peter-toth commented on code in PR #32298: URL: https://github.com/apache/spark/pull/32298#discussion_r853101871
########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/MergeScalarSubqueries.scala: ########## @@ -0,0 +1,382 @@ +/* + * 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.internal.SQLConf +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: + * + * == Optimized Logical Plan == + * Project [scalar-subquery#242 [].avg(a) AS scalarsubquery()#253, + * scalar-subquery#243 [].sum(a) AS scalarsubquery()#254L] + * : :- 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 [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 + * +- OneRowRelation + * + * == Physical Plan == + * *(1) Project [Subquery scalar-subquery#242, [id=#125].avg(a) AS scalarsubquery()#253, + * ReusedSubquery + * Subquery scalar-subquery#242, [id=#125].sum(a) AS scalarsubquery()#254L] + * : :- Subquery scalar-subquery#242, [id=#125] + * : : +- *(2) Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] + * : : +- *(2) HashAggregate(keys=[], functions=[avg(a#244), sum(a#244)], + * output=[avg(a)#247, sum(a)#250L]) + * : : +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#120] + * : : +- *(1) HashAggregate(keys=[], functions=[partial_avg(a#244), partial_sum(a#244)], + * output=[sum#262, count#263L, sum#264L]) + * : : +- *(1) ColumnarToRow + * : : +- FileScan parquet default.t[a#244] ... + * : +- ReusedSubquery Subquery scalar-subquery#242, [id=#125] + * +- *(1) Scan OneRowRelation[] + */ +object MergeScalarSubqueries extends Rule[LogicalPlan] with PredicateHelper { + def apply(plan: LogicalPlan): LogicalPlan = { + plan match { + // Subquery reuse needs to be enabled for this optimization. + case _ if !conf.getConf(SQLConf.SUBQUERY_REUSE_ENABLED) => plan + + // This rule does a whole plan traversal, no need to run on subqueries. + case _: Subquery => plan + + // Plans with CTEs are not supported for now. + case _: WithCTE => plan + + case _ => extractCommonScalarSubqueries(plan) + } + } + + /** + * An item in the cache of merged scalar subqueries. + * + * @param attributes 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 `attributes.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 + * `attributes.size` remains 1 after merging, but the merged flag becomes true. + */ + case class Header(attributes: Seq[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.transformWithSubqueries { + case n => n.transformExpressionsWithPruning(_.containsAnyPattern(SCALAR_SUBQUERY)) { + case s: ScalarSubquery if !s.isCorrelated && s.deterministic => + val (subqueryIndex, headerIndex) = cacheSubquery(insertReferences(s.plan, cache), 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.attributes.indexWhere(_.exprId == mappedOutput.exprId) + subqueryIndex -> headerIndex + }.orElse(tryMergePlans(plan, header.plan).map { + case (mergedPlan, outputMap) => + val mappedOutput = mapAttributes(output, outputMap) + var headerIndex = header.attributes.indexWhere(_.exprId == mappedOutput.exprId) + val newHeaderAttributes = if (headerIndex == -1) { + headerIndex = header.attributes.size + header.attributes :+ mappedOutput + } else { + header.attributes + } + cache(subqueryIndex) = Header(newHeaderAttributes, mergedPlan, true) + subqueryIndex -> headerIndex + }) + }).getOrElse { + cache += Header(Seq(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 + if (mappedNewGroupingExpression.map(_.canonicalized) == + cp.groupingExpressions.map(_.canonicalized)) { + val (mergedAggregateExpressions, newOutputMap) = + mergeNamedExpressions(np.aggregateExpressions, outputMap, cp.aggregateExpressions) + val mergedPlan = + Aggregate(cp.groupingExpressions, mergedAggregateExpressions, mergedChild) + Some(mergedPlan -> newOutputMap) + } else { + None + } + } + + case (np: Filter, cp: Filter) => + tryMergePlans(np.child, cp.child).flatMap { case (mergedChild, outputMap) => + val mappedNewCondition = mapAttributes(np.condition, outputMap) + // Comparing the canonicalized form is required to ignore different forms of the same + // expression. + if (mappedNewCondition.canonicalized == cp.condition.canonicalized) { + val mergedPlan = cp.withNewChildren(Seq(mergedChild)) + Some(mergedPlan -> outputMap) + } else { + None + } + } + + case (np: Join, cp: Join) if np.joinType == cp.joinType && np.hint == cp.hint => + tryMergePlans(np.left, cp.left).flatMap { case (mergedLeft, leftOutputMap) => + tryMergePlans(np.right, cp.right).flatMap { case (mergedRight, rightOutputMap) => + val outputMap = leftOutputMap ++ rightOutputMap + val mappedNewCondition = np.condition.map(mapAttributes(_, outputMap)) + // Comparing the canonicalized form is required to ignore different forms of the same + // expression and `AttributeReference.quailifier`s in `cp.condition`. + if (mappedNewCondition.map(_.canonicalized) == cp.condition.map(_.canonicalized)) { + val mergedPlan = cp.withNewChildren(Seq(mergedLeft, mergedRight)) + Some(mergedPlan -> outputMap) + } else { + None + } + } + } + + // Otherwise merging is not possible. + case _ => None + }) + } + + private def createProject(attributes: Seq[Attribute], plan: LogicalPlan): Project = { + Project( + Seq(Alias( + CreateNamedStruct(attributes.flatMap(a => Seq(Literal(a.name), a))), + "mergedValue")()), + plan) + } + + private def mapAttributes[T <: Expression](expr: T, outputMap: AttributeMap[Attribute]) = { + expr.transform { + case a: Attribute => outputMap.getOrElse(a, a) + }.asInstanceOf[T] + } + + // Applies `outputMap` attribute mapping on attributes of `newExpressions` and merges them into + // `cachedExpressions`. Returns the merged expressions and the attribute mapping from the new to + // the merged version that can be propagated up during merging nodes. + private def mergeNamedExpressions( + newExpressions: Seq[NamedExpression], + outputMap: AttributeMap[Attribute], + cachedExpressions: Seq[NamedExpression]) = { + val mergedExpressions = ListBuffer[NamedExpression](cachedExpressions: _*) + val newOutputMap = AttributeMap(newExpressions.map { ne => + val mapped = mapAttributes(ne, outputMap) + val withoutAlias = mapped match { + case Alias(child, _) => child + case e => e + } + ne.toAttribute -> mergedExpressions.find { + case Alias(child, _) => child semanticEquals withoutAlias + case e => e semanticEquals withoutAlias + }.getOrElse { + mergedExpressions += mapped + mapped + }.toAttribute + }) + (mergedExpressions.toSeq, newOutputMap) + } + + // Only allow aggregates of the same implementation because merging different implementations + // could cause performance regression. + private def supportedAggregateMerge(newPlan: Aggregate, cachedPlan: Aggregate) = { + val newPlanAggregateExpressions = newPlan.aggregateExpressions.flatMap(_.collect { + case a: AggregateExpression => a + }) + val cachedPlanAggregateExpressions = cachedPlan.aggregateExpressions.flatMap(_.collect { + case a: AggregateExpression => a + }) + val newPlanSupportsHashAggregate = Aggregate.supportsHashAggregate( + newPlanAggregateExpressions.flatMap(_.aggregateFunction.aggBufferAttributes)) + val cachedPlanSupportsHashAggregate = Aggregate.supportsHashAggregate( + cachedPlanAggregateExpressions.flatMap(_.aggregateFunction.aggBufferAttributes)) + newPlanSupportsHashAggregate && cachedPlanSupportsHashAggregate || + newPlanSupportsHashAggregate == cachedPlanSupportsHashAggregate && { + val newPlanSupportsObjectHashAggregate = + Aggregate.supportsObjectHashAggregate(newPlanAggregateExpressions) + val cachedPlanSupportsObjectHashAggregate = + Aggregate.supportsObjectHashAggregate(cachedPlanAggregateExpressions) + newPlanSupportsObjectHashAggregate && cachedPlanSupportsObjectHashAggregate || + newPlanSupportsObjectHashAggregate == cachedPlanSupportsObjectHashAggregate + } + } + + // Second traversal replaces `ScalarSubqueryReference`s to either + // `GetStructField(ScalarSubquery(CTERelationRef to the merged plan)` if the plan is merged from + // multiple subqueries or `ScalarSubquery(original plan)` if it isn't. + private def removeReferences( + plan: LogicalPlan, + cache: ListBuffer[Header]): (LogicalPlan, Seq[CTERelationDef]) = { + val subqueryCTEs = mutable.Map.empty[Int, CTERelationDef] Review Comment: That is basically true. This is the relevant conversation we had with @sigmod: https://github.com/apache/spark/pull/32298#discussion_r627800694 Briefly, this PR didn't depend on CTEs originally, but then `MergeScalarSubqueries` evolved to a general rule that might be followed by column pruning. As column pruning might alter the subqueries, subquery reuse might not kick in. So we needed a way to keep merged subqueries untouched till the end of optimization. First I used a custom `CommonScalarSubqueries` root node with `ScalarSubqueryReference` expressions, but then the implementation was changed to use general CTE nodes. -- 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. 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