[jira] [Resolved] (SPARK-19255) SQL Listener is causing out of memory, in case of data size is in petabytes.

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-19255?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-19255.
-------------------------------
    Resolution: Not A Problem

I think the resolution is, if you have incredibly large numbers of partitions, you're going to need a lot of driver memory.

> SQL Listener is causing out of memory, in case of  data size is in petabytes.
> -----------------------------------------------------------------------------
>
>                 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
>
>
> Since its difficult to load huge dataset, below steps will help in reproducing the issue
> 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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