Document that groupByKey will OOM for large keys

This pull request is my own work and I license it under Spark's open-source license.

This contribution is an improvement to the documentation. I documented that the maximum number of values per key for groupByKey is limited by available RAM (see [Datablox][datablox link] and [the spark mailing list][list link]).

Just saying that better performance is available is not sufficient. Sometimes you need to do a group-by - your operation needs all the items available in order to complete. This warning explains the problem.

[datablox link]: http://databricks.gitbooks.io/databricks-spark-knowledge-base/content/best_practices/prefer_reducebykey_over_groupbykey.html
[list link]: http://apache-spark-user-list.1001560.n3.nabble.com/Understanding-RDD-GroupBy-OutOfMemory-Exceptions-tp11427p11466.html

Author: Eric Moyer <eric_moyer@yahoo.com>

Closes #3936 from RadixSeven/better-group-by-docs and squashes the following commits:

5b6f4e9 [Eric Moyer] groupByKey docs naming updates
238e81b [Eric Moyer] Doc that groupByKey will OOM for large keys

core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala

@@ -437,6 +437,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Note: This operation may be very expensive. If you are grouping in order to perform an
    * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
    * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
+   *
+   * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any
+   * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].
    */
   def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = {
     // groupByKey shouldn't use map side combine because map side combine does not
@@ -458,6 +461,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Note: This operation may be very expensive. If you are grouping in order to perform an
    * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
    * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
+   *
+   * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any
+   * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].
    */
   def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = {
     groupByKey(new HashPartitioner(numPartitions))