Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/17138#discussion_r106774778
--- 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. */
+ private def buildJoin(
+ oneJoinPlan: JoinPlan,
+ otherJoinPlan: JoinPlan,
+ conf: CatalystConf,
+ conditions: Set[Expression],
+ topOutput: AttributeSet): JoinPlan = {
+
+ val onePlan = oneJoinPlan.plan
+ val otherPlan = otherJoinPlan.plan
+ // Now both onePlan and otherPlan become intermediate joins, so the
cost of the
+ // new join should also include their own cardinalities and sizes.
+ val newCost = if (isCartesianProduct(onePlan) ||
isCartesianProduct(otherPlan)) {
+ // We consider cartesian product very expensive, thus set a very
large cost for it.
+ // This enables to plan all the cartesian products at the end,
because having a cartesian
+ // product as an intermediate join will significantly increase a
plan's cost, making it
+ // impossible to be selected as the best plan for the items, unless
there's no other choice.
+ Cost(
+ rows = BigInt(Long.MaxValue) * BigInt(Long.MaxValue),
+ size = BigInt(Long.MaxValue) * BigInt(Long.MaxValue))
+ } else {
+ val onePlanStats = onePlan.stats(conf)
+ val otherPlanStats = otherPlan.stats(conf)
+ Cost(
+ rows = oneJoinPlan.cost.rows + onePlanStats.rowCount.get +
+ otherJoinPlan.cost.rows + otherPlanStats.rowCount.get,
+ size = oneJoinPlan.cost.size + onePlanStats.sizeInBytes +
+ otherJoinPlan.cost.size + otherPlanStats.sizeInBytes)
+ }
+
+ // Put the deeper side on the left, tend to build a left-deep tree.
+ val (left, right) = if (oneJoinPlan.itemIds.size >=
otherJoinPlan.itemIds.size) {
+ (onePlan, otherPlan)
+ } else {
+ (otherPlan, onePlan)
+ }
+ val joinConds = conditions
+ .filterNot(l => canEvaluate(l, onePlan))
+ .filterNot(r => canEvaluate(r, otherPlan))
+ .filter(e => e.references.subsetOf(onePlan.outputSet ++
otherPlan.outputSet))
+ // We use inner join whether join condition is empty or not. Since
cross join is
+ // equivalent to inner join without condition.
+ val newJoin = Join(left, right, Inner, joinConds.reduceOption(And))
+ val collectedJoinConds = joinConds ++ oneJoinPlan.joinConds ++
otherJoinPlan.joinConds
+ val remainingConds = conditions -- collectedJoinConds
+ val neededAttr = AttributeSet(remainingConds.flatMap(_.references)) ++
topOutput
+ val neededFromNewJoin = newJoin.outputSet.filter(neededAttr.contains)
+ val newPlan =
+ if ((newJoin.outputSet -- neededFromNewJoin).nonEmpty) {
+ Project(neededFromNewJoin.toSeq, newJoin)
+ } else {
+ newJoin
+ }
+
+ val itemIds = oneJoinPlan.itemIds.union(otherJoinPlan.itemIds)
+ JoinPlan(itemIds, newPlan, collectedJoinConds, newCost)
+ }
+
+ private def isCartesianProduct(plan: LogicalPlan): Boolean = plan match {
+ case Join(_, _, _, None) => true
+ case Project(_, Join(_, _, _, None)) => true
+ case _ => false
+ }
+
+ /** Map[set of item ids, join plan for these items] */
+ type JoinPlanMap = Map[Set[Int], JoinPlan]
+
+ /**
+ * Partial join order in a specific level.
+ *
+ * @param itemIds Set of item ids participating in this partial plan.
+ * @param plan The plan tree with the lowest cost for these items found
so far.
+ * @param joinConds Join conditions included in the plan.
+ * @param cost The cost of this plan is the sum of costs of all
intermediate joins.
+ */
+ case class JoinPlan(itemIds: Set[Int], plan: LogicalPlan, joinConds:
Set[Expression], cost: Cost)
+}
+
+/** This class defines the cost model. */
+case class Cost(rows: BigInt, size: BigInt) {
--- End diff --
`rows` is confusing. Maybe `rowCount` or `numRows`
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
