a69213821c
Instead of recalculating/rewalking over the visible scopes of atoms just calculate the scope once and cache it (as it currently does not change at runtime) and return the cached tuple instead to avoid the needless recreation whenever a scope is requested for a given atom. Change-Id: I47d24054c63e8620d26e7ade4baa239295daed0a
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 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 retry_action as ra
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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.engines.action_engine import task_action as ta
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from taskflow import retry as retry_atom
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from taskflow import states as st
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from taskflow import task as task_atom
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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, self._task_executor,
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self._atom_notifier, self._fetch_scopes_for)
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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 isinstance(node, task_atom.BaseTask):
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self.task_action.change_state(node, state, progress=0.0)
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elif isinstance(node, retry_atom.Retry):
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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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