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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-6698) RandomForest.scala (et al) hardcodes usage of StorageLevel.MEMORY_AND_DISK
Date Fri, 03 Apr 2015 16:56:04 GMT

     [ https://issues.apache.org/jira/browse/SPARK-6698?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-6698:
-----------------------------------

    Assignee:     (was: Apache Spark)

> 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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