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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