Joao created SPARK-17381: ---------------------------- Summary: 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: Bug 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 Priority: Blocker 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]]) => { //load data 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 bring referenced by SQLTaskMetrics. The strangest thing is that those Array[Byte] were basically text that were loaded in the executors so they never should 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(), I noticed that 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) that 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 that I could run my streaming app for a long period of time, but I think there will be always some performance lost. -- 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