spark CostBasedJoinReorder 源码

  • 2022-10-20
  • 浏览 (251)

spark CostBasedJoinReorder 代码

文件路径:/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala

/*
 * 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.internal.Logging
import org.apache.spark.sql.catalyst.expressions.{And, Attribute, AttributeSet, Expression, ExpressionSet, PredicateHelper}
import org.apache.spark.sql.catalyst.plans.{Inner, InnerLike, JoinType}
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.catalyst.trees.TreePattern.INNER_LIKE_JOIN
import org.apache.spark.sql.internal.SQLConf


/**
 * 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.
 */
object CostBasedJoinReorder extends Rule[LogicalPlan] with PredicateHelper {

  def apply(plan: LogicalPlan): LogicalPlan = {
    if (!conf.cboEnabled || !conf.joinReorderEnabled) {
      plan
    } else {
      val result = plan.transformDownWithPruning(_.containsPattern(INNER_LIKE_JOIN), ruleId) {
        // Start reordering with a joinable item, which is an InnerLike join with conditions.
        // Avoid reordering if a join hint is present.
        case j @ Join(_, _, _: InnerLike, Some(cond), JoinHint.NONE) =>
          reorder(j, j.output)
        case p @ Project(projectList, Join(_, _, _: InnerLike, Some(cond), JoinHint.NONE))
          if projectList.forall(_.isInstanceOf[Attribute]) =>
          reorder(p, p.output)
      }
      // After reordering is finished, convert OrderedJoin back to Join.
      result transform {
        case OrderedJoin(left, right, jt, cond) => Join(left, right, jt, cond, JoinHint.NONE)
      }
    }
  }

  private def reorder(plan: LogicalPlan, output: Seq[Attribute]): 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.rowCount.isDefined)) {
        JoinReorderDP.search(conf, items, conditions, output)
      } else {
        plan
      }
    // Set consecutive join nodes ordered.
    replaceWithOrderedJoin(result)
  }

  /**
   * Extracts items of consecutive inner joins and join conditions.
   * This method works for bushy trees and left/right deep trees.
   */
  private def extractInnerJoins(plan: LogicalPlan): (Seq[LogicalPlan], ExpressionSet) = {
    plan match {
      case Join(left, right, _: InnerLike, Some(cond), JoinHint.NONE) =>
        val (leftPlans, leftConditions) = extractInnerJoins(left)
        val (rightPlans, rightConditions) = extractInnerJoins(right)
        (leftPlans ++ rightPlans, leftConditions ++ rightConditions ++
          splitConjunctivePredicates(cond))
      case Project(projectList, j @ Join(_, _, _: InnerLike, Some(cond), JoinHint.NONE))
        if projectList.forall(_.isInstanceOf[Attribute]) =>
        extractInnerJoins(j)
      case _ =>
        (Seq(plan), ExpressionSet())
    }
  }

  private def replaceWithOrderedJoin(plan: LogicalPlan): LogicalPlan = plan match {
    case j @ Join(left, right, jt: InnerLike, Some(cond), JoinHint.NONE) =>
      val replacedLeft = replaceWithOrderedJoin(left)
      val replacedRight = replaceWithOrderedJoin(right)
      OrderedJoin(replacedLeft, replacedRight, jt, Some(cond))
    case p @ Project(projectList, j @ Join(_, _, _: InnerLike, Some(cond), JoinHint.NONE)) =>
      p.copy(child = replaceWithOrderedJoin(j))
    case _ =>
      plan
  }
}

/** This is a mimic class for a join node that has been ordered. */
case class OrderedJoin(
    left: LogicalPlan,
    right: LogicalPlan,
    joinType: JoinType,
    condition: Option[Expression]) extends BinaryNode {
  override def output: Seq[Attribute] = left.output ++ right.output
  override protected def withNewChildrenInternal(
      newLeft: LogicalPlan, newRight: LogicalPlan): OrderedJoin =
    copy(left = newLeft, right = newRight)
}

/**
 * Reorder the joins using a dynamic programming algorithm. This implementation is based on the
 * paper: Access Path Selection in a Relational Database Management System.
 * https://dl.acm.org/doi/10.1145/582095.582099
 *
 * 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.
 * We also prune cartesian product candidates when building a new plan if there exists no join
 * condition involving references from both left and right. This pruning strategy significantly
 * reduces the search space.
 * E.g., given A J B J C J D with join conditions A.k1 = B.k1 and B.k2 = C.k2 and C.k3 = D.k3,
 * plans maintained for each level are as follows:
 * level 0: p({A}), p({B}), p({C}), p({D})
 * level 1: p({A, B}), p({B, C}), p({C, D})
 * level 2: p({A, B, C}), 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 with Logging {

  def search(
      conf: SQLConf,
      items: Seq[LogicalPlan],
      conditions: ExpressionSet,
      output: Seq[Attribute]): LogicalPlan = {

    val startTime = System.nanoTime()
    // 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]({
      // SPARK-32687: Change to use `LinkedHashMap` to make sure that items are
      // inserted and iterated in the same order.
      val joinPlanMap = new JoinPlanMap
      itemIndex.foreach {
        case (item, id) =>
          joinPlanMap.put(Set(id), JoinPlan(Set(id), item, ExpressionSet(), Cost(0, 0)))
      }
      joinPlanMap
    })

    // Build filters from the join graph to be used by the search algorithm.
    val filters = JoinReorderDPFilters.buildJoinGraphInfo(conf, items, conditions, itemIndex)

    // Build plans for next levels until the last level has only one plan. This plan contains
    // all items that can be joined, so there's no need to continue.
    val topOutputSet = AttributeSet(output)
    while (foundPlans.size < items.length) {
      // Build plans for the next level.
      foundPlans += searchLevel(foundPlans.toSeq, conf, conditions, topOutputSet, filters)
    }

    val durationInMs = (System.nanoTime() - startTime) / (1000 * 1000)
    logDebug(s"Join reordering finished. Duration: $durationInMs ms, number of items: " +
      s"${items.length}, number of plans in memo: ${foundPlans.map(_.size).sum}")

    // The last level must have one and only one plan, because all items are joinable.
    assert(foundPlans.size == items.length && foundPlans.last.size == 1)
    foundPlans.last.head._2.plan match {
      case p @ Project(projectList, j: Join) if projectList != output =>
        assert(topOutputSet == p.outputSet)
        // Keep the same order of final output attributes.
        p.copy(projectList = output)
      case finalPlan if !sameOutput(finalPlan, output) =>
        Project(output, finalPlan)
      case finalPlan =>
        finalPlan
    }
  }

  private def sameOutput(plan: LogicalPlan, expectedOutput: Seq[Attribute]): Boolean = {
    val thisOutput = plan.output
    thisOutput.length == expectedOutput.length && thisOutput.zip(expectedOutput).forall {
      case (a1, a2) => a1.semanticEquals(a2)
    }
  }

  /** Find all possible plans at the next level, based on existing levels. */
  private def searchLevel(
      existingLevels: Seq[JoinPlanMap],
      conf: SQLConf,
      conditions: ExpressionSet,
      topOutput: AttributeSet,
      filters: Option[JoinGraphInfo]): JoinPlanMap = {

    val nextLevel = new JoinPlanMap
    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 =>
          buildJoin(oneSidePlan, otherSidePlan, conf, conditions, topOutput, filters) match {
            case Some(newJoinPlan) =>
              // 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(newJoinPlan.itemIds)
              if (existingPlan.isEmpty || newJoinPlan.betterThan(existingPlan.get, conf)) {
                nextLevel.update(newJoinPlan.itemIds, newJoinPlan)
              }
            case None =>
          }
        }
      }
      k += 1
    }
    nextLevel
  }

  /**
   * Builds a new JoinPlan if the following conditions hold:
   * - the sets of items contained in left and right sides do not overlap.
   * - there exists at least one join condition involving references from both sides.
   * - if star-join filter is enabled, allow the following combinations:
   *         1) (oneJoinPlan U otherJoinPlan) is a subset of star-join
   *         2) star-join is a subset of (oneJoinPlan U otherJoinPlan)
   *         3) (oneJoinPlan U otherJoinPlan) is a subset of non star-join
   *
   * @param oneJoinPlan One side JoinPlan for building a new JoinPlan.
   * @param otherJoinPlan The other side JoinPlan for building a new join node.
   * @param conf SQLConf for statistics computation.
   * @param conditions The overall set of join conditions.
   * @param topOutput The output attributes of the final plan.
   * @param filters Join graph info to be used as filters by the search algorithm.
   * @return Builds and returns a new JoinPlan if both conditions hold. Otherwise, returns None.
   */
  private def buildJoin(
      oneJoinPlan: JoinPlan,
      otherJoinPlan: JoinPlan,
      conf: SQLConf,
      conditions: ExpressionSet,
      topOutput: AttributeSet,
      filters: Option[JoinGraphInfo]): Option[JoinPlan] = {

    if (oneJoinPlan.itemIds.intersect(otherJoinPlan.itemIds).nonEmpty) {
      // Should not join two overlapping item sets.
      return None
    }

    if (filters.isDefined) {
      // Apply star-join filter, which ensures that tables in a star schema relationship
      // are planned together. The star-filter will eliminate joins among star and non-star
      // tables until the star joins are built. The following combinations are allowed:
      // 1. (oneJoinPlan U otherJoinPlan) is a subset of star-join
      // 2. star-join is a subset of (oneJoinPlan U otherJoinPlan)
      // 3. (oneJoinPlan U otherJoinPlan) is a subset of non star-join
      val isValidJoinCombination =
        JoinReorderDPFilters.starJoinFilter(oneJoinPlan.itemIds, otherJoinPlan.itemIds,
          filters.get)
      if (!isValidJoinCombination) return None
    }

    val onePlan = oneJoinPlan.plan
    val otherPlan = otherJoinPlan.plan
    val joinConds = conditions
      .filterNot(l => canEvaluate(l, onePlan))
      .filterNot(r => canEvaluate(r, otherPlan))
      .filter(e => e.references.subsetOf(onePlan.outputSet ++ otherPlan.outputSet))
    if (joinConds.isEmpty) {
      // Cartesian product is very expensive, so we exclude them from candidate plans.
      // This also significantly reduces the search space.
      return None
    }

    // 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 newJoin = Join(left, right, Inner, joinConds.reduceOption(And), JoinHint.NONE)
    val collectedJoinConds = joinConds ++ oneJoinPlan.joinConds ++ otherJoinPlan.joinConds
    val remainingConds = conditions -- collectedJoinConds
    val neededAttr = AttributeSet(remainingConds.flatMap(_.references)) ++ topOutput
    val neededFromNewJoin = newJoin.output.filter(neededAttr.contains)
    val newPlan =
      if ((newJoin.outputSet -- neededFromNewJoin).nonEmpty) {
        Project(neededFromNewJoin, newJoin)
      } else {
        newJoin
      }

    val itemIds = oneJoinPlan.itemIds.union(otherJoinPlan.itemIds)
    // Now the root node of onePlan/otherPlan becomes an intermediate join (if it's a non-leaf
    // item), so the cost of the new join should also include its own cost.
    val newPlanCost = oneJoinPlan.planCost + oneJoinPlan.rootCost(conf) +
      otherJoinPlan.planCost + otherJoinPlan.rootCost(conf)
    Some(JoinPlan(itemIds, newPlan, collectedJoinConds, newPlanCost))
  }

  /** Map[set of item ids, join plan for these items] */
  type JoinPlanMap = mutable.LinkedHashMap[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 planCost The cost of this plan tree is the sum of costs of all intermediate joins.
   */
  case class JoinPlan(
      itemIds: Set[Int],
      plan: LogicalPlan,
      joinConds: ExpressionSet,
      planCost: Cost) {

    /** Get the cost of the root node of this plan tree. */
    def rootCost(conf: SQLConf): Cost = {
      if (itemIds.size > 1) {
        val rootStats = plan.stats
        Cost(rootStats.rowCount.get, rootStats.sizeInBytes)
      } else {
        // If the plan is a leaf item, it has zero cost.
        Cost(0, 0)
      }
    }

    /**
     * To identify the plan with smaller computational cost,
     * we use the weighted geometric mean of ratio of rows and the ratio of sizes in bytes.
     *
     * There are other ways to combine these values as a cost comparison function.
     * Some of these, that we have experimented with, but have gotten worse result,
     * than with the current one:
     * 1) Weighted arithmetic mean of these two ratios - adding up fractions puts
     * less emphasis on ratios between 0 and 1. Ratios 10 and 0.1 should be considered
     * to be just as strong evidences in opposite directions. The arithmetic mean of these
     * would be heavily biased towards the 10.
     * 2) Absolute cost (cost = weight * rowCount + (1 - weight) * size) - when adding up
     * two numeric measurements that have different units we can easily end up with one
     * overwhelming the other.
     */
    def betterThan(other: JoinPlan, conf: SQLConf): Boolean = {
      if (other.planCost.card == 0 || other.planCost.size == 0) {
        false
      } else {
        val relativeRows = BigDecimal(this.planCost.card) / BigDecimal(other.planCost.card)
        val relativeSize = BigDecimal(this.planCost.size) / BigDecimal(other.planCost.size)
        Math.pow(relativeRows.doubleValue, conf.joinReorderCardWeight) *
          Math.pow(relativeSize.doubleValue, 1 - conf.joinReorderCardWeight) < 1
      }
    }
  }
}

/**
 * This class defines the cost model for a plan.
 * @param card Cardinality (number of rows).
 * @param size Size in bytes.
 */
case class Cost(card: BigInt, size: BigInt) {
  def +(other: Cost): Cost = Cost(this.card + other.card, this.size + other.size)
}

/**
 * Implements optional filters to reduce the search space for join enumeration.
 *
 * 1) Star-join filters: Plan star-joins together since they are assumed
 *    to have an optimal execution based on their RI relationship.
 * 2) Cartesian products: Defer their planning later in the graph to avoid
 *    large intermediate results (expanding joins, in general).
 * 3) Composite inners: Don't generate "bushy tree" plans to avoid materializing
 *   intermediate results.
 *
 * Filters (2) and (3) are not implemented.
 */
object JoinReorderDPFilters {
  /**
   * Builds join graph information to be used by the filtering strategies.
   * Currently, it builds the sets of star/non-star joins.
   * It can be extended with the sets of connected/unconnected joins, which
   * can be used to filter Cartesian products.
   */
  def buildJoinGraphInfo(
      conf: SQLConf,
      items: Seq[LogicalPlan],
      conditions: ExpressionSet,
      itemIndex: Seq[(LogicalPlan, Int)]): Option[JoinGraphInfo] = {

    if (conf.joinReorderDPStarFilter) {
      // Compute the tables in a star-schema relationship.
      val starJoin = StarSchemaDetection.findStarJoins(items, conditions.toSeq)
      val nonStarJoin = items.filterNot(starJoin.contains(_))

      if (starJoin.nonEmpty && nonStarJoin.nonEmpty) {
        val itemMap = itemIndex.toMap
        Some(JoinGraphInfo(starJoin.map(itemMap).toSet, nonStarJoin.map(itemMap).toSet))
      } else {
        // Nothing interesting to return.
        None
      }
    } else {
      // Star schema filter is not enabled.
      None
    }
  }

  /**
   * Applies the star-join filter that eliminates join combinations among star
   * and non-star tables until the star join is built.
   *
   * Given the oneSideJoinPlan/otherSideJoinPlan, which represent all the plan
   * permutations generated by the DP join enumeration, and the star/non-star plans,
   * the following plan combinations are allowed:
   * 1. (oneSideJoinPlan U otherSideJoinPlan) is a subset of star-join
   * 2. star-join is a subset of (oneSideJoinPlan U otherSideJoinPlan)
   * 3. (oneSideJoinPlan U otherSideJoinPlan) is a subset of non star-join
   *
   * It assumes the sets are disjoint.
   *
   * Example query graph:
   *
   * t1   d1 - t2 - t3
   *  \  /
   *   f1
   *   |
   *   d2
   *
   * star: {d1, f1, d2}
   * non-star: {t2, t1, t3}
   *
   * level 0: (f1 ), (d2 ), (t3 ), (d1 ), (t1 ), (t2 )
   * level 1: {t3 t2 }, {f1 d2 }, {f1 d1 }
   * level 2: {d2 f1 d1 }
   * level 3: {t1 d1 f1 d2 }, {t2 d1 f1 d2 }
   * level 4: {d1 t2 f1 t1 d2 }, {d1 t3 t2 f1 d2 }
   * level 5: {d1 t3 t2 f1 t1 d2 }
   *
   * @param oneSideJoinPlan One side of the join represented as a set of plan ids.
   * @param otherSideJoinPlan The other side of the join represented as a set of plan ids.
   * @param filters Star and non-star plans represented as sets of plan ids
   */
  def starJoinFilter(
      oneSideJoinPlan: Set[Int],
      otherSideJoinPlan: Set[Int],
      filters: JoinGraphInfo) : Boolean = {
    val starJoins = filters.starJoins
    val nonStarJoins = filters.nonStarJoins
    val join = oneSideJoinPlan.union(otherSideJoinPlan)

    // Disjoint sets
    oneSideJoinPlan.intersect(otherSideJoinPlan).isEmpty &&
      // Either star or non-star is empty
      (starJoins.isEmpty || nonStarJoins.isEmpty ||
        // Join is a subset of the star-join
        join.subsetOf(starJoins) ||
        // Star-join is a subset of join
        starJoins.subsetOf(join) ||
        // Join is a subset of non-star
        join.subsetOf(nonStarJoins))
  }
}

/**
 * Helper class that keeps information about the join graph as sets of item/plan ids.
 * It currently stores the star/non-star plans. It can be
 * extended with the set of connected/unconnected plans.
 */
case class JoinGraphInfo (starJoins: Set[Int], nonStarJoins: Set[Int])

相关信息

spark 源码目录

相关文章

spark ComplexTypes 源码

spark DecorrelateInnerQuery 源码

spark EliminateResolvedHint 源码

spark InjectRuntimeFilter 源码

spark InlineCTE 源码

spark LimitPushDownThroughWindow 源码

spark MergeScalarSubqueries 源码

spark NestedColumnAliasing 源码

spark NormalizeFloatingNumbers 源码

spark OptimizeCsvJsonExprs 源码

0  赞