cloud-fan commented on a change in pull request #32602:
URL: https://github.com/apache/spark/pull/32602#discussion_r637769570
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File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelation.scala
##########
@@ -20,65 +20,73 @@ package org.apache.spark.sql.catalyst.optimizer
import org.apache.spark.sql.catalyst.analysis.CastSupport
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.Literal.FalseLiteral
+import
org.apache.spark.sql.catalyst.planning.ExtractSingleColumnNullAwareAntiJoin
import org.apache.spark.sql.catalyst.plans._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules._
import org.apache.spark.sql.catalyst.trees.TreePattern.{LOCAL_RELATION,
TRUE_OR_FALSE_LITERAL}
/**
- * Collapse plans consisting empty local relations generated by
[[PruneFilters]].
- * 1. Binary(or Higher)-node Logical Plans
- * - Union with all empty children.
- * - Join with one or two empty children (including Intersect/Except).
- * 2. Unary-node Logical Plans
- * - Project/Filter/Sample/Join/Limit/Repartition with all empty children.
- * - Join with false condition.
- * - Aggregate with all empty children and at least one grouping expression.
- * - Generate(Explode) with all empty children. Others like Hive UDTF may
return results.
+ * The rule used by both normal Optimizer and AQE Optimizer for:
+ * 1. Binary-node Logical Plans
+ * - Join with one or two empty children (including Intersect/Except).
+ * - Join is single column NULL-aware anti join (NAAJ)
+ * Broadcasted [[HashedRelation]] is [[HashedRelationWithAllNullKeys]].
Eliminate join to an
+ * empty [[LocalRelation]].
+ * - Left semi Join
+ * Right side is non-empty and condition is empty. Eliminate join to its
left side.
+ * - Left anti join
+ * Right side is non-empty and condition is empty. Eliminate join to an
empty
+ * [[LocalRelation]].
+ * 2. Unary-node Logical Plans
+ * - Limit/Repartition with all empty children.
+ * - Aggregate with all empty children and at least one grouping
expression.
+ * - Generate(Explode) with all empty children. Others like Hive UDTF may
return results.
*/
-object PropagateEmptyRelation extends Rule[LogicalPlan] with PredicateHelper
with CastSupport {
- private def isEmptyLocalRelation(plan: LogicalPlan): Boolean = plan match {
+abstract class PropagateEmptyRelationBase extends Rule[LogicalPlan] with
CastSupport {
+ /**
+ * At AQE side, we use this function to check if a plan has output rows or
not
+ */
+ protected def checkRowCount: Option[(LogicalPlan, Boolean) => Boolean] = None
+
+ /**
+ * At AQE side, we use the broadcast query stage to do the check
+ */
+ protected def isRelationWithAllNullKeys: Option[LogicalPlan => Boolean] =
None
+
+ protected def isEmptyLocalRelation(plan: LogicalPlan): Boolean = plan match {
case p: LocalRelation => p.data.isEmpty
case _ => false
}
- private def empty(plan: LogicalPlan) =
+ private def isEmptyLocalRelationWithRowCount(plan: LogicalPlan): Boolean = {
+ val defaultEmptyRelation: Boolean = isEmptyLocalRelation(plan)
+ if (checkRowCount.isDefined) {
+ checkRowCount.get.apply(plan, false) || defaultEmptyRelation
+ } else {
+ defaultEmptyRelation
+ }
+ }
+
+ protected def empty(plan: LogicalPlan): LocalRelation =
LocalRelation(plan.output, data = Seq.empty, isStreaming =
plan.isStreaming)
// Construct a project list from plan's output, while the value is always
NULL.
private def nullValueProjectList(plan: LogicalPlan): Seq[NamedExpression] =
plan.output.map{ a => Alias(cast(Literal(null), a.dataType),
a.name)(a.exprId) }
- def apply(plan: LogicalPlan): LogicalPlan = plan.transformUpWithPruning(
- _.containsAnyPattern(LOCAL_RELATION, TRUE_OR_FALSE_LITERAL), ruleId) {
- case p: Union if p.children.exists(isEmptyLocalRelation) =>
- val newChildren = p.children.filterNot(isEmptyLocalRelation)
- if (newChildren.isEmpty) {
- empty(p)
- } else {
- val newPlan = if (newChildren.size > 1) Union(newChildren) else
newChildren.head
- val outputs = newPlan.output.zip(p.output)
- // the original Union may produce different output attributes than the
new one so we alias
- // them if needed
- if (outputs.forall { case (newAttr, oldAttr) => newAttr.exprId ==
oldAttr.exprId }) {
- newPlan
- } else {
- val outputAliases = outputs.map { case (newAttr, oldAttr) =>
- val newExplicitMetadata =
- if (oldAttr.metadata != newAttr.metadata) Some(oldAttr.metadata)
else None
- Alias(newAttr, oldAttr.name)(oldAttr.exprId, explicitMetadata =
newExplicitMetadata)
- }
- Project(outputAliases, newPlan)
- }
- }
+ protected def propagateEmptyRelationAdvanced: PartialFunction[LogicalPlan,
LogicalPlan] = {
Review comment:
`single column NULL-aware anti join (NAAJ)` is AQE only, I think it's
better to match it only in the AQE rule
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