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

["shashank (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-12837?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17377990#comment-17377990 ] 

shashank commented on SPARK-12837:
----------------------------------

Seeing the same issue on 2.4.3
{code:java}
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Total size of serialized results of 104904 tasks (8.0 GB) is bigger than spark.driver.maxResultSize (8.0 GB)
 at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2041)
 at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2029)
 at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2028)
 at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
 at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
 at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2028)
 at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
 at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
 at scala.Option.foreach(Option.scala:257)
 at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:966)
 at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2262)
 at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2211)
 at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2200)
 at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
 at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777)
 at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
 at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
 at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
 at org.apache.spark.internal.io.SparkHadoopWriter$.write(SparkHadoopWriter.scala:78){code}

 ... 54 more

> 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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