sunchao commented on code in PR #57576: URL: https://github.com/apache/spark/pull/57576#discussion_r3694500019
########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CombineApproximatePercentiles.scala: ########## @@ -0,0 +1,205 @@ +/* + * 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 org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Expression, ExprId, GetArrayItem, LeafExpression, Literal, NamedExpression} +import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateMode, ApproximatePercentile} +import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback +import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, LogicalPlan} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.catalyst.trees.TreePattern.AGGREGATE +import org.apache.spark.sql.catalyst.util.GenericArrayData +import org.apache.spark.sql.types.{ArrayType, DoubleType} + +private[optimizer] case class PercentileFusionIdentity( + aggregateFunctions: Seq[Expression], + mode: AggregateMode, + isDistinct: Boolean, + filter: Option[Expression], + percentageBits: Seq[Long]) + +/** + * Foldable percentage array that retains the original scalar aggregate structures in equality. + * + * Fusion removes those structures from the physical aggregate. Keeping them here prevents + * subquery or exchange reuse from equating plans that were distinct before fusion. + */ +private[optimizer] case class PercentileFusionArray(identity: PercentileFusionIdentity) + extends LeafExpression with CodegenFallback { + override def foldable: Boolean = true + override def nullable: Boolean = false + override def dataType: ArrayType = ArrayType(DoubleType, containsNull = false) + + private lazy val value = new GenericArrayData( + identity.percentageBits.map(java.lang.Double.longBitsToDouble)) + private lazy val literal = Literal(value, dataType) + + override def eval(input: InternalRow): Any = value + override def toString: String = literal.toString + override def sql: String = literal.sql +} + +/** + * Combines scalar approximate percentiles that can share the same percentile digest. + * + * An approximate percentile digest depends on its input, accuracy, filter, distinctness, and + * aggregate mode, but not on the percentile requested from the completed digest. Consequently, + * compatible scalar percentiles can be calculated by one array-valued aggregate and projected + * back to their original scalar outputs. + * + * Inputs and filters must retain their original expression structure so that floating-point + * evaluation and ANSI overflow behavior are preserved. Streaming aggregates are left unchanged + * to preserve the value schemas of existing checkpoints. + */ +object CombineApproximatePercentiles extends Rule[LogicalPlan] { + + private case class CompatibilityKey( + child: Expression, + accuracy: Long, + mode: AggregateMode, + isDistinct: Boolean, + filter: Option[Expression]) + + private case class PhysicalCompatibilityKey( + child: Expression, + accuracy: Expression, + mode: AggregateMode, + isDistinct: Boolean, + filter: Option[Expression]) + + private def structurallyNormalize( + expression: Expression, + input: Seq[Attribute]): Expression = expression.transformUp { + case attribute: AttributeReference => + val ordinal = input.indexWhere(_.exprId == attribute.exprId) + if (ordinal < 0) { + attribute + } else { + AttributeReference("none", attribute.dataType)(ExprId(ordinal)) + } + } + + private def physicalCompatibilityKey( + key: CompatibilityKey, + accuracy: Expression): PhysicalCompatibilityKey = PhysicalCompatibilityKey( + key.child.canonicalized, + accuracy.canonicalized, + key.mode, + key.isDistinct, Review Comment: Fixed in `61eec1ff`. I kept `isDistinct` in the physical key so the safe mixed-DISTINCT case still fuses, but now also inspect the opposite-DISTINCT entry in the existing compatibility map. Fusion is rejected when that entry has a structurally different input or filter, which closes the later `OptimizeOneRowPlan` collision without introducing a second map. I added Catalyst coverage for both cross-DISTINCT input/filter collisions and your exact SQL reproducer. The SQL test compares against a dynamically collected fusion-disabled baseline rather than hard-coding Spark's existing incorrect all-zero result. ########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CombineApproximatePercentiles.scala: ########## @@ -0,0 +1,205 @@ +/* + * 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 org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Expression, ExprId, GetArrayItem, LeafExpression, Literal, NamedExpression} +import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateMode, ApproximatePercentile} +import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback +import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, LogicalPlan} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.catalyst.trees.TreePattern.AGGREGATE +import org.apache.spark.sql.catalyst.util.GenericArrayData +import org.apache.spark.sql.types.{ArrayType, DoubleType} + +private[optimizer] case class PercentileFusionIdentity( + aggregateFunctions: Seq[Expression], + mode: AggregateMode, + isDistinct: Boolean, + filter: Option[Expression], + percentageBits: Seq[Long]) + +/** + * Foldable percentage array that retains the original scalar aggregate structures in equality. + * + * Fusion removes those structures from the physical aggregate. Keeping them here prevents + * subquery or exchange reuse from equating plans that were distinct before fusion. + */ +private[optimizer] case class PercentileFusionArray(identity: PercentileFusionIdentity) + extends LeafExpression with CodegenFallback { + override def foldable: Boolean = true + override def nullable: Boolean = false + override def dataType: ArrayType = ArrayType(DoubleType, containsNull = false) + + private lazy val value = new GenericArrayData( + identity.percentageBits.map(java.lang.Double.longBitsToDouble)) + private lazy val literal = Literal(value, dataType) + + override def eval(input: InternalRow): Any = value + override def toString: String = literal.toString + override def sql: String = literal.sql +} + +/** + * Combines scalar approximate percentiles that can share the same percentile digest. + * + * An approximate percentile digest depends on its input, accuracy, filter, distinctness, and + * aggregate mode, but not on the percentile requested from the completed digest. Consequently, + * compatible scalar percentiles can be calculated by one array-valued aggregate and projected + * back to their original scalar outputs. + * + * Inputs and filters must retain their original expression structure so that floating-point + * evaluation and ANSI overflow behavior are preserved. Streaming aggregates are left unchanged + * to preserve the value schemas of existing checkpoints. + */ +object CombineApproximatePercentiles extends Rule[LogicalPlan] { + + private case class CompatibilityKey( + child: Expression, + accuracy: Long, + mode: AggregateMode, + isDistinct: Boolean, + filter: Option[Expression]) + + private case class PhysicalCompatibilityKey( + child: Expression, + accuracy: Expression, + mode: AggregateMode, + isDistinct: Boolean, + filter: Option[Expression]) + + private def structurallyNormalize( + expression: Expression, + input: Seq[Attribute]): Expression = expression.transformUp { + case attribute: AttributeReference => + val ordinal = input.indexWhere(_.exprId == attribute.exprId) + if (ordinal < 0) { + attribute + } else { + AttributeReference("none", attribute.dataType)(ExprId(ordinal)) + } + } + + private def physicalCompatibilityKey( + key: CompatibilityKey, + accuracy: Expression): PhysicalCompatibilityKey = PhysicalCompatibilityKey( + key.child.canonicalized, + accuracy.canonicalized, + key.mode, + key.isDistinct, + key.filter.map(_.canonicalized)) + + private def hasSafePhysicalFusion( + expressions: scala.collection.Iterable[AggregateExpression]): Boolean = { + val physicalGroups = expressions.groupBy(_.canonicalized) + // PhysicalAggregation already shares a digest within each canonical group. Fusion must both + // remove a digest and preserve cases where canonical percentages evaluate differently. + physicalGroups.sizeCompare(1) > 0 && physicalGroups.values.forall { group => + group.iterator.map { expression => + expression.aggregateFunction + .asInstanceOf[ApproximatePercentile] + .percentageExpression + .eval() + }.toSet.sizeCompare(1) == 0 + } + } + + override def apply(plan: LogicalPlan): LogicalPlan = plan.transformUpWithPruning( + _.containsPattern(AGGREGATE), ruleId) { + case aggregate: Aggregate if aggregate.resolved && !aggregate.isStreaming => + combine(aggregate) + } + + private def combine(aggregate: Aggregate): Aggregate = { + val compatible = mutable.LinkedHashMap.empty[ + CompatibilityKey, mutable.ArrayBuffer[AggregateExpression]] + // PhysicalAggregation deduplicates semantically equivalent aggregates. Track every logical + // key that shares a physical key so fusion does not change that existing deduplication. + val physicalCompatibilityKeys = mutable.HashMap.empty[ + PhysicalCompatibilityKey, mutable.HashSet[CompatibilityKey]] + + aggregate.aggregateExpressions.foreach(_.foreach { + case expression @ AggregateExpression( + percentile: ApproximatePercentile, mode, isDistinct, filter, _) + if percentile.child.deterministic && + filter.forall(_.deterministic) => + val key = CompatibilityKey( + percentile.child, + // Analysis already validates that accuracy is foldable, non-null, and in range. + percentile.accuracyExpression.eval().asInstanceOf[Number].longValue, + mode, + isDistinct, + filter) + physicalCompatibilityKeys.getOrElseUpdate( + physicalCompatibilityKey(key, percentile.accuracyExpression), + mutable.HashSet.empty) += key + if (percentile.percentageExpression.dataType == DoubleType) { + compatible.getOrElseUpdate(key, mutable.ArrayBuffer.empty) += expression + } + case _ => + }) + + val replacements = mutable.HashMap.empty[ExprId, (AggregateExpression, Int)] + compatible.iterator.map { case (key, expressions) => + key -> expressions.distinctBy(_.resultId) + }.filter { case (key, expressions) => + hasSafePhysicalFusion(expressions) && expressions.forall { expression => + val percentile = expression.aggregateFunction.asInstanceOf[ApproximatePercentile] + physicalCompatibilityKeys( + physicalCompatibilityKey(key, percentile.accuracyExpression)).sizeCompare(1) == 0 + } + }.foreach { case (key, expressions) => + val first = expressions.head + val percentile = first.aggregateFunction.asInstanceOf[ApproximatePercentile] + val percentages = expressions.map { expression => + expression.aggregateFunction + .asInstanceOf[ApproximatePercentile] + .percentageExpression + } + val percentageValues = percentages.map(_.eval().asInstanceOf[Double]).toSeq + val identity = PercentileFusionIdentity( + expressions.map { expression => + structurallyNormalize(expression.aggregateFunction, aggregate.child.output) + }.toSeq, + key.mode, + key.isDistinct, + key.filter.map(structurallyNormalize(_, aggregate.child.output)), + percentageValues.map(java.lang.Double.doubleToRawLongBits)) + val combined = first.copy(aggregateFunction = percentile.copy( Review Comment: Fixed in `61eec1ff`. The copied fused aggregate now calls `copyTagsFrom(percentile)`, so `FUNC_ALIAS` survives the rewrite. I extended both the Catalyst regression and the end-to-end SQL fusion test to verify that `approx_percentile` remains `approx_percentile` in the optimized plan. ########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CombineApproximatePercentiles.scala: ########## @@ -0,0 +1,205 @@ +/* + * 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 org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Expression, ExprId, GetArrayItem, LeafExpression, Literal, NamedExpression} +import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateMode, ApproximatePercentile} +import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback +import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, LogicalPlan} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.catalyst.trees.TreePattern.AGGREGATE +import org.apache.spark.sql.catalyst.util.GenericArrayData +import org.apache.spark.sql.types.{ArrayType, DoubleType} + +private[optimizer] case class PercentileFusionIdentity( + aggregateFunctions: Seq[Expression], + mode: AggregateMode, + isDistinct: Boolean, + filter: Option[Expression], + percentageBits: Seq[Long]) + +/** + * Foldable percentage array that retains the original scalar aggregate structures in equality. + * + * Fusion removes those structures from the physical aggregate. Keeping them here prevents + * subquery or exchange reuse from equating plans that were distinct before fusion. + */ +private[optimizer] case class PercentileFusionArray(identity: PercentileFusionIdentity) + extends LeafExpression with CodegenFallback { + override def foldable: Boolean = true + override def nullable: Boolean = false + override def dataType: ArrayType = ArrayType(DoubleType, containsNull = false) + + private lazy val value = new GenericArrayData( + identity.percentageBits.map(java.lang.Double.longBitsToDouble)) + private lazy val literal = Literal(value, dataType) + + override def eval(input: InternalRow): Any = value + override def toString: String = literal.toString + override def sql: String = literal.sql +} + +/** + * Combines scalar approximate percentiles that can share the same percentile digest. + * + * An approximate percentile digest depends on its input, accuracy, filter, distinctness, and + * aggregate mode, but not on the percentile requested from the completed digest. Consequently, + * compatible scalar percentiles can be calculated by one array-valued aggregate and projected + * back to their original scalar outputs. + * + * Inputs and filters must retain their original expression structure so that floating-point + * evaluation and ANSI overflow behavior are preserved. Streaming aggregates are left unchanged + * to preserve the value schemas of existing checkpoints. + */ +object CombineApproximatePercentiles extends Rule[LogicalPlan] { + + private case class CompatibilityKey( + child: Expression, + accuracy: Long, + mode: AggregateMode, + isDistinct: Boolean, + filter: Option[Expression]) + + private case class PhysicalCompatibilityKey( + child: Expression, + accuracy: Expression, + mode: AggregateMode, + isDistinct: Boolean, + filter: Option[Expression]) + + private def structurallyNormalize( + expression: Expression, + input: Seq[Attribute]): Expression = expression.transformUp { + case attribute: AttributeReference => + val ordinal = input.indexWhere(_.exprId == attribute.exprId) Review Comment: Addressed in `61eec1ff`. `combine` now builds one lazy `ExprId`-to-ordinal map per aggregate and reuses it for every normalized input and filter. `getOrElseUpdate` preserves the previous first-match behavior if child output contains duplicate `ExprId`s, and the map is never allocated when no percentiles fuse. -- This is an automated message from the Apache Git Service. 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