Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/2868#issuecomment-59974518
@chouqin Checkpointing is helpful since it is more persistent than
persist(). Checkpointing stores data to HDFS (with replication), so that the
RDD is stored even if a worker dies. With persist(), part of the RDD will be
lost when a worker dies. For my big decision tree tests, I do see EC2 workers
die periodically (though not that often), and I am sure it is a bigger issue
for corporate (big) clusters.
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