Github user ioana-delaney commented on a diff in the pull request:
https://github.com/apache/spark/pull/17546#discussion_r110318101
--- 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 --
@viirya The cost-based optimizer will find the best plan for the star-join.
The star filter is a heuristic within join enumeration to limit the join
sequences evaluated.
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