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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.
#
#!/usr/bin/env python
import re, sys
from operator import add
from pyspark import SparkContext
def computeContribs(urls, rank):
"""Calculates URL contributions to the rank of other URLs."""
num_urls = len(urls)
for url in urls: yield (url, rank / num_urls)
def parseNeighbors(urls):
"""Parses a urls pair string into urls pair."""
parts = re.split(r'\s+', urls)
return parts[0], parts[1]
if __name__ == "__main__":
if len(sys.argv) < 3:
print >> sys.stderr, "Usage: pagerank <master> <file> <number_of_iterations>"
exit(-1)
# Initialize the spark context.
sc = SparkContext(sys.argv[1], "PythonPageRank")
# Loads in input file. It should be in format of:
# URL neighbor URL
# URL neighbor URL
# URL neighbor URL
# ...
lines = sc.textFile(sys.argv[2], 1)
# Loads all URLs from input file and initialize their neighbors.
links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()
# Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one.
ranks = links.map(lambda (url, neighbors): (url, 1.0))
# Calculates and updates URL ranks continuously using PageRank algorithm.
for iteration in xrange(int(sys.argv[3])):
# Calculates URL contributions to the rank of other URLs.
contribs = links.join(ranks).flatMap(lambda (url, (urls, rank)):
computeContribs(urls, rank))
# Re-calculates URL ranks based on neighbor contributions.
ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15)
# Collects all URL ranks and dump them to console.
for (link, rank) in ranks.collect():
print "%s has rank: %s." % (link, rank)
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