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https://issues.apache.org/jira/browse/SPARK-1855?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14073784#comment-14073784
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koert kuipers commented on SPARK-1855:
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i think this makes sense. we have iterative queries that should be very quick.
in case of machine failure i am ok if query fails, we will simply repeat. so i
do not care about checkpoint to disk in this situation. but i do care about
checkpoint to memory to cut my dependencies, which means they get garbage
collected and cached rdds get cleaned up.
> Provide memory-and-local-disk RDD checkpointing
> -----------------------------------------------
>
> Key: SPARK-1855
> URL: https://issues.apache.org/jira/browse/SPARK-1855
> Project: Spark
> Issue Type: New Feature
> Components: MLlib, Spark Core
> Affects Versions: 1.0.0
> Reporter: Xiangrui Meng
>
> Checkpointing is used to cut long lineage while maintaining fault tolerance.
> The current implementation is HDFS-based. Using the BlockRDD we can create
> in-memory-and-local-disk (with replication) checkpoints that are not as
> reliable as HDFS-based solution but faster.
> It can help applications that require many iterations.
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