asugranyes commented on code in PR #56180:
URL: https://github.com/apache/spark/pull/56180#discussion_r3329366271
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala:
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@@ -973,8 +973,19 @@ object LimitPushDown extends Rule[LogicalPlan] {
val newAgg = EliminateSorts(a.copy(child = LocalLimit(le,
a.child))).asInstanceOf[Aggregate]
Limit(le, p.copy(child = Project(newAgg.aggregateExpressions,
newAgg.child)))
// Merge offset value and limit value into LocalLimit and pushes down
LocalLimit through Offset.
+ // Fold the sum eagerly when both operands are integer literals so this
rule produces a
+ // planable logical plan in a single application. Otherwise physical
planning
+ // (BasicOperators in SparkStrategies) would fail with `AssertionError: No
plan for
+ // LocalLimit (a + b)` when ConstantFolding is excluded from the optimizer
pipeline, since
+ // BasicOperators only matches LocalLimit(IntegerLiteral, _). In practice
both operands
+ // come from `LIMIT N OFFSET M` clauses (already folded to literals) so
this single-case
+ // fold covers all realistic inputs.
case LocalLimit(le, Offset(oe, grandChild)) =>
- Offset(oe, LocalLimit(Add(le, oe), grandChild))
+ val mergedLimit = (le, oe) match {
+ case (IntegerLiteral(l), IntegerLiteral(o)) => Literal(l + o,
IntegerType)
Review Comment:
Using `Add(le, oe).eval(EmptyRow) ` reuses the same path
`ConstantFolding.tryFold ` takes — folds correctly for normal values and
surfaces ANSI overflow errors when applicable, without reimplementing the
checks here.
```suggestion
case (IntegerLiteral(l), IntegerLiteral(o)) => Literal(Add(le,
oe).eval(EmptyRow), IntegerType)
```
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