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# -*- coding: utf-8 -*-
# Copyright (C) 2014 Yahoo! Inc. All Rights Reserved.
#
# 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.
import logging
import os
import sys
logging.basicConfig(level=logging.ERROR)
self_dir = os.path.abspath(os.path.dirname(__file__))
top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
os.pardir,
os.pardir))
sys.path.insert(0, top_dir)
sys.path.insert(0, self_dir)
# INTRO: These examples show a simplistic map/reduce implementation where
# a set of mapper(s) will sum a series of input numbers (in parallel) and
# return their individual summed result. A reducer will then use those
# produced values and perform a final summation and this result will then be
# printed (and verified to ensure the calculation was as expected).
import six
from taskflow import engines
from taskflow.patterns import linear_flow
from taskflow.patterns import unordered_flow
from taskflow import task
class SumMapper(task.Task):
def execute(self, inputs):
# Sums some set of provided inputs.
return sum(inputs)
class TotalReducer(task.Task):
def execute(self, *args, **kwargs):
# Reduces all mapped summed outputs into a single value.
total = 0
for (k, v) in six.iteritems(kwargs):
# If any other kwargs was passed in, we don't want to use those
# in the calculation of the total...
if k.startswith('reduction_'):
total += v
return total
def chunk_iter(chunk_size, upperbound):
"""Yields back chunk size pieces from zero to upperbound - 1."""
chunk = []
for i in range(0, upperbound):
chunk.append(i)
if len(chunk) == chunk_size:
yield chunk
chunk = []
# Upper bound of numbers to sum for example purposes...
UPPER_BOUND = 10000
# How many mappers we want to have.
SPLIT = 10
# How big of a chunk we want to give each mapper.
CHUNK_SIZE = UPPER_BOUND // SPLIT
# This will be the workflow we will compose and run.
w = linear_flow.Flow("root")
# The mappers will run in parallel.
store = {}
provided = []
mappers = unordered_flow.Flow('map')
for i, chunk in enumerate(chunk_iter(CHUNK_SIZE, UPPER_BOUND)):
mapper_name = 'mapper_%s' % i
# Give that mapper some information to compute.
store[mapper_name] = chunk
# The reducer uses all of the outputs of the mappers, so it needs
# to be recorded that it needs access to them (under a specific name).
provided.append("reduction_%s" % i)
mappers.add(SumMapper(name=mapper_name,
rebind={'inputs': mapper_name},
provides=provided[-1]))
w.add(mappers)
# The reducer will run last (after all the mappers).
w.add(TotalReducer('reducer', requires=provided))
# Now go!
e = engines.load(w, engine='parallel', store=store, max_workers=4)
print("Running a parallel engine with options: %s" % e.options)
e.run()
# Now get the result the reducer created.
total = e.storage.get('reducer')
print("Calculated result = %s" % total)
# Calculate it manually to verify that it worked...
calc_total = sum(range(0, UPPER_BOUND))
if calc_total != total:
sys.exit(1)
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