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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