Github user chenghao-intel commented on a diff in the pull request:

    https://github.com/apache/spark/pull/7417#discussion_r41467358
  
    --- Diff: 
sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala ---
    @@ -274,12 +275,30 @@ private[sql] abstract class SparkStrategies extends 
QueryPlanner[SparkPlan] {
       }
     
       object CartesianProduct extends Strategy {
    +    def getSmallSide(left: LogicalPlan, right: LogicalPlan): BuildSide = {
    +      if (right.statistics.sizeInBytes < left.statistics.sizeInBytes) {
    +        joins.BuildRight
    +      } else {
    +        joins.BuildLeft
    +      }
    +    }
    +
         def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
    +      // If plan can broadcast we use BroadcastNestedLoopJoin, as we know 
for inner join with true
    +      // condition is same as Cartesian.
    +      case logical.Join(CanBroadcast(left), right, joinType, condition) =>
    +        execution.joins.BroadcastNestedLoopJoin(
    +          planLater(left), planLater(right), joins.BuildLeft, joinType, 
condition) :: Nil
    +      case logical.Join(left, CanBroadcast(right), joinType, condition) =>
    +        execution.joins.BroadcastNestedLoopJoin(
    +          planLater(left), planLater(right), joins.BuildRight, joinType, 
condition) :: Nil
           case logical.Join(left, right, _, None) =>
    -        execution.joins.CartesianProduct(planLater(left), 
planLater(right)) :: Nil
    +        execution.joins.CartesianProduct(planLater(left), planLater(right),
    +          getSmallSide(left, right)) :: Nil
           case logical.Join(left, right, Inner, Some(condition)) =>
             execution.Filter(condition,
    -          execution.joins.CartesianProduct(planLater(left), 
planLater(right))) :: Nil
    +          execution.joins.CartesianProduct(planLater(left), 
planLater(right),
    +            getSmallSide(left, right))) :: Nil
    --- End diff --
    
    Actually I am a little concern about the side switch based on the 
statistic, as I commented previously. And also as @cloud-fan comment out:
    > ```scala
    for (x <- rdd1.iterator(currSplit.s1, context);
         y <- rdd2.iterator(currSplit.s2, context)) yield (x, y)
    ```
    
    What we actually cared is the `average amount of records` in each partition 
in both sides, and, I don't think we can say, the one take the bigger file size 
in statistics will also with more `average amount of records` in its 
partition(most likely the average amount of records in each partition should be 
same).
    
    Probably we'd better add more statistic info says partition number logical 
plan or average file size of each partition, and in order not to make confusing 
for the further improvement, I think we'd better remove this optimization rule 
for cartesian join. And that's why I didn't do that at #8652
    
    What do you think?


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