gengliangwang commented on code in PR #54492:
URL: https://github.com/apache/spark/pull/54492#discussion_r2886755590
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala:
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@@ -370,7 +370,16 @@ abstract class Optimizer(catalogManager: CatalogManager)
s.withNewPlan(removeTopLevelSort(newPlan))
}
- def apply(plan: LogicalPlan): LogicalPlan =
plan.transformAllExpressionsWithPruning(
+ // optimizes subquery expressions, ignoring row-level operation conditions
+ def apply(plan: LogicalPlan): LogicalPlan = {
+ plan.transformWithPruning(_.containsPattern(PLAN_EXPRESSION), ruleId) {
+ case wd: WriteDelta => wd
+ case rd: ReplaceData => rd
Review Comment:
Minor: consider matching on `RowLevelWrite` instead of listing `WriteDelta`
and `ReplaceData` individually. This would be more future-proof if new
`RowLevelWrite` subtypes are added, and `RowLevelWrite` is already available
via the `plans.logical._` wildcard import.
```scala
case _: RowLevelWrite => plan // or use a named binding
```
That said, the explicit listing is fine too — it makes the scope of the
short-circuit clear.
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