Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/17138#discussion_r106799242
--- 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) =>
+ p.copy(child = replaceWithOrderedJoin(join))
+ case _ =>
+ plan
+ }
+
+ /** This is a wrapper class for a join node that has been ordered. */
+ private case class OrderedJoin(join: Join) extends BinaryNode {
+ override def left: LogicalPlan = join.left
+ override def right: LogicalPlan = join.right
+ override def output: Seq[Attribute] = join.output
+ }
+}
+
+/**
+ * Reorder the joins using a dynamic programming algorithm. This
implementation is based on the
+ * paper: Access Path Selection in a Relational Database Management System.
+ * http://www.inf.ed.ac.uk/teaching/courses/adbs/AccessPath.pdf
+ *
+ * First we put all items (basic joined nodes) into level 0, then we build
all two-way joins
+ * at level 1 from plans at level 0 (single items), then build all 3-way
joins from plans
+ * at previous levels (two-way joins and single items), then 4-way joins
... etc, until we
+ * build all n-way joins and pick the best plan among them.
+ *
+ * When building m-way joins, we only keep the best plan (with the lowest
cost) for the same set
+ * of m items. E.g., for 3-way joins, we keep only the best plan for items
{A, B, C} among
+ * plans (A J B) J C, (A J C) J B and (B J C) J A.
+ *
+ * Thus the plans maintained for each level when reordering four items A,
B, C, D are as follows:
+ * level 0: p({A}), p({B}), p({C}), p({D})
+ * level 1: p({A, B}), p({A, C}), p({A, D}), p({B, C}), p({B, D}), p({C,
D})
+ * level 2: p({A, B, C}), p({A, B, D}), p({A, C, D}), p({B, C, D})
+ * level 3: p({A, B, C, D})
+ * where p({A, B, C, D}) is the final output plan.
+ *
+ * For cost evaluation, since physical costs for operators are not
available currently, we use
+ * cardinalities and sizes to compute costs.
+ */
+object JoinReorderDP extends PredicateHelper {
+
+ def search(
+ conf: CatalystConf,
+ items: Seq[LogicalPlan],
+ conditions: Set[Expression],
+ topOutput: AttributeSet): Option[LogicalPlan] = {
+
+ // Level i maintains all found plans for i + 1 items.
+ // Create the initial plans: each plan is a single item with zero cost.
+ val itemIndex = items.zipWithIndex
+ val foundPlans = mutable.Buffer[JoinPlanMap](itemIndex.map {
+ case (item, id) => Set(id) -> JoinPlan(Set(id), item, Set(), Cost(0,
0))
+ }.toMap)
+
+ for (lev <- 1 until items.length) {
+ // Build plans for the next level.
+ foundPlans += searchLevel(foundPlans, conf, conditions, topOutput)
+ }
+
+ val plansLastLevel = foundPlans(items.length - 1)
+ if (plansLastLevel.isEmpty) {
+ // Failed to find a plan, fall back to the original plan
+ None
+ } else {
+ // There must be only one plan at the last level, which contains all
items.
+ assert(plansLastLevel.size == 1 && plansLastLevel.head._1.size ==
items.length)
+ Some(plansLastLevel.head._2.plan)
+ }
+ }
+
+ /** Find all possible plans at the next level, based on existing levels.
*/
+ private def searchLevel(
+ existingLevels: Seq[JoinPlanMap],
+ conf: CatalystConf,
+ conditions: Set[Expression],
+ topOutput: AttributeSet): JoinPlanMap = {
+
+ val nextLevel = mutable.Map.empty[Set[Int], JoinPlan]
+ var k = 0
+ val lev = existingLevels.length - 1
+ // Build plans for the next level from plans at level k (one side of
the join) and level
+ // lev - k (the other side of the join).
+ // For the lower level k, we only need to search from 0 to lev - k,
because when building
+ // a join from A and B, both A J B and B J A are handled.
+ while (k <= lev - k) {
+ val oneSideCandidates = existingLevels(k).values.toSeq
+ for (i <- oneSideCandidates.indices) {
+ val oneSidePlan = oneSideCandidates(i)
+ val otherSideCandidates = if (k == lev - k) {
+ // Both sides of a join are at the same level, no need to repeat
for previous ones.
+ oneSideCandidates.drop(i)
+ } else {
+ existingLevels(lev - k).values.toSeq
+ }
+
+ otherSideCandidates.foreach { otherSidePlan =>
+ // Should not join two overlapping item sets.
+ if
(oneSidePlan.itemIds.intersect(otherSidePlan.itemIds).isEmpty) {
+ val joinPlan = buildJoin(oneSidePlan, otherSidePlan, conf,
conditions, topOutput)
+ // Check if it's the first plan for the item set, or it's a
better plan than
+ // the existing one due to lower cost.
+ val existingPlan = nextLevel.get(joinPlan.itemIds)
+ if (existingPlan.isEmpty ||
joinPlan.cost.lessThan(existingPlan.get.cost)) {
+ nextLevel.update(joinPlan.itemIds, joinPlan)
+ }
+ }
+ }
+ }
+ k += 1
+ }
+ nextLevel.toMap
+ }
+
+ /** Build a new join node. */
--- End diff --
We need a better description for this function. Also need to document the
input parameters and the potential outputs.
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