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diff --git a/examples/src/main/python/streaming/direct_kafka_wordcount.py b/examples/src/main/python/streaming/direct_kafka_wordcount.py
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+++ b/examples/src/main/python/streaming/direct_kafka_wordcount.py
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+#
+# 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 UTF8 encoded, '\n' delimited text directly received from Kafka in every 2 seconds.
+ Usage: direct_kafka_wordcount.py <broker_list> <topic>
+
+ To run this on your local machine, you need to setup Kafka and create a producer first, see
+ http://kafka.apache.org/documentation.html#quickstart
+
+ and then run the example
+    `$ bin/spark-submit --jars external/kafka-assembly/target/scala-*/\
+      spark-streaming-kafka-assembly-*.jar \
+      examples/src/main/python/streaming/direct_kafka_wordcount.py \
+      localhost:9092 test`
+"""
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.streaming import StreamingContext
+from pyspark.streaming.kafka import KafkaUtils
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: direct_kafka_wordcount.py <broker_list> <topic>"
+        exit(-1)
+
+    sc = SparkContext(appName="PythonStreamingDirectKafkaWordCount")
+    ssc = StreamingContext(sc, 2)
+
+    brokers, topic = sys.argv[1:]
+    kvs = KafkaUtils.createDirectStream(ssc, [topic], {"metadata.broker.list": brokers})
+    lines = kvs.map(lambda x: x[1])
+    counts = lines.flatMap(lambda line: line.split(" ")) \
+        .map(lambda word: (word, 1)) \
+        .reduceByKey(lambda a, b: a+b)
+    counts.pprint()
+
+    ssc.start()
+    ssc.awaitTermination()