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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2015/06/17 17:23:01 UTC
[jira] [Resolved] (SPARK-6698) RandomForest.scala (et al) hardcodes
usage of StorageLevel.MEMORY_AND_DISK
[ https://issues.apache.org/jira/browse/SPARK-6698?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-6698.
------------------------------
Resolution: Won't Fix
Closing per PR
> 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
>
> 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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