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https://issues.apache.org/jira/browse/SPARK-10942?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14948928#comment-14948928
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Nick Pritchard commented on SPARK-10942:
----------------------------------------
Thanks [~sowen] for trying! I'll let it go.
> Not all cached RDDs are unpersisted
> -----------------------------------
>
> Key: SPARK-10942
> URL: https://issues.apache.org/jira/browse/SPARK-10942
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Reporter: Nick Pritchard
> Priority: Minor
> Attachments: SPARK-10942_1.png, SPARK-10942_2.png, SPARK-10942_3.png
>
>
> I have a Spark Streaming application that caches RDDs inside of a
> {{transform}} closure. Looking at the Spark UI, it seems that most of these
> RDDs are unpersisted after the batch completes, but not all.
> I have copied a minimal reproducible example below to highlight the problem.
> I run this and monitor the Spark UI "Storage" tab. The example generates and
> caches 30 RDDs, and I see most get cleaned up. However in the end, some still
> remain cached. There is some randomness going on because I see different RDDs
> remain cached for each run.
> I have marked this as Major because I haven't been able to workaround it and
> it is a memory leak for my application. I tried setting {{spark.cleaner.ttl}}
> but that did not change anything.
> {code}
> val inputRDDs = mutable.Queue.tabulate(30) { i =>
> sc.parallelize(Seq(i))
> }
> val input: DStream[Int] = ssc.queueStream(inputRDDs)
> val output = input.transform { rdd =>
> if (rdd.isEmpty()) {
> rdd
> } else {
> val rdd2 = rdd.map(identity)
> rdd2.setName(rdd.first().toString)
> rdd2.cache()
> val rdd3 = rdd2.map(identity)
> rdd3
> }
> }
> output.print()
> ssc.start()
> ssc.awaitTermination()
> {code}
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