2017-03-05 08:06:17 +03:00
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# -*- coding: utf-8 -*-
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from mrjob.job import MRJob
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class SalesRanker(MRJob):
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def within_past_week(self, timestamp):
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"""Return True if timestamp is within past week, False otherwise."""
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...
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2018-04-26 04:19:04 +03:00
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def mapper(self, _, line):
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2017-03-05 08:06:17 +03:00
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"""Parse each log line, extract and transform relevant lines.
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Emit key value pairs of the form:
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(foo, p1), 2
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(bar, p1), 2
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(bar, p1), 1
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(foo, p2), 3
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(bar, p3), 10
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(foo, p4), 1
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"""
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timestamp, product_id, category, quantity = line.split('\t')
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if self.within_past_week(timestamp):
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yield (category, product_id), quantity
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2018-04-26 04:19:04 +03:00
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def reducer(self, key, values):
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2017-03-05 08:06:17 +03:00
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"""Sum values for each key.
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(foo, p1), 2
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(bar, p1), 3
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(foo, p2), 3
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(bar, p3), 10
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(foo, p4), 1
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"""
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yield key, sum(values)
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def mapper_sort(self, key, value):
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"""Construct key to ensure proper sorting.
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Transform key and value to the form:
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(foo, 2), p1
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(bar, 3), p1
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(foo, 3), p2
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(bar, 10), p3
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(foo, 1), p4
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The shuffle/sort step of MapReduce will then do a
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distributed sort on the keys, resulting in:
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(category1, 1), product4
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(category1, 2), product1
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(category1, 3), product2
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(category2, 3), product1
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(category2, 7), product3
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"""
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category, product_id = key
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quantity = value
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yield (category, quantity), product_id
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def reducer_identity(self, key, value):
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yield key, value
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def steps(self):
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"""Run the map and reduce steps."""
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return [
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self.mr(mapper=self.mapper,
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reducer=self.reducer),
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self.mr(mapper=self.mapper_sort,
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reducer=self.reducer_identity),
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]
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if __name__ == '__main__':
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2018-04-26 04:19:04 +03:00
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SalesRanker.run()
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