatible with older versions of Spark? ie spark 1.6 can still talk to 2.2.0 shuffle service.
> Fetch big blocks to disk when shuffle-read
> ------------------------------------------
>
> Key: SPARK-19659
> URL: https://issues.apache.org/jira/browse/SPARK-19659
> Project: Spark
> Issue Type: Improvement
> Components: Shuffle
> Affects Versions: 2.1.0
> Reporter: jin xing
> Assignee: jin xing
> Fix For: 2.2.0
>
> Attachments: SPARK-19659-design-v1.pdf, SPARK-19659-design-v2.pdf
>
>
> Currently the whole block is fetched into memory(offheap by default) when shuffle-read. A block is defined by (shuffleId, mapId, reduceId). Thus it can be large when skew situations. If OOM happens during shuffle read, job will be killed and users will be notified to "Consider boosting spark.yarn.executor.memoryOverhead". Adjusting parameter and allocating more memory can resolve the OOM. However the approach is not perfectly suitable for production environment, especially for data warehouse.
> Using Spark SQL as data engine in warehouse, users hope to have a unified parameter(e.g. memory) but less resource wasted(resource is allocated but not used),
> It's not always easy to predict skew situations, when happen, it make sense to fetch remote blocks to disk for shuffle-read, rather than
> kill the job because of OOM. This approach is mentioned during the discussion in SPARK-3019, by [~sandyr] and [~mridulm80]
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