mirror of
https://github.com/vale981/ray
synced 2025-03-06 18:41:40 -05:00
104 lines
3.6 KiB
Python
104 lines
3.6 KiB
Python
import argparse
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from collections import Counter, defaultdict
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import heapq
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import numpy as np
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import os
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import ray
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import wikipedia
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parser = argparse.ArgumentParser()
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parser.add_argument("--num-mappers",
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help="number of mapper actors used", default=3, type=int)
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parser.add_argument("--num-reducers",
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help="number of reducer actors used", default=4, type=int)
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@ray.remote
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class Mapper(object):
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def __init__(self, title_stream):
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self.title_stream = title_stream
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self.num_articles_processed = 0
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self.articles = []
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self.word_counts = []
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def get_new_article(self):
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# Get the next wikipedia article.
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article = wikipedia.page(self.title_stream.next()).content
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# Count the words and store the result.
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self.word_counts.append(Counter(article.split(" ")))
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self.num_articles_processed += 1
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def get_range(self, article_index, keys):
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# Process more articles if this Mapper hasn't processed enough yet.
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while self.num_articles_processed < article_index + 1:
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self.get_new_article()
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# Return the word counts from within a given character range.
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return [(k, v) for k, v in self.word_counts[article_index].items()
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if len(k) >= 1 and k[0] >= keys[0] and k[0] <= keys[1]]
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@ray.remote
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class Reducer(object):
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def __init__(self, keys, *mappers):
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self.mappers = mappers
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self.keys = keys
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def next_reduce_result(self, article_index):
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word_count_sum = defaultdict(lambda: 0)
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# Get the word counts for this Reducer's keys from all of the Mappers
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# and aggregate the results.
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count_ids = [mapper.get_range.remote(article_index, self.keys)
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for mapper in self.mappers]
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# TODO(rkn): We should process these out of order using ray.wait.
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for count_id in count_ids:
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for k, v in ray.get(count_id):
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word_count_sum[k] += v
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return word_count_sum
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class Stream(object):
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def __init__(self, elements):
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self.elements = elements
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def next(self):
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i = np.random.randint(0, len(self.elements))
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return self.elements[i]
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init()
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# Create one streaming source of articles per mapper.
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directory = os.path.dirname(os.path.realpath(__file__))
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streams = []
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for _ in range(args.num_mappers):
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with open(os.path.join(directory, "articles.txt")) as f:
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streams.append(Stream([line.strip() for line in f.readlines()]))
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# Partition the keys among the reducers.
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chunks = np.array_split([chr(i) for i in range(ord("a"), ord("z") + 1)],
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args.num_reducers)
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keys = [[chunk[0], chunk[-1]] for chunk in chunks]
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# Create a number of mappers.
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mappers = [Mapper.remote(stream) for stream in streams]
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# Create a number of reduces, each responsible for a different range of
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# keys. This gives each Reducer actor a handle to each Mapper actor.
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reducers = [Reducer.remote(key, *mappers) for key in keys]
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article_index = 0
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while True:
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print("article index = {}".format(article_index))
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wordcounts = {}
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counts = ray.get([reducer.next_reduce_result.remote(article_index)
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for reducer in reducers])
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for count in counts:
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wordcounts.update(count)
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most_frequent_words = heapq.nlargest(10, wordcounts,
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key=wordcounts.get)
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for word in most_frequent_words:
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print(" ", word, wordcounts[word])
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article_index += 1
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