[jira] [Updated] (SPARK-12837) Spark driver requires large memory space for serialized results even there are no data collected to the driver

2017-05-11 Thread Xiao Li (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-12837?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Xiao Li updated SPARK-12837:

Fix Version/s: (was: 2.2.1)
   (was: 2.3.0)
   2.2.0

> 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: 2.2.0
>
>
> 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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[jira] [Updated] (SPARK-12837) Spark driver requires large memory space for serialized results even there are no data collected to the driver

2016-05-03 Thread Davies Liu (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-12837?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Davies Liu updated SPARK-12837:
---
Target Version/s: 2.0.0
Priority: Critical  (was: Major)

> 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
>
> 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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[jira] [Updated] (SPARK-12837) Spark driver requires large memory space for serialized results even there are no data collected to the driver

2016-05-03 Thread Davies Liu (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-12837?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Davies Liu updated SPARK-12837:
---
Assignee: Wenchen Fan

> 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
>
> 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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[jira] [Updated] (SPARK-12837) Spark driver requires large memory space for serialized results even there are no data collected to the driver

2016-01-15 Thread Tien-Dung LE (JIRA) 

 [ 
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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