cloud-fan commented on a change in pull request #30368:
URL: https://github.com/apache/spark/pull/30368#discussion_r549942541
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File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
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@@ -1452,11 +1452,27 @@ object PushPredicateThroughJoin extends
Rule[LogicalPlan] with PredicateHelper {
}
/**
- * Combines two adjacent [[Limit]] operators into one, merging the
- * expressions into one single expression.
+ * 1. Eliminate [[Limit]] operators if it's child max row <= limit.
+ * 2. Combines two adjacent [[Limit]] operators into one, merging the
+ * expressions into one single expression.
*/
-object CombineLimits extends Rule[LogicalPlan] {
- def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+object EliminateLimits extends Rule[LogicalPlan] {
+ private def canEliminate(limitExpr: Expression, child: LogicalPlan): Boolean
= {
+ // We skip such case that Sort is after Limit since
+ // SparkStrategies will convert them to TakeOrderedAndProjectExec
+ val skipEliminate = child match {
+ case Sort(_, true, _) => true
Review comment:
This reminds me that we can optimize root `SortExec` by collecting data
to the driver and sort locally, to avoid the shuffle, if the input data size is
small. @ulysses-you do you have interests to run some experiments?
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