zinking commented on a change in pull request #32298: URL: https://github.com/apache/spark/pull/32298#discussion_r709869699
########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/MergeScalarSubqueries.scala ########## @@ -0,0 +1,413 @@ +/* + * 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.catalyst.optimizer + +import scala.collection.mutable.ListBuffer + +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression +import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, CommonScalarSubqueries, Filter, Join, LogicalPlan, Project, Subquery} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.catalyst.trees.TreePattern.{SCALAR_SUBQUERY, SCALAR_SUBQUERY_REFERENCE, TreePattern} +import org.apache.spark.sql.types.DataType + +/** + * This rule tries to merge multiple non-correlated [[ScalarSubquery]]s to compute multiple scalar + * values once. + * + * The process is the following: + * - While traversing through the plan each [[ScalarSubquery]] plan is tried to merge into the cache + * of already seen subquery plans. If merge is possible then cache is updated with the merged + * subquery plan, if not then the new subquery plan is added to the cache. + * During this first traversal each [[ScalarSubquery]] expression is replaced to a + * [[ScalarSubqueryReference]] pointing to its cached version. + * The cache uses a flag to keep track of if a cache entry is a results of merging 2 or more + * plans, or it is a plan that was seen only once. + * Merged plans in the cache get a "header" that is is basically + * `CreateNamedStructure(name1, attribute1, name2, attribute2, ...)` + * expression in new root [[Project]] node. This expression ensures that the merged plan is a + * valid scalar subquery that returns only one value. + * - A second traversal checks if a [[ScalarSubqueryReference]] is pointing to a merged subquery + * plan or not and either keeps the reference or restores the original [[ScalarSubquery]]. + * If there are [[ScalarSubqueryReference]] nodes remained a [[CommonScalarSubqueries]] root node + * is added to the plan with the referenced scalar subqueries. + * - [[PlanSubqueries]] or [[PlanAdaptiveSubqueries]] rule does the physical planning of scalar + * subqueries including the ones under [[CommonScalarSubqueriesExec]] node and replaces + * each [[ScalarSubqueryReference]] to their referenced physical plan in + * `GetStructField(ScalarSubquery(merged plan with CreateNamedStruct() header))` form. + * It is important that references pointing to the same merged subquery are replaced to the same + * planned instance to make sure that each merged subquery runs only once (even without a wrapping + * [[ReuseSubquery]] node). + * Finally, the [[CommonScalarSubqueriesExec]] node is removed from the physical plan. + * - The [[ReuseExchangeAndSubquery]] rule wraps the second, third, ... instances of the same + * subquery into a [[ReuseSubquery]] node, but this just a cosmetic change in the plan. + * + * Eg. the following query: + * + * SELECT + * (SELECT avg(a) FROM t GROUP BY b), + * (SELECT sum(b) FROM t GROUP BY b) + * + * is optimized from: + * + * Project [scalar-subquery#231 [] AS scalarsubquery()#241, + * scalar-subquery#232 [] AS scalarsubquery()#242L] + * : :- Aggregate [b#234], [avg(a#233) AS avg(a)#236] + * : : +- Relation default.t[a#233,b#234] parquet + * : +- Aggregate [b#240], [sum(b#240) AS sum(b)#238L] + * : +- Project [b#240] + * : +- Relation default.t[a#239,b#240] parquet + * +- OneRowRelation + * + * to: + * + * CommonScalarSubqueries [scalar-subquery#250 []] + * : +- Project [named_struct(avg(a), avg(a)#236, sum(b), sum(b)#238L) AS mergedValue#249] + * : +- Aggregate [b#234], [avg(a#233) AS avg(a)#236, sum(b#234) AS sum(b)#238L] + * : +- Project [a#233, b#234] + * : +- Relation default.t[a#233,b#234] parquet + * +- Project [scalarsubqueryreference(0, 0, DoubleType, 231) AS scalarsubquery()#241, + * scalarsubqueryreference(0, 1, LongType, 232) AS scalarsubquery()#242L] + * +- OneRowRelation + */ +object MergeScalarSubqueries extends Rule[LogicalPlan] with PredicateHelper { + def apply(plan: LogicalPlan): LogicalPlan = { + if (conf.subqueryReuseEnabled) { + plan match { + case Subquery(_: CommonScalarSubqueries, _) => plan + case s: Subquery => s.copy(child = extractCommonScalarSubqueries(s.child)) + case _: CommonScalarSubqueries => plan + case _ => extractCommonScalarSubqueries(plan) + } + } else { + plan + } + } + + private def extractCommonScalarSubqueries(plan: LogicalPlan) = { + // Plan of subqueries and a flag is the plan is merged + val cache = ListBuffer.empty[(Project, Boolean)] Review comment: is it worthwhile to alias Project to some different type name that hint this is the merged header of scalar query ? -- This is an automated message from the Apache Git Service. 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