Python 神器 Celery 源码解析（5）
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2021-11-27 21:01
Celery是一款非常简单、灵活、可靠的分布式系统，可用于处理大量消息，并且提供了一整套操作此系统的工具。Celery 也是一款消息队列工具，可用于处理实时数据以及任务调度。
本文是是celery源码解析的第五篇，在前4篇里分别介绍了vine, py-amqp和kombu:
神器 celery 源码解析- vine实现Promise功能 神器 celery 源码解析- py-amqp实现AMQP协议 神器 celery 源码解析- kombu，一个python实现的消息库 神器 celery 源码解析- kombu的企业级算法
基本扫清celery的基础库后，我们正式进入celery的源码解析，本文包括下面几个部分:
celery应用示例 celery项目概述 worker启动流程跟踪 client启动流程跟踪 celery的app worker模式启动流程 小结
celery应用示例
启动celery之前，我们先使用docker启动一个redis服务，作为broker:
$ docker run -p 6379:6379 --name redis -d redis:6.2.3-alpine
使用telnet监控redis服务，观测任务调度情况:
$ telnet 127.0.0.1 6379
Trying 127.0.0.1...
Connected to localhost.
Escape character is '^]'.
monitor
+OK
下面是我们的celery服务代码 myapp.py :
# myapp.py
from celery import Celery
app = Celery(
'myapp',
broker='redis://localhost:6379/0',
result_backend='redis://localhost:6379/0'
)
@app.task
def add(x, y):
print("add", x, y)
return x + y
if __name__ == '__main__':
app.start()
打开一个新的终端，使用下面的命令启动celery的worker服务:
$ python myapp.py worker -l DEBUG
正常情况下，可以看到worker正常启动。启动的时候会显示一些banner信息，包括AMQP的实现协议，任务等:
$ celery -A myapp worker -l DEBUG
-------------- celery@bogon v5.1.2 (sun-harmonics)
--- ***** -----
-- ******* ---- macOS-10.16-x86_64-i386-64bit 2021-09-08 20:33:45
- *** --- * ---
- ** ---------- [config]
- ** ---------- .> app: myapp:0x7f855079e730
- ** ---------- .> transport: redis://localhost:6379/0
- ** ---------- .> results: disabled://
- *** --- * --- .> concurrency: 12 (prefork)
-- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker)
--- ***** -----
-------------- [queues]
.> celery exchange=celery(direct) key=celery
[tasks]
. myapp.add
[2021-09-08 20:33:46,220: INFO/MainProcess] Connected to redis://localhost:6379/0
[2021-09-08 20:33:46,234: INFO/MainProcess] mingle: searching for neighbors
[2021-09-08 20:33:47,279: INFO/MainProcess] mingle: all alone
[2021-09-08 20:33:47,315: INFO/MainProcess] celery@bogon ready.
再开启一个终端窗口，作为client执行下面的代码, 可以看到add函数正确的执行，获取到计算 16+16 的结果 32。注意: 这个过程是远程执行的，使用的是delay方法，函数的打印print("add", x, y)并没有输出:
$ python
>>> from myapp import add
>>> task = add.delay(16,16)
>>> task
>>> task.get()
32
在celery的worker服务窗口，可以看到类似下面的输出。收到一个执行任务 myapp.add 的请求, 请求的uuid是 5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b ，参数数组是 [16, 16] 正常执行后返回结果32。
[2021-11-11 20:13:48,040: INFO/MainProcess] Task myapp.add[5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b] received
[2021-11-11 20:13:48,040: DEBUG/MainProcess] TaskPool: Apply <function fast_trace_task at 0x7fda086baa60> (args:('myapp.add', '5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b', {'lang': 'py', 'task': 'myapp.add', 'id': '5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b', 'shadow': None, 'eta': None, 'expires': None, 'group': None, 'group_index': None, 'retries': 0, 'timelimit': [None, None], 'root_id': '5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b', 'parent_id': None, 'argsrepr': '(16, 16)', 'kwargsrepr': '{}', 'origin': 'gen63119@localhost', 'ignore_result': False, 'reply_to': '97a3e117-c8cf-3d4c-97c0-c0a76aaf9a16', 'correlation_id': '5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b', 'hostname': 'celery@localhost', 'delivery_info': {'exchange': '', 'routing_key': 'celery', 'priority': 0, 'redelivered': None}, 'args': [16, 16], 'kwargs': {}}, b'[[16, 16], {}, {"callbacks": null, "errbacks": null, "chain": null, "chord": null}]', 'application/json', 'utf-8') kwargs:{})
[2021-11-11 20:13:49,059: INFO/ForkPoolWorker-8] Task myapp.add[5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b] succeeded in 1.0166977809999995s: 32
在redis的monitor窗口，也可以可以看到类似的输出，展示了过程中一些对redis的操作命令:
+1636632828.304020 [0 172.16.0.117:51127] "SUBSCRIBE" "celery-task-meta-5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b"
+1636632828.304447 [0 172.16.0.117:51129] "PING"
+1636632828.305448 [0 172.16.0.117:51129] "LPUSH" "celery" "{\"body\": \"W1sxNiwgMTZdLCB7fSwgeyJjYWxsYmFja3MiOiBudWxsLCAiZXJyYmFja3MiOiBudWxsLCAiY2hhaW4iOiBudWxsLCAiY2hvcmQiOiBudWxsfV0=\", \"content-encoding\": \"utf-8\", \"content-type\": \"application/json\", \"headers\": {\"lang\": \"py\", \"task\": \"myapp.add\", \"id\": \"5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b\", \"shadow\": null, \"eta\": null, \"expires\": null, \"group\": null, \"group_index\": null, \"retries\": 0, \"timelimit\": [null, null], \"root_id\": \"5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b\", \"parent_id\": null, \"argsrepr\": \"(16, 16)\", \"kwargsrepr\": \"{}\", \"origin\": \"gen63119@localhost\", \"ignore_result\": false}, \"properties\": {\"correlation_id\": \"5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b\", \"reply_to\": \"97a3e117-c8cf-3d4c-97c0-c0a76aaf9a16\", \"delivery_mode\": 2, \"delivery_info\": {\"exchange\": \"\", \"routing_key\": \"celery\"}, \"priority\": 0, \"body_encoding\": \"base64\", \"delivery_tag\": \"20dbd584-b669-4ef0-8a3b-41d19b354690\"}}"
+1636632828.307040 [0 172.16.0.117:52014] "MULTI"
+1636632828.307075 [0 172.16.0.117:52014] "ZADD" "unacked_index" "1636632828.038743" "20dbd584-b669-4ef0-8a3b-41d19b354690"
+1636632828.307088 [0 172.16.0.117:52014] "HSET" "unacked" "20dbd584-b669-4ef0-8a3b-41d19b354690" "[{\"body\": \"W1sxNiwgMTZdLCB7fSwgeyJjYWxsYmFja3MiOiBudWxsLCAiZXJyYmFja3MiOiBudWxsLCAiY2hhaW4iOiBudWxsLCAiY2hvcmQiOiBudWxsfV0=\", \"content-encoding\": \"utf-8\", \"content-type\": \"application/json\", \"headers\": {\"lang\": \"py\", \"task\": \"myapp.add\", \"id\": \"5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b\", \"shadow\": null, \"eta\": null, \"expires\": null, \"group\": null, \"group_index\": null, \"retries\": 0, \"timelimit\": [null, null], \"root_id\": \"5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b\", \"parent_id\": null, \"argsrepr\": \"(16, 16)\", \"kwargsrepr\": \"{}\", \"origin\": \"gen63119@localhost\", \"ignore_result\": false}, \"properties\": {\"correlation_id\": \"5aabfc0b-04b5-4a51-86b0-6a7263e2ef3b\", \"reply_to\": \"97a3e117-c8cf-3d4c-97c0-c0a76aaf9a16\", \"delivery_mode\": 2, \"delivery_info\": {\"exchange\": \"\", \"routing_key\": \"celery\"}, \"priority\": 0, \"body_encoding\": \"base64\", \"delivery_tag\": \"20dbd584-b669-4ef0-8a3b-41d19b354690\"}}, \"\", \"celery\"]"
...
我们再一次回顾下图，对比一下示例，加强理解：
我们先启动一个celery的worker服务作为消费者 再启动一个窗口作为生产者执行task 使用redis作为broker，负责生产者和消费者之间的消息通讯 最终生成者的task，作为消息发送到远程的消费者上执行，执行的结果又通过网络回传给生产者
上面示例展示了celery作为一个分布式任务调度系统的执行过程，本地的任务调用，通过AMQP协议的包装，作为消息发送到远程的消费者执行。
celery项目概述
解析celery采用的代码版本5.0.5, 主要模块结构:
| 模块 | 描述 |
|---|---|
| app | celery的app实现 |
| apps | celery服务的三种主要模式，worker，beat和multi |
| backends | 任务结果存储 |
| bin | 命令行工具实现 |
| concurrency | 各种并发实现，包括线程，gevent，asyncpool等 |
| events | 事件实现 |
| worker | 服务启动环节实现 |
| beat.py&&schedules.py | 定时和调度实现 |
| result.py | 任务结果实现 |
| signals.py | 一些信号定义 |
| status.py | 一些状态定义 |
从项目结构看，模块较多，功能复杂。不过我们已经搞定了vine, py-amqp和kombu三个库，接下来只需要理解worker，beat和multi三种服务模型，就可以较好的了解celery这个分布式系统如何构建。
worker启动流程跟踪
worker的启动命令 celery -A myapp worker -l DEBUG 使celery作为一个模块，入口在main文件的main函数:
# ch23-celery/celery-5.0.5/celery/__main__.py
def main():
"""Entrypoint to the ``celery`` umbrella command."""
"""celery命令入口"""
...
# 具体执行的main函数
from celery.bin.celery import main as _main
sys.exit(_main())
celery命令作为主命令，加载celery-app的同时，还会启动worker子命令:
# ch23-celery/celery-5.0.5/celery/bin/celery.py
def celery(ctx, app, broker, result_backend, loader, config, workdir,
no_color, quiet, version):
"""Celery command entrypoint."""
...
ctx.obj = CLIContext(app=app, no_color=no_color, workdir=workdir,
quiet=quiet)
# worker/beat/events三个主要子命令参数
# User options
worker.params.extend(ctx.obj.app.user_options.get('worker', []))
beat.params.extend(ctx.obj.app.user_options.get('beat', []))
events.params.extend(ctx.obj.app.user_options.get('events', []))
def main() -> int:
"""Start celery umbrella command.
This function is the main entrypoint for the CLI.
:return: The exit code of the CLI.
"""
return celery(auto_envvar_prefix="CELERY")
在worker子命令中创建worker并启动:
# ch23-celery/celery-5.0.5/celery/bin/worker.py
def worker(ctx, hostname=None, pool_cls=None, app=None, uid=None, gid=None,
loglevel=None, logfile=None, pidfile=None, statedb=None,
**kwargs):
# 创建和启动worker
worker = app.Worker(
hostname=hostname, pool_cls=pool_cls, loglevel=loglevel,
logfile=logfile, # node format handled by celery.app.log.setup
pidfile=node_format(pidfile, hostname),
statedb=node_format(statedb, hostname),
no_color=ctx.obj.no_color,
quiet=ctx.obj.quiet,
**kwargs)
worker.start()
下面是创建worker的方式，创一个 celery.apps.worker:Worker 对象:
# ch23-celery/celery-5.0.5/celery/app/base.py
def Worker(self):
# 创建worker
return self.subclass_with_self('celery.apps.worker:Worker')
服务启动过程中，调用链路如下：
+----------+
+--->app.celery|
| +----------+
+---------+ +----------+ |
|main.main+--->bin.celery+---+
+---------+ +----------+ |
| +----------+ +-----------+
+--->bin.worker+--->apps.worker|
+----------+ +-----------+
在这个服务启动过程中，创建了celery-application和worker-application两个应用程序。至于具体的启动流程，我们暂时跳过，先看看客户端的流程。
client启动流程分析
示例client的启动过程包括下面4步: 1 创建celery-application， 2 创建task 3 调用task的delay方法执行任务得到一个异步结果 4 最后使用异步结果的get方法获取真实结果
task是通过app创建的装饰器创建的Promise对象:
# ch23-celery/celery-5.0.5/celery/app/base.py
task_cls = 'celery.app.task:Task'
def task(self, *args, **opts):
"""Decorator to create a task class out of any callable.
"""
def inner_create_task_cls(shared=True, filter=None, lazy=True, **opts):
def _create_task_cls(fun):
ret = PromiseProxy(self._task_from_fun, (fun,), opts,
__doc__=fun.__doc__)
return ret
return _create_task_cls
return inner_create_task_cls(**opts)
task实际上是一个由Task基类动态创建的子类:
def _task_from_fun(self, fun, name=None, base=None, bind=False, **options):
base = base or self.Task
task = type(fun.__name__, (base,), dict({
'app': self,
'name': name,
'run': run,
'_decorated': True,
'__doc__': fun.__doc__,
'__module__': fun.__module__,
'__annotations__': fun.__annotations__,
'__header__': staticmethod(head_from_fun(fun, bound=bind)),
'__wrapped__': run}, **options))
add_autoretry_behaviour(task, **options)
# 增加task
self._tasks[task.name] = task
task.bind(self) # connects task to this app
add_autoretry_behaviour(task, **options)
return task
任务的执行使用app的send_task方法进行:
# ch23-celery/celery-5.0.5/celery/app/task.py
def delay(self, *args, **kwargs):
...
return app.send_task(
self.name, args, kwargs, task_id=task_id, producer=producer,
link=link, link_error=link_error, result_cls=self.AsyncResult,
shadow=shadow, task_type=self,
**options
)
可以看到，client作为生产者启动任务，也需要创建celery-application，下面我们就先看celery-application的实现。
celery的app两大功能
Celery的构造函数:
class Celery:
# 协议类
amqp_cls = 'celery.app.amqp:AMQP'
backend_cls = None
# 事件类
events_cls = 'celery.app.events:Events'
loader_cls = None
log_cls = 'celery.app.log:Logging'
# 控制类
control_cls = 'celery.app.control:Control'
# 任务类
task_cls = 'celery.app.task:Task'
# 任务注册中心
registry_cls = 'celery.app.registry:TaskRegistry'
...
def __init__(self, main=None, loader=None, backend=None,
amqp=None, events=None, log=None, control=None,
set_as_current=True, tasks=None, broker=None, include=None,
changes=None, config_source=None, fixups=None, task_cls=None,
autofinalize=True, namespace=None, strict_typing=True,
**kwargs):
# 启动步骤
self.steps = defaultdict(set)
# 待执行的task
self._pending = deque()
# 所有任务
self._tasks = self.registry_cls(self._tasks or {})
...
self.__autoset('broker_url', broker)
self.__autoset('result_backend', backend)
...
self.on_init()
_register_app(self)
可以看到celery类提供了一些默认模块类的名称，可以根据这些类名动态创建对象。app对象任务的处理使用一个队列作为pending状态的任务容器，使用TaskRegistry来管理任务的注册。
任务通过task装饰器，记录到celery的TaskRegistry中:
def task(self, *args, **opts):
...
# 增加task
self._tasks[task.name] = task
task.bind(self) # connects task to this app
add_autoretry_behaviour(task, **options)
...
celery另外一个核心功能是提供到broker的连接:
def _connection(self, url, userid=None, password=None,
virtual_host=None, port=None, ssl=None,
connect_timeout=None, transport=None,
transport_options=None, heartbeat=None,
login_method=None, failover_strategy=None, **kwargs):
conf = self.conf
return self.amqp.Connection(
url,
userid or conf.broker_user,
password or conf.broker_password,
virtual_host or conf.broker_vhost,
port or conf.broker_port,
transport=transport or conf.broker_transport,
ssl=self.either('broker_use_ssl', ssl),
heartbeat=heartbeat,
login_method=login_method or conf.broker_login_method,
failover_strategy=(
failover_strategy or conf.broker_failover_strategy
),
transport_options=dict(
conf.broker_transport_options, **transport_options or {}
),
connect_timeout=self.either(
'broker_connection_timeout', connect_timeout
),
)
broker_connection = connection
@cached_property
def amqp(self):
"""AMQP related functionality: :class:`~@amqp`."""
return instantiate(self.amqp_cls, app=self)
AMQP的实现，是依赖kombu提供的AMQP协议封装:
from kombu import Connection, Consumer, Exchange, Producer, Queue, pools
class AMQP:
"""App AMQP API: app.amqp."""
Connection = Connection
然后使用我们熟悉的Queue，Consumer，Producer进行消息的生成和消费:
def Queues(self, queues, create_missing=None,
autoexchange=None, max_priority=None):
...
return self.Queues(
queues, self.default_exchange, create_missing,
autoexchange, max_priority, default_routing_key,
)
def TaskConsumer(self, channel, queues=None, accept=None, **kw):
...
return self.Consumer(
channel, accept=accept,
queues=queues or list(self.queues.consume_from.values()),
**kw
)
def _create_task_sender(self):
...
producer.publish(
body,
exchange=exchange,
routing_key=routing_key,
serializer=serializer or default_serializer,
compression=compression or default_compressor,
retry=retry, retry_policy=_rp,
delivery_mode=delivery_mode, declare=declare,
headers=headers2,
**properties
)
...
celery-app的两大功能，管理task和管理AMQP连接，我们有一个大概的了解。
worker模式启动流程
worker模式启动在WorkController中，将服务分成不同的阶段，然后将各个阶段组装成一个叫做蓝图(Blueprint)的方式进行管理:
class WorkController:
# 内部类
class Blueprint(bootsteps.Blueprint):
"""Worker bootstep blueprint."""
name = 'Worker'
default_steps = {
'celery.worker.components:Hub',
'celery.worker.components:Pool',
'celery.worker.components:Beat',
'celery.worker.components:Timer',
'celery.worker.components:StateDB',
'celery.worker.components:Consumer',
'celery.worker.autoscale:WorkerComponent',
}
def __init__(self, app=None, hostname=None, **kwargs):
self.blueprint = self.Blueprint(
steps=self.app.steps['worker'],
on_start=self.on_start,
on_close=self.on_close,
on_stopped=self.on_stopped,
)
self.blueprint.apply(self, **kwargs)
启动蓝图:
def start(self):
try:
# 启动worker
self.blueprint.start(self)
except WorkerTerminate:
self.terminate()
except Exception as exc:
logger.critical('Unrecoverable error: %r', exc, exc_info=True)
self.stop(exitcode=EX_FAILURE)
except SystemExit as exc:
self.stop(exitcode=exc.code)
except KeyboardInterrupt:
self.stop(exitcode=EX_FAILURE)
启动步骤，比较简单，大概代码如下:
class StepType(type):
"""Meta-class for steps."""
name = None
requires = None
class Step(metaclass=StepType):
...
def instantiate(self, name, *args, **kwargs):
return symbol_by_name(name)(*args, **kwargs)
def include_if(self, parent):
return self.enabled
def _should_include(self, parent):
if self.include_if(parent):
return True, self.create(parent)
return False, None
def create(self, parent):
"""Create the step."""
从Step大概可以看出:
每个步骤，可以有依赖requires 每个步骤，可以有具体的动作instantiate 步骤具有树状的父子结构，可以自动创建上级步骤
比如一个消费者步骤, 依赖Connection步骤。启动的时候对Connection进行消费。两者代码如下:
class ConsumerStep(StartStopStep):
"""Bootstep that starts a message consumer."""
requires = ('celery.worker.consumer:Connection',)
consumers = None
def start(self, c):
channel = c.connection.channel()
self.consumers = self.get_consumers(channel)
for consumer in self.consumers or []:
consumer.consume()
class Connection(bootsteps.StartStopStep):
"""Service managing the consumer broker connection."""
def __init__(self, c, **kwargs):
c.connection = None
super().__init__(c, **kwargs)
def start(self, c):
c.connection = c.connect()
info('Connected to %s', c.connection.as_uri())
在Blueprint中创建和管理这些step:
class Blueprint:
def __init__(self, steps=None, name=None,
on_start=None, on_close=None, on_stopped=None):
self.name = name or self.name or qualname(type(self))
# 并集
self.types = set(steps or []) | set(self.default_steps)
...
self.steps = {}
def apply(self, parent, **kwargs):
steps = self.steps = dict(symbol_by_name(step) for step in self.types)
self._debug('Building graph...')
for S in self._finalize_steps(steps):
step = S(parent, **kwargs)
steps[step.name] = step
order.append(step)
self._debug('New boot order: {%s}',
', '.join(s.alias for s in self.order))
for step in order:
step.include(parent)
return self
启动Blueprint:
def start(self, parent):
self.state = RUN
if self.on_start:
self.on_start()
for i, step in enumerate(s for s in parent.steps if s is not None):
self._debug('Starting %s', step.alias)
self.started = i + 1
step.start(parent)
logger.debug('^-- substep ok')
通过将启动过程拆分成多个step单元，然后组合单元构建成graph，逐一启动。
小结
本篇我们正式学习了一下celery的使用流程，了解celery如果使用redis作为broker，利用服务作为消费者，使用客户端作为生成者，完成一次远程任务的执行。简单探索worker服务模式的启动流程，重点分析celery-application的管理task和管理连接两大功能实现。
小技巧
celery中展示了一种动态创建类和对象的方法:
task = type(fun.__name__, (Task,), dict({
'app': self,
'name': name,
'run': run,
'_decorated': True,
'__doc__': fun.__doc__,
'__module__': fun.__module__,
'__annotations__': fun.__annotations__,
'__header__': staticmethod(head_from_fun(fun, bound=bind)),
'__wrapped__': run}, **options))()
通过type函数创了一个动态的task子类，然后执行 () 实例化一个task子对象。
参考链接
以编程方式定义类 https://python3-cookbook.readthedocs.io/zh_CN/latest/c09/p18_define_classes_programmatically.html
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