celery源码分析-Task的初始化与发送任务
celery源码分析
本文环境python3.5.2，celery4.0.2，django1.10.x系列
celery的任务发送
在Django项目中使用了装饰器来包装待执行任务，
from celery import shared_task, app
@shared_task
def add(x, y):
return x + y
@app.task(bind=True)
def debug_task(self):
print('Request: {0!r}'.format(self.request))
此时分析一下，Task是怎样在celery中执行的。
首先，先看shared_task函数，
def shared_task(*args, **kwargs):
"""Create shared task (decorator).
This can be used by library authors to create tasks that'll work
for any app environment. # 由shared_task装饰的任务可以被任何app调用
Returns:
~celery.local.Proxy: A proxy that always takes the task from the
current apps task registry.
Example:
>>> from celery import Celery, shared_task
>>> @shared_task
... def add(x, y):
... return x + y
...
>>> app1 = Celery(broker='amqp://')
>>> add.app is app1
True
>>> app2 = Celery(broker='redis://')
>>> add.app is app2
True
"""
def create_shared_task(**options):
def __inner(fun):
name = options.get('name') # 从装饰器中获取是否传入name参数
# Set as shared task so that unfinalized apps,
# and future apps will register a copy of this task.
_state.connect_on_app_finalize(
lambda app: app._task_from_fun(fun, **options)
) # 将该task添加到全局变量中，当其他app调用该函数时会将该任务添加到app任务列表中，以此达到所有任务共享
# Force all finalized apps to take this task as well.
for app in _state._get_active_apps(): # 获取所有app的弱引用
if app.finalized: # 是否任务初始化过
with app._finalize_mutex: # 获取线程锁
app._task_from_fun(fun, **options) # 加载该任务
# Return a proxy that always gets the task from the current
# apps task registry.
def task_by_cons():
app = _state.get_current_app() # 获取当前的app
return app.tasks[
name or app.gen_task_name(fun.__name__, fun.__module__)
] # 根据task的name或者fun来获取对应的task
return Proxy(task_by_cons) # 通过代理类实例化task_by_cons
return __inner # 返回被__inner
if len(args) == 1 and callable(args[0]): # 如果装饰器传入参数就1个并且是可调用的，即shared_task没有传入参数
return create_shared_task(**kwargs)(args[0]) # 直接调用该函数并传入该函数
return create_shared_task(*args, **kwargs) # 处理shared_task中的传入参数
按照示例中的无参数调用则返回了Proxy的实例，传入参数就是task_by_cons，此时查看一下Proxy类的实现，该类位于celery/local.py中，
class Proxy(object):
"""Proxy to another object."""
# Code stolen from werkzeug.local.Proxy.
__slots__ = ('__local', '__args', '__kwargs', '__dict__')
def __init__(self, local,
args=None, kwargs=None, name=None, __doc__=None):
object.__setattr__(self, '_Proxy__local', local) # 将传入参数local设置到_Proxy__local属性中
object.__setattr__(self, '_Proxy__args', args or ()) # 设置列表属性
object.__setattr__(self, '_Proxy__kwargs', kwargs or {}) # 设置键值属性
if name is not None:
object.__setattr__(self, '__custom_name__', name)
if __doc__ is not None:
object.__setattr__(self, '__doc__', __doc__)
...
def _get_current_object(self):
"""Get current object.
This is useful if you want the real
object behind the proxy at a time for performance reasons or because
you want to pass the object into a different context.
"""
loc = object.__getattribute__(self, '_Proxy__local') # 获取初始化传入的local
if not hasattr(loc, '__release_local__'): # 如果没有__release_local__属性
return loc(*self.__args, **self.__kwargs) # 函数调用，将初始化的值传入调用该函数
try: # pragma: no cover
# not sure what this is about
return getattr(loc, self.__name__) # 获取当前__name__属性值
except AttributeError: # pragma: no cover
raise RuntimeError('no object bound to {0.__name__}'.format(self))
...
def __getattr__(self, name):
if name == '__members__':
return dir(self._get_current_object())
return getattr(self._get_current_object(), name) # 获取obj的属性
def __setitem__(self, key, value):
self._get_current_object()[key] = value # 设置key val
def __delitem__(self, key):
del self._get_current_object()[key] # 删除对应key
def __setslice__(self, i, j, seq):
self._get_current_object()[i:j] = seq # 列表操作
def __delslice__(self, i, j):
del self._get_current_object()[i:j]
def __setattr__(self, name, value):
setattr(self._get_current_object(), name, value) # 设置属性
def __delattr__(self, name):
delattr(self._get_current_object(), name) # 删除对应属性
只选取了部分属性分析如上，主要是根据传入的是否local是否是函数，或者包含release_local来判断是否是调用函数，或是获取属性来处理。
此时在初始化过程中，为每个app添加该任务时，会调用到app._task_from_fun(fun, **options)，
def _task_from_fun(self, fun, name=None, base=None, bind=False, **options):
if not self.finalized and not self.autofinalize:
raise RuntimeError('Contract breach: app not finalized')
name = name or self.gen_task_name(fun.__name__, fun.__module__) # 如果传入了名字则使用，否则就使用moudle name的形式
base = base or self.Task # 是否传入Task，否则用类自己的Task类 默认celery.app.task:Task
if name not in self._tasks: # 如果要加入的任务名称不再_tasks中
run = fun if bind else staticmethod(fun) # 是否bind该方法是则直接使用该方法，否则就置为静态方法
task = type(fun.__name__, (base,), dict({
'app': self, # 动态创建Task类实例
'name': name, # Task的name
'run': run, # task的run方法
'_decorated': True, # 是否装饰
'__doc__': fun.__doc__,
'__module__': fun.__module__,
'__header__': staticmethod(head_from_fun(fun, bound=bind)),
'__wrapped__': run}, **options))()
# for some reason __qualname__ cannot be set in type()
# so we have to set it here.
try:
task.__qualname__ = fun.__qualname__
except AttributeError:
pass
self._tasks[task.name] = task # 将任务添加到_tasks任务中
task.bind(self) # connects task to this app # 调用task的bind方法绑定相关属性到该实例上
autoretry_for = tuple(options.get('autoretry_for', ()))
retry_kwargs = options.get('retry_kwargs', {})
if autoretry_for and not hasattr(task, '_orig_run'):
@wraps(task.run)
def run(*args, **kwargs):
try:
return task._orig_run(*args, **kwargs)
except autoretry_for as exc:
raise task.retry(exc=exc, **retry_kwargs)
task._orig_run, task.run = task.run, run
else:
task = self._tasks[name] # 否则获取该task
return task # 返回该task
其中task在默认情况下是celery.app.task:Task，在动态生成该实例后，滴啊用了task.bind(self)方法，
@classmethod
def bind(cls, app):
was_bound, cls.__bound__ = cls.__bound__, True
cls._app = app # 设置类的_app属性
conf = app.conf # 获取app的配置信息
cls._exec_options = None # clear option cache
if cls.typing is None:
cls.typing = app.strict_typing
for attr_name, config_name in cls.from_config: # 设置类中的默认值
if getattr(cls, attr_name, None) is None: # 如果获取该属性为空
setattr(cls, attr_name, conf[config_name]) # 使用app配置中的默认值
# decorate with annotations from config.
if not was_bound:
cls.annotate()
from celery.utils.threads import LocalStack
cls.request_stack = LocalStack() # 使用线程栈保存数据
# PeriodicTask uses this to add itself to the PeriodicTask schedule.
cls.on_bound(app)
return app
此时在Django项目中调用该异步任务时，如下调用，
add.delay(1,2)
此时就是通过代理类获取task的delay方法，
def delay(self, *args, **kwargs):
"""Star argument version of :meth:`apply_async`.
Does not support the extra options enabled by :meth:`apply_async`.
Arguments:
*args (Any): Positional arguments passed on to the task.
**kwargs (Any): Keyword arguments passed on to the task.
Returns:
celery.result.AsyncResult: Future promise.
"""
return self.apply_async(args, kwargs)
此时直接调用了self.apply_async方法，
def apply_async(self, args=None, kwargs=None, task_id=None, producer=None,
link=None, link_error=None, shadow=None, **options):
"""Apply tasks asynchronously by sending a message.
Arguments:
args (Tuple): The positional arguments to pass on to the task.
kwargs (Dict): The keyword arguments to pass on to the task.
countdown (float): Number of seconds into the future that the
task should execute. Defaults to immediate execution.
eta (~datetime.datetime): Absolute time and date of when the task
should be executed. May not be specified if `countdown`
is also supplied.
expires (float, ~datetime.datetime): Datetime or
seconds in the future for the task should expire.
The task won't be executed after the expiration time.
shadow (str): Override task name used in logs/monitoring.
Default is retrieved from :meth:`shadow_name`.
connection (kombu.Connection): Re-use existing broker connection
instead of acquiring one from the connection pool.
retry (bool): If enabled sending of the task message will be
retried in the event of connection loss or failure.
Default is taken from the :setting:`task_publish_retry`
setting. Note that you need to handle the
producer/connection manually for this to work.
retry_policy (Mapping): Override the retry policy used.
See the :setting:`task_publish_retry_policy` setting.
queue (str, kombu.Queue): The queue to route the task to.
This must be a key present in :setting:`task_queues`, or
:setting:`task_create_missing_queues` must be
enabled. See :ref:`guide-routing` for more
information.
exchange (str, kombu.Exchange): Named custom exchange to send the
task to. Usually not used in combination with the ``queue``
argument.
routing_key (str): Custom routing key used to route the task to a
worker server. If in combination with a ``queue`` argument
only used to specify custom routing keys to topic exchanges.
priority (int): The task priority, a number between 0 and 9.
Defaults to the :attr:`priority` attribute.
serializer (str): Serialization method to use.
Can be `pickle`, `json`, `yaml`, `msgpack` or any custom
serialization method that's been registered
with :mod:`kombu.serialization.registry`.
Defaults to the :attr:`serializer` attribute.
compression (str): Optional compression method
to use. Can be one of ``zlib``, ``bzip2``,
or any custom compression methods registered with
:func:`kombu.compression.register`.
Defaults to the :setting:`task_compression` setting.
link (~@Signature): A single, or a list of tasks signatures
to apply if the task returns successfully.
link_error (~@Signature): A single, or a list of task signatures
to apply if an error occurs while executing the task.
producer (kombu.Producer): custom producer to use when publishing
the task.
add_to_parent (bool): If set to True (default) and the task
is applied while executing another task, then the result
will be appended to the parent tasks ``request.children``
attribute. Trailing can also be disabled by default using the
:attr:`trail` attribute
publisher (kombu.Producer): Deprecated alias to ``producer``.
headers (Dict): Message headers to be included in the message.
Returns:
~@AsyncResult: Promise of future evaluation.
Raises:
TypeError: If not enough arguments are passed, or too many
arguments are passed. Note that signature checks may
be disabled by specifying ``@task(typing=False)``.
kombu.exceptions.OperationalError: If a connection to the
transport cannot be made, or if the connection is lost.
Note:
Also supports all keyword arguments supported by
:meth:`kombu.Producer.publish`.
"""
if self.typing:
try:
check_arguments = self.__header__ # 获取参数
except AttributeError: # pragma: no cover
pass
else:
check_arguments(*(args or ()), **(kwargs or {}))
app = self._get_app() # 获取当前app
if app.conf.task_always_eager: # 如果该配置为true
return self.apply(args, kwargs, task_id=task_id or uuid(),
link=link, link_error=link_error, **options) # 本地执行该任务并返回结果
# add 'self' if this is a "task_method".
if self.__self__ is not None:
args = args if isinstance(args, tuple) else tuple(args or ())
args = (self.__self__,) + args
shadow = shadow or self.shadow_name(args, kwargs, options)
preopts = self._get_exec_options() # 获取队列等信息
options = dict(preopts, **options) if options else preopts # 设置成字典类型
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
) # 调用app发送send_task
该方法比较复杂，主要是进行了组装待发送任务的任务的参数，如connection，queue，exchange，routing_key等，如果是配置了本地直接执行则本地执行直接返回结果，否则调用app实例的send_task发送任务。
def send_task(self, name, args=None, kwargs=None, countdown=None,
eta=None, task_id=None, producer=None, connection=None,
router=None, result_cls=None, expires=None,
publisher=None, link=None, link_error=None,
add_to_parent=True, group_id=None, retries=0, chord=None,
reply_to=None, time_limit=None, soft_time_limit=None,
root_id=None, parent_id=None, route_name=None,
shadow=None, chain=None, task_type=None, **options):
"""Send task by name.
Supports the same arguments as :meth:`@-Task.apply_async`.
Arguments:
name (str): Name of task to call (e.g., `"tasks.add"`).
result_cls (~@AsyncResult): Specify custom result class.
"""
parent = have_parent = None
amqp = self.amqp # 获取amqp实例
task_id = task_id or uuid() # 设置任务id，如果没有传入则生成任务id
producer = producer or publisher # XXX compat # 生成这
router = router or amqp.router # 路由值，如果没有则使用amqp的router
conf = self.conf # 获取配置信息
if conf.task_always_eager: # pragma: no cover # 如果配置了本地执行则打印信息
warnings.warn(AlwaysEagerIgnored(
'task_always_eager has no effect on send_task',
), stacklevel=2)
options = router.route(
options, route_name or name, args, kwargs, task_type) # 生成route信息
if not root_id or not parent_id:
parent = self.current_worker_task
if parent:
if not root_id:
root_id = parent.request.root_id or parent.request.id
if not parent_id:
parent_id = parent.request.id
message = amqp.create_task_message(
task_id, name, args, kwargs, countdown, eta, group_id,
expires, retries, chord,
maybe_list(link), maybe_list(link_error),
reply_to or self.oid, time_limit, soft_time_limit,
self.conf.task_send_sent_event,
root_id, parent_id, shadow, chain,
) # 生成任务信息
if connection:
producer = amqp.Producer(connection) # 如果有连接则生成生产者
with self.producer_or_acquire(producer) as P:
with P.connection._reraise_as_library_errors():
self.backend.on_task_call(P, task_id)
amqp.send_task_message(P, name, message, **options) # 发送任务消息
result = (result_cls or self.AsyncResult)(task_id) # 生成异步任务实例
if add_to_parent:
if not have_parent:
parent, have_parent = self.current_worker_task, True
if parent:
parent.add_trail(result)
return result # 返回结果
至此一个任务就发送出去，等待着消费者消费掉任务。
worker消费task的概述
在分析celery的worker的启动过程中，最后开启了loop等待任务来消费，启动定义的回调函数就是on_task_received，
def on_task_received(message):
# payload will only be set for v1 protocol, since v2
# will defer deserializing the message body to the pool.
payload = None
try:
type_ = message.headers['task'] # protocol v2 # 获取任务
except TypeError:
return on_unknown_message(None, message) # 如果解析失败
except KeyError:
try:
payload = message.decode() # 再次解析消息
except Exception as exc: # pylint: disable=broad-except
return self.on_decode_error(message, exc)
try:
type_, payload = payload['task'], payload # protocol v1 # 利用协议解析任务
except (TypeError, KeyError):
return on_unknown_message(payload, message)
try:
strategy = strategies[type_] # 获取type_的对应stratepy
except KeyError as exc:
return on_unknown_task(None, message, exc)
else:
try:
strategy(
message, payload,
promise(call_soon, (message.ack_log_error,)),
promise(call_soon, (message.reject_log_error,)),
callbacks,
) # 处理获取的信息内容
except InvalidTaskError as exc:
return on_invalid_task(payload, message, exc)
至此，从Django应用客户端发送的消息就到达了启动的worker的进程并被消费掉。
大概的消费流程如下，
此时的strategies就是在consumer的task实例在启动start时，调用的update_strategies方法，
def update_strategies(self):
loader = self.app.loader # app的加载器
for name, task in items(self.app.tasks): # 遍历所有的任务
self.strategies[name] = task.start_strategy(self.app, self) # 将task的name设为key 将task调用的返回值作为key
task.__trace__ = build_tracer(name, task, loader, self.hostname,
app=self.app) # 处理相关执行结果的函数
此时我们继续查看task.start_strategy函数，
def start_strategy(self, app, consumer, **kwargs):
return instantiate(self.Strategy, self, app, consumer, **kwargs) # 生成task实例
此时self.Strategy的默认值是celery.worker.strategy:default，
def default(task, app, consumer,
info=logger.info, error=logger.error, task_reserved=task_reserved,
to_system_tz=timezone.to_system, bytes=bytes, buffer_t=buffer_t,
proto1_to_proto2=proto1_to_proto2):
"""Default task execution strategy.
Note:
Strategies are here as an optimization, so sadly
it's not very easy to override.
"""
hostname = consumer.hostname # 设置相关的消费者信息
connection_errors = consumer.connection_errors # 设置错误值
_does_info = logger.isEnabledFor(logging.INFO)
# task event related
# (optimized to avoid calling request.send_event)
eventer = consumer.event_dispatcher
events = eventer and eventer.enabled
send_event = eventer.send
task_sends_events = events and task.send_events
call_at = consumer.timer.call_at
apply_eta_task = consumer.apply_eta_task
rate_limits_enabled = not consumer.disable_rate_limits
get_bucket = consumer.task_buckets.__getitem__
handle = consumer.on_task_request
limit_task = consumer._limit_task
body_can_be_buffer = consumer.pool.body_can_be_buffer
Req = create_request_cls(Request, task, consumer.pool, hostname, eventer) # 返回一个请求类
revoked_tasks = consumer.controller.state.revoked
def task_message_handler(message, body, ack, reject, callbacks,
to_timestamp=to_timestamp):
if body is None:
body, headers, decoded, utc = (
message.body, message.headers, False, True,
)
if not body_can_be_buffer:
body = bytes(body) if isinstance(body, buffer_t) else body
else:
body, headers, decoded, utc = proto1_to_proto2(message, body) # 解析接受的数据
req = Req(
message,
on_ack=ack, on_reject=reject, app=app, hostname=hostname,
eventer=eventer, task=task, connection_errors=connection_errors,
body=body, headers=headers, decoded=decoded, utc=utc,
) # 实例化请求
if _does_info:
info('Received task: %s', req)
if (req.expires or req.id in revoked_tasks) and req.revoked():
return
if task_sends_events:
send_event(
'task-received',
uuid=req.id, name=req.name,
args=req.argsrepr, kwargs=req.kwargsrepr,
root_id=req.root_id, parent_id=req.parent_id,
retries=req.request_dict.get('retries', 0),
eta=req.eta and req.eta.isoformat(),
expires=req.expires and req.expires.isoformat(),
) # 如果需要发送接受请求则发送
if req.eta: # 时间相关处理
try:
if req.utc:
eta = to_timestamp(to_system_tz(req.eta))
else:
eta = to_timestamp(req.eta, timezone.local)
except (OverflowError, ValueError) as exc:
error("Couldn't convert ETA %r to timestamp: %r. Task: %r",
req.eta, exc, req.info(safe=True), exc_info=True)
req.reject(requeue=False)
else:
consumer.qos.increment_eventually()
call_at(eta, apply_eta_task, (req,), priority=6)
else:
if rate_limits_enabled: # 速率限制
bucket = get_bucket(task.name)
if bucket:
return limit_task(req, bucket, 1)
task_reserved(req) #
if callbacks:
[callback(req) for callback in callbacks]
handle(req) # 处理接受的请求
return task_message_handler
此时处理的handler就是在consumer初始化的时候传入的w.process_task，
def _process_task(self, req):
"""Process task by sending it to the pool of workers."""
try:
req.execute_using_pool(self.pool)
except TaskRevokedError:
try:
self._quick_release() # Issue 877
except AttributeError:
pass
接着就会调用，req.execute_using_pool来执行该任务，该request位于create_request_cls中的Request类的方法，
class Request(base):
def execute_using_pool(self, pool, **kwargs):
task_id = self.id # 获取任务id
if (self.expires or task_id in revoked_tasks) and self.revoked(): # 检查是否过期或者是否已经执行过
raise TaskRevokedError(task_id)
time_limit, soft_time_limit = self.time_limits # 获取时间
result = apply_async( # 执行对应的func并返回结果
trace,
args=(self.type, task_id, self.request_dict, self.body,
self.content_type, self.content_encoding),
accept_callback=self.on_accepted,
timeout_callback=self.on_timeout,
callback=self.on_success,
error_callback=self.on_failure,
soft_timeout=soft_time_limit or default_soft_time_limit,
timeout=time_limit or default_time_limit,
correlation_id=task_id,
)
# cannot create weakref to None
# pylint: disable=attribute-defined-outside-init
self._apply_result = maybe(ref, result)
return result
此时调用的apply_async其实就是pool.apply_async的方法，传入的执行方法就是trace_task_ret,
def trace_task(task, uuid, args, kwargs, request={}, **opts):
"""Trace task execution."""
try:
if task.__trace__ is None:
task.__trace__ = build_tracer(task.name, task, **opts)
return task.__trace__(uuid, args, kwargs, request) # 调用在strategy更新时写入的方法
except Exception as exc:
return trace_ok_t(report_internal_error(task, exc), None, 0.0, None)
def _trace_task_ret(name, uuid, request, body, content_type,
content_encoding, loads=loads_message, app=None,
**extra_request):
app = app or current_app._get_current_object() # 获取app
embed = None
if content_type:
accept = prepare_accept_content(app.conf.accept_content)
args, kwargs, embed = loads(
body, content_type, content_encoding, accept=accept,
)
else:
args, kwargs, embed = body
hostname = gethostname()
request.update({
'args': args, 'kwargs': kwargs,
'hostname': hostname, 'is_eager': False,
}, **embed or {})
R, I, T, Rstr = trace_task(app.tasks[name],
uuid, args, kwargs, request, app=app) # 调用trace_task执行task
return (1, R, T) if I else (0, Rstr, T)
trace_task_ret = _trace_task_ret
在update_stragegy时传入的方法是,
task.__trace__ = build_tracer(name, task, loader, self.hostname,
app=self.app)
build_tracer函数的部分解析是，
def build_tracer(name, task, loader=None, hostname=None, store_errors=True,
Info=TraceInfo, eager=False, propagate=False, app=None,
monotonic=monotonic, truncate=truncate,
trace_ok_t=trace_ok_t, IGNORE_STATES=IGNORE_STATES):
fun = task if task_has_custom(task, '__call__') else task.run # 获取task对应的run函数
...
def trace_task(uuid, args, kwargs, request=None):
# R - is the possibly prepared return value.
# I - is the Info object.
# T - runtime
# Rstr - textual representation of return value
# retval - is the always unmodified return value.
# state - is the resulting task state.
# This function is very long because we've unrolled all the calls
# for performance reasons, and because the function is so long
# we want the main variables (I, and R) to stand out visually from the
# the rest of the variables, so breaking PEP8 is worth it ;)
R = I = T = Rstr = retval = state = None
task_request = None
time_start = monotonic()
...
# -*- TRACE -*-
try:
R = retval = fun(*args, **kwargs) # 执行对应的函数
state = SUCCESS
except Reject as exc:
...
return trace_task
此时调用的fun函数就是task本来应该执行的函数，此时就执行了对应task并获得了函数执行的返回结果。
至此，一个简单的消息的发送和消费的过程就完成了。
本文总结
主要是讲述了一个task任务从客户端的发送过程，然后服务端获得任务后并消费掉该任务，从而完成任务的消费，虽然本文的分析略显粗略，只是大致描述了任务的发送和消费，其中很多细节没有一一分析，大家如有兴趣可自行分析。