98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2012-2013 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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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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import taskflow.engines
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from taskflow.patterns import linear_flow as lf
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from taskflow.patterns import unordered_flow as uf
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from taskflow import task
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# INTRO: These examples show how a linear flow and an unordered flow can be
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# used together to execute calculations in parallel and then use the
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# result for the next task/s. The adder task is used for all calculations
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# and argument bindings are used to set correct parameters for each task.
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# This task provides some values from as a result of execution, this can be
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# useful when you want to provide values from a static set to other tasks that
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# depend on those values existing before those tasks can run.
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#
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# NOTE(harlowja): this usage is *depreciated* in favor of a simpler mechanism
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# that provides those values on engine running by prepopulating the storage
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# backend before your tasks are ran (which accomplishes a similar goal in a
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# more uniform manner).
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class Provider(task.Task):
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def __init__(self, name, *args, **kwargs):
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super(Provider, self).__init__(name=name, **kwargs)
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self._provide = args
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def execute(self):
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return self._provide
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# This task adds two input variables and returns the result of that addition.
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#
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# Note that since this task does not have a revert() function (since addition
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# is a stateless operation) there are no side-effects that this function needs
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# to undo if some later operation fails.
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class Adder(task.Task):
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def execute(self, x, y):
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return x + y
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flow = lf.Flow('root').add(
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# Provide the initial values for other tasks to depend on.
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#
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# x1 = 2, y1 = 3, x2 = 5, x3 = 8
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Provider("provide-adder", 2, 3, 5, 8,
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provides=('x1', 'y1', 'x2', 'y2')),
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# Note here that we define the flow that contains the 2 adders to be an
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# unordered flow since the order in which these execute does not matter,
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# another way to solve this would be to use a graph_flow pattern, which
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# also can run in parallel (since they have no ordering dependencies).
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uf.Flow('adders').add(
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# Calculate 'z1 = x1+y1 = 5'
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#
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# Rebind here means that the execute() function x argument will be
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# satisfied from a previous output named 'x1', and the y argument
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# of execute() will be populated from the previous output named 'y1'
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#
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# The output (result of adding) will be mapped into a variable named
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# 'z1' which can then be refereed to and depended on by other tasks.
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Adder(name="add", provides='z1', rebind=['x1', 'y1']),
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# z2 = x2+y2 = 13
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Adder(name="add-2", provides='z2', rebind=['x2', 'y2']),
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),
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# r = z1+z2 = 18
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Adder(name="sum-1", provides='r', rebind=['z1', 'z2']))
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# The result here will be all results (from all tasks) which is stored in an
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# in-memory storage location that backs this engine since it is not configured
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# with persistence storage.
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result = taskflow.engines.run(flow, engine='parallel')
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print(result)
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