Github user Sephiroth-Lin commented on a diff in the pull request:
https://github.com/apache/spark/pull/7417#discussion_r34754893
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala
---
@@ -34,7 +34,15 @@ case class CartesianProduct(left: SparkPlan, right:
SparkPlan) extends BinaryNod
val leftResults = left.execute().map(_.copy())
val rightResults = right.execute().map(_.copy())
- leftResults.cartesian(rightResults).mapPartitions { iter =>
+ val cartesianRdd = if (leftResults.partitions.size >
rightResults.partitions.size) {
+ rightResults.cartesian(leftResults).mapPartitions { iter =>
+ iter.map(tuple => (tuple._2, tuple._1))
+ }
+ } else {
+ leftResults.cartesian(rightResults)
+ }
+
+ cartesianRdd.mapPartitions { iter =>
val joinedRow = new JoinedRow
--- End diff --
@hvanhovell Yes, use sizeInBytes is better, but also have a problem, if
leftResults only have 1 record and this record size are big, and rightResults
have many records and these records total size are small, then at this scenario
will cause worse performance. The best way is we check the total records for
the partition, but now we can not get it.
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