# -*- coding: utf-8 -*-
# Copyright (C) 2013 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 abc
import futurist
import six
from taskflow import task as ta
from taskflow.types import failure
from taskflow.types import notifier
# Execution and reversion outcomes.
EXECUTED = 'executed'
REVERTED = 'reverted'
def _execute_retry(retry, arguments):
try:
result = retry.execute(**arguments)
except Exception:
result = failure.Failure()
return (EXECUTED, result)
def _revert_retry(retry, arguments):
try:
result = retry.revert(**arguments)
except Exception:
result = failure.Failure()
return (REVERTED, result)
def _execute_task(task, arguments, progress_callback=None):
with notifier.register_deregister(task.notifier,
ta.EVENT_UPDATE_PROGRESS,
callback=progress_callback):
try:
task.pre_execute()
result = task.execute(**arguments)
except Exception:
# NOTE(imelnikov): wrap current exception with Failure
# object and return it.
result = failure.Failure()
finally:
task.post_execute()
return (EXECUTED, result)
def _revert_task(task, arguments, result, failures, progress_callback=None):
arguments = arguments.copy()
arguments[ta.REVERT_RESULT] = result
arguments[ta.REVERT_FLOW_FAILURES] = failures
with notifier.register_deregister(task.notifier,
ta.EVENT_UPDATE_PROGRESS,
callback=progress_callback):
try:
task.pre_revert()
result = task.revert(**arguments)
except Exception:
# NOTE(imelnikov): wrap current exception with Failure
# object and return it.
result = failure.Failure()
finally:
task.post_revert()
return (REVERTED, result)
[docs]class SerialRetryExecutor(object):
"""Executes and reverts retries."""
def __init__(self):
self._executor = futurist.SynchronousExecutor()
[docs] def execute_retry(self, retry, arguments):
"""Schedules retry execution."""
fut = self._executor.submit(_execute_retry, retry, arguments)
fut.atom = retry
return fut
[docs] def revert_retry(self, retry, arguments):
"""Schedules retry reversion."""
fut = self._executor.submit(_revert_retry, retry, arguments)
fut.atom = retry
return fut
[docs]@six.add_metaclass(abc.ABCMeta)
class TaskExecutor(object):
"""Executes and reverts tasks.
This class takes task and its arguments and executes or reverts it.
It encapsulates knowledge on how task should be executed or reverted:
right now, on separate thread, on another machine, etc.
"""
[docs] @abc.abstractmethod
def execute_task(self, task, task_uuid, arguments,
progress_callback=None):
"""Schedules task execution."""
[docs] @abc.abstractmethod
def revert_task(self, task, task_uuid, arguments, result, failures,
progress_callback=None):
"""Schedules task reversion."""
[docs]class SerialTaskExecutor(TaskExecutor):
"""Executes tasks one after another."""
def __init__(self):
self._executor = futurist.SynchronousExecutor()
def start(self):
self._executor.restart()
def stop(self):
self._executor.shutdown()
def execute_task(self, task, task_uuid, arguments, progress_callback=None):
fut = self._executor.submit(_execute_task,
task, arguments,
progress_callback=progress_callback)
fut.atom = task
return fut
def revert_task(self, task, task_uuid, arguments, result, failures,
progress_callback=None):
fut = self._executor.submit(_revert_task,
task, arguments, result, failures,
progress_callback=progress_callback)
fut.atom = task
return fut
[docs]class ParallelTaskExecutor(TaskExecutor):
"""Executes tasks in parallel.
Submits tasks to an executor which should provide an interface similar
to concurrent.Futures.Executor.
"""
constructor_options = [
('max_workers', lambda v: v if v is None else int(v)),
]
"""
Optional constructor keyword arguments this executor supports. These will
typically be passed via engine options (by a engine user) and converted
into the correct type before being sent into this
classes ``__init__`` method.
"""
def __init__(self, executor=None, max_workers=None):
self._executor = executor
self._max_workers = max_workers
self._own_executor = executor is None
@abc.abstractmethod
def _create_executor(self, max_workers=None):
"""Called when an executor has not been provided to make one."""
def _submit_task(self, func, task, *args, **kwargs):
fut = self._executor.submit(func, task, *args, **kwargs)
fut.atom = task
return fut
def execute_task(self, task, task_uuid, arguments, progress_callback=None):
return self._submit_task(_execute_task, task, arguments,
progress_callback=progress_callback)
def revert_task(self, task, task_uuid, arguments, result, failures,
progress_callback=None):
return self._submit_task(_revert_task, task, arguments, result,
failures, progress_callback=progress_callback)
def start(self):
if self._own_executor:
self._executor = self._create_executor(
max_workers=self._max_workers)
def stop(self):
if self._own_executor:
self._executor.shutdown(wait=True)
self._executor = None
[docs]class ParallelThreadTaskExecutor(ParallelTaskExecutor):
"""Executes tasks in parallel using a thread pool executor."""
def _create_executor(self, max_workers=None):
return futurist.ThreadPoolExecutor(max_workers=max_workers)
[docs]class ParallelGreenThreadTaskExecutor(ParallelThreadTaskExecutor):
"""Executes tasks in parallel using a greenthread pool executor."""
DEFAULT_WORKERS = 1000
"""
Default number of workers when ``None`` is passed; being that
greenthreads don't map to native threads or processors very well this
is more of a guess/somewhat arbitrary, but it does match what the eventlet
greenpool default size is (so at least it's consistent with what eventlet
does).
"""
def _create_executor(self, max_workers=None):
if max_workers is None:
max_workers = self.DEFAULT_WORKERS
return futurist.GreenThreadPoolExecutor(max_workers=max_workers)
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