Github user viirya commented on a diff in the pull request: https://github.com/apache/spark/pull/17546#discussion_r110300420 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala --- @@ -327,3 +345,104 @@ object JoinReorderDP extends PredicateHelper with Logging { 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. + */ +case class JoinReorderDPFilters(conf: SQLConf) extends PredicateHelper { + /** + * 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( + items: Seq[LogicalPlan], + conditions: Set[Expression], + planIndex: Seq[(LogicalPlan, Int)]): Option[JoinGraphInfo] = { + + // Compute the tables in a star-schema relationship. + val starJoin = StarSchemaDetection(conf).findStarJoins(items, conditions.toSeq) + val nonStarJoin = items.filterNot(starJoin.contains(_)) + + if (starJoin.nonEmpty && nonStarJoin.nonEmpty) { + val (starInt, nonStarInt) = planIndex.collect { + case (p, i) if starJoin.contains(p) => + (Some(i), None) + case (p, i) if nonStarJoin.contains(p) => + (None, Some(i)) + case _ => + (None, None) + }.unzip + Some(JoinGraphInfo(starInt.flatten.toSet, nonStarInt.flatten.toSet)) + } else { + // Nothing interesting to return. + None + } + } + + /** + * Applies star-join filter. + * + * Given the outer/inner and the star/non-star sets, + * the following plan combinations are allowed: + * 1) (outer U inner) is a subset of star-join + * 2) star-join is a subset of (outer U inner) + * 3) (outer U inner) 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 } + */ + def starJoinFilter( + outer: Set[Int], + inner: Set[Int], + filters: JoinGraphInfo) : Boolean = { + val starJoins = filters.starJoins + val nonStarJoins = filters.nonStarJoins + val join = outer.union(inner) + + // Disjoint sets + outer.intersect(inner).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) || --- End diff -- `ReorderJoin` will reorder the star join plans. Doesn't this cost-based join reorder rule breaks the order created by `ReorderJoin`? Here we only ask this rule doesn't try to reorder part of star join plans and non-star join plans, but it still can reorder the order among star join plans.
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