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Posted to issues@spark.apache.org by "Tien-Dung LE (JIRA)" <ji...@apache.org> on 2016/01/15 14:46:39 UTC
[jira] [Updated] (SPARK-12837) Spark driver requires large memory
space for serialized results even there are no data collected to the driver
[ https://issues.apache.org/jira/browse/SPARK-12837?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Tien-Dung LE updated SPARK-12837:
---------------------------------
Description:
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}
was:
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:shell}
bin/spark-shell --driver-memory=1g --conf spark.driver.maxResultSize=1m
{code}
2. Execute the code
{code:scala}
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:scala}
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}
> 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
>
> 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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