Github user ron8hu commented on a diff in the pull request:
https://github.com/apache/spark/pull/15363#discussion_r106271250
--- 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 --
@ioana-delaney Thanks for sharing your thought. This is helpful. As of
today, Spark's join reorder computation is still at its early stage. Your
comments above can serve as a guideline for future enhancement. Can you point
to a paper/article for your optimization idea? Thanks.
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