From c51e047252fbcf602886d5b555d8aace22158fb2 Mon Sep 17 00:00:00 2001
From: Jimmy-Newtron <enrico.mano@protonmail.com>
Date: Thu, 8 Dec 2016 18:36:30 +0100
Subject: [PATCH] Update PairDStreamFunctions.scala

Added two methods that are available into the PairRDDFunctions.scala
---
 .../spark/streaming/dstream/PairDStreamFunctions.scala | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
index f38c1e7996595..88d0421316009 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
@@ -45,6 +45,16 @@ class PairDStreamFunctions[K, V](self: DStream[(K, V)])
     new HashPartitioner(numPartitions)
   }
 
+  /**
+   * Return a DStream with the keys of each tuple.
+   */
+  def keys: DStream[K] = self.map(_._1)
+
+  /**
+   * Return a DStream with the values of each tuple.
+   */
+  def values: DStream[V] = self.map(_._2)
+      
   /**
    * Return a new DStream by applying `groupByKey` to each RDD. Hash partitioning is used to
    * generate the RDDs with Spark's default number of partitions.