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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2021/09/15 16:26:23 UTC
[GitHub] [spark] peter-toth commented on a change in pull request #32298: [SPARK-34079][SQL] Merge non-correlated scalar subqueries for better reuse
peter-toth commented on a change in pull request #32298:
URL: https://github.com/apache/spark/pull/32298#discussion_r709351389
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File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/MergeScalarSubqueries.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.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)]
+ val newPlan = removeReferences(insertReferences(plan, cache), cache)
+ if (cache.nonEmpty) {
+ val scalarSubqueries = cache.map { case (header, _) => ScalarSubquery(header) }.toSeq
+ CommonScalarSubqueries(scalarSubqueries, newPlan)
+ } else {
+ newPlan
+ }
+ }
+
+ // First traversal builds up the cache and inserts `ScalarSubqueryReference`s to the plan.
+ private def insertReferences(
+ plan: LogicalPlan,
+ cache: ListBuffer[(Project, Boolean)]): LogicalPlan = {
+ plan.transformAllExpressionsWithPruning(_.containsAnyPattern(SCALAR_SUBQUERY)) {
+ case s: ScalarSubquery if s.children.isEmpty =>
+ 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
+ // `CreateNamedStruct` header.
+ private def cacheSubquery(
+ plan: LogicalPlan,
+ cache: ListBuffer[(Project, Boolean)]): (Int, Int) = {
+ val firstOutput = plan.output.head
+ cache.zipWithIndex.collectFirst(Function.unlift { case ((header, merged), subqueryIndex) =>
+ checkIdenticalPlans(plan, header.child)
+ .map((subqueryIndex, header, header.child, _, merged))
+ .orElse(tryMergePlans(plan, header.child).map {
+ case (mergedPlan, outputMap) => (subqueryIndex, header, mergedPlan, outputMap, true)
+ })
+ }).map { case (subqueryIndex, header, mergedPlan, outputMap, merged) =>
+ val mappedFirstOutput = mapAttributes(firstOutput, outputMap)
+ val headerElements = getHeaderElements(header)
+ var headerIndex = headerElements.indexWhere {
+ case (_, attribute) => attribute.exprId == mappedFirstOutput.exprId
+ }
+ if (headerIndex == -1) {
+ val newHeaderElements = headerElements :+ (Literal(firstOutput.name) -> mappedFirstOutput)
+ cache(subqueryIndex) = createHeader(newHeaderElements, mergedPlan) -> merged
+ headerIndex = headerElements.size
+ }
+ subqueryIndex -> headerIndex
+ }.getOrElse {
+ cache += createHeader(Seq(Literal(firstOutput.name) -> firstOutput), 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) = {
+ 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))
+ if (ExpressionSet(mappedNewGroupingExpression) ==
+ ExpressionSet(cp.groupingExpressions)) {
+ val (mergedAggregateExpressions, newOutputMap) =
+ mergeNamedExpressions(np.aggregateExpressions, outputMap, cp.aggregateExpressions)
+ val mergedPlan =
+ Aggregate(cp.groupingExpressions, mergedAggregateExpressions, mergedChild)
+ Some(mergedPlan -> newOutputMap)
+ } else {
+ None
+ }
+ }
+
+ // Merging general nodes is complicated and this implementation supports only those nodes in
+ // which the order and the number of output attributes are not relevant (see
+ // `supportedMerge()` whitelist).
+ // Also, this implementation supports only those nodes in which children can be merged in
+ // the same order.
+ case (np, cp) if supportedMerge(np) && np.getClass == cp.getClass &&
+ np.children.size == cp.children.size =>
+ val merged = np.children.zip(cp.children).map {
+ case (npChild, cpChild) => tryMergePlans(npChild, cpChild)
+ }
+ if (merged.forall(_.isDefined)) {
+ val (mergedChildren, outputMaps) = merged.map(_.get).unzip
+ val outputMap = AttributeMap(outputMaps.map(_.iterator).reduce(_ ++ _).toSeq)
+ val mappedNewPlan = mapAttributes(np.withNewChildren(mergedChildren), outputMap)
+ val mergedPlan = cp.withNewChildren(mergedChildren)
+ if (mappedNewPlan.canonicalized == mergedPlan.canonicalized) {
+ Some(mergedPlan -> outputMap)
+ } else {
+ None
+ }
+ } else {
+ None
+ }
+
+ // As a follow-up, it would be possible to merge `CommonScalarSubqueries` nodes, which would
Review comment:
I don't think it is straightforward to refer to a subquery in a common root node of the whole plan, from another subquery, but it is probably doable. Unfortunately, even this simpler change hasn't got much reviews...
Anyway, I will update this PR soon to resolve the conflicts.
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