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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-19255) SQL Listener is causing out of memory, in case of large no of shuffle partition
Date Wed, 18 Jan 2017 09:46:26 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19255?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15827740#comment-15827740
] 

Sean Owen commented on SPARK-19255:
-----------------------------------

I think 10m partitions is an unrealistic number. Surely you can repartition that down?

> SQL Listener is causing out of memory, in case of large no of shuffle partition
> -------------------------------------------------------------------------------
>
>                 Key: SPARK-19255
>                 URL: https://issues.apache.org/jira/browse/SPARK-19255
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>         Environment: Linux
>            Reporter: Ashok Kumar
>            Priority: Minor
>         Attachments: spark_sqllistener_oom.png
>
>
> Test steps.
> 1.CREATE TABLE sample(imei string,age int,task bigint,num double,level decimal(10,3),productdate
timestamp,name string,point int)USING com.databricks.spark.csv OPTIONS (path "data.csv", header
"false", inferSchema "false");
> 2. set spark.sql.shuffle.partitions=100000;
> 3. select count(*) from (select task,sum(age) from sample group by task) t;
> After running above query, number of objects in map variable _stageIdToStageMetrics has
increase to very high number , this increment is proportional to number of shuffle partition.
> Please have a look at attached screenshot



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