# -*- coding: utf-8 -*- # 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 compiler from taskflow.engines.action_engine import executor from taskflow.engines.action_engine import runtime from taskflow.engines import base from taskflow import exceptions as exc from taskflow.openstack.common import excutils from taskflow import retry from taskflow import states from taskflow import storage as atom_storage 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 compiles the flow (and any subflows) into a compilation unit which contains the full runtime definition to be executed and then uses this compilation unit in combination with the executor, runtime, runner and storage classes to attempt to run your flow (and any subflows & contained atoms) to completion. NOTE(harlowja): during this process it is permissible and valid to have a task or multiple tasks in the execution graph fail (at the same time even), which will cause the process of reversion or retrying to commence. See the valid states in the states module to learn more about what other states the tasks and flow being ran can go through. """ _compiler_factory = compiler.PatternCompiler _task_executor_factory = executor.SerialTaskExecutor def __init__(self, flow, flow_detail, backend, conf): super(ActionEngine, self).__init__(flow, flow_detail, backend, conf) self._runtime = None self._compiled = False self._compilation = None self._lock = threading.RLock() self._state_lock = threading.RLock() self._storage_ensured = False def __str__(self): return "%s: %s" % (reflection.get_class_name(self), id(self)) def suspend(self): if not self._compiled: raise exc.InvalidState("Can not suspend an engine" " which has not been compiled") self._change_state(states.SUSPENDING) @property def compilation(self): """The compilation result. NOTE(harlowja): Only accessible after compilation has completed (None will be returned when this property is accessed before compilation has completed successfully). """ if self._compiled: return self._compilation else: return None def run(self): with lock_utils.try_lock(self._lock) as was_locked: if not was_locked: raise exc.ExecutionFailure("Engine currently locked, please" " try again later") for _state in self.run_iter(): pass def run_iter(self, timeout=None): """Runs the engine using iteration (or die trying). :param timeout: timeout to wait for any tasks to complete (this timeout will be used during the waiting period that occurs after the waiting state is yielded when unfinished tasks are being waited for). Instead of running to completion in a blocking manner, this will return a generator which will yield back the various states that the engine is going through (and can be used to run multiple engines at once using a generator per engine). the iterator returned also responds to the send() method from pep-0342 and will attempt to suspend itself if a truthy value is sent in (the suspend may be delayed until all active tasks have finished). NOTE(harlowja): using the run_iter method will **not** retain the engine lock while executing so the user should ensure that there is only one entity using a returned engine iterator (one per engine) at a given time. """ self.compile() self.prepare() self._task_executor.start() state = None runner = self._runtime.runner try: self._change_state(states.RUNNING) for state in runner.run_iter(timeout=timeout): try: try_suspend = yield state except GeneratorExit: break else: if try_suspend: self.suspend() except Exception: with excutils.save_and_reraise_exception(): self._change_state(states.FAILURE) else: ignorable_states = getattr(runner, 'ignorable_states', []) if state and state not in ignorable_states: self._change_state(state) if state != states.SUSPENDED and state != states.SUCCESS: failures = self.storage.get_failures() misc.Failure.reraise_if_any(failures.values()) finally: self._task_executor.stop() def _change_state(self, state): with self._state_lock: 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 _ensure_storage(self): # 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 node in self._compilation.execution_graph.nodes_iter(): version = misc.get_version_string(node) if isinstance(node, retry.Retry): self.storage.ensure_retry(node.name, version, node.save_as) else: self.storage.ensure_task(node.name, version, node.save_as) if node.inject: self.storage.inject_atom_args(node.name, node.inject) self._change_state(states.SUSPENDED) # does nothing in PENDING state @lock_utils.locked def prepare(self): if not self._compiled: raise exc.InvalidState("Can not prepare an engine" " which has not been compiled") if not self._storage_ensured: self._ensure_storage() self._storage_ensured = True # At this point we can check to ensure all dependencies are either # flow/task provided or storage provided, if there are still missing # dependencies then this flow will fail at runtime (which we can avoid # by failing at preparation time). external_provides = set(self.storage.fetch_all().keys()) missing = self._flow.requires - external_provides if missing: raise exc.MissingDependencies(self._flow, sorted(missing)) # Reset everything back to pending (if we were previously reverted). if self.storage.get_flow_state() == states.REVERTED: self._runtime.reset_all() self._change_state(states.PENDING) @misc.cachedproperty def _task_executor(self): return self._task_executor_factory() @misc.cachedproperty def _compiler(self): return self._compiler_factory() @lock_utils.locked def compile(self): if self._compiled: return self._compilation = self._compiler.compile(self._flow) self._runtime = runtime.Runtime(self._compilation, self.storage, self.task_notifier, self._task_executor) self._compiled = True class SingleThreadedActionEngine(ActionEngine): """Engine that runs tasks in serial manner.""" _storage_factory = atom_storage.SingleThreadedStorage class MultiThreadedActionEngine(ActionEngine): """Engine that runs tasks in parallel manner.""" _storage_factory = atom_storage.MultiThreadedStorage def _task_executor_factory(self): return executor.ParallelTaskExecutor(self._executor) def __init__(self, flow, flow_detail, backend, conf, **kwargs): super(MultiThreadedActionEngine, self).__init__( flow, flow_detail, backend, conf) self._executor = kwargs.get('executor')