5996c8f25e
Instead of having the fetch arguments functions need to be provided a scope walker to correctly find the right arguments, which only the internals of the action engine know about provide a default scope walker (that is the same one the action engine internal uses) to the storage unit and have it be the default strategy used so that users need not know how to pass it in (which they should not care about). This allows for users to fetch the same mapped arguments as the internals of the engine will fetch. Change-Id: I1beca532b2b7c7ad98b09265a0c4477658052d16
124 lines
4.5 KiB
Python
124 lines
4.5 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,
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self._atom_notifier)
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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,
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self._task_executor)
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def fetch_scopes_for(self, atom_name):
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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_name]
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except KeyError:
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atom = None
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for node in self.analyzer.iterate_all_nodes():
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if node.name == atom_name:
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atom = node
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break
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if atom is not None:
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walker = sc.ScopeWalker(self.compilation, atom,
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names_only=True)
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self._scopes[atom_name] = visible_to = tuple(walker)
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else:
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visible_to = tuple([])
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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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