[SPARK-35455][SQL] Unify empty relation optimization between normal and AQE optimizer

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/PropagateEmptyRelation.scala

@@ -26,59 +26,46 @@ 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 base class of two rules in the normal and AQE Optimizer. It simplifies query plans with
+ * empty or non-empty relations:
+ *  1. Binary-node Logical Plans
+ *     - Join with one or two empty children (including Intersect/Except).
+ *     - 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 {
+  protected def isEmpty(plan: LogicalPlan): Boolean = plan match {
     case p: LocalRelation => p.data.isEmpty
     case _ => false
   }
 
-  private def empty(plan: LogicalPlan) =
+  protected def nonEmpty(plan: LogicalPlan): Boolean = plan match {
+    case p: LocalRelation => p.data.nonEmpty
+    case _ => false
+  }
+
+  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 commonApplyFunc: PartialFunction[LogicalPlan, LogicalPlan] = {
     // Joins on empty LocalRelations generated from streaming sources are not eliminated
     // as stateful streaming joins need to perform other state management operations other than
     // just processing the input data.
     case p @ Join(_, _, joinType, conditionOpt, _)
         if !p.children.exists(_.isStreaming) =>
-      val isLeftEmpty = isEmptyLocalRelation(p.left)
-      val isRightEmpty = isEmptyLocalRelation(p.right)
+      val isLeftEmpty = isEmpty(p.left)
+      val isRightEmpty = isEmpty(p.right)
       val isFalseCondition = conditionOpt match {
         case Some(FalseLiteral) => true
         case _ => false
@@ -103,14 +90,15 @@ object PropagateEmptyRelation extends Rule[LogicalPlan] with PredicateHelper wit
             Project(nullValueProjectList(p.left) ++ p.right.output, p.right)
           case _ => p
         }
+      } else if (joinType == LeftSemi && conditionOpt.isEmpty && nonEmpty(p.right)) {
+        p.left
+      } else if (joinType == LeftAnti && conditionOpt.isEmpty && nonEmpty(p.right)) {
+        empty(p)
       } else {
         p
       }
 
-    case p: UnaryNode if p.children.nonEmpty && p.children.forall(isEmptyLocalRelation) => p match {
-      case _: Project => empty(p)
-      case _: Filter => empty(p)
-      case _: Sample => empty(p)
+    case p: UnaryNode if p.children.nonEmpty && p.children.forall(isEmpty) => p match {
       case _: Sort => empty(p)
       case _: GlobalLimit if !p.isStreaming => empty(p)
       case _: LocalLimit if !p.isStreaming => empty(p)
@@ -137,3 +125,55 @@ object PropagateEmptyRelation extends Rule[LogicalPlan] with PredicateHelper wit
     }
   }
 }
+
+/**
+ * This rule runs in the normal optimizer and optimizes more cases
+ * compared to [[PropagateEmptyRelationBase]]:
+ * 1. Higher-node Logical Plans
+ *    - Union with all empty children.
+ * 2. Unary-node Logical Plans
+ *    - Project/Filter/Sample with all empty children.
+ *
+ * The reason why we don't apply this rule at AQE optimizer side is: the benefit is not big enough
+ * and it may introduce extra exchanges.
+ */
+object PropagateEmptyRelation extends PropagateEmptyRelationBase {
+  private def applyFunc: PartialFunction[LogicalPlan, LogicalPlan] = {
+    case p: Union if p.children.exists(isEmpty) =>
+      val newChildren = p.children.filterNot(isEmpty)
+      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)
+        }
+      }
+
+    case p: UnaryNode if p.children.nonEmpty && p.children.forall(isEmpty) && canPropagate(p) =>
+      empty(p)
+  }
+
+  // extract the pattern avoid conflict with propagateEmptyRelationAdvanced
+  private def canPropagate(plan: LogicalPlan): Boolean = plan match {
+    case _: Project => true
+    case _: Filter => true
+    case _: Sample => true
+    case _ => false
+  }
+
+  override def apply(plan: LogicalPlan): LogicalPlan = plan.transformUpWithPruning(
+    _.containsAnyPattern(LOCAL_RELATION, TRUE_OR_FALSE_LITERAL), ruleId) {
+    applyFunc.orElse(commonApplyFunc)
+  }
+}

sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AQEOptimizer.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.adaptive
 
+import org.apache.spark.sql.catalyst.analysis.UpdateAttributeNullability
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LogicalPlanIntegrity, PlanHelper}
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.internal.SQLConf
@@ -27,7 +28,9 @@ import org.apache.spark.util.Utils
  */
 class AQEOptimizer(conf: SQLConf) extends RuleExecutor[LogicalPlan] {
   private val defaultBatches = Seq(
-    Batch("Eliminate Unnecessary Join", Once, EliminateUnnecessaryJoin),
+    Batch("Propagate Empty Relations", Once,
+      AQEPropagateEmptyRelation,
+      UpdateAttributeNullability),
     Batch("Demote BroadcastHashJoin", Once, DemoteBroadcastHashJoin)
   )
 

sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AQEPropagateEmptyRelation.scala

@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.adaptive
+
+import org.apache.spark.sql.catalyst.optimizer.PropagateEmptyRelationBase
+import org.apache.spark.sql.catalyst.planning.ExtractSingleColumnNullAwareAntiJoin
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.joins.HashedRelationWithAllNullKeys
+
+/**
+ * This rule runs in the AQE optimizer and optimizes more cases
+ * compared to [[PropagateEmptyRelationBase]]:
+ * 1. Join is single column NULL-aware anti join (NAAJ)
+ *    Broadcasted [[HashedRelation]] is [[HashedRelationWithAllNullKeys]]. Eliminate join to an
+ *    empty [[LocalRelation]].
+ */
+object AQEPropagateEmptyRelation extends PropagateEmptyRelationBase {
+  override protected def isEmpty(plan: LogicalPlan): Boolean =
+    super.isEmpty(plan) || getRowCount(plan).contains(0)
+
+  override protected def nonEmpty(plan: LogicalPlan): Boolean =
+    super.nonEmpty(plan) || getRowCount(plan).exists(_ > 0)
+
+  private def getRowCount(plan: LogicalPlan): Option[BigInt] = plan match {
+    case LogicalQueryStage(_, stage: QueryStageExec) if stage.resultOption.get().isDefined =>
+      stage.getRuntimeStatistics.rowCount
+    case _ => None
+  }
+
+  private def isRelationWithAllNullKeys(plan: LogicalPlan): Boolean = plan match {
+    case LogicalQueryStage(_, stage: BroadcastQueryStageExec)
+      if stage.resultOption.get().isDefined =>
+      stage.broadcast.relationFuture.get().value == HashedRelationWithAllNullKeys
+    case _ => false
+  }
+
+  private def eliminateSingleColumnNullAwareAntiJoin: PartialFunction[LogicalPlan, LogicalPlan] = {
+    case j @ ExtractSingleColumnNullAwareAntiJoin(_, _) if isRelationWithAllNullKeys(j.right) =>
+      empty(j)
+  }
+
+  // TODO we need use transformUpWithPruning instead of transformUp
+  def apply(plan: LogicalPlan): LogicalPlan = plan.transformUp {
+    eliminateSingleColumnNullAwareAntiJoin.orElse(commonApplyFunc)
+  }
+}

sql/core/src/test/scala/org/apache/spark/sql/DynamicPartitionPruningSuite.scala

@@ -1382,7 +1382,7 @@ abstract class DynamicPartitionPruningSuiteBase
     withSQLConf(
       SQLConf.DYNAMIC_PARTITION_PRUNING_ENABLED.key -> "true",
       SQLConf.DYNAMIC_PARTITION_PRUNING_REUSE_BROADCAST_ONLY.key -> "true",
-      SQLConf.ADAPTIVE_OPTIMIZER_EXCLUDED_RULES.key -> EliminateUnnecessaryJoin.ruleName) {
+      SQLConf.ADAPTIVE_OPTIMIZER_EXCLUDED_RULES.key -> AQEPropagateEmptyRelation.ruleName) {
       val df = sql(
         """
           |SELECT * FROM fact_sk f