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https://issues.apache.org/jira/browse/SPARK-37502?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-37502:
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Assignee: 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
> Assignee: Apache Spark
> 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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