beliefer commented on code in PR #38799:
URL: https://github.com/apache/spark/pull/38799#discussion_r1036903257
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala:
##########
@@ -627,6 +627,87 @@ abstract class SparkStrategies extends
QueryPlanner[SparkPlan] {
}
}
+ /**
+ * Optimize the filter based on rank-like window function by reduce not
required rows.
+ * This rule optimizes the following cases:
+ * {{{
+ * SELECT *, ROW_NUMBER() OVER(PARTITION BY k ORDER BY a) AS rn FROM Tab1
WHERE rn = 5
+ * SELECT *, ROW_NUMBER() OVER(PARTITION BY k ORDER BY a) AS rn FROM Tab1
WHERE 5 = rn
+ * SELECT *, ROW_NUMBER() OVER(PARTITION BY k ORDER BY a) AS rn FROM Tab1
WHERE rn < 5
+ * SELECT *, ROW_NUMBER() OVER(PARTITION BY k ORDER BY a) AS rn FROM Tab1
WHERE 5 > rn
+ * SELECT *, ROW_NUMBER() OVER(PARTITION BY k ORDER BY a) AS rn FROM Tab1
WHERE rn <= 5
+ * SELECT *, ROW_NUMBER() OVER(PARTITION BY k ORDER BY a) AS rn FROM Tab1
WHERE 5 >= rn
+ * }}}
+ */
+ object WindowGroupLimit extends Strategy with PredicateHelper {
+
+ /**
+ * Extract all the limit values from predicates.
+ */
+ def extractLimits(condition: Expression, attr: Attribute): Option[Int] = {
+ val limits = splitConjunctivePredicates(condition).collect {
+ case EqualTo(IntegerLiteral(limit), e) if e.semanticEquals(attr) =>
limit
+ case EqualTo(e, IntegerLiteral(limit)) if e.semanticEquals(attr) =>
limit
+ case LessThan(e, IntegerLiteral(limit)) if e.semanticEquals(attr) =>
limit - 1
+ case GreaterThan(IntegerLiteral(limit), e) if e.semanticEquals(attr)
=> limit - 1
+ case LessThanOrEqual(e, IntegerLiteral(limit)) if
e.semanticEquals(attr) => limit
+ case GreaterThanOrEqual(IntegerLiteral(limit), e) if
e.semanticEquals(attr) => limit
+ }
+
+ if (limits.nonEmpty) Some(limits.min) else None
+ }
+
+ private def supports(
+ windowExpressions: Seq[NamedExpression]): Boolean =
windowExpressions.exists {
+ case Alias(WindowExpression(_: Rank | _: DenseRank | _: RowNumber,
WindowSpecDefinition(_, _,
+ SpecifiedWindowFrame(RowFrame, UnboundedPreceding, CurrentRow))), _) =>
true
+ case _ => false
+ }
+
+ def apply(plan: LogicalPlan): Seq[SparkPlan] = {
+ if (conf.windowGroupLimitThreshold == -1) return Nil
+
+ plan match {
+ case filter @ Filter(condition,
+ window @ logical.Window(windowExpressions, partitionSpec, orderSpec,
child))
+ if !child.isInstanceOf[logical.Window] &&
+ supports(windowExpressions) && orderSpec.nonEmpty =>
Review Comment:
No, if the window exists at least one rank like function, we can apply the
optimization.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowGroupLimitExec.scala:
##########
@@ -0,0 +1,235 @@
+/*
+ * 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] = {
+
+ abstract class WindowIterator extends Iterator[InternalRow] {
Review Comment:
OK
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