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diff --git a/examples/src/main/python/streaming/recoverable_network_wordcount.py b/examples/src/main/python/streaming/recoverable_network_wordcount.py
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+++ b/examples/src/main/python/streaming/recoverable_network_wordcount.py
@@ -0,0 +1,80 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+ Counts words in text encoded with UTF8 received from the network every second.
+
+ Usage: recoverable_network_wordcount.py <hostname> <port> <checkpoint-directory> <output-file>
+   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
+   data. <checkpoint-directory> directory to HDFS-compatible file system which checkpoint data
+   <output-file> file to which the word counts will be appended
+
+ To run this on your local machine, you need to first run a Netcat server
+    `$ nc -lk 9999`
+
+ and then run the example
+    `$ bin/spark-submit examples/src/main/python/streaming/recoverable_network_wordcount.py \
+        localhost 9999 ~/checkpoint/ ~/out`
+
+ If the directory ~/checkpoint/ does not exist (e.g. running for the first time), it will create
+ a new StreamingContext (will print "Creating new context" to the console). Otherwise, if
+ checkpoint data exists in ~/checkpoint/, then it will create StreamingContext from
+ the checkpoint data.
+"""
+
+import os
+import sys
+
+from pyspark import SparkContext
+from pyspark.streaming import StreamingContext
+
+
+def createContext(host, port, outputPath):
+    # If you do not see this printed, that means the StreamingContext has been loaded
+    # from the new checkpoint
+    print "Creating new context"
+    if os.path.exists(outputPath):
+        os.remove(outputPath)
+    sc = SparkContext(appName="PythonStreamingRecoverableNetworkWordCount")
+    ssc = StreamingContext(sc, 1)
+
+    # Create a socket stream on target ip:port and count the
+    # words in input stream of \n delimited text (eg. generated by 'nc')
+    lines = ssc.socketTextStream(host, port)
+    words = lines.flatMap(lambda line: line.split(" "))
+    wordCounts = words.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x + y)
+
+    def echo(time, rdd):
+        counts = "Counts at time %s %s" % (time, rdd.collect())
+        print counts
+        print "Appending to " + os.path.abspath(outputPath)
+        with open(outputPath, 'a') as f:
+            f.write(counts + "\n")
+
+    wordCounts.foreachRDD(echo)
+    return ssc
+
+if __name__ == "__main__":
+    if len(sys.argv) != 5:
+        print >> sys.stderr, "Usage: recoverable_network_wordcount.py <hostname> <port> "\
+                             "<checkpoint-directory> <output-file>"
+        exit(-1)
+    host, port, checkpoint, output = sys.argv[1:]
+    ssc = StreamingContext.getOrCreate(checkpoint,
+                                       lambda: createContext(host, int(port), output))
+    ssc.start()
+    ssc.awaitTermination()