Github user wzhfy commented on a diff in the pull request: https://github.com/apache/spark/pull/17240#discussion_r105815087 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala --- @@ -204,63 +206,37 @@ object JoinReorderDP extends PredicateHelper { oneJoinPlan: JoinPlan, otherJoinPlan: JoinPlan, conf: CatalystConf, - conditions: Set[Expression], - topOutput: AttributeSet): JoinPlan = { + conditions: Set[Expression]): Option[JoinPlan] = { val onePlan = oneJoinPlan.plan val otherPlan = otherJoinPlan.plan - // Now both onePlan and otherPlan become intermediate joins, so the cost of the - // new join should also include their own cardinalities and sizes. - val newCost = if (isCartesianProduct(onePlan) || isCartesianProduct(otherPlan)) { - // We consider cartesian product very expensive, thus set a very large cost for it. - // This enables to plan all the cartesian products at the end, because having a cartesian - // product as an intermediate join will significantly increase a plan's cost, making it - // impossible to be selected as the best plan for the items, unless there's no other choice. - Cost( - rows = BigInt(Long.MaxValue) * BigInt(Long.MaxValue), - size = BigInt(Long.MaxValue) * BigInt(Long.MaxValue)) - } else { - val onePlanStats = onePlan.stats(conf) - val otherPlanStats = otherPlan.stats(conf) - Cost( - rows = oneJoinPlan.cost.rows + onePlanStats.rowCount.get + - otherJoinPlan.cost.rows + otherPlanStats.rowCount.get, - size = oneJoinPlan.cost.size + onePlanStats.sizeInBytes + - otherJoinPlan.cost.size + otherPlanStats.sizeInBytes) - } - - // Put the deeper side on the left, tend to build a left-deep tree. - val (left, right) = if (oneJoinPlan.itemIds.size >= otherJoinPlan.itemIds.size) { - (onePlan, otherPlan) - } else { - (otherPlan, onePlan) - } val joinConds = conditions .filterNot(l => canEvaluate(l, onePlan)) .filterNot(r => canEvaluate(r, otherPlan)) .filter(e => e.references.subsetOf(onePlan.outputSet ++ otherPlan.outputSet)) - // We use inner join whether join condition is empty or not. Since cross join is - // equivalent to inner join without condition. - val newJoin = Join(left, right, Inner, joinConds.reduceOption(And)) - val collectedJoinConds = joinConds ++ oneJoinPlan.joinConds ++ otherJoinPlan.joinConds - val remainingConds = conditions -- collectedJoinConds - val neededAttr = AttributeSet(remainingConds.flatMap(_.references)) ++ topOutput - val neededFromNewJoin = newJoin.outputSet.filter(neededAttr.contains) - val newPlan = - if ((newJoin.outputSet -- neededFromNewJoin).nonEmpty) { - Project(neededFromNewJoin.toSeq, newJoin) + if (joinConds.isEmpty) { + // Cartesian product is very expensive, so we exclude them from candidate plans. + // This also helps us to reduce the search space. Unjoinable items will be put at the end + // of the plan when the reordering phase finishes. + None + } else { + // Put the deeper side on the left, tend to build a left-deep tree. + val (left, right) = if (oneJoinPlan.itemIds.size >= otherJoinPlan.itemIds.size) { + (onePlan, otherPlan) } else { - newJoin + (otherPlan, onePlan) } + val newJoin = Join(left, right, Inner, joinConds.reduceOption(And)) + val itemIds = oneJoinPlan.itemIds.union(otherJoinPlan.itemIds) - val itemIds = oneJoinPlan.itemIds.union(otherJoinPlan.itemIds) - JoinPlan(itemIds, newPlan, collectedJoinConds, newCost) - } + // Now onePlan/otherPlan becomes an intermediate join (if it's a non-leaf item), + // so the cost of the new join should also include their own cardinalities. + val newCost = oneJoinPlan.cost + otherJoinPlan.cost + + (if (oneJoinPlan.itemIds.size > 1) onePlan.stats(conf).rowCount.get else 0) + + (if (otherJoinPlan.itemIds.size > 1) otherPlan.stats(conf).rowCount.get else 0) --- End diff -- Filtering factor is considered in `def stats`.
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