Sean Owen resolved SPARK-17381.
    Resolution: Not A Problem

As far as I can tell this is answered by spark.sql.ui.retainedExecutions

> 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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