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From chenghao-intel <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-10484][SQL] Optimize the cartesian join...
Date Tue, 20 Oct 2015 01:29:12 GMT
Github user chenghao-intel commented on a diff in the pull request:

    https://github.com/apache/spark/pull/8652#discussion_r42446830
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
---
    @@ -268,6 +268,27 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan]
{
     
       object CartesianProduct extends Strategy {
         def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
    +      // Not like the equal-join, BroadcastNestedLoopJoin doesn't support condition
    +      // for cartesian join, as in cartesian join, probably, the records satisfy the
    +      // condition, but exists in another partition of the large table, so we may not
able
    +      // to eliminate the duplicates.
    +      case logical.Join(
    +        CanBroadcast(left), right, joinType @ (FullOuter | LeftOuter | RightOuter), None)
=>
    +        execution.joins.BroadcastNestedLoopJoin(
    +          planLater(left), planLater(right), joins.BuildLeft, joinType, None) :: Nil
    --- End diff --
    
    Yes, I noticed that also, however, without these changes, the cases I added will transform
into the operator `CartesianProduct` instead of `BroadcastNestedLoopJoin`, as the strategy
`BroadcastNestedLoopJoin` is the rule after `CartesianProduct`.
    
    Besides, explicitly providing the rules for the optimization, probably will be helpful
for people to understand how the logic behind.
    
    PS: The rule `BroadcastNestedLoopJoin` has to be the last gate, as it's supposed to handle
all kinds of joins.


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