reduce.py

#
# Copyright 2011 Twitter, Inc.
# Licensed 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.
#

"""Example showing how to use filters and buffers.

A buffer UDF is similar to the built-in Python reduce function. It takes a
group of tuples that have been previously grouped by group_by, and yields an
arbitrary number of new tuples for the group (it is most useful though to do
some aggregation on the group). The tuples are fetched using an iterator.
"""

from pycascading.helpers import *


@udf_filter
def starts_with_letter(tuple, letter):
    try:
        return tuple.get(1)[0].upper() == letter
    except:
        return False


@udf_map
def word_count(tuple):
    return [len(tuple.get(1).split()), tuple.get(1)]


def main():
    flow = Flow()
    input = flow.source(Hfs(TextLine(), 'pycascading_data/town.txt'))
    output = flow.tsv_sink('pycascading_data/out')

    p = input | filter_by(starts_with_letter('A')) | \
    map_replace(word_count(), ['word_count', 'line'])

    @udf_buffer(produces=['word_count', 'count', 'first_chars'])
    def count(group, tuples):
        """Counts the number of tuples in the group, and also emits a string
        that is the first character of the 'line' column repeated this many
        times."""
        c = 0
        first_char = ''
        for tuple in tuples:
            c += 1
            first_char += tuple.get('line')[0]
        yield [group.get(0), c, first_char]

    p | group_by('word_count', count()) | output

    flow.run(num_reducers=2)