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https://issues.apache.org/jira/browse/SPARK-21795?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16273642#comment-16273642
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Anton commented on SPARK-21795:
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A simple example of when you would like to broadcast a cached dataframe is when
you have a dataframe that is the result of a long pipeline of transformations -
e.g. a few large table joins and then some aggregations to make it small enough
to be broadcasted. If you want to broadcast it to two different tables (or do
multiple actions on that dataframe), it would be better to cache it rather than
running the transformation pipeline multiple times.
I was able to replicate the issue and it appears that the the hint is ignored
on cached dataframes however if the size of the dataframe is below the current
spark.sql.autoBroadcastJoinThreshold value then it will still be broadcasted.
This makes it feel more like a bug than by design.
> Broadcast hint ignored when dataframe is cached
> -----------------------------------------------
>
> Key: SPARK-21795
> URL: https://issues.apache.org/jira/browse/SPARK-21795
> Project: Spark
> Issue Type: Question
> Components: Documentation, SQL
> Affects Versions: 2.2.0
> Reporter: Lior Chaga
> Priority: Minor
>
> Not sure if it's a bug or by design, but if a DF is cached, the broadcast
> hint is ignored, and spark uses SortMergeJoin.
> {code}
> val largeDf = ...
> val smalDf = ...
> smallDf = smallDf.cache
> largeDf.join(broadcast(smallDf))
> {code}
> It make sense there's no need to use cache when using broadcast join,
> however, I wonder if it's the correct behavior for spark to ignore the
> broadcast hint just because the DF is cached. Consider a case when a DF
> should be cached for several queries, and on different queries it should be
> broadcasted.
> If this is the correct behavior, at least it's worth documenting that cached
> DF cannot be broadcasted.
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