5544d71bc8
Some things that popped out while reading the comments/documentation. Change-Id: I0ccecae3381447ede44bb855d91f997349be1562
116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2014 Yahoo! Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import logging
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import os
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import sys
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logging.basicConfig(level=logging.ERROR)
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self_dir = os.path.abspath(os.path.dirname(__file__))
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top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
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os.pardir,
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os.pardir))
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sys.path.insert(0, top_dir)
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sys.path.insert(0, self_dir)
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# INTRO: These examples show a simplistic map/reduce implementation where
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# a set of mapper(s) will sum a series of input numbers (in parallel) and
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# return their individual summed result. A reducer will then use those
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# produced values and perform a final summation and this result will then be
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# printed (and verified to ensure the calculation was as expected).
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import six
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from taskflow import engines
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from taskflow.patterns import linear_flow
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from taskflow.patterns import unordered_flow
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from taskflow import task
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class SumMapper(task.Task):
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def execute(self, inputs):
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# Sums some set of provided inputs.
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return sum(inputs)
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class TotalReducer(task.Task):
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def execute(self, *args, **kwargs):
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# Reduces all mapped summed outputs into a single value.
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total = 0
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for (k, v) in six.iteritems(kwargs):
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# If any other kwargs was passed in, we don't want to use those
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# in the calculation of the total...
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if k.startswith('reduction_'):
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total += v
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return total
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def chunk_iter(chunk_size, upperbound):
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"""Yields back chunk size pieces from zero to upperbound - 1."""
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chunk = []
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for i in range(0, upperbound):
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chunk.append(i)
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if len(chunk) == chunk_size:
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yield chunk
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chunk = []
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# Upper bound of numbers to sum for example purposes...
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UPPER_BOUND = 10000
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# How many mappers we want to have.
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SPLIT = 10
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# How big of a chunk we want to give each mapper.
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CHUNK_SIZE = UPPER_BOUND // SPLIT
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# This will be the workflow we will compose and run.
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w = linear_flow.Flow("root")
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# The mappers will run in parallel.
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store = {}
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provided = []
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mappers = unordered_flow.Flow('map')
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for i, chunk in enumerate(chunk_iter(CHUNK_SIZE, UPPER_BOUND)):
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mapper_name = 'mapper_%s' % i
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# Give that mapper some information to compute.
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store[mapper_name] = chunk
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# The reducer uses all of the outputs of the mappers, so it needs
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# to be recorded that it needs access to them (under a specific name).
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provided.append("reduction_%s" % i)
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mappers.add(SumMapper(name=mapper_name,
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rebind={'inputs': mapper_name},
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provides=provided[-1]))
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w.add(mappers)
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# The reducer will run last (after all the mappers).
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w.add(TotalReducer('reducer', requires=provided))
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# Now go!
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e = engines.load(w, engine='parallel', store=store, max_workers=4)
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print("Running a parallel engine with options: %s" % e.options)
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e.run()
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# Now get the result the reducer created.
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total = e.storage.get('reducer')
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print("Calculated result = %s" % total)
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# Calculate it manually to verify that it worked...
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calc_total = sum(range(0, UPPER_BOUND))
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if calc_total != total:
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sys.exit(1)
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