# -*- coding: utf-8 -*- # vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright (C) 2012 Yahoo! Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import threading from taskflow.engines.action_engine import executor from taskflow.engines.action_engine import graph_action from taskflow.engines.action_engine import graph_analyzer from taskflow.engines.action_engine import task_action from taskflow.engines import base from taskflow import exceptions as exc from taskflow.openstack.common import excutils from taskflow import states from taskflow import storage as t_storage from taskflow.utils import flow_utils from taskflow.utils import lock_utils from taskflow.utils import misc from taskflow.utils import reflection class ActionEngine(base.EngineBase): """Generic action-based engine. This engine flattens the flow (and any subflows) into a execution graph which contains the full runtime definition to be executed and then uses this graph in combination with the action classes & storage to attempt to run your flow (and any subflows & contained tasks) to completion. During this process it is permissible and valid to have a task or multiple tasks in the execution graph fail, which will cause the process of reversion to commence. See the valid states in the states module to learn more about what other states the tasks & flow being ran can go through. """ _graph_action_cls = graph_action.FutureGraphAction _graph_analyzer_cls = graph_analyzer.GraphAnalyzer _task_action_cls = task_action.TaskAction _task_executor_cls = executor.SerialTaskExecutor def __init__(self, flow, flow_detail, backend, conf): super(ActionEngine, self).__init__(flow, flow_detail, backend, conf) self._analyzer = None self._root = None self._compiled = False self._lock = threading.RLock() self._state_lock = threading.RLock() self._task_executor = None self._task_action = None def _revert(self, current_failure=None): self._change_state(states.REVERTING) try: state = self._root.revert() except Exception: with excutils.save_and_reraise_exception(): self._change_state(states.FAILURE) self._change_state(state) if state == states.SUSPENDED: return failures = self.storage.get_failures() misc.Failure.reraise_if_any(failures.values()) if current_failure: current_failure.reraise() def __str__(self): return "%s: %s" % (reflection.get_class_name(self), id(self)) def suspend(self): if not self._compiled: raise exc.InvariantViolation("Can not suspend an engine" " which has not been compiled") self._change_state(states.SUSPENDING) @property def execution_graph(self): self.compile() return self._analyzer.execution_graph @lock_utils.locked def run(self): """Runs the flow in the engine to completion.""" if self.storage.get_flow_state() == states.REVERTED: self._reset() self.compile() external_provides = set(self.storage.fetch_all().keys()) missing = self._flow.requires - external_provides if missing: raise exc.MissingDependencies(self._flow, sorted(missing)) self._task_executor.start() try: if self.storage.has_failures(): self._revert() else: self._run() finally: self._task_executor.stop() def _run(self): self._change_state(states.RUNNING) try: state = self._root.execute() except Exception: self._change_state(states.FAILURE) self._revert(misc.Failure()) else: self._change_state(state) @lock_utils.locked(lock='_state_lock') def _change_state(self, state): old_state = self.storage.get_flow_state() if not states.check_flow_transition(old_state, state): return self.storage.set_flow_state(state) try: flow_uuid = self._flow.uuid except AttributeError: # NOTE(harlowja): if the flow was just a single task, then it will # not itself have a uuid, but the constructed flow_detail will. if self._flow_detail is not None: flow_uuid = self._flow_detail.uuid else: flow_uuid = None details = dict(engine=self, flow_name=self._flow.name, flow_uuid=flow_uuid, old_state=old_state) self.notifier.notify(state, details) def _reset(self): for name, uuid in self.storage.reset_tasks(): details = dict(engine=self, task_name=name, task_uuid=uuid, result=None) self.task_notifier.notify(states.PENDING, details) self._change_state(states.PENDING) def _ensure_storage_for(self, task_graph): # NOTE(harlowja): signal to the tasks that exist that we are about to # resume, if they have a previous state, they will now transition to # a resuming state (and then to suspended). self._change_state(states.RESUMING) # does nothing in PENDING state for task in task_graph.nodes_iter(): task_version = misc.get_version_string(task) self.storage.ensure_task(task.name, task_version, task.save_as) self._change_state(states.SUSPENDED) # does nothing in PENDING state @lock_utils.locked def compile(self): if self._compiled: return task_graph = flow_utils.flatten(self._flow) if task_graph.number_of_nodes() == 0: raise exc.EmptyFlow("Flow %s is empty." % self._flow.name) self._analyzer = self._graph_analyzer_cls(task_graph, self.storage) if self._task_executor is None: self._task_executor = self._task_executor_cls() if self._task_action is None: self._task_action = self._task_action_cls(self.storage, self._task_executor, self.task_notifier) self._root = self._graph_action_cls(self._analyzer, self.storage, self._task_action) # NOTE(harlowja): Perform initial state manipulation and setup. # # TODO(harlowja): This doesn't seem like it should be in a compilation # function since compilation seems like it should not modify any # external state. self._ensure_storage_for(task_graph) self._compiled = True class SingleThreadedActionEngine(ActionEngine): """Engine that runs tasks in serial manner.""" _storage_cls = t_storage.Storage class MultiThreadedActionEngine(ActionEngine): """Engine that runs tasks in parallel manner.""" _storage_cls = t_storage.ThreadSafeStorage def _task_executor_cls(self): return executor.ParallelTaskExecutor(self._executor) def __init__(self, flow, flow_detail, backend, conf): super(MultiThreadedActionEngine, self).__init__( flow, flow_detail, backend, conf) self._executor = conf.get('executor', None)