yeshengm 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_r311793676
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala
##########
@@ -470,3 +397,451 @@ 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
+}
+
+/**
+ * This class implements the Genetic Edge Recombination algorithm. The
algorithm generates
+ * a new traveling path by choosing the edges in certain order from the
parents' edge table,
+ * where the edge table contains links of vertexes of the parents' traveling
paths.
+ *
+ * Here's an example. Suppose we have two traveling paths on a graph,
+ * I, [A B C D E F]
+ * II, [B D C A E F]
+ *
+ * The algorithm works as follows,
+ * 1. find the links of each vertex, then we have an 'Edge Table'
+ * A: B F C E
+ * B: A C D F
+ * C: B D A
+ * D: C E B
+ * E: D F A
+ * F: A E B
+ * 2. from one vertex, say A, choose one of it's neighbours as it's new
neighbour, say F, we have
+ * {A F}
+ * 3. then choose one from F's neighbours(note that vertex that has been
chosen should not
+ * be chosen again),
+ * {A F E}
+ * 4. go on,
+ * {A F E D}
+ * 5. go on,
+ * {A F E D B}
+ * 6. go on,
+ * {A F E D B C}
+ * Note if the procedure ends before all of vertexes been chosen, start from
another vertex that
+ * has not been chosen, and go on the procedure.
+ *
+ * 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) : Map[JoinPlan,
Seq[JoinPlan]] = {
+ val fatherTable = father.basicPlans.map(g => g ->
findNeighbours(father.basicPlans, g)).toMap
+ val motherTable = mother.basicPlans.map(g => g ->
findNeighbours(mother.basicPlans, g)).toMap
+
+ fatherTable.map(entry => entry._1 -> (entry._2 ++ motherTable(entry._1)))
+ }
+
+ def findNeighbours(genes: Seq[JoinPlan], g: JoinPlan) : Seq[JoinPlan] = {
Review comment:
An elegant approach is to calculate `(i-1+seq.length) % seq.length`/`(i+1) %
seq.length`. No need for a single `if`.
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