Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/1380#discussion_r14851298
--- Diff: core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
---
@@ -353,9 +353,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
* Group the values for each key in the RDD into a single sequence.
Allows controlling the
* partitioning of the resulting key-value pair RDD by passing a
Partitioner.
*
- * Note: If you are grouping in order to perform an aggregation (such as
a sum or average) over
- * each key, using [[PairRDDFunctions.reduceByKey]] or
[[PairRDDFunctions.combineByKey]]
- * will provide much better performance.
+ * Note: This operation may be very expensive. If you are grouping in
order to perform an
--- End diff --
It might be good to mention `aggregateByKey` first actually (and maybe
remove combineByKey). I think that `reduceByKey` and `aggregateByKey` cover
almost every use case.
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