Files
deb-python-taskflow/taskflow/engines/action_engine/engine.py
Joshua Harlow e9a319d7d3 Use reader/writer locks in storage
Switch to using a reader/writer lock scheme to
protect against simultaneous storage mutations,
typically seen when running in a multi-threaded
mode.

For the single-threaded mode provide a dummy
reader/writer lock which will mimic the locking
api but not actually lock anything.

Closes-Bug: 1273146
Change-Id: I954f542d9ab34b693e8da71c9fc913f823e869ba
2014-02-05 13:47:42 +04:00

208 lines
8.0 KiB
Python

# -*- 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.SingleThreadedStorage
class MultiThreadedActionEngine(ActionEngine):
"""Engine that runs tasks in parallel manner."""
_storage_cls = t_storage.MultiThreadedStorage
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)