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https://issues.apache.org/jira/browse/SPARK-6698?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14394681#comment-14394681
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Apache Spark commented on SPARK-6698:
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User 'bien' has created a pull request for this issue:
https://github.com/apache/spark/pull/5351
> RandomForest.scala (et al) hardcodes usage of StorageLevel.MEMORY_AND_DISK
> --------------------------------------------------------------------------
>
> Key: SPARK-6698
> URL: https://issues.apache.org/jira/browse/SPARK-6698
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Michael Bieniosek
> Priority: Minor
> Attachments: SPARK-6698.patch
>
>
> In RandomForest.scala the feature input is persisted with
> StorageLevel.MEMORY_AND_DISK during the bagging phase, even if the bagging
> rate is set at 100%. This forces the RDD to be stored unserialized, which
> causes major JVM GC headaches if the RDD is sizable.
> Something similar happens in NodeIdCache.scala though I believe in this case
> the RDD is smaller.
> A simple fix would be to use the same StorageLevel as the input RDD.
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