Github user wzhfy commented on a diff in the pull request:
https://github.com/apache/spark/pull/17138#discussion_r104281554
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala
---
@@ -0,0 +1,274 @@
+/*
+ * 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, AttributeSet,
Expression, PredicateHelper}
+import org.apache.spark.sql.catalyst.plans.{Inner, InnerLike}
+import org.apache.spark.sql.catalyst.plans.logical.{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 {
+ plan transform {
+ case p @ Project(projectList, j @ Join(_, _, _: InnerLike, _)) if
!j.ordered =>
+ reorder(j, p.outputSet)
+ case j @ Join(_, _, _: InnerLike, _) if !j.ordered =>
+ reorder(j, j.outputSet)
+ }
+ }
+ }
+
+ def reorder(plan: LogicalPlan, output: AttributeSet): LogicalPlan = {
+ val (items, conditions) = extractInnerJoins(plan)
+ val result =
+ if (items.size > 2 && items.size <= conf.joinReorderDPThreshold &&
conditions.nonEmpty) {
+ JoinReorderDP(conf, items, conditions,
output).search().getOrElse(plan)
+ } else {
+ plan
+ }
+ // Set all inside joins ordered.
+ setOrdered(result)
+ result
+ }
+
+ /**
+ * Extract inner joinable items and join conditions.
+ * This method works for bushy trees and left/right deep trees.
+ */
+ def extractInnerJoins(plan: LogicalPlan): (Seq[LogicalPlan],
Set[Expression]) = plan match {
+ case j @ Join(left, right, _: InnerLike, cond) =>
+ val (leftPlans, leftConditions) = extractInnerJoins(left)
+ val (rightPlans, rightConditions) = extractInnerJoins(right)
+ (leftPlans ++ rightPlans,
cond.map(splitConjunctivePredicates).getOrElse(Nil).toSet ++
+ leftConditions ++ rightConditions)
+ case Project(_, j @ Join(left, right, _: InnerLike, cond)) =>
+ val (leftPlans, leftConditions) = extractInnerJoins(left)
+ val (rightPlans, rightConditions) = extractInnerJoins(right)
+ (leftPlans ++ rightPlans,
cond.map(splitConjunctivePredicates).getOrElse(Nil).toSet ++
+ leftConditions ++ rightConditions)
+ case _ =>
+ (Seq(plan), Set())
+ }
+
+ def setOrdered(plan: LogicalPlan): Unit = plan match {
+ case j @ Join(left, right, _: InnerLike, cond) =>
+ j.ordered = true
+ setOrdered(left)
+ setOrdered(right)
+ case Project(_, j @ Join(left, right, _: InnerLike, cond)) =>
+ j.ordered = true
+ setOrdered(left)
+ setOrdered(right)
+ case _ =>
+ }
+}
+
+/**
+ * Reorder the joins using a dynamic programming algorithm:
+ * First we put all items (basic joined nodes) into level 1, then we build
all two-way joins
+ * at level 2 from plans at level 1 (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 1: p({A}), p({B}), p({C}), p({D})
+ * level 2: p({A, B}), p({A, C}), p({A, D}), p({B, C}), p({B, D}), p({C,
D})
+ * level 3: p({A, B, C}), p({A, B, D}), p({A, C, D}), p({B, C, D})
+ * level 4: 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.
+ */
+case class JoinReorderDP(
+ conf: CatalystConf,
+ items: Seq[LogicalPlan],
+ conditions: Set[Expression],
+ topOutput: AttributeSet) extends PredicateHelper{
+
+ /** Level i maintains all found plans for sets of i joinable items. */
+ val foundPlans = new Array[mutable.Map[Set[Int], JoinPlan]](items.length
+ 1)
+ for (i <- 1 to items.length) foundPlans(i) = mutable.Map.empty
+
+ def search(): Option[LogicalPlan] = {
+ // Start from the first level: each plan is a single item with zero
cost.
+ val itemIndex = items.zipWithIndex
+ foundPlans(1) ++=
+ itemIndex.map { case (item, id) => Set(id) -> JoinPlan(Set(id),
item, Cost(0, 0)) }
+
+ for (lev <- 2 to items.length) {
+ searchForLevel(lev)
+ }
+
+ val plansLastLevel = foundPlans(items.length)
+ 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 in one level, based on previous levels. */
+ private def searchForLevel(level: Int): Unit = {
+ val foundPlansCurLevel = foundPlans(level)
+ var k = 1
+ var continue = true
+ while (continue) {
+ val otherLevel = level - k
+ if (k > otherLevel) {
+ // We can break from here, because when building a join from A and
B, both A J B and B J A
+ // are handled.
+ continue = false
+ } else {
+ val joinPlansLevelK = foundPlans(k).values.toSeq
+ for (i <- joinPlansLevelK.indices) {
+ val curJoinPlan = joinPlansLevelK(i)
+
+ val joinPlansOtherLevel = if (k == otherLevel) {
+ // Both sides of a join are at the same level, no need to
repeat for previous ones.
+ joinPlansLevelK.drop(i)
+ } else {
+ foundPlans(otherLevel).values.toSeq
+ }
+
+ joinPlansOtherLevel.foreach { otherJoinPlan =>
+ // Should not join two overlapping item sets.
+ if
(curJoinPlan.itemIds.intersect(otherJoinPlan.itemIds).isEmpty) {
+ val joinPlan = buildJoin(curJoinPlan, otherJoinPlan)
+ if (joinPlan.nonEmpty) {
+ // 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 =
foundPlansCurLevel.get(joinPlan.get.itemIds)
+ if (existingPlan.isEmpty || joinPlan.get.cost <
existingPlan.get.cost) {
+ foundPlansCurLevel.update(joinPlan.get.itemIds,
joinPlan.get)
+ }
+ }
+ }
+ }
+ }
+
+ k += 1
+ }
+ }
+ }
+
+ /** Build a new join node. */
+ private def buildJoin(curJoinPlan: JoinPlan, otherJoinPlan: JoinPlan):
Option[JoinPlan] = {
+ // Check if these two nodes are inner joinable. We consider cartesian
product very
+ // costly, thus exclude such plans. This also helps us to reduce the
search space.
+ val curPlan = curJoinPlan.plan
+ val otherPlan = otherJoinPlan.plan
+ val joinCond = conditions
+ .filterNot(l => canEvaluate(l, curPlan))
+ .filterNot(r => canEvaluate(r, otherPlan))
+ .filter(e => e.references.subsetOf(curPlan.outputSet ++
otherPlan.outputSet))
+
+ if (joinCond.nonEmpty) {
+ val curPlanStats = curPlan.stats(conf)
+ val otherPlanStats = otherPlan.stats(conf)
+ if (curPlanStats.rowCount.nonEmpty &&
otherPlanStats.rowCount.nonEmpty) {
+ // Now both curPlan and otherPlan become intermediate joins, so
the cost of the
+ // new join should also include their costs.
+ val cost = curJoinPlan.cost + otherJoinPlan.cost +
+ Cost(curPlanStats.rowCount.get, curPlanStats.sizeInBytes) +
+ Cost(otherPlanStats.rowCount.get, otherPlanStats.sizeInBytes)
+
+ // Put the deeper side on the left, tend to build a left-deep tree.
+ val (left, right) = if (curJoinPlan.itemIds.size >=
otherJoinPlan.itemIds.size) {
+ (curPlan, otherPlan)
+ } else {
+ (otherPlan, curPlan)
+ }
+ val newJoin = Join(left, right, Inner, joinCond.reduceOption(And))
+ val remainingConds = conditions -- collectJoinConds(newJoin)
+ val neededAttr =
AttributeSet(remainingConds.flatMap(_.references)) ++ topOutput
+ val newPlan =
+ if ((newJoin.outputSet --
newJoin.outputSet.filter(neededAttr.contains)).nonEmpty) {
+ Project(newJoin.output.filter(neededAttr.contains), newJoin)
+ } else {
+ newJoin
+ }
+ val itemIds = curJoinPlan.itemIds.union(otherJoinPlan.itemIds)
+ return Some(JoinPlan(itemIds, newPlan, cost))
+ }
+ }
+ None
+ }
+
+ private def collectJoinConds(plan: LogicalPlan): Set[Expression] = plan
match {
+ case j @ Join(left, right, _: InnerLike, cond) =>
+ val leftConditions = collectJoinConds(left)
+ val rightConditions = collectJoinConds(right)
+ cond.map(splitConjunctivePredicates).getOrElse(Nil).toSet ++
leftConditions ++ rightConditions
+ case Project(_, j @ Join(left, right, _: InnerLike, cond)) =>
+ val leftConditions = collectJoinConds(left)
+ val rightConditions = collectJoinConds(right)
+ cond.map(splitConjunctivePredicates).getOrElse(Nil).toSet ++
leftConditions ++ rightConditions
+ case _ =>
+ Set()
+ }
+
+ /**
+ * 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 cost The cost of this plan is the sum of costs of all
intermediate joins.
+ */
+ case class JoinPlan(itemIds: Set[Int], plan: LogicalPlan, cost: Cost)
+}
+
+/** This class defines the cost model. */
+case class Cost(rows: BigInt, sizeInBytes: BigInt) {
+ /**
+ * An empirical value for the weights of cardinality (number of rows) in
the cost formula:
+ * cost = rows * weight + size * (1 - weight), usually cardinality is
more important than size.
+ */
+ val weight = 0.7
+
+ def +(other: Cost): Cost = Cost(rows + other.rows, sizeInBytes +
other.sizeInBytes)
--- End diff --
We need to compute a relative value inside the comparison logic, how about
change the name to `def lt` or `def lessThan`?
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