#
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# 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
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"""
This is an example implementation of PageRank. For more conventional use,
Please refer to PageRank implementation provided by graphx
"""

import re
import 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 <file> <iterations>"
        exit(-1)

    print >> sys.stderr,  """WARN: This is a naive implementation of PageRank and is
          given as an example! Please refer to PageRank implementation provided by graphx"""

    # Initialize the spark context.
    sc = SparkContext(appName="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[1], 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[2])):
        # 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)

    sc.stop()