pan3793 commented on code in PR #44352:
URL: https://github.com/apache/spark/pull/44352#discussion_r1427566432
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sql/core/src/test/resources/sql-tests/analyzer-results/udf/postgreSQL/udf-select_having.sql.out:
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@@ -102,12 +102,11 @@ Project [udf(b)#x, udf(c)#x]
SELECT udf(b), udf(c) FROM test_having
GROUP BY b, c HAVING udf(b) = 3 ORDER BY udf(b), udf(c)
-- !query analysis
-Project [udf(b)#x, udf(c)#x]
Review Comment:
Though this query has both `HAVING` and `ORDER BY` clauses, but only scalar
function is present in `ORDER BY` clause, I think the previous resolution
matches item 4 of `ResolveReferencesInSort` comments.
> 4. Resolves the column to [[AttributeReference]] with the output of a
descendant plan node.
> Spark will propagate the missing attributes from the descendant plan
node to the Sort node.
> This is to allow users to ORDER BY columns that are not in the SELECT
clause, which is
> widely supported in other SQL dialects. For example, `SELECT a FROM t
ORDER BY b`.
With this patch, it should match item 3
> 3. If the child plan is Aggregate or Filter(_, Aggregate), resolves the
column to
> [[TempResolvedColumn]] with the output of Aggregate's child plan.
> This is to allow Sort to host grouping expressions and aggregate
functions, which can
> be pushed down to the Aggregate later. For example,
> `SELECT max(a) FROM t GROUP BY b HAVING max(a) > 1 ORDER BY min(a)`.
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