cde0dee14e
Previously to run a flow client code had to put together the flow,
an engine, logbook, flowdetail, and storage backend. This commit
adds two helper functions, run() and load(), so that simplest usecase
now looks like
taskflow.engines.run(flow)
Client code may also provide configuration for storage and engine if
needed, but if not needed it just works with defaults.
Engines are loaded via stevedore, as drivers in 'taskflow.engines'
backend. Now three entry points are defined in that namespace:
- 'default', for SingleThreadedActionEngine, used by default;
- 'serial', as another synonym for SingleThreadedActionEngine;
- 'parallel', for MultiThreadedActionEngine.
Closes-bug: #1224726
Change-Id: I7f4cb5c8ff7f5f12831ddd0952c202d2fd8cd6ef
113 lines
4.2 KiB
Python
113 lines
4.2 KiB
Python
# -*- coding: utf-8 -*-
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# vim: tabstop=4 shiftwidth=4 softtabstop=4
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# Copyright (C) 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 six
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import stevedore.driver
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from taskflow.persistence import backends as p_backends
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from taskflow.utils import persistence_utils as p_utils
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# NOTE(imelnikov): this is the entrypoint namespace, not the module namespace.
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ENGINES_NAMESPACE = 'taskflow.engines'
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def load(flow, store=None, flow_detail=None, book=None,
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engine_conf=None, backend=None, namespace=ENGINES_NAMESPACE):
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"""Load flow into engine
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This function creates and prepares engine to run the
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flow. All that is left is to run the engine with 'run()' method.
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Which engine to load is specified in 'engine_conf' parameter. It
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can be a string that names engine type or a dictionary which holds
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engine type (with 'engine' key) and additional engine-specific
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configuration (for example, executor for multithreaded engine).
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Which storage backend to use is defined by backend parameter. It
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can be backend itself, or a dictionary that is passed to
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taskflow.persistence.backends.fetch to obtain backend.
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:param flow: flow to load
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:param store: dict -- data to put to storage to satisfy flow requirements
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:param flow_detail: FlowDetail that holds state of the flow
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:param book: LogBook to create flow detail in if flow_detail is None
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:param engine_conf: engine type and configuration configuration
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:param backend: storage backend to use or configuration
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:param namespace: driver namespace for stevedore (default is fine
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if you don't know what is it)
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:returns: engine
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"""
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if engine_conf is None:
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engine_conf = {'engine': 'default'}
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# NOTE(imelnikov): this allows simpler syntax
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if isinstance(engine_conf, six.string_types):
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engine_conf = {'engine': engine_conf}
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if isinstance(backend, dict):
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backend = p_backends.fetch(backend)
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if flow_detail is None:
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if book is None:
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_lb, flow_detail = p_utils.temporary_flow_detail(backend)
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else:
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flow_detail = p_utils.create_flow_detail(flow, book=book,
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backend=backend)
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mgr = stevedore.driver.DriverManager(
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namespace, engine_conf['engine'],
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invoke_on_load=True,
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invoke_kwds={
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'conf': engine_conf.copy(),
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'flow': flow,
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'flow_detail': flow_detail,
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'backend': backend
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})
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engine = mgr.driver
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if store:
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engine.storage.inject(store)
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return engine
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def run(flow, store=None, engine_conf=None, backend=None):
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"""Run the flow
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This function load the flow into engine (with 'load' function)
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and runs the engine.
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Which engine to load is specified in 'engine_conf' parameter. It
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can be a string that names engine type or a dictionary which holds
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engine type (with 'engine' key) and additional engine-specific
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configuration (for example, executor for multithreaded engine).
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Which storage backend to use is defined by backend parameter. It
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can be backend itself, or a dictionary that is passed to
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taskflow.persistence.backends.fetch to obtain backend.
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:param flow: flow to load
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:param store: dict -- data to put to storage to satisfy flow requirements
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:param engine_conf: engine type and configuration configuration
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:param backend: storage backend to use or configuration
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:returns: dictionary of all named task results (see Storage.fetch_all)
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"""
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engine = load(flow, store=store, engine_conf=engine_conf, backend=backend)
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engine.run()
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return engine.storage.fetch_all()
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