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Posted to issues@spark.apache.org by "Deenbandhu Agarwal (JIRA)" <ji...@apache.org> on 2017/06/19 05:33:00 UTC
[jira] [Commented] (SPARK-17381) Memory leak
org.apache.spark.sql.execution.ui.SQLTaskMetrics
[ https://issues.apache.org/jira/browse/SPARK-17381?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16053488#comment-16053488 ]
Deenbandhu Agarwal commented on SPARK-17381:
--------------------------------------------
[~joaomaiaduarte] I am facing a similar kind of issue. I am running spark streaming in the production environment with 6 executors and 1 GB memory and 1 core each and driver with 3 GB.Spark Version used is 2.0.1. Objects of some linked list are getting accumulated over the time in the JVM Heap of driver and after 2-3 hours the GC become very frequent and jobs starts queuing up. I tried your solution but in vain. We are not using linked list anywhere.
> Memory leak org.apache.spark.sql.execution.ui.SQLTaskMetrics
> -------------------------------------------------------------
>
> Key: SPARK-17381
> URL: https://issues.apache.org/jira/browse/SPARK-17381
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.0.0
> Environment: EMR 5.0.0 (submitted as yarn-client)
> Java Version 1.8.0_101 (Oracle Corporation)
> Scala Version version 2.11.8
> Problem also happens when I run locally with similar versions of java/scala. OS: Ubuntu 16.04
> Reporter: Joao Duarte
>
> I am running a Spark Streaming application from a Kinesis stream. After some hours running it gets out of memory. After a driver heap dump I found two problems:
> 1) huge amount of org.apache.spark.sql.execution.ui.SQLTaskMetrics (It seems this was a problem before:
> https://issues.apache.org/jira/browse/SPARK-11192);
> To replicate the org.apache.spark.sql.execution.ui.SQLTaskMetrics leak just needed to run the code below:
> {code}
> val dstream = ssc.union(kinesisStreams)
> dstream.foreachRDD((streamInfo: RDD[Array[Byte]]) => {
> val toyDF = streamInfo.map(_ =>
> (1, "data","more data "
> ))
> .toDF("Num", "Data", "MoreData" )
> toyDF.agg(sum("Num")).first().get(0)
> }
> )
> {code}
> 2) huge amount of Array[Byte] (9Gb+)
> After some analysis, I noticed that most of the Array[Byte] where being referenced by objects that were being referenced by SQLTaskMetrics. The strangest thing is that those Array[Byte] were basically text that were loaded in the executors, so they should never be in the driver at all!
> Still could not replicate the 2nd problem with a simple code (the original was complex with data coming from S3, DynamoDB and other databases). However, when I debug the application I can see that in Executor.scala, during reportHeartBeat(), the data that should not be sent to the driver is being added to "accumUpdates" which, as I understand, will be sent to the driver for reporting.
> To be more precise, one of the taskRunner in the loop "for (taskRunner <- runningTasks.values().asScala)" contains a GenericInternalRow with a lot of data that should not go to the driver. The path would be in my case: taskRunner.task.metrics.externalAccums[2]._list[0]. This data is similar (if not the same) to the data I see when I do a driver heap dump.
> I guess that if the org.apache.spark.sql.execution.ui.SQLTaskMetrics leak is fixed I would have less of this undesirable data in the driver and I could run my streaming app for a long period of time, but I think there will always be some performance lost.
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