Nick Pritchard created SPARK-10942:
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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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