Shay Elbaz created SPARK-24904:
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Summary: Join with broadcasted dataframe causes shuffle of
redundant data
Key: SPARK-24904
URL: https://issues.apache.org/jira/browse/SPARK-24904
Project: Spark
Issue Type: Question
Components: SQL
Affects Versions: 2.1.2
Reporter: Shay Elbaz
When joining a "large" dataframe with broadcasted small one, and join-type is
on the small DF side (see right-join below), the physical plan does not include
broadcasting the small table. But when the join is on the large DF side, the
broadcast does take place. Is there a good reason for this? In the below
example it sure doesn't make any sense to shuffle the entire large table:
{code:java}
val small = spark.range(1, 10)
val big = spark.range(1, 1 << 30)
.withColumnRenamed("id", "id2")
big.join(broadcast(small), $"id" === $"id2", "right")
.explain
== Physical Plan ==
SortMergeJoin [id2#16307L], [id#16310L], RightOuter
:- *Sort [id2#16307L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(id2#16307L, 1000)
: +- *Project [id#16304L AS id2#16307L]
: +- *Range (1, 1073741824, step=1, splits=Some(600))
+- *Sort [id#16310L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(id#16310L, 1000)
+- *Range (1, 10, step=1, splits=Some(600))
{code}
As a workaround, users need to perform inner instead of right join, and then
join the result back with the small DF to fill the missing rows.
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