[GitHub] [spark] LuciferYang commented on a diff in pull request #38799: [SPARK-37099][SQL] Introduce the group limit of Window for rank-based filter to optimize top-k computation

[GitBox <gi...@apache.org>]

LuciferYang commented on code in PR #38799:
URL: https://github.com/apache/spark/pull/38799#discussion_r1035453396


##########
sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala:
##########
@@ -627,6 +627,22 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
   }
 
+  object WindowGroupLimit extends Strategy {
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case logical.WindowGroupLimit(partitionSpec, orderSpec, rankLikeFunction, limit, child) =>
+        // TODO: add a physical rule to remove the partialWindowGroupLimit node, if there is no

Review Comment:
   should we file a new jira to tracking this TODO



##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/InsertWindowGroupLimit.scala:
##########
@@ -0,0 +1,97 @@
+/*
+ * 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.{Alias, Attribute, CurrentRow, DenseRank, EqualTo, Expression, GreaterThan, GreaterThanOrEqual, IntegerLiteral, LessThan, LessThanOrEqual, NamedExpression, PredicateHelper, Rank, RowFrame, RowNumber, SpecifiedWindowFrame, UnboundedPreceding, WindowExpression, WindowSpecDefinition}
+import org.apache.spark.sql.catalyst.plans.logical.{Filter, LocalRelation, LogicalPlan, Window, WindowGroupLimit}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.catalyst.trees.TreePattern.{FILTER, WINDOW}
+
+/**
+ * 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 InsertWindowGroupLimit extends Rule[LogicalPlan] 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): LogicalPlan = {
+    if (conf.windowGroupLimitThreshold == -1) return plan
+
+    plan.transformWithPruning(
+      _.containsAllPatterns(FILTER, WINDOW), ruleId) {
+      case filter @ Filter(condition,
+        window @ Window(windowExpressions, partitionSpec, orderSpec, child))
+          if !child.isInstanceOf[WindowGroupLimit] && !child.isInstanceOf[Window] &&
+            supports(windowExpressions) && orderSpec.nonEmpty =>
+        val limits = windowExpressions.collect {
+          case alias @ Alias(WindowExpression(rankLikeFunction, _), _) =>
+            extractLimits(condition, alias.toAttribute).map((_, rankLikeFunction))
+        }
+
+        // multiple different rank-like functions unsupported.
+        if (limits.filter(_.isDefined).groupBy(_.get._2).size > 1) {
+          return filter
+        }
+        val minLimit = limits.sortBy(_.get._1).head

Review Comment:
   could we change to use `minBy`?



##########
sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowGroupLimitExec.scala:
##########
@@ -0,0 +1,237 @@
+/*
+ * 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] {
+
+      def stream: Iterator[InternalRow]
+
+      val grouping = UnsafeProjection.create(partitionSpec, child.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 = {
+        val found = (bufferIterator != null && bufferIterator.hasNext) || nextRowAvailable

Review Comment:
   need the local `val found`?



##########
sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowGroupLimitExec.scala:
##########
@@ -0,0 +1,237 @@
+/*
+ * 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] {
+
+      def stream: Iterator[InternalRow]
+
+      val grouping = UnsafeProjection.create(partitionSpec, child.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 = {
+        val found = (bufferIterator != null && bufferIterator.hasNext) || nextRowAvailable
+        found
+      }
+
+      override final def next(): InternalRow = {
+        // Load the next partition if we need to.
+        if ((bufferIterator == null || !bufferIterator.hasNext) && nextRowAvailable) {

Review Comment:
   Is this condition equal to `!hasNext`? not sure
   
   



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