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From hvanhovell <...@git.apache.org>
Subject [GitHub] spark pull request #17138: [SPARK-17080] [SQL] join reorder
Date Fri, 03 Mar 2017 13:46:57 GMT
Github user hvanhovell commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17138#discussion_r104135795
  
    --- 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)
    --- End diff --
    
    I am not really sure you need this. Just internalize this in the `search` and `searchForLevel`
functions. That will also allow you to turn this into an object.
    
    BTW, if ever need to do something like this again. Use: `Array.fill(items.length)(mutable.Map.empty)`



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