[SPARK-20233] [SQL] Apply star-join filter heuristics to dynamic programming join enumeration

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala

@@ -54,8 +54,6 @@ case class CostBasedJoinReorder(conf: SQLConf) extends Rule[LogicalPlan] with Pr
 
   private def reorder(plan: LogicalPlan, output: Seq[Attribute]): LogicalPlan = {
     val (items, conditions) = extractInnerJoins(plan)
-    // TODO: Compute the set of star-joins and use them in the join enumeration
-    // algorithm to prune un-optimal plan choices.
     val result =
       // Do reordering if the number of items is appropriate and join conditions exist.
       // We also need to check if costs of all items can be evaluated.
@@ -150,12 +148,15 @@ object JoinReorderDP extends PredicateHelper with Logging {
       case (item, id) => Set(id) -> JoinPlan(Set(id), item, Set(), Cost(0, 0))
     }.toMap)
 
+    // Build filters from the join graph to be used by the search algorithm.
+    val filters = JoinReorderDPFilters.buildJoinGraphInfo(conf, items, conditions, itemIndex)
+
     // Build plans for next levels until the last level has only one plan. This plan contains
     // all items that can be joined, so there's no need to continue.
     val topOutputSet = AttributeSet(output)
-    while (foundPlans.size < items.length && foundPlans.last.size > 1) {
+    while (foundPlans.size < items.length) {
       // Build plans for the next level.
-      foundPlans += searchLevel(foundPlans, conf, conditions, topOutputSet)
+      foundPlans += searchLevel(foundPlans, conf, conditions, topOutputSet, filters)
     }
 
     val durationInMs = (System.nanoTime() - startTime) / (1000 * 1000)
@@ -179,7 +180,8 @@ object JoinReorderDP extends PredicateHelper with Logging {
       existingLevels: Seq[JoinPlanMap],
       conf: SQLConf,
       conditions: Set[Expression],
-      topOutput: AttributeSet): JoinPlanMap = {
+      topOutput: AttributeSet,
+      filters: Option[JoinGraphInfo]): JoinPlanMap = {
 
     val nextLevel = mutable.Map.empty[Set[Int], JoinPlan]
     var k = 0
@@ -200,7 +202,7 @@ object JoinReorderDP extends PredicateHelper with Logging {
         }
 
         otherSideCandidates.foreach { otherSidePlan =>
-          buildJoin(oneSidePlan, otherSidePlan, conf, conditions, topOutput) match {
+          buildJoin(oneSidePlan, otherSidePlan, conf, conditions, topOutput, filters) match {
             case Some(newJoinPlan) =>
               // Check if it's the first plan for the item set, or it's a better plan than
               // the existing one due to lower cost.
@@ -218,28 +220,48 @@ object JoinReorderDP extends PredicateHelper with Logging {
   }
 
   /**
-   * Builds a new JoinPlan when both conditions hold:
+   * Builds a new JoinPlan if the following conditions hold:
    * - the sets of items contained in left and right sides do not overlap.
    * - there exists at least one join condition involving references from both sides.
+   * - if star-join filter is enabled, allow the following combinations:
+   *         1) (oneJoinPlan U otherJoinPlan) is a subset of star-join
+   *         2) star-join is a subset of (oneJoinPlan U otherJoinPlan)
+   *         3) (oneJoinPlan U otherJoinPlan) is a subset of non star-join
+   *
    * @param oneJoinPlan One side JoinPlan for building a new JoinPlan.
    * @param otherJoinPlan The other side JoinPlan for building a new join node.
    * @param conf SQLConf for statistics computation.
    * @param conditions The overall set of join conditions.
    * @param topOutput The output attributes of the final plan.
+   * @param filters Join graph info to be used as filters by the search algorithm.
    * @return Builds and returns a new JoinPlan if both conditions hold. Otherwise, returns None.
    */
   private def buildJoin(
       oneJoinPlan: JoinPlan,
       otherJoinPlan: JoinPlan,
       conf: SQLConf,
       conditions: Set[Expression],
-      topOutput: AttributeSet): Option[JoinPlan] = {
+      topOutput: AttributeSet,
+      filters: Option[JoinGraphInfo]): Option[JoinPlan] = {
 
     if (oneJoinPlan.itemIds.intersect(otherJoinPlan.itemIds).nonEmpty) {
       // Should not join two overlapping item sets.
       return None
     }
 
+    if (filters.isDefined) {
+      // Apply star-join filter, which ensures that tables in a star schema relationship
+      // are planned together. The star-filter will eliminate joins among star and non-star
+      // tables until the star joins are built. The following combinations are allowed:
+      // 1. (oneJoinPlan U otherJoinPlan) is a subset of star-join
+      // 2. star-join is a subset of (oneJoinPlan U otherJoinPlan)
+      // 3. (oneJoinPlan U otherJoinPlan) is a subset of non star-join
+      val isValidJoinCombination =
+        JoinReorderDPFilters.starJoinFilter(oneJoinPlan.itemIds, otherJoinPlan.itemIds,
+          filters.get)
+      if (!isValidJoinCombination) return None
+    }
+
     val onePlan = oneJoinPlan.plan
     val otherPlan = otherJoinPlan.plan
     val joinConds = conditions
@@ -327,3 +349,109 @@ 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.
+ */
+object JoinReorderDPFilters 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(
+      conf: SQLConf,
+      items: Seq[LogicalPlan],
+      conditions: Set[Expression],
+      itemIndex: Seq[(LogicalPlan, Int)]): Option[JoinGraphInfo] = {
+
+    if (conf.joinReorderDPStarFilter) {
+      // 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 itemMap = itemIndex.toMap
+        Some(JoinGraphInfo(starJoin.map(itemMap).toSet, nonStarJoin.map(itemMap).toSet))
+      } else {
+        // Nothing interesting to return.
+        None
+      }
+    } else {
+      // Star schema filter is not enabled.
+      None
+    }
+  }
+
+  /**
+   * Applies the star-join filter that eliminates join combinations among star
+   * and non-star tables until the star join is built.
+   *
+   * Given the oneSideJoinPlan/otherSideJoinPlan, which represent all the plan
+   * permutations generated by the DP join enumeration, and the star/non-star plans,
+   * the following plan combinations are allowed:
+   * 1. (oneSideJoinPlan U otherSideJoinPlan) is a subset of star-join
+   * 2. star-join is a subset of (oneSideJoinPlan U otherSideJoinPlan)
+   * 3. (oneSideJoinPlan U otherSideJoinPlan) 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 }
+   *
+   * @param oneSideJoinPlan One side of the join represented as a set of plan ids.
+   * @param otherSideJoinPlan The other side of the join represented as a set of plan ids.
+   * @param filters Star and non-star plans represented as sets of plan ids
+   */
+  def starJoinFilter(
+      oneSideJoinPlan: Set[Int],
+      otherSideJoinPlan: Set[Int],
+      filters: JoinGraphInfo) : Boolean = {
+    val starJoins = filters.starJoins
+    val nonStarJoins = filters.nonStarJoins
+    val join = oneSideJoinPlan.union(otherSideJoinPlan)
+
+    // Disjoint sets
+    oneSideJoinPlan.intersect(otherSideJoinPlan).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) ||
+        // Join is a subset of non-star
+        join.subsetOf(nonStarJoins))
+  }
+}
+
+/**
+ * Helper class that keeps information about the join graph as sets of item/plan ids.
+ * It currently stores the star/non-star plans. It can be
+ * extended with the set of connected/unconnected plans.
+ */
+case class JoinGraphInfo (starJoins: Set[Int], nonStarJoins: Set[Int])

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/StarSchemaDetection.scala

@@ -76,7 +76,7 @@ case class StarSchemaDetection(conf: SQLConf) extends PredicateHelper {
 
     val emptyStarJoinPlan = Seq.empty[LogicalPlan]
 
-    if (!conf.starSchemaDetection || input.size < 2) {
+    if (input.size < 2) {
       emptyStarJoinPlan
     } else {
       // Find if the input plans are eligible for star join detection.

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -736,6 +736,12 @@ object SQLConf {
       .checkValue(weight => weight >= 0 && weight <= 1, "The weight value must be in [0, 1].")
       .createWithDefault(0.7)
 
+  val JOIN_REORDER_DP_STAR_FILTER =
+    buildConf("spark.sql.cbo.joinReorder.dp.star.filter")
+      .doc("Applies star-join filter heuristics to cost based join enumeration.")
+      .booleanConf
+      .createWithDefault(false)
+
   val STARSCHEMA_DETECTION = buildConf("spark.sql.cbo.starSchemaDetection")
     .doc("When true, it enables join reordering based on star schema detection. ")
     .booleanConf
@@ -1011,6 +1017,8 @@ class SQLConf extends Serializable with Logging {
 
   def joinReorderCardWeight: Double = getConf(SQLConf.JOIN_REORDER_CARD_WEIGHT)
 
+  def joinReorderDPStarFilter: Boolean = getConf(SQLConf.JOIN_REORDER_DP_STAR_FILTER)
+
   def windowExecBufferSpillThreshold: Int = getConf(WINDOW_EXEC_BUFFER_SPILL_THRESHOLD)
 
   def sortMergeJoinExecBufferSpillThreshold: Int =