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

["XiDuo You (Jira)" <ji...@apache.org>]

XiDuo You created SPARK-37502:
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             Summary: 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


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 simply the plan using two shuffle nodes:
{code:java}
SortMergeJoin
  HashAggregate
    Exchange(c1)
      Scan v1
  Exchange(c2)
    Scan v2
{code}



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