bfc11369f0
Instead of saving task state in a class variable that is later introspected by further test code just remove that concept (which doesn't work in multiprocessing or worker engines which can not have access those types of shared/globally available concepts due to how they run) and use a specialized listener that can gather the same information in a more decoupled manner (and it will work in multiprocessing and worker engines correctly). This allows our engine test cases to work in those engine types which increases those engines test coverage (and future coverage and engine tests that are added). Fixes a bunch of occurrences of bug 1357117 as well that were removed during this cleanup and adjustment process... Change-Id: Ic9901de2902ac28ec255bef146be5846d18f9bfb
117 lines
4.2 KiB
Python
117 lines
4.2 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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from taskflow.engines.action_engine.actions import retry as ra
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from taskflow.engines.action_engine.actions import task as ta
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from taskflow.engines.action_engine import analyzer as an
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from taskflow.engines.action_engine import completer as co
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from taskflow.engines.action_engine import runner as ru
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from taskflow.engines.action_engine import scheduler as sched
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from taskflow.engines.action_engine import scopes as sc
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from taskflow import states as st
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from taskflow.utils import misc
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class Runtime(object):
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"""A aggregate of runtime objects, properties, ... used during execution.
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This object contains various utility methods and properties that represent
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the collection of runtime components and functionality needed for an
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action engine to run to completion.
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"""
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def __init__(self, compilation, storage, atom_notifier, task_executor):
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self._atom_notifier = atom_notifier
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self._task_executor = task_executor
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self._storage = storage
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self._compilation = compilation
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self._scopes = {}
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@property
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def compilation(self):
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return self._compilation
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@property
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def storage(self):
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return self._storage
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@misc.cachedproperty
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def analyzer(self):
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return an.Analyzer(self._compilation, self._storage)
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@misc.cachedproperty
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def runner(self):
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return ru.Runner(self, self._task_executor)
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@misc.cachedproperty
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def completer(self):
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return co.Completer(self)
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@misc.cachedproperty
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def scheduler(self):
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return sched.Scheduler(self)
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@misc.cachedproperty
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def retry_action(self):
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return ra.RetryAction(self._storage, self._atom_notifier,
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self._fetch_scopes_for)
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@misc.cachedproperty
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def task_action(self):
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return ta.TaskAction(self._storage,
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self._atom_notifier, self._fetch_scopes_for,
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self._task_executor)
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def _fetch_scopes_for(self, atom):
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"""Fetches a tuple of the visible scopes for the given atom."""
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try:
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return self._scopes[atom]
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except KeyError:
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walker = sc.ScopeWalker(self.compilation, atom,
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names_only=True)
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visible_to = tuple(walker)
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self._scopes[atom] = visible_to
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return visible_to
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# Various helper methods used by the runtime components; not for public
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# consumption...
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def reset_nodes(self, nodes, state=st.PENDING, intention=st.EXECUTE):
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for node in nodes:
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if state:
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if self.task_action.handles(node):
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self.task_action.change_state(node, state,
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progress=0.0)
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elif self.retry_action.handles(node):
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self.retry_action.change_state(node, state)
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else:
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raise TypeError("Unknown how to reset atom '%s' (%s)"
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% (node, type(node)))
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if intention:
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self.storage.set_atom_intention(node.name, intention)
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def reset_all(self, state=st.PENDING, intention=st.EXECUTE):
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self.reset_nodes(self.analyzer.iterate_all_nodes(),
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state=state, intention=intention)
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def reset_subgraph(self, node, state=st.PENDING, intention=st.EXECUTE):
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self.reset_nodes(self.analyzer.iterate_subgraph(node),
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state=state, intention=intention)
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def retry_subflow(self, retry):
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self.storage.set_atom_intention(retry.name, st.EXECUTE)
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self.reset_subgraph(retry)
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