Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/5351#issuecomment-92520525
@bien Did it help? I'm not sure if it will, but I could imagine it helping
depending on whether the RDDs are getting materialized multiple times.
Adjusting executor memory does sound good, regardless; I tend to use between 2
and 20 GB per executor for my ML tests on clusters.
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