cloud-fan commented on code in PR #32298: URL: https://github.com/apache/spark/pull/32298#discussion_r852165221
########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/MergeScalarSubqueries.scala: ########## @@ -0,0 +1,357 @@ +/* + * 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 +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, CTERelationDef, CTERelationRef, Filter, Join, LogicalPlan, Project, Subquery, WithCTE} +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 temporal + * [[ScalarSubqueryReference]] reference pointing to its cached version. + * The cache uses a flag to keep track of if a cache entry is a result 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 contains the list of attributes form the scalar + * return value of a merged subquery. + * - A second traversal checks if there are merged subqueries in the cache and builds a `WithCTE` + * node from these queries. The `CTERelationDef` nodes contain the merged subquery in the + * following form: + * `Project(Seq(CreateNamedStruct(name1, attribute1, ...) AS mergedValue), mergedSubqueryPlan)` + * and the definitions are flagged that they host a subquery, that can return maximum one row. + * During the second traversal [[ScalarSubqueryReference]] expressions that pont to a merged + * subquery is either transformed to a `GetStructField(ScalarSubquery(CTERelationRef(...)))` + * expression or restored to the original [[ScalarSubquery]]. + * + * Eg. the following query: + * + * SELECT + * (SELECT avg(a) FROM t), + * (SELECT sum(b) FROM t) + * + * is optimized from: + * + * == Optimized Logical Plan == + * Project [scalar-subquery#242 [] AS scalarsubquery()#253, + * scalar-subquery#243 [] AS scalarsubquery()#254L] + * : :- Aggregate [avg(a#244) AS avg(a)#247] + * : : +- Project [a#244] + * : : +- Relation default.t[a#244,b#245] parquet + * : +- Aggregate [sum(a#251) AS sum(a)#250L] + * : +- Project [a#251] + * : +- Relation default.t[a#251,b#252] parquet + * +- OneRowRelation + * + * to: + * + * WithCTE + * :- CTERelationDef 0 + * : +- Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] + * : +- Aggregate [avg(a#244) AS avg(a)#247, sum(a#244) AS sum(a)#250L] + * : +- Project [a#244] + * : +- Relation default.t[a#244,b#245] parquet + * +- Project [scalar-subquery#242 [].avg(a) AS scalarsubquery()#253, + * scalar-subquery#243 [].sum(a) AS scalarsubquery()#254L] + * : :- CTERelationRef 0, true, [mergedValue#260], true + * : +- CTERelationRef 0, true, [mergedValue#260], true + * +- OneRowRelation + */ +object MergeScalarSubqueries extends Rule[LogicalPlan] with PredicateHelper { + def apply(plan: LogicalPlan): LogicalPlan = { + plan match { + case s: Subquery => s.copy(child = extractCommonScalarSubqueries(s.child)) + case _ => extractCommonScalarSubqueries(plan) + } + } + + /** + * An item in the cache of merged scalar subqueries. + * + * @param elements List of member names and attributes that form the struct scalar return value + * of a merged subquery. + * @param plan The plan of a merged scalar subquery. + * @param merged A flag to identify if this item is the result of merging subqueries. + * Please note that `elements.size == 1` doesn't always mean that the plan is not + * merged as there can be subqueries that are different ([[checkIdenticalPlans]] + * is false) due to an extra [[Project]] node in one of them. In that case + * `elements.size` remains 1 after merging, but the merged flag becomes true. + */ + case class Header(elements: Seq[(String, Attribute)], plan: LogicalPlan, merged: Boolean) + + private def extractCommonScalarSubqueries(plan: LogicalPlan) = { + val cache = ListBuffer.empty[Header] + val (newPlan, subqueryCTEs) = removeReferences(insertReferences(plan, cache), cache) + if (subqueryCTEs.nonEmpty) { + WithCTE(newPlan, subqueryCTEs) + } else { + newPlan + } + } + + // First traversal builds up the cache and inserts `ScalarSubqueryReference`s to the plan. + private def insertReferences(plan: LogicalPlan, cache: ListBuffer[Header]): LogicalPlan = { + plan.transformAllExpressionsWithPruning(_.containsAnyPattern(SCALAR_SUBQUERY)) { + case s: ScalarSubquery if !s.isCorrelated && s.deterministic => + val (subqueryIndex, headerIndex) = cacheSubquery(s.plan, cache) + ScalarSubqueryReference(subqueryIndex, headerIndex, s.dataType, s.exprId) + } + } + + // Caching returns the index of the subquery in the cache and the index of scalar member in the + // "Header". + private def cacheSubquery(plan: LogicalPlan, cache: ListBuffer[Header]): (Int, Int) = { + val output = plan.output.head + cache.zipWithIndex.collectFirst(Function.unlift { case (header, subqueryIndex) => + checkIdenticalPlans(plan, header.plan).map { outputMap => + val mappedOutput = mapAttributes(output, outputMap) + val headerIndex = header.elements.indexWhere { + case (_, attribute) => attribute.exprId == mappedOutput.exprId + } + subqueryIndex -> headerIndex + }.orElse(tryMergePlans(plan, header.plan).map { + case (mergedPlan, outputMap) => + val mappedOutput = mapAttributes(output, outputMap) + var headerIndex = header.elements.indexWhere { + case (_, attribute) => attribute.exprId == mappedOutput.exprId + } + val newHeaderElements = if (headerIndex == -1) { + headerIndex = header.elements.size + header.elements :+ (output.name -> mappedOutput) + } else { + header.elements + } + cache(subqueryIndex) = Header(newHeaderElements, mergedPlan, true) + subqueryIndex -> headerIndex + }) + }).getOrElse { + cache += Header(Seq(output.name -> output), plan, false) + cache.length - 1 -> 0 + } + } + + // If 2 plans are identical return the attribute mapping from the new to the cached version. + private def checkIdenticalPlans( + newPlan: LogicalPlan, + cachedPlan: LogicalPlan): Option[AttributeMap[Attribute]] = { + if (newPlan.canonicalized == cachedPlan.canonicalized) { + Some(AttributeMap(newPlan.output.zip(cachedPlan.output))) + } else { + None + } + } + + // Recursively traverse down and try merging 2 plans. If merge is possible then return the merged + // plan with the attribute mapping from the new to the merged version. + // Please note that merging arbitrary plans can be complicated, the current version supports only + // some of the most important nodes. + private def tryMergePlans( + newPlan: LogicalPlan, + cachedPlan: LogicalPlan): Option[(LogicalPlan, AttributeMap[Attribute])] = { + checkIdenticalPlans(newPlan, cachedPlan).map(cachedPlan -> _).orElse( + (newPlan, cachedPlan) match { + case (np: Project, cp: Project) => + tryMergePlans(np.child, cp.child).map { case (mergedChild, outputMap) => + val (mergedProjectList, newOutputMap) = + mergeNamedExpressions(np.projectList, outputMap, cp.projectList) + val mergedPlan = Project(mergedProjectList, mergedChild) + mergedPlan -> newOutputMap + } + case (np, cp: Project) => + tryMergePlans(np, cp.child).map { case (mergedChild, outputMap) => + val (mergedProjectList, newOutputMap) = + mergeNamedExpressions(np.output, outputMap, cp.projectList) + val mergedPlan = Project(mergedProjectList, mergedChild) + mergedPlan -> newOutputMap + } + case (np: Project, cp) => + tryMergePlans(np.child, cp).map { case (mergedChild, outputMap) => + val (mergedProjectList, newOutputMap) = + mergeNamedExpressions(np.projectList, outputMap, cp.output) + val mergedPlan = Project(mergedProjectList, mergedChild) + mergedPlan -> newOutputMap + } + case (np: Aggregate, cp: Aggregate) if supportedAggregateMerge(np, cp) => + tryMergePlans(np.child, cp.child).flatMap { case (mergedChild, outputMap) => + val mappedNewGroupingExpression = + np.groupingExpressions.map(mapAttributes(_, outputMap)) + // Order of grouping expression doesn't matter so we can compare sets Review Comment: are we sure this doesn't matter? What I can think of: 1. for sort-based aggregate, the grouping keys decide the required child ordering. Changing the order of grouping keys may introduce extra sorts. 2. for hash aggregate, the grouping keys decide the output partitioning. Changing the order of grouping keys may introduce extra shuffles. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
