Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/10554#discussion_r48938992
--- Diff: core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
---
@@ -288,17 +288,18 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
* immediately to the master as a Map. This will also perform the
merging locally on each mapper
* before sending results to a reducer, similarly to a "combiner" in
MapReduce.
*/
- def reduceByKeyLocally(func: JFunction2[V, V, V]): java.util.Map[K, V] =
+ def reduceByKeyLocally(func: JFunction2[V, V, V]): JMap[K, V] =
mapAsSerializableJavaMap(rdd.reduceByKeyLocally(func))
/** Count the number of elements for each key, and return the result to
the master as a Map. */
- def countByKey(): java.util.Map[K, Long] =
mapAsSerializableJavaMap(rdd.countByKey())
+ def countByKey(): JMap[K, JLong] =
+ mapAsSerializableJavaMap(rdd.countByKey().mapValues(JLong.valueOf))
--- End diff --
This is what I was referring to in the last comment -- I realized that this
is how `RDD.countByValue` is implemented, so I was picking consistency. I don't
feel strongly about it.
For the other naming -- yeah I don't know what to be consistent with. For
the files I'm touching, I'll try to use `java.util.` and `jl.Long` consistently
even if it means updating some other bits of these files, and leave it at that.
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