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

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

Sreelal S L created SPARK-17842:
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             Summary: 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. 
 




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