Sreelal S L created SPARK-17842: ----------------------------------- 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org