I have two dataframes which I am joining. small and big size dataframess. The
optimizer suggest to use BroadcastNestedLoopJoin.
number of partitions for the big Dataframe is 200 while small Dataframe has 5
partitions.
The joined dataframe results with 205 partitions (joined.rdd.partitions.size),
I have tried to understand why is this number and figured out that
BroadCastNestedLoopJoin is actually a union.
code :
case class BroadcastNestedLoopJoin{
def doExecuteo(): =
{ ... ... sparkContext.union( matchedStreamRows,
sparkContext.makeRDD(notMatchedBroadcastRows) ) }
}
can someone please explain what exactly the code of doExecute() do? can you
elaborate about all the null checks and why can we have nulls ? Why do we have
205 partitions? link to a JIRA with discussion that can explain the code can
help.
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