Github user ioana-delaney commented on a diff in the pull request:
https://github.com/apache/spark/pull/15363#discussion_r106088842
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/CostBasedJoinReorder.scala
---
@@ -51,6 +51,11 @@ case class CostBasedJoinReorder(conf: CatalystConf)
extends Rule[LogicalPlan] wi
def reorder(plan: LogicalPlan, output: AttributeSet): LogicalPlan = {
val (items, conditions) = extractInnerJoins(plan)
+ // Find the star schema joins. Currently, it returns the star join
with the largest
+ // fact table. In the future, it can return more than one star join
(e.g. F1-D1-D2
+ // and F2-D3-D4).
+ val starJoinPlans = StarSchemaDetection(conf).findStarJoins(items,
conditions.toSeq)
--- End diff --
@ron8hu We already ran TPC-DS with star schema and the results are
documented in the design doc. I don't think there is a question about its
value.
I am familiar with Pat Selinger's paper since I've been working in the IBM
DB2 optimizer for several years. What Zhenhua and I are discussing here is how
to integrate the star join plans with his new DP planning. There are no
competing planning algorithm that needs to be tested.
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