[SPARK-1766] sorted functions to meet pedantic requirements

Pedantry is underrated

Author: Chris Cope <ccope@resilientscience.com>

Closes #1859 from copester/master and squashes the following commits:

0fb4499 [Chris Cope] [SPARK-1766] sorted functions to meet pedantic requirements

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

@@ -237,6 +237,25 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     combineByKey[V]((v: V) => v, func, func, partitioner)
   }
 
+  /**
+   * Merge the values for each key using an associative reduce function. This will also perform
+   * the merging locally on each mapper before sending results to a reducer, similarly to a
+   * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
+   */
+  def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = {
+    reduceByKey(new HashPartitioner(numPartitions), func)
+  }
+
+  /**
+   * Merge the values for each key using an associative reduce function. This will also perform
+   * the merging locally on each mapper before sending results to a reducer, similarly to a
+   * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/
+   * parallelism level.
+   */
+  def reduceByKey(func: (V, V) => V): RDD[(K, V)] = {
+    reduceByKey(defaultPartitioner(self), func)
+  }
+
   /**
    * Merge the values for each key using an associative reduce function, but return the results
    * immediately to the master as a Map. This will also perform the merging locally on each mapper
@@ -374,15 +393,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     countApproxDistinctByKey(relativeSD, defaultPartitioner(self))
   }
 
-  /**
-   * Merge the values for each key using an associative reduce function. This will also perform
-   * the merging locally on each mapper before sending results to a reducer, similarly to a
-   * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
-   */
-  def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = {
-    reduceByKey(new HashPartitioner(numPartitions), func)
-  }
-
   /**
    * 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.
@@ -482,16 +492,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self))
   }
 
-  /**
-   * Merge the values for each key using an associative reduce function. This will also perform
-   * the merging locally on each mapper before sending results to a reducer, similarly to a
-   * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/
-   * parallelism level.
-   */
-  def reduceByKey(func: (V, V) => V): RDD[(K, V)] = {
-    reduceByKey(defaultPartitioner(self), func)
-  }
-
   /**
    * Group the values for each key in the RDD into a single sequence. Hash-partitions the
    * resulting RDD with the existing partitioner/parallelism level.