[jira] [Assigned] (SPARK-16827) Query with Join produces excessive amount of shuffle data
[
https://issues.apache.org/jira/browse/SPARK-16827?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-16827:
Assignee: Apache Spark
> Query with Join produces excessive amount of shuffle data
> -
>
> Key: SPARK-16827
> URL: https://issues.apache.org/jira/browse/SPARK-16827
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
>Affects Versions: 2.0.0
>Reporter: Sital Kedia
>Assignee: Apache Spark
> Labels: performance
>
> One of our hive job which looks like this -
> {code}
> SELECT userid
> FROM table1 a
> JOIN table2 b
> ONa.ds = '2016-07-15'
> AND b.ds = '2016-07-15'
> AND a.source_id = b.id
> {code}
> After upgrade to Spark 2.0 the job is significantly slow. Digging a little
> into it, we found out that one of the stages produces excessive amount of
> shuffle data. Please note that this is a regression from Spark 1.6. Stage 2
> of the job which used to produce 32KB shuffle data with 1.6, now produces
> more than 400GB with Spark 2.0. We also tried turning off whole stage code
> generation but that did not help.
> PS - Even if the intermediate shuffle data size is huge, the job still
> produces accurate output.
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[jira] [Assigned] (SPARK-16827) Query with Join produces excessive amount of shuffle data
[
https://issues.apache.org/jira/browse/SPARK-16827?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-16827:
Assignee: (was: Apache Spark)
> Query with Join produces excessive amount of shuffle data
> -
>
> Key: SPARK-16827
> URL: https://issues.apache.org/jira/browse/SPARK-16827
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
>Affects Versions: 2.0.0
>Reporter: Sital Kedia
> Labels: performance
>
> One of our hive job which looks like this -
> {code}
> SELECT userid
> FROM table1 a
> JOIN table2 b
> ONa.ds = '2016-07-15'
> AND b.ds = '2016-07-15'
> AND a.source_id = b.id
> {code}
> After upgrade to Spark 2.0 the job is significantly slow. Digging a little
> into it, we found out that one of the stages produces excessive amount of
> shuffle data. Please note that this is a regression from Spark 1.6. Stage 2
> of the job which used to produce 32KB shuffle data with 1.6, now produces
> more than 400GB with Spark 2.0. We also tried turning off whole stage code
> generation but that did not help.
> PS - Even if the intermediate shuffle data size is huge, the job still
> produces accurate output.
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