milanisvet commented on code in PR #49518:
URL: https://github.com/apache/spark/pull/49518#discussion_r1925279434
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveWithCTE.scala:
##########
@@ -183,4 +184,52 @@ object ResolveWithCTE extends Rule[LogicalPlan] {
columnNames.map(UnresolvedSubqueryColumnAliases(_, ref)).getOrElse(ref)
}
}
+
+ /**
+ * Checks if data types of anchor and recursive terms of a recursive CTE
definition match.
+ */
+ def checkDataTypesAnchorAndRecursiveTerm(unionLoop: UnionLoop): Unit = {
+ val anchorOutputDatatypes = unionLoop.anchor.output.map(_.dataType)
+ val recursiveTermOutputDatatypes =
unionLoop.recursion.output.map(_.dataType)
+
+ if (!anchorOutputDatatypes.zip(recursiveTermOutputDatatypes).forall {
+ case (anchorDT, recursionDT) => DataType.equalsStructurally(anchorDT,
recursionDT, true)
Review Comment:
Rewritten, but I might completely remove data type checking as I realized
that we might end up in some unexpected behavior.
```
WITH RECURSIVE r(level, data) AS (
VALUES (0, 0)
UNION ALL
SELECT level + 1, r.data
FROM r
WHERE r.level < 9
)
SELECT * FROM r
```
This query will throw data type error although it should not.
The queries for which data type error should indeed be thrown should end up
anyway in `[CANNOT_MERGE_INCOMPATIBLE_DATA_TYPE]`.
I will try with more examples to confirm this
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