[jira] [Created] (SPARK-24904) Join with broadcasted dataframe causes shuffle of redundant data

["Shay Elbaz (JIRA)" <ji...@apache.org>]

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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