Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/17138#discussion_r104880245
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala
---
@@ -0,0 +1,297 @@
+/*
+ * 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.CatalystConf
+import org.apache.spark.sql.catalyst.expressions.{And, Attribute,
AttributeSet, Expression, PredicateHelper}
+import org.apache.spark.sql.catalyst.plans.{Inner, InnerLike}
+import org.apache.spark.sql.catalyst.plans.logical.{BinaryNode, Join,
LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.rules.Rule
+
+
+/**
+ * Cost-based join reorder.
+ * We may have several join reorder algorithms in the future. This class
is the entry of these
+ * algorithms, and chooses which one to use.
+ */
+case class CostBasedJoinReorder(conf: CatalystConf) extends
Rule[LogicalPlan] with PredicateHelper {
+ def apply(plan: LogicalPlan): LogicalPlan = {
+ if (!conf.cboEnabled || !conf.joinReorderEnabled) {
+ plan
+ } else {
+ val result = plan transform {
+ case p @ Project(projectList, j @ Join(_, _, _: InnerLike, _)) =>
+ reorder(p, p.outputSet)
+ case j @ Join(_, _, _: InnerLike, _) =>
+ reorder(j, j.outputSet)
+ }
+ // After reordering is finished, convert OrderedJoin back to Join
+ result transform {
+ case oj: OrderedJoin => oj.join
+ }
+ }
+ }
+
+ def reorder(plan: LogicalPlan, output: AttributeSet): LogicalPlan = {
+ val (items, conditions) = extractInnerJoins(plan)
+ val result =
+ // Do reordering if the number of items is appropriate and join
conditions exist.
+ // We also need to check if costs of all items can be evaluated.
+ if (items.size > 2 && items.size <= conf.joinReorderDPThreshold &&
conditions.nonEmpty &&
+ items.forall(_.stats(conf).rowCount.isDefined)) {
+ JoinReorderDP.search(conf, items, conditions,
output).getOrElse(plan)
+ } else {
+ plan
+ }
+ // Set consecutive join nodes ordered.
+ replaceWithOrderedJoin(result)
+ }
+
+ /**
+ * Extract consecutive inner joinable items and join conditions.
+ * This method works for bushy trees and left/right deep trees.
+ */
+ private def extractInnerJoins(plan: LogicalPlan): (Seq[LogicalPlan],
Set[Expression]) = {
+ plan match {
+ case Join(left, right, _: InnerLike, cond) =>
+ val (leftPlans, leftConditions) = extractInnerJoins(left)
+ val (rightPlans, rightConditions) = extractInnerJoins(right)
+ (leftPlans ++ rightPlans,
cond.toSet.flatMap(splitConjunctivePredicates) ++
+ leftConditions ++ rightConditions)
+ case Project(projectList, join) if
projectList.forall(_.isInstanceOf[Attribute]) =>
+ extractInnerJoins(join)
+ case _ =>
+ (Seq(plan), Set())
+ }
+ }
+
+ private def replaceWithOrderedJoin(plan: LogicalPlan): LogicalPlan =
plan match {
+ case j @ Join(left, right, _: InnerLike, cond) =>
+ val replacedLeft = replaceWithOrderedJoin(left)
+ val replacedRight = replaceWithOrderedJoin(right)
+ OrderedJoin(j.copy(left = replacedLeft, right = replacedRight))
+ case p @ Project(_, join) =>
--- End diff --
NIT: You can make this pattern match a bit more precise by also checking if
`join` is an actual `Join`.
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