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From "Rekha Joshi (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-10942) Not all cached RDDs are unpersisted
Date Tue, 06 Oct 2015 03:55:26 GMT

    [ https://issues.apache.org/jira/browse/SPARK-10942?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14944465#comment-14944465
] 

Rekha Joshi commented on SPARK-10942:
-------------------------------------

[~pnpritchard] hi.did you try rdd.unpersist()?

> 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
>
> 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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