Fix style issues

Author: holdenk

core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala

@@ -707,7 +707,8 @@ object JavaPairRDD {
 
   private[spark]
   def cogroupResult2ToJava[K: ClassTag, V, W1, W2](
-      rdd: RDD[(K, (Iterator[V], Iterator[W1], Iterator[W2]))]): RDD[(K, (JIterator[V], JIterator[W1], JIterator[W2]))] = {
+      rdd: RDD[(K, (Iterator[V], Iterator[W1], Iterator[W2]))])
+      : RDD[(K, (JIterator[V], JIterator[W1], JIterator[W2]))] = {
     rddToPairRDDFunctions(rdd)
       .mapValues(x => (asJavaIterator(x._1), asJavaIterator(x._2), asJavaIterator(x._3)))
   }

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

@@ -456,7 +456,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)])
    * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
    * list of values for that key in `this` as well as `other`.
    */
-  def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Iterator[V], Iterator[W]))] = {
+  def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner)
+      : RDD[(K, (Iterator[V], Iterator[W]))]  = {
     if (partitioner.isInstanceOf[HashPartitioner] && getKeyClass().isArray) {
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
@@ -477,7 +478,9 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)])
     }
     val cg = new CoGroupedRDD[K](Seq(self, other1, other2), partitioner)
     cg.mapValues { case Seq(vs, w1s, w2s) =>
-      (vs.asInstanceOf[Seq[V]].iterator, w1s.asInstanceOf[Seq[W1]].iterator, w2s.asInstanceOf[Seq[W2]].iterator)
+      (vs.asInstanceOf[Seq[V]].iterator,
+       w1s.asInstanceOf[Seq[W1]].iterator,
+       w2s.asInstanceOf[Seq[W2]].iterator)
     }
   }
 

streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala

@@ -140,7 +140,8 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
    *                       the new DStream will generate RDDs); must be a multiple of this
    *                       DStream's batching interval
    */
-  def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration): DStream[(K, Iterator[V])] =
+  def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration)
+      : DStream[(K, Iterator[V])] =
   {
     groupByKeyAndWindow(windowDuration, slideDuration, defaultPartitioner())
   }