hemanthboyina opened a new pull request, #58544: URL: https://github.com/apache/spark/pull/58544
**What changes were proposed in this pull request?**
When a select item mixes a window function with the GROUP BY expression
(e.g. CASE WHEN ROW_NUMBER() OVER (...) <= 2 THEN UPPER(country) ... END with
GROUP BY UPPER(country)), the ExtractWindowExpressions rule builds an
Aggregate under the Window and decides which inputs the Aggregate must output.
While scanning that select item, the rule had no case for a plain
expression like UPPER(country), so it looked inside it and pushed down the raw
column country instead of the whole UPPER(country). The resulting Aggregate
outputs a country column that is not in the GROUP BY, which is an invalid plan.
This PR passes the grouping expressions to extract and adds a case that
pushes a sub-expression down as a whole when it matches a grouping expression.
The Aggregate now outputs UPPER(country) instead of country. Bare grouping
columns keep their existing behavior, so only queries that used to fail are
affected.
**Why are the changes needed?**
The query is valid SQL but fails analysis with:
[MISSING_AGGREGATION] The non-aggregating expression "country" is based on
columns which are not participating in the GROUP BY clause. SQLSTATE: 42803
This is wrong: country is never selected directly, only UPPER(country),
which is the GROUP BY expression.
**Does this PR introduce any user-facing change?**
Yes. A query that combines a window function with the GROUP BY expression
in the same select item used to fail with MISSING_AGGREGATION; it now runs and
returns the correct result. Queries that already
worked are unaffected.
**How was this patch tested?**
Added a test in DataFrameWindowFunctionsSuite covering the reported query
plus two regression cases (grouping by the bare column, and the window function
in a separate select item).
**Was this patch authored or co-authored using generative AI tooling?**
No
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