xianyinxin commented on a change in pull request #24983:
[SPARK-27714][SQL][CBO] Support Genetic Algorithm based join reorder
URL: https://github.com/apache/spark/pull/24983#discussion_r311352767
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala
##########
@@ -470,3 +397,427 @@ object JoinReorderDPFilters extends PredicateHelper {
* extended with the set of connected/unconnected plans.
*/
case class JoinGraphInfo (starJoins: Set[Int], nonStarJoins: Set[Int])
+
+/**
+ * Reorder the joins using a genetic algorithm. The algorithm treat the
reorder problem
+ * to a traveling salesmen problem, and use genetic algorithm give an
optimized solution.
+ *
+ * The implementation refs the geqo in postgresql, which is contibuted by
Darrell Whitley:
+ * https://www.postgresql.org/docs/9.1/geqo-pg-intro.html
+ *
+ * For more info about genetic algorithm and the edge recombination crossover,
pls see:
+ * "A Genetic Algorithm Tutorial, Darrell Whitley"
+ * https://link.springer.com/article/10.1007/BF00175354
+ * and "Scheduling Problems and Traveling Salesmen: The Genetic Edge
Recombination Operator,
+ * Darrell Whitley et al." https://dl.acm.org/citation.cfm?id=657238
+ * respectively.
+ */
+object JoinReorderGA extends PredicateHelper with Logging {
+
+ def search(
+ conf: SQLConf,
+ items: Seq[LogicalPlan],
+ conditions: Set[Expression],
+ output: Seq[Attribute]): Option[LogicalPlan] = {
+
+ val startTime = System.nanoTime()
+
+ val itemsWithIndex = items.zipWithIndex.map {
+ case (plan, id) => id -> JoinPlan(Set(id), plan, Set.empty, Cost(0, 0))
+ }.toMap
+
+ val topOutputSet = AttributeSet(output)
+
+ val pop = Population(conf, itemsWithIndex, conditions, topOutputSet).evolve
+
+ val durationInMs = (System.nanoTime() - startTime) / (1000 * 1000)
+ logInfo(s"Join reordering finished. Duration: $durationInMs ms, number of
items: " +
+ s"${items.length}, number of plans in memo: ${ pop.chromos.size}")
+
+ assert(pop.chromos.head.basicPlans.size == items.length)
+ pop.chromos.head.integratedPlan match {
+ case Some(joinPlan) => joinPlan.plan match {
+ case p @ Project(projectList, _: Join) if projectList != output =>
+ assert(topOutputSet == p.outputSet)
+ // Keep the same order of final output attributes.
+ Some(p.copy(projectList = output))
+ case finalPlan if !sameOutput(finalPlan, output) =>
+ Some(Project(output, finalPlan))
+ case finalPlan =>
+ Some(finalPlan)
+ }
+ case _ => None
+ }
+ }
+}
+
+/**
+ * A pair of parent individuals can breed a child with certain crossover
process.
+ * With crossover, child can inherit gene from its parents, and these gene
snippets
+ * finally compose a new [[Chromosome]].
+ */
+@DeveloperApi
+trait Crossover {
+
+ /**
+ * Generate a new [[Chromosome]] from the given parent [[Chromosome]]s,
+ * with this crossover algorithm.
+ */
+ def newChromo(father: Chromosome, mother: Chromosome) : Chromosome
+}
+
+case class EdgeTable(table: Map[JoinPlan, Seq[JoinPlan]])
+
+/**
+ * This class implements the Genetic Edge Recombination algorithm.
+ * For more information about the Genetic Edge Recombination,
+ * see "Scheduling Problems and Traveling Salesmen: The Genetic Edge
+ * Recombination Operator" by Darrell Whitley et al.
+ * https://dl.acm.org/citation.cfm?id=657238
+ */
+object EdgeRecombination extends Crossover {
+
+ def genEdgeTable(father: Chromosome, mother: Chromosome) : EdgeTable = {
+ val fatherTable = father.basicPlans.map(g => g ->
findNeighbours(father.basicPlans, g)).toMap
+ val motherTable = mother.basicPlans.map(g => g ->
findNeighbours(mother.basicPlans, g)).toMap
+ EdgeTable(
+ fatherTable.map(entry => entry._1 -> (entry._2 ++
motherTable(entry._1))))
+ }
+
+ def findNeighbours(genes: Seq[JoinPlan], g: JoinPlan) : Seq[JoinPlan] = {
+ val genesIndexed = genes.toIndexedSeq
+ val index = genesIndexed.indexOf(g)
+ val length = genes.size
+ if (index > 0 && index < length - 1) {
+ Seq(genesIndexed(index - 1), genesIndexed(index + 1))
+ } else if (index == 0) {
+ Seq(genesIndexed(1), genesIndexed(length - 1))
+ } else if (index == length - 1) {
+ Seq(genesIndexed(0), genesIndexed(length - 2))
+ } else {
+ Seq()
+ }
+ }
+
+ override def newChromo(father: Chromosome, mother: Chromosome): Chromosome =
{
+ var newGenes: Seq[JoinPlan] = Seq()
+ // 1. Generate the edge table.
+ var table = genEdgeTable(father, mother).table
+ // 2. Choose a start point randomly from the heads of father/mother.
+ var current =
+ if (util.Random.nextInt(2) == 0) father.basicPlans.head else
mother.basicPlans.head
+ newGenes :+= current
+
+ var stop = false
+ while (!stop) {
+ // 3. Filter out the chosen point from the edge table.
+ table = table.map(
+ entry => entry._1 -> entry._2.filter(g => if (g == current) false else
true)
+ )
+ // 4. Choose next point among its neighbours. The criterion for choosing
which point
+ // is that the one who has fewest neighbours. If two or more points has
the same num
+ // of neighbours, choose one randomly. If there's no neighbours
available for this
+ // point but there're still other remaining points, choose one from them
randomly.
+ val tobeVisited = table(current)
+ val neighboursTable = tobeVisited.map(g => g ->
table(g)).sortBy(-_._2.size).toMap
+ val filteredTable = table.filter(_._2.nonEmpty)
+ if (neighboursTable.nonEmpty) {
+ val numBase = neighboursTable.head._2.size
+ var numCand = 0
+ neighboursTable.foreach(entry => if (entry._2.size == numBase) numCand
+= 1)
+ current = neighboursTable.toIndexedSeq(util.Random.nextInt(numCand))._1
+ newGenes :+= current
+ } else if (filteredTable.nonEmpty) {
+ current =
filteredTable.toIndexedSeq(util.Random.nextInt(filteredTable.size))._2.head
+ newGenes :+= current
+ } else {
+ stop = true
+ }
+ }
+
+ Chromosome(father.conf, newGenes, father.conditions, father.topOutputSet)
+ }
+}
+
+/**
+ * A sequence of genes(each represents a single relation) which represents
+ * a joined plan with determined join order.
+ */
+case class Chromosome(
+ conf: SQLConf,
+ basicPlans: Seq[JoinPlan],
+ conditions: Set[Expression],
+ topOutputSet: AttributeSet) {
+
+ lazy val fitness: Double = evalFitness(integratedPlan)
+
+ lazy val integratedPlan: Option[JoinPlan] = makePlan
+
+ private def makePlan: Option[JoinPlan] = {
+ val semiFinished = mutable.Buffer[JoinPlan]()
+ basicPlans.foreach(mergeSemi(semiFinished, _))
+ if (semiFinished.head.itemIds.size == basicPlans.size) {
+ Some(semiFinished.head)
+ } else {
+ None
+ }
+ }
+
+ private def mergeSemi(semiFinished: mutable.Buffer[JoinPlan], right:
JoinPlan): Unit = {
+ val filters = None: Option[JoinGraphInfo]
+ for (left <- semiFinished) {
+ JoinReorderUtils.buildJoin(left, right, conf, conditions, topOutputSet,
filters) match {
+ case Some(joined) =>
+ semiFinished.remove(semiFinished.indexOf(left))
+ mergeSemi(semiFinished, joined)
+ case _ =>
+ None
+ }
+ }
+
+ if (semiFinished.isEmpty || right.itemIds.size == 1) {
+ semiFinished.append(right)
+ return
+ }
+
+ insertPlan(semiFinished, right)
+ }
+
+ private def insertPlan(semiFinished: mutable.Buffer[JoinPlan], plan:
JoinPlan): Unit = {
+ var criticalSize = if (semiFinished.head.itemIds.size > plan.itemIds.size)
{
+ semiFinished.head.itemIds.size
+ } else {
+ plan.itemIds.size
+ }
+ var criticalIndex = 0
+ var break: Boolean = false
+ for (p <- semiFinished if !break) {
+ if (plan.itemIds.size > p.itemIds.size && plan.itemIds.size <=
criticalSize) {
+ break = true
+ } else {
+ criticalIndex += 1
+ criticalSize = p.itemIds.size
+ }
+ }
+
+ semiFinished.insert(criticalIndex, plan)
+ }
+
+ private def evalFitness(plan: Option[JoinPlan]): Double = {
+ plan match {
+ case Some(joinPlan) =>
+ // We use the negative cost as fitness.
+ - joinPlan.planCost.card.toDouble * conf.joinReorderCardWeight -
+ joinPlan.planCost.size.toDouble * (1 - conf.joinReorderCardWeight)
+ case _ =>
+ - Double.MaxValue
+ }
+ }
+}
+
+/**
+ * A live space which has multi individuals(represented by [[Chromosome]]).
+ * The population can evolve, generation by generation. The child individual
+ * is generated by the [[Crossover]] procedure from its parents.
+ */
+object Population {
+ def apply(
+ conf: SQLConf,
+ itemsMap: Map[Int, JoinPlan],
+ conditions: Set[Expression],
+ topOutputSet: AttributeSet) : Population = {
+
+ val chromos: Seq[Chromosome] = Seq.fill(determinePopSize(conf,
itemsMap.size)) {
+ Chromosome(conf, shuffle(itemsMap), conditions, topOutputSet)
+ }
+
+ Population(conf, chromos)
+ }
+
+ private def determinePopSize(conf: SQLConf, numRelations: Int): Int = {
+ val relaxFactor = conf.joinReorderGARelaxFactor
+ // The default population size:
+ // # of relations | pop size (RF=3) | pop size (RF=3.5)| pop size (RF=4)
+ // < 13 | DP based | DP based | DP based
+ // 13 | 20 | 16 (13<16) | 16
+ // 14 | 25 | 16 | 16
+ // 15 | 32 | 19 | 16
+ // 16 | 40 | 23 | 16
+ // 17 | 50 | 28 | 19
+ // 18 | 64 | 35 | 22
+ // 19 | 80 | 43 | 26
+ // 20 | 101 | 52 | 32
+ // 21 | 128 | 64 | 38
+ // 22 | 128 | 78 | 45
+ // 23 | 128 | 95 | 53
+ // 24 | 128 | 115 | 64
+ // 25 | 128 | 128 | 76
+ // 26 | 128 | 128 | 90
+ // 27 | 128 | 128 | 90
+ // 28 | 128 | 128 | 128
+ // > 28 | 128 | 128 | 128
+ val size = math.pow(2.0, numRelations / relaxFactor)
+ val max = conf.joinReorderGAMaxPoPSize
+ val min = conf.joinReorderGAMinPoPSize
+
+ math.ceil(math.max(math.min(max, size), min)).toInt
+ }
+
+ private def shuffle(itemsMap: Map[Int, JoinPlan]) : Seq[JoinPlan] = {
+ util.Random.shuffle(itemsMap.values).toSeq
+ }
+}
+
+case class Population(conf: SQLConf, chromos: Seq[Chromosome]) extends Logging
{
+
+ def evolve: Population = {
+ // Sort chromos in the population first.
+ var tempChromos = chromos.sortWith((left, right) => left.fitness >
right.fitness)
Review comment:
Done.
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