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https://issues.apache.org/jira/browse/SPARK-12837?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15962731#comment-15962731
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Wenchen Fan edited comment on SPARK-12837 at 4/10/17 11:54 AM:
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[~jbherman] I tried and debugged your example, the actual task result is around
1kb, when the number of shuffle partitions is 1k, the total result size will be
around 1mb. I'm trying to reduce the overhead of accumulator, but you have to
increase the `spark.driver.maxResultSize` anyway, 1mb is too small for this job.
was (Author: cloud_fan):
[~jbherman] I tried and debugged your example, the actual task result is around
1kb, when the number of shuffle partitions is 1k, the total result size will be
around 1mb. I'm trying to reduce the overhead of accumulator, but you have to
increase the `spark.driver.maxResultSize` anyway.
> Spark driver requires large memory space for serialized results even there
> are no data collected to the driver
> --------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-12837
> URL: https://issues.apache.org/jira/browse/SPARK-12837
> Project: Spark
> Issue Type: Question
> Components: SQL
> Affects Versions: 1.5.2, 1.6.0
> Reporter: Tien-Dung LE
> Assignee: Wenchen Fan
> Priority: Critical
> Fix For: 2.0.0
>
>
> Executing a sql statement with a large number of partitions requires a high
> memory space for the driver even there are no requests to collect data back
> to the driver.
> Here are steps to re-produce the issue.
> 1. Start spark shell with a spark.driver.maxResultSize setting
> {code:java}
> bin/spark-shell --driver-memory=1g --conf spark.driver.maxResultSize=1m
> {code}
> 2. Execute the code
> {code:java}
> case class Toto( a: Int, b: Int)
> val df = sc.parallelize( 1 to 1e6.toInt).map( i => Toto( i, i)).toDF
> sqlContext.setConf( "spark.sql.shuffle.partitions", "200" )
> df.groupBy("a").count().saveAsParquetFile( "toto1" ) // OK
> sqlContext.setConf( "spark.sql.shuffle.partitions", 1e3.toInt.toString )
> df.repartition(1e3.toInt).groupBy("a").count().repartition(1e3.toInt).saveAsParquetFile(
> "toto2" ) // ERROR
> {code}
> The error message is
> {code:java}
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
> Total size of serialized results of 393 tasks (1025.9 KB) is bigger than
> spark.driver.maxResultSize (1024.0 KB)
> {code}
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