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https://issues.apache.org/jira/browse/SPARK-12590?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-12590.
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Resolution: Not A Problem
Yes, I think you've hit it on the head: the issue is that you're recomputing a
non-deterministic RDD, and so you aren't seeing consistent results. However,
the non-determinism isn't actually the randomSplit call (it's seeded even), but
the repartition with a shuffle. Generally, this behavior is as expected, and
indeed, you have to cache rdd2 in order to not recompute it.
> Inconsistent behavior of randomSplit in YARN mode
> -------------------------------------------------
>
> Key: SPARK-12590
> URL: https://issues.apache.org/jira/browse/SPARK-12590
> Project: Spark
> Issue Type: Bug
> Components: MLlib, Spark Core
> Affects Versions: 1.5.2
> Environment: YARN mode
> Reporter: Gaurav Kumar
>
> I noticed an inconsistent behavior when using rdd.randomSplit when the source
> rdd is repartitioned, but only in YARN mode. It works fine in local mode
> though.
> *Code:*
> val rdd = sc.parallelize(1 to 1000000)
> val rdd2 = rdd.repartition(64)
> rdd.partitions.size
> rdd2.partitions.size
> val Array(train, test) = rdd2.randomSplit(Array(70, 30), 1)
> train.takeOrdered(10)
> test.takeOrdered(10)
> *Master: local*
> Both the take statements produce consistent results and have no overlap in
> numbers being outputted.
> *Master: YARN*
> However, when these are run on YARN mode, these produce random results every
> time and also the train and test have overlap in the numbers being outputted.
> If I use rdd.randomSplit, then it works fine even on YARN.
> So, it concludes that the repartition is being evaluated every time the
> splitting occurs.
> Interestingly, if I cache the rdd2 before splitting it, then we can expect
> consistent behavior since repartition is not evaluated again and again.
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