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Posted to issues@spark.apache.org by "Shay Elbaz (JIRA)" <ji...@apache.org> on 2018/07/24 11:27:00 UTC
[jira] [Updated] (SPARK-24904) Join with broadcasted dataframe
causes shuffle of redundant data
[ https://issues.apache.org/jira/browse/SPARK-24904?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Shay Elbaz updated SPARK-24904:
-------------------------------
Description:
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
//OUTPUT:
== 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.
was:
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.
> 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
> Priority: Minor
>
> 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
> //OUTPUT:
> == 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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