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From "Nick Pritchard (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-10942) Not all cached RDDs are unpersisted
Date Tue, 06 Oct 2015 02:49:26 GMT
Nick Pritchard created SPARK-10942:
--------------------------------------

             Summary: 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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