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https://issues.apache.org/jira/browse/SPARK-19255?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15826041#comment-15826041
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Takeshi Yamamuro commented on SPARK-19255:
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Yea, If #partition increases, the size of `taskIdToMetricUpdates` grows. Then,
since `_stageIdToStageMetrics` holds `taskIdToMetricUpdates`,
`_stageIdToStageMetrics` seems big as described in your png.
Anyway, IIUC, you took 100MB for `_stageIdToStageMetrics` in a driver and hit
OOM there, right?
If so, you'd be better to make driver memory more bigger, I think.
> 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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