wangyum commented on a change in pull request #35789: URL: https://github.com/apache/spark/pull/35789#discussion_r824384997
########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/InjectRuntimeFilter.scala ########## @@ -0,0 +1,311 @@ +/* + * 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 org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, BloomFilterAggregate, Complete} +import org.apache.spark.sql.catalyst.planning.{ExtractEquiJoinKeys, PhysicalOperation} +import org.apache.spark.sql.catalyst.plans._ +import org.apache.spark.sql.catalyst.plans.logical._ +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.catalyst.trees.TreePattern.{INVOKE, JSON_TO_STRUCT, LIKE_FAMLIY, PYTHON_UDF, REGEXP_EXTRACT_FAMILY, REGEXP_REPLACE, SCALA_UDF} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types._ + +/** + * Insert a filter on one side of the join if the other side has a selective predicate. + * The filter could be an IN subquery (converted to a semi join), a bloom filter, or something + * else in the future. + */ +object InjectRuntimeFilter extends Rule[LogicalPlan] with PredicateHelper with JoinSelectionHelper { + + // Wraps `expr` with a hash function if its byte size is larger than an integer. + private def mayWrapWithHash(expr: Expression): Expression = { + if (expr.dataType.defaultSize > IntegerType.defaultSize) { + new Murmur3Hash(Seq(expr)) + } else { + expr + } + } + + private def injectFilter( + filterApplicationSideExp: Expression, + filterApplicationSidePlan: LogicalPlan, + filterCreationSideExp: Expression, + filterCreationSidePlan: LogicalPlan + ): LogicalPlan = { + require(conf.runtimeFilterBloomFilterEnabled || conf.runtimeFilterSemiJoinReductionEnabled) + if (conf.runtimeFilterBloomFilterEnabled) { + injectBloomFilter( + filterApplicationSideExp, + filterApplicationSidePlan, + filterCreationSideExp, + filterCreationSidePlan + ) + } else { + injectInSubqueryFilter( + filterApplicationSideExp, + filterApplicationSidePlan, + filterCreationSideExp, + filterCreationSidePlan + ) + } + } + + private def injectBloomFilter( + filterApplicationSideExp: Expression, + filterApplicationSidePlan: LogicalPlan, + filterCreationSideExp: Expression, + filterCreationSidePlan: LogicalPlan + ): LogicalPlan = { + // Skip if the filter creation side is too big + if (filterCreationSidePlan.stats.sizeInBytes > conf.runtimeFilterBloomFilterThreshold) { + return filterApplicationSidePlan + } + val rowCount = filterCreationSidePlan.stats.rowCount + val bloomFilterAgg = + if (rowCount.isDefined && rowCount.get.longValue > 0L) { + new BloomFilterAggregate(new XxHash64(Seq(filterCreationSideExp)), + Literal(rowCount.get.longValue)) + } else { + new BloomFilterAggregate(new XxHash64(Seq(filterCreationSideExp))) + } + val aggExp = AggregateExpression(bloomFilterAgg, Complete, isDistinct = false, None) + val alias = Alias(aggExp, "bloomFilter")() + val aggregate = ConstantFolding(Aggregate(Nil, Seq(alias), filterCreationSidePlan)) + val bloomFilterSubquery = ScalarSubquery(aggregate, Nil) + val filter = BloomFilterMightContain(bloomFilterSubquery, + new XxHash64(Seq(filterApplicationSideExp))) + Filter(filter, filterApplicationSidePlan) + } + + private def injectInSubqueryFilter( + filterApplicationSideExp: Expression, + filterApplicationSidePlan: LogicalPlan, + filterCreationSideExp: Expression, + filterCreationSidePlan: LogicalPlan + ): LogicalPlan = { + require(filterApplicationSideExp.dataType == filterCreationSideExp.dataType) + val actualFilterKeyExpr = mayWrapWithHash(filterCreationSideExp) + val alias = Alias(actualFilterKeyExpr, actualFilterKeyExpr.toString)() + val aggregate = Aggregate(Seq(alias), Seq(alias), filterCreationSidePlan) + if (!canBroadcastBySize(aggregate, conf)) { + // Skip the InSubquery filter if the size of `aggregate` is beyond broadcast join threshold, + // i.e., the semi-join will be a shuffled join, which is not worthwhile. + return filterApplicationSidePlan + } + val filter = InSubquery(Seq(mayWrapWithHash(filterApplicationSideExp)), + ListQuery(aggregate, childOutputs = aggregate.output)) + Filter(filter, filterApplicationSidePlan) + } + + /** + * Returns whether the plan is a simple filter over scan and the filter is likely selective + * Also check if the plan only has simple expressions (attribute reference, literals) so that we + * do not add a subquery that might have an expensive computation + */ + private def isSelectiveFilterOverScan(plan: LogicalPlan): Boolean = { + plan.expressions + val ret = plan match { + case PhysicalOperation(_, filters, child) if child.isInstanceOf[LeafNode] => + filters.forall(isSimpleExpression) && + filters.exists(isLikelySelective) + case _ => false + } + !plan.isStreaming && ret + } + + private def isSimpleExpression(e: Expression): Boolean = { + !e.containsAnyPattern(PYTHON_UDF, SCALA_UDF, INVOKE, JSON_TO_STRUCT, LIKE_FAMLIY, + REGEXP_EXTRACT_FAMILY, REGEXP_REPLACE) + } + + /** + * Returns whether an expression is likely to be selective + */ + private def isLikelySelective(e: Expression): Boolean = e match { + case Not(expr) => isLikelySelective(expr) + case And(l, r) => isLikelySelective(l) || isLikelySelective(r) + case Or(l, r) => isLikelySelective(l) && isLikelySelective(r) + case _: StringRegexExpression => true + case _: BinaryComparison => true + case _: In | _: InSet => true + case _: StringPredicate => true + case _: MultiLikeBase => true + case _ => false + } + + private def canFilterLeft(joinType: JoinType): Boolean = joinType match { + case Inner | RightOuter => true + case _ => false + } + + private def canFilterRight(joinType: JoinType): Boolean = joinType match { + case Inner | LeftOuter => true + case _ => false + } Review comment: Duplicate code: https://github.com/apache/spark/blob/2da0d0722d5a0aa337d5744a51075449b224e421/sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/PartitionPruning.scala#L197-L241 -- This is an automated message from the Apache Git Service. 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