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https://issues.apache.org/jira/browse/SPARK-3621?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14342119#comment-14342119
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Sean Owen commented on SPARK-3621:
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I'd like to resolve what the use case is here:
Is the request to "load an entire RDD into memory on every executor"?
If so, what is the use case? the setup implies this is a situation where the
data is large to be handled by the driver, but then putting it in memory
everywhere is expensive.
If the goal is sharing between stages, how is this different from persisting an
RDD, either on disk or in memory?
> Provide a way to broadcast an RDD (instead of just a variable made of the
> RDD) so that a job can access
> -------------------------------------------------------------------------------------------------------
>
> Key: SPARK-3621
> URL: https://issues.apache.org/jira/browse/SPARK-3621
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 1.0.0, 1.1.0
> Reporter: Xuefu Zhang
>
> In some cases, such as Hive's way of doing map-side join, it would be
> benefcial to allow client program to broadcast RDDs rather than just
> variables made of these RDDs. Broadcasting a variable made of RDDs requires
> all RDD data be collected to the driver and that the variable be shipped to
> the cluster after being made. It would be more performing if driver just
> broadcasts the RDDs and uses the corresponding data in jobs (such building
> hashmaps at executors).
> Tez has a broadcast edge which can ship data from previous stage to the next
> stage, which doesn't require driver side processing.
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