[GitHub] [spark] viirya commented on a change in pull request #24706: [SPARK-23128][SQL] A new approach to do adaptive execution in Spark SQL

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viirya commented on a change in pull request #24706: [SPARK-23128][SQL] A new approach to do adaptive execution in Spark SQL
URL: https://github.com/apache/spark/pull/24706#discussion_r287548684
 
 

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 File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AdaptiveSparkPlanExec.scala
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+/*
+ * 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 java.util.concurrent.LinkedBlockingQueue
+
+import scala.collection.concurrent.TrieMap
+import scala.collection.mutable
+import scala.concurrent.{ExecutionContext, ExecutionContextExecutorService}
+
+import org.apache.spark.SparkException
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, ReturnAnswer}
+import org.apache.spark.sql.catalyst.rules.{Rule, RuleExecutor}
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.exchange._
+import org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.ThreadUtils
+
+/**
+ * A root node to execute the query plan adaptively. It splits the query plan into independent
+ * stages and executes them in order according to their dependencies. The query stage
+ * materializes its output at the end. When one stage completes, the data statistics of the
+ * materialized output will be used to optimize the remainder of the query.
+ *
+ * To create query stages, we traverse the query tree bottom up. When we hit an exchange node,
+ * and if all the child query stages of this exchange node are materialized, we create a new
+ * query stage for this exchange node. The new stage is then materialized asynchronously once it
+ * is created.
+ *
+ * When one query stage finishes materialization, the rest query is re-optimized and planned based
+ * on the latest statistics provided by all materialized stages. Then we traverse the query plan
+ * again and create more stages if possible. After all stages have been materialized, we execute
+ * the rest of the plan.
+ */
+case class AdaptiveSparkPlanExec(
+    initialPlan: SparkPlan,
+    session: SparkSession,
+    subqueryMap: Map[Long, ExecSubqueryExpression],
+    stageCache: TrieMap[StructType, mutable.Buffer[(Exchange, QueryStageExec)]])
+  extends LeafExecNode {
+
+  def executedPlan: SparkPlan = currentPhysicalPlan
+
+  override def output: Seq[Attribute] = initialPlan.output
+
+  override def doCanonicalize(): SparkPlan = initialPlan.canonicalized
 
 Review comment:
   Why not currentPhysicalPlan.canonicalized but initialPlan.canonicalized?

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