cloud-fan commented on code in PR #38799: URL: https://github.com/apache/spark/pull/38799#discussion_r1046827770
########## sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowGroupLimitExec.scala: ########## @@ -0,0 +1,251 @@ +/* + * 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.execution.window + +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute, DenseRank, Expression, Rank, RowNumber, SortOrder, UnsafeProjection, UnsafeRow} +import org.apache.spark.sql.catalyst.expressions.codegen.GenerateOrdering +import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, Partitioning} +import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode} + +sealed trait WindowGroupLimitMode + +case object Partial extends WindowGroupLimitMode + +case object Final extends WindowGroupLimitMode + +/** + * This operator is designed to filter out unnecessary rows before WindowExec + * for top-k computation. + * @param partitionSpec Should be the same as [[WindowExec#partitionSpec]] + * @param orderSpec Should be the same as [[WindowExec#orderSpec]] + * @param rankLikeFunction The function to compute row rank, should be RowNumber/Rank/DenseRank. + */ +case class WindowGroupLimitExec( + partitionSpec: Seq[Expression], + orderSpec: Seq[SortOrder], + rankLikeFunction: Expression, + limit: Int, + mode: WindowGroupLimitMode, + child: SparkPlan) extends UnaryExecNode { + + override def output: Seq[Attribute] = child.output + + override def requiredChildDistribution: Seq[Distribution] = mode match { + case Partial => super.requiredChildDistribution + case Final => + if (partitionSpec.isEmpty) { + AllTuples :: Nil + } else { + ClusteredDistribution(partitionSpec) :: Nil + } + } + + override def requiredChildOrdering: Seq[Seq[SortOrder]] = + Seq(partitionSpec.map(SortOrder(_, Ascending)) ++ orderSpec) + + override def outputOrdering: Seq[SortOrder] = child.outputOrdering + + override def outputPartitioning: Partitioning = child.outputPartitioning + + protected override def doExecute(): RDD[InternalRow] = rankLikeFunction match { + case _: RowNumber => + child.execute().mapPartitions(SimpleGroupLimitIterator(partitionSpec, output, _, limit)) + case _: Rank => + child.execute().mapPartitions( + RankGroupLimitIterator(partitionSpec, output, _, orderSpec, limit)) + case _: DenseRank => + child.execute().mapPartitions( + DenseRankGroupLimitIterator(partitionSpec, output, _, orderSpec, limit)) + } + + override protected def withNewChildInternal(newChild: SparkPlan): WindowGroupLimitExec = + copy(child = newChild) +} + +abstract class WindowIterator extends Iterator[InternalRow] { + + def partitionSpec: Seq[Expression] + + def output: Seq[Attribute] + + def stream: Iterator[InternalRow] + + val grouping = UnsafeProjection.create(partitionSpec, output) + + // Manage the stream and the grouping. + var nextRow: UnsafeRow = null + var nextGroup: UnsafeRow = null + var nextRowAvailable: Boolean = false + protected[this] def fetchNextRow(): Unit = { + nextRowAvailable = stream.hasNext + if (nextRowAvailable) { + nextRow = stream.next().asInstanceOf[UnsafeRow] + nextGroup = grouping(nextRow) + } else { + nextRow = null + nextGroup = null + } + } + fetchNextRow() + + // Whether or not the rank exceeding the window group limit value. + def exceedingLimit(): Boolean + + // Increase the rank value. + def increaseRank(): Unit + + // Clear the rank value. + def clearRank(): Unit + + var bufferIterator: Iterator[InternalRow] = _ + + private[this] def fetchNextPartition(): Unit = { + clearRank() + bufferIterator = createGroupIterator() + } + + override final def hasNext: Boolean = + (bufferIterator != null && bufferIterator.hasNext) || nextRowAvailable + + override final def next(): InternalRow = { + // Load the next partition if we need to. + if ((bufferIterator == null || !bufferIterator.hasNext) && nextRowAvailable) { + fetchNextPartition() + } + + if (bufferIterator.hasNext) { + bufferIterator.next() + } else { + throw new NoSuchElementException + } + } + + private def createGroupIterator(): Iterator[InternalRow] = { + new Iterator[InternalRow] { + // Before we start to fetch new input rows, make a copy of nextGroup. + val currentGroup = nextGroup.copy() + + def hasNext: Boolean = { + if (nextRowAvailable) { + if (exceedingLimit() && nextGroup == currentGroup) { + do { + fetchNextRow() + } while (nextRowAvailable && nextGroup == currentGroup) + } + nextRowAvailable && nextGroup == currentGroup + } else { + nextRowAvailable + } + } + + def next(): InternalRow = { + val currentRow = nextRow.copy() + increaseRank() + fetchNextRow() + currentRow + } + } + } +} + +case class SimpleGroupLimitIterator( + partitionSpec: Seq[Expression], + output: Seq[Attribute], + stream: Iterator[InternalRow], + limit: Int) extends WindowIterator { + var count = 0 + + override def exceedingLimit(): Boolean = { + count >= limit + } + + override def increaseRank(): Unit = { + count += 1 + } + + override def clearRank(): Unit = { + count = 0 + } +} + +case class RankGroupLimitIterator( + partitionSpec: Seq[Expression], + output: Seq[Attribute], + stream: Iterator[InternalRow], + orderSpec: Seq[SortOrder], + limit: Int) extends WindowIterator { + val ordering = GenerateOrdering.generate(orderSpec, output) + var count = 0 + var rank = 0 + var currentRank: UnsafeRow = null + + override def exceedingLimit(): Boolean = { + rank >= limit + } + + override def increaseRank(): Unit = { + if (count == 0) { + currentRank = nextRow.copy() + } else { + if (ordering.compare(currentRank, nextRow) != 0) { + rank = count + currentRank = nextRow.copy() + } + } + count += 1 + } + + override def clearRank(): Unit = { + count = 0 + rank = 0 + currentRank = null + } +} + +case class DenseRankGroupLimitIterator( + partitionSpec: Seq[Expression], + output: Seq[Attribute], + stream: Iterator[InternalRow], + orderSpec: Seq[SortOrder], + limit: Int) extends WindowIterator { + val ordering = GenerateOrdering.generate(orderSpec, output) + var rank = 0 Review Comment: We can move `var rand = 0` to the parent class, then we don't need to add `def exceedingLimit` ########## sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowGroupLimitExec.scala: ########## @@ -0,0 +1,251 @@ +/* + * 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.execution.window + +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute, DenseRank, Expression, Rank, RowNumber, SortOrder, UnsafeProjection, UnsafeRow} +import org.apache.spark.sql.catalyst.expressions.codegen.GenerateOrdering +import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, Partitioning} +import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode} + +sealed trait WindowGroupLimitMode + +case object Partial extends WindowGroupLimitMode + +case object Final extends WindowGroupLimitMode + +/** + * This operator is designed to filter out unnecessary rows before WindowExec + * for top-k computation. + * @param partitionSpec Should be the same as [[WindowExec#partitionSpec]] + * @param orderSpec Should be the same as [[WindowExec#orderSpec]] + * @param rankLikeFunction The function to compute row rank, should be RowNumber/Rank/DenseRank. + */ +case class WindowGroupLimitExec( + partitionSpec: Seq[Expression], + orderSpec: Seq[SortOrder], + rankLikeFunction: Expression, + limit: Int, + mode: WindowGroupLimitMode, + child: SparkPlan) extends UnaryExecNode { + + override def output: Seq[Attribute] = child.output + + override def requiredChildDistribution: Seq[Distribution] = mode match { + case Partial => super.requiredChildDistribution + case Final => + if (partitionSpec.isEmpty) { + AllTuples :: Nil + } else { + ClusteredDistribution(partitionSpec) :: Nil + } + } + + override def requiredChildOrdering: Seq[Seq[SortOrder]] = + Seq(partitionSpec.map(SortOrder(_, Ascending)) ++ orderSpec) + + override def outputOrdering: Seq[SortOrder] = child.outputOrdering + + override def outputPartitioning: Partitioning = child.outputPartitioning + + protected override def doExecute(): RDD[InternalRow] = rankLikeFunction match { + case _: RowNumber => + child.execute().mapPartitions(SimpleGroupLimitIterator(partitionSpec, output, _, limit)) + case _: Rank => + child.execute().mapPartitions( + RankGroupLimitIterator(partitionSpec, output, _, orderSpec, limit)) + case _: DenseRank => + child.execute().mapPartitions( + DenseRankGroupLimitIterator(partitionSpec, output, _, orderSpec, limit)) + } + + override protected def withNewChildInternal(newChild: SparkPlan): WindowGroupLimitExec = + copy(child = newChild) +} + +abstract class WindowIterator extends Iterator[InternalRow] { + + def partitionSpec: Seq[Expression] + + def output: Seq[Attribute] + + def stream: Iterator[InternalRow] + + val grouping = UnsafeProjection.create(partitionSpec, output) + + // Manage the stream and the grouping. + var nextRow: UnsafeRow = null + var nextGroup: UnsafeRow = null + var nextRowAvailable: Boolean = false + protected[this] def fetchNextRow(): Unit = { + nextRowAvailable = stream.hasNext + if (nextRowAvailable) { + nextRow = stream.next().asInstanceOf[UnsafeRow] + nextGroup = grouping(nextRow) + } else { + nextRow = null + nextGroup = null + } + } + fetchNextRow() + + // Whether or not the rank exceeding the window group limit value. + def exceedingLimit(): Boolean + + // Increase the rank value. + def increaseRank(): Unit + + // Clear the rank value. + def clearRank(): Unit + + var bufferIterator: Iterator[InternalRow] = _ + + private[this] def fetchNextPartition(): Unit = { + clearRank() + bufferIterator = createGroupIterator() + } + + override final def hasNext: Boolean = + (bufferIterator != null && bufferIterator.hasNext) || nextRowAvailable + + override final def next(): InternalRow = { + // Load the next partition if we need to. + if ((bufferIterator == null || !bufferIterator.hasNext) && nextRowAvailable) { + fetchNextPartition() + } + + if (bufferIterator.hasNext) { + bufferIterator.next() + } else { + throw new NoSuchElementException + } + } + + private def createGroupIterator(): Iterator[InternalRow] = { + new Iterator[InternalRow] { + // Before we start to fetch new input rows, make a copy of nextGroup. + val currentGroup = nextGroup.copy() + + def hasNext: Boolean = { + if (nextRowAvailable) { + if (exceedingLimit() && nextGroup == currentGroup) { + do { + fetchNextRow() + } while (nextRowAvailable && nextGroup == currentGroup) + } + nextRowAvailable && nextGroup == currentGroup + } else { + nextRowAvailable + } + } + + def next(): InternalRow = { + val currentRow = nextRow.copy() + increaseRank() + fetchNextRow() + currentRow + } + } + } +} + +case class SimpleGroupLimitIterator( + partitionSpec: Seq[Expression], + output: Seq[Attribute], + stream: Iterator[InternalRow], + limit: Int) extends WindowIterator { + var count = 0 + + override def exceedingLimit(): Boolean = { + count >= limit + } + + override def increaseRank(): Unit = { + count += 1 + } + + override def clearRank(): Unit = { + count = 0 + } +} + +case class RankGroupLimitIterator( + partitionSpec: Seq[Expression], + output: Seq[Attribute], + stream: Iterator[InternalRow], + orderSpec: Seq[SortOrder], + limit: Int) extends WindowIterator { + val ordering = GenerateOrdering.generate(orderSpec, output) + var count = 0 + var rank = 0 + var currentRank: UnsafeRow = null + + override def exceedingLimit(): Boolean = { + rank >= limit + } + + override def increaseRank(): Unit = { + if (count == 0) { + currentRank = nextRow.copy() + } else { + if (ordering.compare(currentRank, nextRow) != 0) { + rank = count + currentRank = nextRow.copy() + } + } + count += 1 + } + + override def clearRank(): Unit = { + count = 0 + rank = 0 + currentRank = null + } +} + +case class DenseRankGroupLimitIterator( + partitionSpec: Seq[Expression], + output: Seq[Attribute], + stream: Iterator[InternalRow], + orderSpec: Seq[SortOrder], + limit: Int) extends WindowIterator { + val ordering = GenerateOrdering.generate(orderSpec, output) + var rank = 0 Review Comment: We can move `var rank = 0` to the parent class, then we don't need to add `def exceedingLimit` -- This is an automated message from the Apache Git Service. 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