apache
diff --git a/‎sql/catalyst/src/main/scala/org/apache/spark/sql/util/ReuseMap.scala‎
Lines changed: 73 additions & 0 deletions b/‎sql/catalyst/src/main/scala/org/apache/spark/sql/util/ReuseMap.scala‎
Lines changed: 73 additions & 0 deletions
diff --git a/‎sql/catalyst/src/test/scala/org/apache/spark/sql/util/ReuseMapSuite.scala‎
Lines changed: 73 additions & 0 deletions b/‎sql/catalyst/src/test/scala/org/apache/spark/sql/util/ReuseMapSuite.scala‎
Lines changed: 73 additions & 0 deletions
diff --git a/‎sql/core/src/main/scala/org/apache/spark/sql/execution/ExplainUtils.scala‎
Lines changed: 85 additions & 71 deletions b/‎sql/core/src/main/scala/org/apache/spark/sql/execution/ExplainUtils.scala‎
Lines changed: 85 additions & 71 deletions
@@ -0,0 +1,73 @@
+/*
+ * 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.util
+
+import scala.collection.mutable.{ArrayBuffer, Map}
+
+import org.apache.spark.sql.catalyst.plans.QueryPlan
+import org.apache.spark.sql.types.StructType
+
+/**
+ * Map of canonicalized plans that can be used to find reuse possibilities.
+ *
+ * To avoid costly canonicalization of a plan:
+ * - we use its schema first to check if it can be replaced to a reused one at all
+ * - we insert it into the map of canonicalized plans only when at least 2 have the same schema
+ *
+ * @tparam T the type of the node we want to reuse
+ * @tparam T2 the type of the canonicalized node
+ */
+class ReuseMap[T <: T2, T2 <: QueryPlan[T2]] {
+  private val map = Map[StructType, ArrayBuffer[T]]()
+
+  /**
+   * Find a matching plan with the same canonicalized form in the map or add the new plan to the
+   * map otherwise.
+   *
+   * @param plan the input plan
+   * @return the matching plan or the input plan
+   */
+  private def lookupOrElseAdd(plan: T): T = {
+    val sameSchema = map.getOrElseUpdate(plan.schema, ArrayBuffer())
+    val samePlan = sameSchema.find(plan.sameResult)
+    if (samePlan.isDefined) {
+      samePlan.get
+    } else {
+      sameSchema += plan
+      plan
+    }
+  }
+
+  /**
+   * Find a matching plan with the same canonicalized form in the map and apply `f` on it or add
+   * the new plan to the map otherwise.
+   *
+   * @param plan the input plan
+   * @param f the function to apply
+   * @tparam T2 the type of the reuse node
+   * @return the matching plan with `f` applied or the input plan
+   */
+  def reuseOrElseAdd[T2 >: T](plan: T, f: T => T2): T2 = {
+    val found = lookupOrElseAdd(plan)
+    if (found eq plan) {
+      plan
+    } else {
+      f(found)
+    }
+  }
+}
@@ -0,0 +1,73 @@
+/*
+ * 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.util
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, UnaryNode}
+import org.apache.spark.sql.types.IntegerType
+
+case class TestNode(children: Seq[TestNode], output: Seq[Attribute]) extends LogicalPlan {
+  override protected def withNewChildrenInternal(
+      newChildren: IndexedSeq[LogicalPlan]): LogicalPlan = copy(children = children)
+}
+case class TestReuseNode(child: LogicalPlan) extends UnaryNode {
+  override def output: Seq[Attribute] = child.output
+
+  override protected def withNewChildInternal(newChild: LogicalPlan): LogicalPlan =
+    copy(child = newChild)
+}
+
+class ReuseMapSuite extends SparkFunSuite {
+  private val leafNode1 = TestNode(Nil, Seq(AttributeReference("a", IntegerType)()))
+  private val leafNode2 = TestNode(Nil, Seq(AttributeReference("b", IntegerType)()))
+  private val parentNode1 = TestNode(Seq(leafNode1), Seq(AttributeReference("a", IntegerType)()))
+  private val parentNode2 = TestNode(Seq(leafNode2), Seq(AttributeReference("b", IntegerType)()))
+
+  private def reuse(testNode: TestNode) = TestReuseNode(testNode)
+
+  test("no reuse if same instance") {
+    val reuseMap = new ReuseMap[TestNode, LogicalPlan]()
+
+    reuseMap.reuseOrElseAdd(leafNode1, reuse)
+    reuseMap.reuseOrElseAdd(parentNode1, reuse)
+
+    assert(reuseMap.reuseOrElseAdd(leafNode1, reuse) == leafNode1)
+    assert(reuseMap.reuseOrElseAdd(parentNode1, reuse) == parentNode1)
+  }
+
+  test("reuse if different instance with same canonicalized plan") {
+    val reuseMap = new ReuseMap[TestNode, LogicalPlan]()
+    reuseMap.reuseOrElseAdd(leafNode1, reuse)
+    reuseMap.reuseOrElseAdd(parentNode1, reuse)
+
+    assert(reuseMap.reuseOrElseAdd(leafNode1.clone.asInstanceOf[TestNode], reuse) ==
+      reuse(leafNode1))
+    assert(reuseMap.reuseOrElseAdd(parentNode1.clone.asInstanceOf[TestNode], reuse) ==
+      reuse(parentNode1))
+  }
+
+  test("no reuse if different canonicalized plan") {
+    val reuseMap = new ReuseMap[TestNode, LogicalPlan]()
+    reuseMap.reuseOrElseAdd(leafNode1, reuse)
+    reuseMap.reuseOrElseAdd(parentNode1, reuse)
+
+    assert(reuseMap.reuseOrElseAdd(leafNode2, reuse) == leafNode2)
+    assert(reuseMap.reuseOrElseAdd(parentNode2, reuse) == parentNode2)
+  }
+}
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.execution
 
-import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.sql.AnalysisException
@@ -28,11 +27,9 @@ import org.apache.spark.sql.execution.adaptive.{AdaptiveSparkPlanExec, AdaptiveS
 object ExplainUtils extends AdaptiveSparkPlanHelper {
   /**
    * Given a input physical plan, performs the following tasks.
-   *   1. Computes the operator id for current operator and records it in the operator
-   *      by setting a tag.
-   *   2. Computes the whole stage codegen id for current operator and records it in the
+   *   1. Computes the whole stage codegen id for current operator and records it in the
    *      operator by setting a tag.
-   *   3. Generate the two part explain output for this plan.
+   *   2. Generate the two part explain output for this plan.
    *      1. First part explains the operator tree with each operator tagged with an unique
    *         identifier.
    *      2. Second part explains each operator in a verbose manner.
@@ -41,22 +38,11 @@ object ExplainUtils extends AdaptiveSparkPlanHelper {
    *
    * @param plan Input query plan to process
    * @param append function used to append the explain output
-   * @param startOperatorID The start value of operation id. The subsequent operations will
-   *                         be assigned higher value.
-   *
-   * @return The last generated operation id for this input plan. This is to ensure we
-   *         always assign incrementing unique id to each operator.
-   *
    */
   private def processPlanSkippingSubqueries[T <: QueryPlan[T]](
-      plan: => QueryPlan[T],
-      append: String => Unit,
-      startOperatorID: Int): Int = {
-
-    val operationIDs = new mutable.ArrayBuffer[(Int, QueryPlan[_])]()
-    var currentOperatorID = startOperatorID
+      plan: T,
+      append: String => Unit): Unit = {
     try {
-      currentOperatorID = generateOperatorIDs(plan, currentOperatorID, operationIDs)
       generateWholeStageCodegenIds(plan)
 
       QueryPlan.append(
@@ -67,31 +53,36 @@ object ExplainUtils extends AdaptiveSparkPlanHelper {
         printOperatorId = true)
 
       append("\n")
-      var i: Integer = 0
-      for ((opId, curPlan) <- operationIDs) {
-        append(curPlan.verboseStringWithOperatorId())
-      }
+
+      val operationsWithID = ArrayBuffer.empty[QueryPlan[_]]
+      collectOperatorsWithID(plan, operationsWithID)
+      operationsWithID.foreach(p => append(p.verboseStringWithOperatorId()))
+
     } catch {
       case e: AnalysisException => append(e.toString)
     }
-    currentOperatorID
   }
 
   /**
    * Given a input physical plan, performs the following tasks.
    *   1. Generates the explain output for the input plan excluding the subquery plans.
    *   2. Generates the explain output for each subquery referenced in the plan.
    */
-  def processPlan[T <: QueryPlan[T]](
-      plan: => QueryPlan[T],
-      append: String => Unit): Unit = {
+  def processPlan[T <: QueryPlan[T]](plan: T, append: String => Unit): Unit = {
     try {
-      val subqueries = ArrayBuffer.empty[(SparkPlan, Expression, BaseSubqueryExec)]
       var currentOperatorID = 0
-      currentOperatorID = processPlanSkippingSubqueries(plan, append, currentOperatorID)
+      currentOperatorID = generateOperatorIDs(plan, currentOperatorID)
+
+      val subqueries = ArrayBuffer.empty[(SparkPlan, Expression, BaseSubqueryExec)]
       getSubqueries(plan, subqueries)
-      var i = 0
 
+      subqueries.foldLeft(currentOperatorID) {
+        (curId, plan) => generateOperatorIDs(plan._3.child, curId)
+      }
+
+      processPlanSkippingSubqueries(plan, append)
+
+      var i = 0
       for (sub <- subqueries) {
         if (i == 0) {
           append("\n===== Subqueries =====\n\n")
@@ -104,10 +95,7 @@ object ExplainUtils extends AdaptiveSparkPlanHelper {
         // the explain output. In case of subquery reuse, we don't print subquery plan more
         // than once. So we skip [[ReusedSubqueryExec]] here.
         if (!sub._3.isInstanceOf[ReusedSubqueryExec]) {
-          currentOperatorID = processPlanSkippingSubqueries(
-            sub._3.child,
-            append,
-            currentOperatorID)
+          processPlanSkippingSubqueries(sub._3.child, append)
         }
         append("\n")
       }
@@ -117,59 +105,85 @@ object ExplainUtils extends AdaptiveSparkPlanHelper {
   }
 
   /**
-   * Traverses the supplied input plan in a bottom-up fashion does the following :
-   *    1. produces a map : operator identifier -> operator
-   *    2. Records the operator id via setting a tag in the operator.
+   * Traverses the supplied input plan in a bottom-up fashion and records the operator id via
+   * setting a tag in the operator.
    * Note :
-   *    1. Operator such as WholeStageCodegenExec and InputAdapter are skipped as they don't
-   *       appear in the explain output.
-   *    2. operator identifier starts at startOperatorID + 1
+   * - Operator such as WholeStageCodegenExec and InputAdapter are skipped as they don't
+   *     appear in the explain output.
+   * - Operator identifier starts at startOperatorID + 1
+   *
    * @param plan Input query plan to process
-   * @param startOperatorID The start value of operation id. The subsequent operations will
-   *                         be assigned higher value.
-   * @param operatorIDs A output parameter that contains a map of operator id and query plan. This
-   *                    is used by caller to print the detail portion of the plan.
-   * @return The last generated operation id for this input plan. This is to ensure we
-   *         always assign incrementing unique id to each operator.
+   * @param startOperatorID The start value of operation id. The subsequent operations will be
+   *                        assigned higher value.
+   * @return The last generated operation id for this input plan. This is to ensure we always
+   *         assign incrementing unique id to each operator.
    */
-  private def generateOperatorIDs(
-      plan: QueryPlan[_],
-      startOperatorID: Int,
-      operatorIDs: mutable.ArrayBuffer[(Int, QueryPlan[_])]): Int = {
+  private def generateOperatorIDs(plan: QueryPlan[_], startOperatorID: Int): Int = {
     var currentOperationID = startOperatorID
     // Skip the subqueries as they are not printed as part of main query block.
     if (plan.isInstanceOf[BaseSubqueryExec]) {
       return currentOperationID
     }
-    plan.foreachUp {
-      case p: WholeStageCodegenExec =>
-      case p: InputAdapter =>
-      case other: QueryPlan[_] =>
 
-        def setOpId(): Unit = if (other.getTagValue(QueryPlan.OP_ID_TAG).isEmpty) {
-          currentOperationID += 1
-          other.setTagValue(QueryPlan.OP_ID_TAG, currentOperationID)
-          operatorIDs += ((currentOperationID, other))
-        }
+    def setOpId(plan: QueryPlan[_]): Unit = if (plan.getTagValue(QueryPlan.OP_ID_TAG).isEmpty) {
+      currentOperationID += 1
+      plan.setTagValue(QueryPlan.OP_ID_TAG, currentOperationID)
+    }
 
-        other match {
-          case p: AdaptiveSparkPlanExec =>
-            currentOperationID =
-              generateOperatorIDs(p.executedPlan, currentOperationID, operatorIDs)
-            setOpId()
-          case p: QueryStageExec =>
-            currentOperationID = generateOperatorIDs(p.plan, currentOperationID, operatorIDs)
-            setOpId()
-          case _ =>
-            setOpId()
-            other.innerChildren.foldLeft(currentOperationID) {
-              (curId, plan) => generateOperatorIDs(plan, curId, operatorIDs)
-            }
+    plan.foreachUp {
+      case _: WholeStageCodegenExec =>
+      case _: InputAdapter =>
+      case p: AdaptiveSparkPlanExec =>
+        currentOperationID = generateOperatorIDs(p.executedPlan, currentOperationID)
+        setOpId(p)
+      case p: QueryStageExec =>
+        currentOperationID = generateOperatorIDs(p.plan, currentOperationID)
+        setOpId(p)
+      case other: QueryPlan[_] =>
+        setOpId(other)
+        other.innerChildren.foldLeft(currentOperationID) {
+          (curId, plan) => generateOperatorIDs(plan, curId)
         }
     }
     currentOperationID
   }
 
+  /**
+   * Traverses the supplied input plan in a bottom-up fashion and collects operators with assigned
+   * ids.
+   *
+   * @param plan Input query plan to process
+   * @param operators An output parameter that contains the operators.
+   */
+  private def collectOperatorsWithID(
+      plan: QueryPlan[_],
+      operators: ArrayBuffer[QueryPlan[_]]): Unit = {
+    // Skip the subqueries as they are not printed as part of main query block.
+    if (plan.isInstanceOf[BaseSubqueryExec]) {
+      return
+    }
+
+    def collectOperatorWithID(plan: QueryPlan[_]): Unit = {
+      if (plan.getTagValue(QueryPlan.OP_ID_TAG).isDefined) {
+        operators += plan
+      }
+    }
+
+    plan.foreachUp {
+      case _: WholeStageCodegenExec =>
+      case _: InputAdapter =>
+      case p: AdaptiveSparkPlanExec =>
+        collectOperatorsWithID(p.executedPlan, operators)
+        collectOperatorWithID(p)
+      case p: QueryStageExec =>
+        collectOperatorsWithID(p.plan, operators)
+        collectOperatorWithID(p)
+      case other: QueryPlan[_] =>
+        collectOperatorWithID(other)
+        other.innerChildren.foreach(collectOperatorsWithID(_, operators))
+    }
+  }
+
   /**
    * Traverses the supplied input plan in a top-down fashion and records the
    * whole stage code gen id in the plan via setting a tag.