53dcbd4d97
Instead of blocking the caller when they call run() allow there to be a new api run_iter() that will yield back the engine state transitions while running. This allows for a engine user to do alternate work while an engine is running (and come back to yield on there own time). Implements blueprint iterable-execution Change-Id: Ibb48c6c5618c97c59a6ab170dab5233ed47e5554
243 lines
10 KiB
Python
243 lines
10 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2012 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 threading
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from taskflow.engines.action_engine import executor
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from taskflow.engines.action_engine import graph_action
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from taskflow.engines.action_engine import graph_analyzer
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from taskflow.engines.action_engine import retry_action
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from taskflow.engines.action_engine import task_action
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from taskflow.engines import base
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from taskflow import exceptions as exc
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from taskflow.openstack.common import excutils
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from taskflow import retry
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from taskflow import states
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from taskflow import storage as t_storage
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from taskflow.utils import flow_utils
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from taskflow.utils import lock_utils
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from taskflow.utils import misc
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from taskflow.utils import reflection
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class ActionEngine(base.EngineBase):
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"""Generic action-based engine.
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This engine flattens the flow (and any subflows) into a execution graph
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which contains the full runtime definition to be executed and then uses
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this graph in combination with the action classes & storage to attempt to
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run your flow (and any subflows & contained tasks) to completion.
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During this process it is permissible and valid to have a task or multiple
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tasks in the execution graph fail, which will cause the process of
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reversion to commence. See the valid states in the states module to learn
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more about what other states the tasks & flow being ran can go through.
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"""
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_graph_action_factory = graph_action.FutureGraphAction
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_graph_analyzer_factory = graph_analyzer.GraphAnalyzer
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_task_action_factory = task_action.TaskAction
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_task_executor_factory = executor.SerialTaskExecutor
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_retry_action_factory = retry_action.RetryAction
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def __init__(self, flow, flow_detail, backend, conf):
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super(ActionEngine, self).__init__(flow, flow_detail, backend, conf)
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self._analyzer = None
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self._root = None
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self._compiled = False
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self._lock = threading.RLock()
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self._state_lock = threading.RLock()
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self._task_executor = None
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self._task_action = None
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self._retry_action = None
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self._storage_ensured = False
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def __str__(self):
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return "%s: %s" % (reflection.get_class_name(self), id(self))
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def suspend(self):
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if not self._compiled:
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raise exc.InvalidState("Can not suspend an engine"
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" which has not been compiled")
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self._change_state(states.SUSPENDING)
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@property
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def execution_graph(self):
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"""The graph of nodes to be executed.
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NOTE(harlowja): Only accessible after compilation has completed.
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"""
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g = None
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if self._compiled and self._analyzer:
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g = self._analyzer.execution_graph
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return g
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def run(self):
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with lock_utils.try_lock(self._lock) as was_locked:
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if not was_locked:
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raise exc.ExecutionFailure("Engine currently locked, please"
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" try again later")
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for _state in self.run_iter():
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pass
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def run_iter(self, timeout=None):
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"""Runs the engine using iteration (or die trying).
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:param timeout: timeout to wait for any tasks to complete (this timeout
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will be used during the waiting period that occurs after the
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waiting state is yielded when unfinished tasks are being waited
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for).
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Instead of running to completion in a blocking manner, this will
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return a generator which will yield back the various states that the
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engine is going through (and can be used to run multiple engines at
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once using a generator per engine). the iterator returned also
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responds to the send() method from pep-0342 and will attempt to suspend
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itself if a truthy value is sent in (the suspend may be delayed until
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all active tasks have finished).
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NOTE(harlowja): using the run_iter method will **not** retain the
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engine lock while executing so the user should ensure that there is
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only one entity using a returned engine iterator (one per engine) at a
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given time.
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"""
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self.compile()
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self.prepare()
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self._task_executor.start()
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state = None
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try:
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self._change_state(states.RUNNING)
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for state in self._root.execute_iter(timeout=timeout):
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try:
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try_suspend = yield state
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except GeneratorExit:
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break
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else:
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if try_suspend:
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self.suspend()
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except Exception:
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with excutils.save_and_reraise_exception():
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self._change_state(states.FAILURE)
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else:
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ignorable_states = getattr(self._root, 'ignorable_states', [])
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if state and state not in ignorable_states:
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self._change_state(state)
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if state != states.SUSPENDED and state != states.SUCCESS:
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failures = self.storage.get_failures()
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misc.Failure.reraise_if_any(failures.values())
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finally:
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self._task_executor.stop()
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def _change_state(self, state):
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with self._state_lock:
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old_state = self.storage.get_flow_state()
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if not states.check_flow_transition(old_state, state):
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return
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self.storage.set_flow_state(state)
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try:
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flow_uuid = self._flow.uuid
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except AttributeError:
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# NOTE(harlowja): if the flow was just a single task, then it
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# will not itself have a uuid, but the constructed flow_detail
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# will.
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if self._flow_detail is not None:
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flow_uuid = self._flow_detail.uuid
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else:
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flow_uuid = None
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details = dict(engine=self,
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flow_name=self._flow.name,
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flow_uuid=flow_uuid,
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old_state=old_state)
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self.notifier.notify(state, details)
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def _ensure_storage_for(self, execution_graph):
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# NOTE(harlowja): signal to the tasks that exist that we are about to
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# resume, if they have a previous state, they will now transition to
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# a resuming state (and then to suspended).
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self._change_state(states.RESUMING) # does nothing in PENDING state
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for node in execution_graph.nodes_iter():
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version = misc.get_version_string(node)
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if isinstance(node, retry.Retry):
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self.storage.ensure_retry(node.name, version, node.save_as)
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else:
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self.storage.ensure_task(node.name, version, node.save_as)
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self._change_state(states.SUSPENDED) # does nothing in PENDING state
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@lock_utils.locked
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def prepare(self):
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if not self._compiled:
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raise exc.InvalidState("Can not prepare an engine"
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" which has not been compiled")
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if not self._storage_ensured:
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self._ensure_storage_for(self.execution_graph)
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self._storage_ensured = True
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# At this point we can check to ensure all dependencies are either
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# flow/task provided or storage provided, if there are still missing
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# dependencies then this flow will fail at runtime (which we can avoid
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# by failing at preparation time).
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external_provides = set(self.storage.fetch_all().keys())
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missing = self._flow.requires - external_provides
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if missing:
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raise exc.MissingDependencies(self._flow, sorted(missing))
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# Reset everything back to pending (if we were previously reverted).
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if self.storage.get_flow_state() == states.REVERTED:
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self._root.reset_all()
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self._change_state(states.PENDING)
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@lock_utils.locked
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def compile(self):
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if self._compiled:
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return
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execution_graph = flow_utils.flatten(self._flow)
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if execution_graph.number_of_nodes() == 0:
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raise exc.Empty("Flow %s is empty." % self._flow.name)
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self._analyzer = self._graph_analyzer_factory(execution_graph,
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self.storage)
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if self._task_executor is None:
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self._task_executor = self._task_executor_factory()
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if self._task_action is None:
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self._task_action = self._task_action_factory(self.storage,
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self._task_executor,
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self.task_notifier)
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if self._retry_action is None:
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self._retry_action = self._retry_action_factory(self.storage,
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self.task_notifier)
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self._root = self._graph_action_factory(self._analyzer,
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self.storage,
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self._task_action,
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self._retry_action)
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self._compiled = True
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return
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class SingleThreadedActionEngine(ActionEngine):
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"""Engine that runs tasks in serial manner."""
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_storage_factory = t_storage.SingleThreadedStorage
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class MultiThreadedActionEngine(ActionEngine):
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"""Engine that runs tasks in parallel manner."""
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_storage_factory = t_storage.MultiThreadedStorage
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def _task_executor_factory(self):
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return executor.ParallelTaskExecutor(self._executor)
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def __init__(self, flow, flow_detail, backend, conf, **kwargs):
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super(MultiThreadedActionEngine, self).__init__(
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flow, flow_detail, backend, conf)
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self._executor = kwargs.get('executor')
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