[jira] [Issue Comment Deleted] (SPARK-17842) Thread and memory leak in WindowDstream (UnionRDD ) when parallelPartition computation gets enabled.

["Sreelal S L (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17842?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sreelal S L updated SPARK-17842:
--------------------------------
    Comment: was deleted

(was:   SparkStreaming’s Window internally uses UnionRDD to merge all the RDDs(created during each batch)  in the window time frame. 
  They have a logic    isPartitionListingParallel: Boolean = rdds.length > conf.getInt("spark.rdd.parallelListingThreshold", 10)
 
  In our case , the rdd count per window is well above 10 , and hence it will use parallel partition computation. 
  partitionEvalTaskSupport =new ForkJoinTaskSupport(new ForkJoinPool(8))
  
This pool is created every slide interval but ForkJoinPool.shutdown()  is not called anywhere. 
This can be the reason for scala.concurrent.forkjoin.ForkJoinTask[] to leak . 

This is causing a thread leak. )

> Thread and memory leak in WindowDstream (UnionRDD ) when parallelPartition computation gets enabled. 
> -----------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-17842
>                 URL: https://issues.apache.org/jira/browse/SPARK-17842
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core, Streaming
>    Affects Versions: 2.0.0
>         Environment: Yarn cluster, Eclipse Dev Env
>            Reporter: Sreelal S L
>            Priority: Critical
>
> We noticed a steady increase in ForkJoinTask instances in the driver process heap. Found out the root cause to be UnionRDD.
> WindowDstream internally uses UnionRDD which has a parallel partition computation logic by using parallel collection with ForkJoinPool task support. 
> partitionEvalTaskSupport =new ForkJoinTaskSupport(new ForkJoinPool(8))
> The pool is created each time when a UnionRDD is created , but the pool is not getting shutdown. This is leaking thread/mem every slide interval of the window. 
> Easily reproducible with the below code. Just keep a watch on the number of threads. 
> {code}
>     val sparkConf = new SparkConf().setMaster("local[*]").setAppName("TestLeak")
>     val ssc = new StreamingContext(sparkConf, Seconds(1))
>     ssc.checkpoint("checkpoint")
>     val rdd = ssc.sparkContext.parallelize(List(1,2,3))
>     val constStream = new ConstantInputDStream[Int](ssc,rdd)
>     constStream.window(Seconds(20),Seconds(1)).print()
>     ssc.start()
>     ssc.awaitTermination();
> {code}
> This happens only when the number of rdds to be unioned is above the value spark.rdd.parallelListingThreshold (By default 10)
> Currently i'm working around by setting this threshold be a higher value. 
>  



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org