diff --git a/doc/source/example-streaming.rst b/doc/source/example-streaming.rst
new file mode 100644
index 000000000..a066f2188
--- /dev/null
+++ b/doc/source/example-streaming.rst
@@ -0,0 +1,151 @@
+Streaming MapReduce
+===================
+
+This document walks through how to implement a simple streaming application
+using Ray's actor capabilities. It implements a streaming MapReduce which
+computes word counts on wikipedia articles.
+
+You can view the `code for this example`_.
+
+.. _`code for this example`: https://github.com/ray-project/ray/tree/master/examples/streaming
+
+To run the example, you need to install the dependencies
+
+.. code-block:: bash
+
+  pip install wikipedia
+
+
+and then execute the script as follows:
+
+.. code-block:: bash
+
+  python ray/examples/streaming/streaming.py
+
+For each round of articles read, the script will output
+the top 10 words in these articles together with their word count:
+
+.. code-block:: text
+
+  article index = 0
+     the 2866
+     of 1688
+     and 1448
+     in 1101
+     to 593
+     a 553
+     is 509
+     as 325
+     are 284
+     by 261
+  article index = 1
+     the 3597
+     of 1971
+     and 1735
+     in 1429
+     to 670
+     a 623
+     is 578
+     as 401
+     by 293
+     for 285
+  article index = 2
+     the 3910
+     of 2123
+     and 1890
+     in 1468
+     to 658
+     a 653
+     is 488
+     as 364
+     by 362
+     for 297
+  article index = 3
+     the 2962
+     of 1667
+     and 1472
+     in 1220
+     a 546
+     to 538
+     is 516
+     as 307
+     by 253
+     for 243
+  article index = 4
+     the 3523
+     of 1866
+     and 1690
+     in 1475
+     to 645
+     a 583
+     is 572
+     as 352
+     by 318
+     for 306
+  ...
+
+Note that this examples uses `distributed actor handles`_, which are still
+considered experimental.
+
+.. _`distributed actor handles`: http://ray.readthedocs.io/en/latest/actors.html
+
+There is a ``Mapper`` actor, which has a method ``get_range`` used to retrieve
+word counts for words in a certain range:
+
+.. code-block:: python
+
+  @ray.remote
+  class Mapper(object):
+
+      def __init__(self, title_stream):
+          # Constructor, the title stream parameter is a stream of wikipedia
+          # article titles that will be read by this mapper
+
+      def get_range(self, article_index, keys):
+          # Return counts of all the words with first
+          # letter between keys[0] and keys[1] in the
+          # articles that haven't been read yet with index
+          # up to article_index
+
+The ``Reducer`` actor holds a list of mappers, calls ``get_range`` on them
+and accumulates the results.
+
+.. code-block:: python
+
+  @ray.remote
+  class Reducer(object):
+
+      def __init__(self, keys, *mappers):
+           # Constructor for a reducer that gets input from the list of mappers
+           # in the argument and accumulates word counts for words with first
+           # letter between keys[0] and keys[1]
+
+      def next_reduce_result(self, article_index):
+           # Get articles up to article_index that haven't been read yet,
+           # accumulate the word counts and return them
+
+On the driver, we then create a number of mappers and reducers and run the
+streaming MapReduce:
+
+.. code-block:: python
+
+  streams = # Create list of num_mappers streams
+  keys = # Partition the keys among the reducers.
+
+  # Create a number of mappers.
+  mappers = [Mapper.remote(stream) for stream in streams]
+
+  # Create a number of reduces, each responsible for a different range of keys.
+  # This gives each Reducer actor a handle to each Mapper actor.
+  reducers = [Reducer.remote(key, *mappers) for key in keys]
+
+  article_index = 0
+  while True:
+      counts = ray.get([reducer.next_reduce_result.remote(article_index)
+                        for reducer in reducers])
+      article_index += 1
+
+The actual example reads a list of articles and creates a stream object which
+produces an infinite stream of articles from the list. This is a toy example
+meant to illustrate the idea. In practice we would produce a stream of
+non-repeating items for each mapper.
diff --git a/doc/source/index.rst b/doc/source/index.rst
index 402b0ccef..80c351044 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -59,6 +59,7 @@ Example Program
    example-lbfgs.rst
    example-evolution-strategies.rst
    example-cython.rst
+   example-streaming.rst
    using-ray-with-tensorflow.rst
 
 .. toctree::
diff --git a/examples/streaming/articles.txt b/examples/streaming/articles.txt
new file mode 100644
index 000000000..0bb455fd3
--- /dev/null
+++ b/examples/streaming/articles.txt
@@ -0,0 +1,8 @@
+New York City
+Berlin
+London
+Paris
+United States
+Germany
+France
+United Kingdom
diff --git a/examples/streaming/streaming.py b/examples/streaming/streaming.py
new file mode 100644
index 000000000..92184ef9d
--- /dev/null
+++ b/examples/streaming/streaming.py
@@ -0,0 +1,104 @@
+import argparse
+from collections import Counter, defaultdict
+import heapq
+import numpy as np
+import os
+import ray
+import wikipedia
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--num-mappers",
+                    help="number of mapper actors used", default=3)
+parser.add_argument("--num-reducers",
+                    help="number of reducer actors used", default=4)
+
+
+@ray.remote
+class Mapper(object):
+    def __init__(self, title_stream):
+        self.title_stream = title_stream
+        self.num_articles_processed = 0
+        self.articles = []
+        self.word_counts = []
+
+    def get_new_article(self):
+        # Get the next wikipedia article.
+        article = wikipedia.page(self.title_stream.next()).content
+        # Count the words and store the result.
+        self.word_counts.append(Counter(article.split(" ")))
+        self.num_articles_processed += 1
+
+    def get_range(self, article_index, keys):
+        # Process more articles if this Mapper hasn't processed enough yet.
+        while self.num_articles_processed < article_index + 1:
+            self.get_new_article()
+        # Return the word counts from within a given character range.
+        return [(k, v) for k, v in self.word_counts[article_index].items()
+                if len(k) >= 1 and k[0] >= keys[0] and k[0] <= keys[1]]
+
+
+@ray.remote
+class Reducer(object):
+    def __init__(self, keys, *mappers):
+        self.mappers = mappers
+        self.keys = keys
+
+    def next_reduce_result(self, article_index):
+        word_count_sum = defaultdict(lambda: 0)
+        # Get the word counts for this Reducer's keys from all of the Mappers
+        # and aggregate the results.
+        count_ids = [mapper.get_range.remote(article_index, self.keys)
+                     for mapper in self.mappers]
+        # TODO(rkn): We should process these out of order using ray.wait.
+        for count_id in count_ids:
+            for k, v in ray.get(count_id):
+                word_count_sum[k] += v
+        return word_count_sum
+
+
+class Stream(object):
+    def __init__(self, elements):
+        self.elements = elements
+
+    def next(self):
+        i = np.random.randint(0, len(self.elements))
+        return self.elements[i]
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    ray.init()
+
+    # Create one streaming source of articles per mapper.
+    directory = os.path.dirname(os.path.realpath(__file__))
+    streams = []
+    for _ in range(args.num_mappers):
+        with open(os.path.join(directory, "articles.txt")) as f:
+            streams.append(Stream([line.strip() for line in f.readlines()]))
+
+    # Partition the keys among the reducers.
+    chunks = np.array_split([chr(i) for i in range(ord("a"), ord("z") + 1)],
+                            args.num_reducers)
+    keys = [[chunk[0], chunk[-1]] for chunk in chunks]
+
+    # Create a number of mappers.
+    mappers = [Mapper.remote(stream) for stream in streams]
+
+    # Create a number of reduces, each responsible for a different range of
+    # keys. This gives each Reducer actor a handle to each Mapper actor.
+    reducers = [Reducer.remote(key, *mappers) for key in keys]
+
+    article_index = 0
+    while True:
+        print("article index = {}".format(article_index))
+        wordcounts = dict()
+        counts = ray.get([reducer.next_reduce_result.remote(article_index)
+                          for reducer in reducers])
+        for count in counts:
+            wordcounts.update(count)
+        most_frequent_words = heapq.nlargest(10, wordcounts,
+                                             key=wordcounts.get)
+        for word in most_frequent_words:
+            print("  ", word, wordcounts[word])
+        article_index += 1