Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/6454#discussion_r31267472
--- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala ---
@@ -580,7 +580,18 @@ abstract class RDD[T: ClassTag](
* elements (a, b) where a is in `this` and b is in `other`.
*/
def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope {
- new CartesianRDD(sc, this, other)
+ val numPartitions = {
+ val numExecutors = System.getProperty("spark.executor.instances")
+
+ if (numExecutors != null) {
+ numExecutors.toInt
+ } else {
+ sc.defaultMinPartitions
+ }
+ }
+ val coalesced = coalesce(numPartitions, shuffle = true)
+ val coalescedOther = other.coalesce(numPartitions, shuffle = true)
+ new CartesianRDD(sc, coalesced, coalescedOther)
--- End diff --
Sorry I think I was vague when I originally brought up `coalesce`. I had
intended for it to only be a suggestion to the user, included in the docs, as
you note. I think its really hard to guess how to do the coalescing. In fact,
I just realized there is a downside to coalescing that I hadn't thought of
earlier -- the more you coalesce, then it becomes more likely that you *cannot*
store all of `rdd2.iterator` in memory.
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