sunchao commented on code in PR #57576: URL: https://github.com/apache/spark/pull/57576#discussion_r3696825902
########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CombineApproximatePercentiles.scala: ########## @@ -0,0 +1,216 @@ +/* + * 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.{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, + inputOrdinals: scala.collection.Map[ExprId, Int]): Expression = expression.transformUp { + case attribute: AttributeReference => + inputOrdinals.get(attribute.exprId) match { + case Some(ordinal) => AttributeReference("none", attribute.dataType)(ExprId(ordinal)) + case None => attribute + } + } + + 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)] + lazy val inputOrdinals = { + val ordinals = mutable.HashMap.empty[ExprId, Int] + aggregate.child.output.zipWithIndex.foreach { case (attribute, ordinal) => + ordinals.getOrElseUpdate(attribute.exprId, ordinal) + } + ordinals + } + 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] + val physicalKey = physicalCompatibilityKey(key, percentile.accuracyExpression) + // OptimizeOneRowPlan can erase DISTINCT after fusion. Across distinctness boundaries, + // canonical matches are safe only when their original inputs and filters also match. + physicalCompatibilityKeys(physicalKey).sizeCompare(1) == 0 && + physicalCompatibilityKeys + .get(physicalKey.copy(isDistinct = !physicalKey.isDistinct)) + .forall(_.forall(other => other.child == key.child && other.filter == key.filter)) + } + }.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, inputOrdinals) + }.toSeq, + key.mode, + key.isDistinct, + key.filter.map(structurallyNormalize(_, inputOrdinals)), + percentageValues.map(java.lang.Double.doubleToRawLongBits)) + val combinedFunction = percentile.copy(percentageExpression = PercentileFusionArray(identity)) + combinedFunction.copyTagsFrom(percentile) + val combined = first.copy(aggregateFunction = combinedFunction) Review Comment: Thanks, excellent catch. Fixed in `a04260d9`: the fused array aggregate now receives its own fresh expression ID: ```scala val combined = first.copy( aggregateFunction = combinedFunction, resultId = NamedExpression.newExprId) ``` That keeps the original scalar and fused array attributes distinct when `MergeSubplans` combines CTE references. I added your CTE self-join as an end-to-end regression, plus a Catalyst assertion that the fused aggregate does not retain the first scalar aggregate's `resultId`. The reproducer now returns `(3, 5, 3)` without the `ClassCastException`. ########## sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CombineApproximatePercentiles.scala: ########## @@ -0,0 +1,216 @@ +/* + * 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, CreateArray, 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) Review Comment: Thanks for following up, especially for pointing out the later `ConstantFolding` batch. I kept `PercentileFusionIdentity` and its targeted `ConstantFolding` exemption: structurally different literal accuracies such as `100` and `100L` still need to remain distinguishable after fusion, and the optimization must also stay correct when `ConstantFolding` is excluded. The latest update additionally makes the rule opt-in through `spark.sql.optimizer.combineApproximatePercentiles.enabled`, which defaults to `false`. -- 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: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
