Allison Wang created SPARK-40862:
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Summary: Unexpected operators when rewriting scalar subqueries
with non-deterministic expressions
Key: SPARK-40862
URL: https://issues.apache.org/jira/browse/SPARK-40862
Project: Spark
Issue Type: Sub-task
Components: SQL
Affects Versions: 3.4.0
Reporter: Allison Wang
Since SPARK-28379, Spark has supported non-aggregated single-row correlated
subqueries. SPARK-40800 handles the majority of the cases where projects can be
collapsed. But Spark can throw exceptions for single-row subqueries with
non-deterministic expressions. For example:
{code:java}
CREATE TEMP VIEW t1 AS SELECT ARRAY('a', 'b') a
SELECT (
SELECT array_sort(a, (i, j) -> rank[i] - rank[j])[0] + r + r AS sorted
FROM (SELECT MAP('a', 1, 'b', 2) rank, rand() as r)
) FROM t1{code}
This throws an exception:
{code:java}
Unexpected operator Join Inner
:- Aggregate [[a,b]], [[a,b] AS a#253]
: +- OneRowRelation
+- Project [map(keys: [a,b], values: [1,2]) AS rank#241,
rand(86882494013664043) AS r#242]
+- OneRowRelation
in correlated subquery{code}
This is because when Spark rewrites correlated subqueries, it checks whether a
scalar subquery is subject to the COUNT bug. It splits the query into parts
above the aggregate, the aggregate, and the parts below the aggregate (see
`splitSubquery` in the `RewriteCorrelatedScalarSubquery` rule).
This pattern is very restrictive and does not work well with non-aggregated
single-row subqueries. We should fix this issue.
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