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https://issues.apache.org/jira/browse/SPARK-8582?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16590733#comment-16590733
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Baris ERGUN commented on SPARK-8582:
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+1 when this issue is planned to be resolved. I am facing it on Spark 2.3.1
when using with Dataset Api. It has been long time and old version since it has
been reported? Is this maybe more complex than it seems? Thanks for the help
> Optimize checkpointing to avoid computing an RDD twice
> ------------------------------------------------------
>
> Key: SPARK-8582
> URL: https://issues.apache.org/jira/browse/SPARK-8582
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.0.0
> Reporter: Andrew Or
> Assignee: Shixiong Zhu
> Priority: Major
>
> In Spark, checkpointing allows the user to truncate the lineage of his RDD
> and save the intermediate contents to HDFS for fault tolerance. However, this
> is not currently implemented super efficiently:
> Every time we checkpoint an RDD, we actually compute it twice: once during
> the action that triggered the checkpointing in the first place, and once
> while we checkpoint (we iterate through an RDD's partitions and write them to
> disk). See this line for more detail:
> https://github.com/apache/spark/blob/0401cbaa8ee51c71f43604f338b65022a479da0a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala#L102.
> Instead, we should have a `CheckpointingInterator` that writes checkpoint
> data to HDFS while we run the action. This will speed up many usages of
> `RDD#checkpoint` by 2X.
> (Alternatively, the user can just cache the RDD before checkpointing it, but
> this is not always viable for very large input data. It's also not a great
> API to use in general.)
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