[jira] [Assigned] (SPARK-37502) Support cast aware output partitioning and required if it can up cast

["Apache Spark (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-37502?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-37502:
------------------------------------

    Assignee:     (was: Apache Spark)

> Support cast aware output partitioning and required if it can up cast
> ---------------------------------------------------------------------
>
>                 Key: SPARK-37502
>                 URL: https://issues.apache.org/jira/browse/SPARK-37502
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 3.3.0
>            Reporter: XiDuo You
>            Priority: Major
>
> If a `Cast` is up cast then it should be without any truncating or precision lose or possible runtime failures. So the output partitioning should be same with/without `Cast` if the `Cast` is up cast.
> Let's say we have a query:
> {code:java}
> -- v1: c1 int
> -- v2: c2 long
> SELECT * FROM v2 JOIN (SELECT c1, count(*) FROM v1 GROUP BY c1) v1 ON v1.c1 = v2.c2
> {code}
> The executed plan contains three shuffle nodes which looks like:
> {code:java}
> SortMergeJoin
>   Exchange(cast(c1 as bigint))
>     HashAggregate
>       Exchange(c1)
>         Scan v1
>   Exchange(c2)
>     Scan v2
> {code}
> We can simplify the plan using two shuffle nodes:
> {code:java}
> SortMergeJoin
>   HashAggregate
>     Exchange(c1)
>       Scan v1
>   Exchange(c2)
>     Scan v2
> {code}



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