Github user ioana-delaney commented on a diff in the pull request:
https://github.com/apache/spark/pull/15363#discussion_r106282966
--- 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 There are papers in the field that discuss options for reducing
query compilation times. Some that I found, mainly from the IBM research, are:
âMeasuring the Complexity of Join Enumeration in Query Optimizationâ â
K.Ono, et al., âEstimating Compilation Time of Query Optimizerâ â I.
Ilyas et al.
Regarding Sparkâs cost-based optimizer evolution, I would very interested
to help and contribute in this area. Star schema enhancements would be a
starting point.
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