Add streaming MapReduce example (#1251)

Add streaming MapReduce example.

examples/streaming/articles.txt

@@ -0,0 +1,8 @@
+New York City
+Berlin
+London
+Paris
+United States
+Germany
+France
+United Kingdom

examples/streaming/streaming.py

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+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