Github user ConeyLiu commented on a diff in the pull request:
https://github.com/apache/spark/pull/19317#discussion_r144760426
--- Diff: core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
---
@@ -180,6 +180,56 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
* as in scala.TraversableOnce. The former operation is used for merging
values within a
* partition, and the latter is used for merging values between
partitions. To avoid memory
* allocation, both of these functions are allowed to modify and return
their first argument
+ * instead of creating a new U. This method is different from the
ordinary "aggregateByKey"
+ * method, it directly returns a map to the driver, rather than a rdd.
This will also perform
+ * the merging locally on each mapper before sending results to a
reducer, similarly to a
--- End diff --
`aggregateByKey(...).toLocalIterator` need a shuffle for `aggregateByKey`
and then collect the `RDD` to driver as a iterator. But `aggregateByKeyLocally`
seems like the `aggregateByKey`, while there isn't a shuffle. It calculates the
combines in each task and then collect all the `map` direcly to driver and do
the finally combines on driver.
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