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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