Github user ioana-delaney commented on a diff in the pull request:
https://github.com/apache/spark/pull/17546#discussion_r110987408
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala
---
@@ -327,3 +349,110 @@ 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))
--- End diff --
@wzhfy This is a very useful code simplification. Thank you!
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