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Posted to issues@spark.apache.org by "Michael Bieniosek (JIRA)" <ji...@apache.org> on 2015/04/03 18:04:53 UTC

[jira] [Created] (SPARK-6698) RandomForest.scala (et al) hardcodes usage of StorageLevel.MEMORY_AND_DISK

Michael Bieniosek created SPARK-6698:
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             Summary: 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: Bug
          Components: MLlib
    Affects Versions: 1.3.0
            Reporter: Michael Bieniosek


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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