[源码分析] 分布式任务队列 Celery 多线程模型 之 子进程
[源码分析] 分布式任务队列 Celery 多线程模型 之 子进程
0x00 摘要
Celery是一个简单、灵活且可靠的，处理大量消息的分布式系统，专注于实时处理的异步任务队列，同时也支持任务调度。
在前文中，我们介绍了Celery 多线程模型，但是我们略过了子进程这一个阶段，本文看看子进程如何继续启动。
我们依然先要提出几个问题：
- 在启动子进程之前，需要做哪些准备？
- 如何知道子进程要运行什么命令？
- 如何构建父子进程通讯机制？
- 如何把父进程信息传递给子进程？
- 目前，Celery 应用是在父进程中。
- 子进程如何得到 Celery 应用？
- 如何恢复 Celery 应用？
- 父进程如何知道子进程已经ready，从而可以给子进程安排工作？
- 子进程如何接受父进程安排的任务？
为了便于大家理解，我们先给出本文一个最终关系图。
0x01 前文回顾
1.1 基类作用
前文我们讲到，在 AsynPool 的基类 Pool(object) 之中，建立了各种 消息处理函数，并且建立了子进程。
代码位置在：billiard/pool.py
具体代码如下，这里 _create_worker_process 就建立了子进程。
class Pool(object):
def __init__(self, processes=None, initializer=None, initargs=(),
maxtasksperchild=None, timeout=None, soft_timeout=None,
lost_worker_timeout=None,
max_restarts=None, max_restart_freq=1,
on_process_up=None,
on_process_down=None,
on_timeout_set=None,
on_timeout_cancel=None,
threads=True,
semaphore=None,
putlocks=False,
allow_restart=False,
synack=False,
on_process_exit=None,
context=None,
max_memory_per_child=None,
enable_timeouts=False,
**kwargs):
for i in range(self._processes):
self._create_worker_process(i)
1.2 子进程抽象
如下代码建立子进程抽象。
for i in range(self._processes):
self._create_worker_process(i)
_create_worker_process 主要工作如下：
-
inq, outq, synq = self.get_process_queues() 拿到的是一个读和写的管道的抽象对象。这个管道是之前预先创建好的（就是上面 self.create_process_queues() 创建的）。主要是给即将 fork 的子进程用的，子进程会监听这管道数据结构抽象实例中的读事件，还可以从写管道写数据。
-
w，也就是 self.WorkerProcess 的实例，其实是对 fork 出来的子进程的一个抽象封装。用来方便快捷的管理子进程，抽象成一个进程池，这个 w 会记录 fork 出来的子进程的一些 meta 信息，比如 pid，管道的读写的 fd 等等，并注册在主进程中，主进程可以利用它进行任务分发；
-
把 WorkerProcess 的实例记录在 self._pool；
-
w.start() 中包含具体的 fork 过程；
w.start() 中包含具体的 fork 过程。
def _create_worker_process(self, i):
w = self.WorkerProcess(self.Worker(
inq, outq, synq, self._initializer, self._initargs,
self._maxtasksperchild, sentinel, self._on_process_exit,
# Need to handle all signals if using the ipc semaphore,
# to make sure the semaphore is released.
sigprotection=self.threads,
wrap_exception=self._wrap_exception,
max_memory_per_child=self._max_memory_per_child,
on_ready_counter=on_ready_counter,
))
w.start() # 到了这里
return w
1.3 Fork过程
Fork 的具体代码如下：
class BaseProcess(object):
'''
Process objects represent activity that is run in a separate process
The class is analagous to `threading.Thread`
'''
def run(self):
'''
Method to be run in sub-process; can be overridden in sub-class
'''
if self._target:
self._target(*self._args, **self._kwargs)
def start(self):
'''
Start child process
'''
assert self._popen is None, 'cannot start a process twice'
assert self._parent_pid == os.getpid(), \
'can only start a process object created by current process'
_cleanup()
self._popen = self._Popen(self)
self._sentinel = self._popen.sentinel
_children.add(self)
其中主要是 self._popen = self._Popen(self) 比较重要。
代码位于：billiard/context.py。
其中可以看到，因为操作系统的不同，具体使用也不同。
下面为 *nix 系统的各种 类fork函数。
class ForkProcess(process.BaseProcess):
_start_method = 'fork'
@staticmethod
def _Popen(process_obj):
from .popen_fork import Popen
return Popen(process_obj)
class SpawnProcess(process.BaseProcess):
_start_method = 'spawn'
@staticmethod
def _Popen(process_obj):
from .popen_spawn_posix import Popen
return Popen(process_obj)
class ForkServerProcess(process.BaseProcess):
_start_method = 'forkserver'
@staticmethod
def _Popen(process_obj):
from .popen_forkserver import Popen
return Popen(process_obj)
下面为 windows系统。
class SpawnProcess(process.BaseProcess):
_start_method = 'spawn'
@staticmethod
def _Popen(process_obj):
from .popen_spawn_win32 import Popen
return Popen(process_obj)
于是我们就具体看看子进程如何进行处理。
0x02 预先准备
在子进程启动之前，Celery 会做很多准备，比如构建子进程运行的命令，设置管道，传递父进程信息等等。
2.1 总体准备流程
经过调试我们发现，无论是windows或者*nix系统，调试中都各有不便之处，所以我们下面以windows系统为例分析。
注：
有同学指出，Windows上 Celery 多线程出错。特此说明下。我的环境复杂，有mac, linux, windows，而且有的操作系统有多台，各种切换很无奈。
当分析本文部分代码时候，手上只有Windows，所以就只能贴出来Windows下面的具体调试变量。
其实具体OS不重要，重要的是通过代码来剖析Celery的设计思路。
另外，mac调试Celery，也是各种容易出错，在分析代码这点上，和Windows相比没什么太大优势。
前文因为是 *nix 系统，所以子进程抽象是 ForkProcess， 本文因为是 windows，替换为 SpawnProcess
因为是windows 系统，所以我们调用到：
class SpawnProcess(process.BaseProcess):
_start_method = 'spawn'
@staticmethod
def _Popen(process_obj):
from .popen_spawn_win32 import Popen
return Popen(process_obj)
因此使用 from .popen_spawn_win32 import Popen。
下面代码位于：billiard/popen_spawn_win32.py
主要功能如下：
-
首先调用 _winapi.CreatePipe(None, 0) 来得到 之前建立的 pipe 的读写管道;
-
其次调用 get_command_line 来拼凑出子进程执行命令，注意这里传递的 pipe_handle 为 读管道，parent_pid 就为父进程的pid，子进程中，*nix 和 windows 分别依据 pipe_handle 和 parent_pid 得到读管道；
-
然后打开读管道，这个很重要；
-
再次调用 windows 系统方法 CreateProcess 来执行子进程；
-
因为已经打开了读管道，所以通过 reduction.dump(prep_data, to_child) 把父进程的关键辅助信息传递给子进程，通过这些信息子进程才可以解读父进程信息；
-
通过 reduction.dump(process_obj, to_child) 把父进程信息传递给子进程，父进程信息就为 SpawnProcess；
-
在父进程中，通过 _winapi.CloseHandle(rhandle) 关闭父进程的读管道。这样父进程，子进程就通过子进程的读管道联系;
具体如下：
class Popen(object):
'''
Start a subprocess to run the code of a process object
'''
method = 'spawn'
sentinel = None
def __init__(self, process_obj):
os.environ["MULTIPROCESSING_FORKING_DISABLE"] = "1"
spawn._Django_old_layout_hack__save()
prep_data = spawn.get_preparation_data(process_obj._name)
# read end of pipe will be "stolen" by the child process
# -- see spawn_main() in spawn.py.
rhandle, whandle = _winapi.CreatePipe(None, 0)
wfd = msvcrt.open_osfhandle(whandle, 0)
cmd = spawn.get_command_line(parent_pid=os.getpid(),
pipe_handle=rhandle)
cmd = ' '.join('"%s"' % x for x in cmd)
with io.open(wfd, 'wb', closefd=True) as to_child:
# start process
try:
hp, ht, pid, tid = CreateProcess(
spawn.get_executable(), cmd,
None, None, False, 0, None, None, None)
close_thread_handle(ht)
except:
_winapi.CloseHandle(rhandle)
raise
# set attributes of self
self.pid = pid
self.returncode = None
self._handle = hp
self.sentinel = int(hp)
# send information to child
context.set_spawning_popen(self)
try:
reduction.dump(prep_data, to_child)
reduction.dump(process_obj, to_child)
finally:
context.set_spawning_popen(None)
我们下面具体看看这个准备流程中的几个重要点。
2.2 获取命令
首先，重要点是：调用 get_command_line 来拼凑出子进程执行命令。
代码位于：billiard/spawn.py。
就是拼接出一个celery运行命令。
def get_command_line(**kwds):
'''
Returns prefix of command line used for spawning a child process
'''
if getattr(sys, 'frozen', False):
return ([sys.executable, '--billiard-fork'] +
['%s=%r' % item for item in kwds.items()])
else:
prog = 'from billiard.spawn import spawn_main; spawn_main(%s)'
prog %= ', '.join('%s=%r' % item for item in kwds.items())
opts = util._args_from_interpreter_flags()
return [_python_exe] + opts + ['-c', prog, '--billiard-fork']
命令行结果就是类似于：
python -c 'from billiard.spawn import spawn_main; spawn_main(....)' -billiard+fork ..
2.3 调用 windows 系统方法
然后会调用windows 系统方法启动子进程。
hp, ht, pid, tid = CreateProcess(
spawn.get_executable(), cmd,
None, None, False, 0, None, None, None)
因此，逻辑如下：
+-----------------------------+
| SpawnProcess |
| |
| |
| os.getpid() +-----------------+
| | |
| rhandle +---------------+ |
| Popen | | |
| + whandle | | |
| | | | |
+-----------------------------+ | |
| | |
| | |
| get_command_line | |
| | | .
| | |
v | |
v v
python -c 'from billiard.spawn import spawn_main; spawn_main(....)' --billiard+fork ..
+
|
|
|
| CreateProcess
|
|
|
v
+--------+--------+
| windows kernel |
+-----------------+
2.4 传递父进程信息
因为已经打开了读管道，所以通过 reduction.dump(prep_data, to_child) 把父进程的关键辅助信息传递给子进程，通过这些信息才可以解读父进程信息。这里父进程信息 obj 就为 SpawnProcess 本身。
代码通过 picker 完成，具体如下：
def dump(obj, file, protocol=None):
'''Replacement for pickle.dump() using ForkingPickler.'''
ForkingPickler(file, protocol).dump(obj)
以及：
if PY3:
import copyreg
class ForkingPickler(pickle.Pickler):
'''Pickler subclass used by multiprocessing.'''
_extra_reducers = {}
_copyreg_dispatch_table = copyreg.dispatch_table
def __init__(self, *args):
super(ForkingPickler, self).__init__(*args)
self.dispatch_table = self._copyreg_dispatch_table.copy()
self.dispatch_table.update(self._extra_reducers)
@classmethod
def register(cls, type, reduce):
'''Register a reduce function for a type.'''
cls._extra_reducers[type] = reduce
@classmethod
def dumps(cls, obj, protocol=None):
buf = io.BytesIO()
cls(buf, protocol).dump(obj)
return buf.getbuffer()
@classmethod
def loadbuf(cls, buf, protocol=None):
return cls.loads(buf.getbuffer())
loads = pickle.loads
else:
class ForkingPickler(pickle.Pickler): # noqa
'''Pickler subclass used by multiprocessing.'''
dispatch = pickle.Pickler.dispatch.copy()
@classmethod
def register(cls, type, reduce):
'''Register a reduce function for a type.'''
def dispatcher(self, obj):
rv = reduce(obj)
self.save_reduce(obj=obj, *rv)
cls.dispatch[type] = dispatcher
@classmethod
def dumps(cls, obj, protocol=None):
buf = io.BytesIO()
cls(buf, protocol).dump(obj)
return buf.getvalue()
@classmethod
def loadbuf(cls, buf, protocol=None):
return cls.loads(buf.getvalue())
@classmethod
def loads(cls, buf, loads=pickle.loads):
if isinstance(buf, io.BytesIO):
buf = buf.getvalue()
return loads(buf)
至此，准备工作完毕，将会进入到子进程。
0x03 子进程启动
既然已经通知了 windows，所以 windows 就进行系统调用。
3.1 从命令行进入
既然前面的命令行结果中明确提到了spawn_main：
python -c 'from billiard.spawn import spawn_main; spawn_main(....)' -billiard+fork ..
于是子进程从spawn_main启动。
代码位于：billiard/spawn.py
def spawn_main(pipe_handle, parent_pid=None, tracker_fd=None):
'''
Run code specified by data received over pipe
'''
assert is_forking(sys.argv)
if sys.platform == 'win32':
import msvcrt
from .reduction import steal_handle
new_handle = steal_handle(parent_pid, pipe_handle)
fd = msvcrt.open_osfhandle(new_handle, os.O_RDONLY)
else:
from . import semaphore_tracker
semaphore_tracker._semaphore_tracker._fd = tracker_fd
fd = pipe_handle
exitcode = _main(fd) # 将会调用到这里。
sys.exit(exitcode)
注意：
这里的 pipe_handle 就为 传递进来的读管道。parent_pid为父进程ID。子进程中，*nix 和 windows 分别依据 pipe_handle 和 parent_pid 得到读管道。
此时逻辑为：
+
parent process | child process
+-----------------------------+ |
| SpawnProcess | |
| | |
| os.getpid()+-----------------+ |
| | | |
| rhandle +---------------+ | | +---------------+
| Popen | | | | | spawn_main |
| + whandle | | | | parent_pid | |
| | | | | | | |
+---+-------------------------+ | | | +---------------> | |
| | | | | | | fd |
| | | | | | +-----------> | ^ |
| | get_command_line | | | | | pipe_handle | | |
| | | | | | | +---------------+
| | | | | | | |
| v | | | | | ^ |
| v v | | | | |
python -c 'from billi|rd.spawn import spawn_main; spawn_main(....)' --billiard-fork ... | | | | |
| + + + | | | | |
| | | | | | | | |
| | | | | | | | |
| | | | | | | | |
| | CreateProcess | | | | | | |
| | | +---------------------------------+ | | |
| | +---------------------------------------+ | |
| | | |
| | 1 +-----------------+ 2 | |
| +----------------------------------------------> | windows kernel | +---------------------+ |
| +-----------------+ |
| |
| |
+-----------------------------------------------------------------------------------------------------------+
3 reduction.dump(process_obj, to_child)
手机如下：
因此，程序进行调用 _main。
3.2 _main 读取父进程关键信息
前面提到，父进程会写入关键信息。所以子进程这里打开了读管道，读取父进程的关键信息，这里父进程信息 就为 SpawnProcess 本身，因此子进程可以操作 SpawnProcess。
def _main(fd):
_Django_old_layout_hack__load()
with io.open(fd, 'rb', closefd=True) as from_parent:
process.current_process()._inheriting = True
try:
preparation_data = pickle.load(from_parent)
prepare(preparation_data)
_setup_logging_in_child_hack()
self = pickle.load(from_parent) #读取父进程的关键信息，就为SpawnProcess
finally:
del process.current_process()._inheriting
return self._bootstrap()
逻辑如下：
+
parent process | child process
+-----------------------------+ |
| SpawnProcess | |
| | |
| os.getpid()+-----------------+ |
| | | |
| rhandle +---------------+ | | +---------------+
| Popen | | | | | spawn_main |
| + whandle | | | | parent_pid | | 4 +-------------+
| | | | | | | self+--------> |SpawnProcess |
+---+-------------------------+ | | | +---------------> | | +------+------+
| | | | | | | fd | |
| | | | | | +-----------> | ^ | |
| | get_command_line | | | | | pipe_handle | | | 5 | _bootstrap()
| | | | | | | +---------------+ |
| | | | | | | | v
| v | | | | | ^ |
| v v | | | | |
python -c 'from billi|rd.spawn import spawn_main; spawn_main(....)' --billiard-fork ... | | | | |
| + + + | | | | |
| | | | | | | | |
| | | | | | | | |
| | | | | | | | |
| | CreateProcess | | | | | | |
| | | +---------------------------------+ | | |
| | +---------------------------------------+ | |
| | | |
| | 1 +-----------------+ 2 | |
| +----------------------------------------------> | windows kernel | +---------------------+ |
| +-----------------+ |
| |
| |
+-----------------------------------------------------------------------------------------------------------+
3 reduction.dump(process_obj, to_child)
手机如下：
3.3 SpawnProcess 启动
既然子进程已经知道了SpawnProcess，因此调用到了 SpawnProcess 的基类。
代码位于：billiard/process.py。
class BaseProcess(object):
'''
Process objects represent activity that is run in a separate process
The class is analagous to `threading.Thread`
'''
3.3.1 _bootstrap 配置必要信息
基类 billiard/process.py 之中，会通过 _bootstrap 来 配置必要信息，比如 stdin ，然后调用 run。
def _bootstrap(self):
from . import util, context
global _current_process, _process_counter, _children
try:
# 设置 stdin等等
if self._start_method is not None:
context._force_start_method(self._start_method)
_process_counter = itertools.count(1)
_children = set()
if sys.stdin is not None:
try:
sys.stdin.close()
sys.stdin = open(os.devnull)
except (EnvironmentError, OSError, ValueError):
pass
old_process = _current_process
_set_current_process(self)
# 设置 logger等等
loggerDict = logging.Logger.manager.loggerDict
logger_names = list(loggerDict.keys())
logger_names.append(None) # for root logger
for name in logger_names:
if not name or not isinstance(loggerDict[name],
logging.PlaceHolder):
for handler in logging.getLogger(name).handlers:
handler.createLock()
logging._lock = threading.RLock()
try:
util._finalizer_registry.clear()
util._run_after_forkers()
finally:
# delay finalization of the old process object until after
# _run_after_forkers() is executed
del old_process
util.info('child process %s calling self.run()', self.pid)
try:
self.run() # 运行到这里
exitcode = 0
finally:
util._exit_function()
return exitcode
3.3.2 启动服务 Worker
SpawnProcess 继续调用 run。
def run(self):
'''
Method to be run in sub-process; can be overridden in sub-class
'''
if self._target:
self._target(*self._args, **self._kwargs)
由前文可知道，
_target = {Worker} <celery.concurrency.asynpool.Worker object at 0x7f9ad358b240>
因此来到了 celery.concurrency.asynpool.Worker ，这就是子进程工作循环。
如下：
+
parent process | child process
+-----------------------------+ |
| SpawnProcess | |
| | |
| os.getpid()+-----------------+ |
| | | |
| rhandle +---------------+ | | +---------------+
| Popen | | | | | spawn_main |
| + whandle | | | | parent_pid | | 4 +-------------+
| | | | | | | self+--------> |SpawnProcess |
+---+-------------------------+ | | | +---------------> | | +------+------+
| | | | | | | fd | |
| | | | | | +-----------> | ^ | |
| | get_command_line | | | | | pipe_handle | | | 5 | _bootstrap()
| | | | | | | +---------------+ |
| | | | | | | | v
| v | | | | | ^ |
| v v | | | | | +----------+
python -c 'from billi|rd.spawn import spawn_main; spawn_main(....)' --billiard-fork ... | | | | | | Worker |
| + + + | | | | | | |
| | | | | | | | | +-----+----+
| | | | | | | | | |
| | | | | | | | | |
| | CreateProcess | | | | | | | |
| | | +---------------------------------+ | | | |
| | +---------------------------------------+ | | |
| | | | v
| | 1 +-----------------+ 2 | |
| +----------------------------------------------> | windows kernel | +---------------------+ |
| +-----------------+ |
| |
| |
+-----------------------------------------------------------------------------------------------------------+
3 reduction.dump(process_obj, to_child)
手机如下：
3.4 Worker 服务
代码位于 ：celery/billiard/pool.py
进入 Worker 之后，就来到了 __call__，主要功能如下：
+
parent process | child process
+-----------------------------+ |
| SpawnProcess | |
| | |
| os.getpid()+-----------------+ |
| | | |
| rhandle +---------------+ | | +---------------+
| Popen | | | | | spawn_main |
| + whandle | | | | parent_pid | | 4 +-------------+
| | | | | | | self+--------> |SpawnProcess |
+---+-------------------------+ | | | +---------------> | | +------+------+
| | | | | | | fd | |
| | | | | | +-----------> | ^ | |
| | get_command_line | | | | | pipe_handle | | | 5 | _bootstrap()
| | | | | | | +---------------+ |
| | | | | | | | v
| v | | | | | ^ |
| v v | | | | | +----------+
python -c 'from billi|rd.spawn import spawn_main; spawn_main(....)' --billiard-fork ... | | | | | | Worker |
| + + + | | | | | | |
| | | | | | | | | +-----+----+
| | | | | | | | | |
| | | | | | | | | |
| | CreateProcess | | | | | | | |
| | | +---------------------------------+ | | | |
| | +---------------------------------------+ | | |
| | | | v
| | 1 +-----------------+ 2 | |
| +----------------------------------------------> | windows kernel | +---------------------+ | __call__
| +-----------------+ |
| |
| |
+-----------------------------------------------------------------------------------------------------------+
3 reduction.dump(process_obj, to_child)
+
|
|
|
+
手机如下：
__call__，主要功能如下：
-
使用 _make_child_methods 配置 监听 任务 和 同步的方法；
-
使用 after_fork 来恢复应用信息；
-
使用 on_loop_start 来发送一个 WORKER_UP，以此通知父进程；
-
使用 sys.exit(self.workloop(pid=pid)) 正式进入循环；
class Worker(object):
def __call__(self):
_exit = sys.exit
_exitcode = [None]
def exit(status=None):
_exitcode[0] = status
return _exit(status)
sys.exit = exit
pid = os.getpid()
self._make_child_methods()
self.after_fork()
self.on_loop_start(pid=pid) # callback on loop start
try:
sys.exit(self.workloop(pid=pid))
except Exception as exc:
error('Pool process %r error: %r', self, exc, exc_info=1)
self._do_exit(pid, _exitcode[0], exc)
finally:
self._do_exit(pid, _exitcode[0], None)
我们下面详细分析。
3.4.1 配置 监听 任务 和 同步的方法
子进程 使用 _make_child_methods 配置 监听 任务 和 同步的方法；
def _make_child_methods(self, loads=pickle_loads):
self.wait_for_job = self._make_protected_receive(self.inq)
self.wait_for_syn = (self._make_protected_receive(self.synq)
if self.synq else None)
3.4.2 配置应用相关信息
于是我们遇到一个问题：Celery 应用是在父进程中，子进程如何得到。
虽然在一些多进程机制中，父进程的变量是会复制到子进程中，但是这并不是一定的，所以必然有一个父进程把 Celery 应用 设置给子进程的机制。
所以，我们需要梳理父进程是如何给子进程配置 Celery应用，以及子进程如何得到这个应用的。
3.4.2.1 应用信息来源
之前在父进程中，当启动进程池时候， class Pool(object): 对应配置如下（路径在 ：billiard/pool.py）：
需要注意的是：
- 这里是回到父进程来探讨；
- 参数 initializer 就是 Celery 变量本身。
代码为：
class Pool(object):
'''
Class which supports an async version of applying functions to arguments.
'''
_wrap_exception = True
Worker = Worker
Supervisor = Supervisor
TaskHandler = TaskHandler
TimeoutHandler = TimeoutHandler
ResultHandler = ResultHandler
SoftTimeLimitExceeded = SoftTimeLimitExceeded
def __init__(self, processes=None, initializer=None, initargs=(),
......
**kwargs):
self._ctx = context or get_context()
self.synack = synack
self._setup_queues()
self._taskqueue = Queue()
self._cache = {}
self._state = RUN
self.timeout = timeout
self.soft_timeout = soft_timeout
self._maxtasksperchild = maxtasksperchild
self._max_memory_per_child = max_memory_per_child
self._initializer = initializer
self._initargs = initargs
于是 Pool 类的相关变量为如下，这里的 Celery myTest 就是 Celery 应用本身：
self._initializer = {function} <function process_initializer at 0x7f90c9387488>
self._initargs = {tuple: 2} (<Celery myTest at 0x7f90c8812f98>, 'celery')
self = {AsynPool} <celery.concurrency.asynpool.AsynPool object at 0x7f90c97379b0>
从而父进程中 class Worker(object): 配置如下，可以看到设置了 initializer：
class Worker(object):
def __init__(self, inq, outq, synq=None, initializer=None, initargs=(),
maxtasks=None, sentinel=None, on_exit=None,
sigprotection=True, wrap_exception=True,
max_memory_per_child=None, on_ready_counter=None):
assert maxtasks is None or (type(maxtasks) == int and maxtasks > 0)
self.initializer = initializer
self.initargs = initargs
3.4.2.2 调用恢复
前面提到，在子进程启动之后，会调用 after_fork 来进行恢复应用。
process_initializer, prefork.py:44
after_fork, pool.py:421
__call__, pool.py:289
run, process.py:114
_bootstrap, process.py:327
_main, spawn.py:210
spawn_main, spawn.py:165
<frame not available>
具体看看，发现 after_fork 通过 self.initializer(*self.initargs) 恢复应用信息。
def after_fork(self):
if hasattr(self.inq, '_writer'):
self.inq._writer.close()
if hasattr(self.outq, '_reader'):
self.outq._reader.close()
if self.initializer is not None:
self.initializer(*self.initargs)
# Make sure all exiting signals call finally: blocks.
# This is important for the semaphore to be released.
reset_signals(full=self.sigprotection)
# install signal handler for soft timeouts.
if SIG_SOFT_TIMEOUT is not None:
signal.signal(SIG_SOFT_TIMEOUT, soft_timeout_sighandler)
try:
signal.signal(signal.SIGINT, signal.SIG_IGN)
except AttributeError:
pass
3.4.2.3 恢复应用信息
具体恢复方法在 process_initializer。
代码位置为 ：celery/concurrency/prefork.py
这里重要的是 app.set_current()，就是把 传入的 Celery 配置到 子进程本身之中。
具体代码为：
def process_initializer(app, hostname):
"""Pool child process initializer.
Initialize the child pool process to ensure the correct
app instance is used and things like logging works.
"""
_set_task_join_will_block(True)
platforms.signals.reset(*WORKER_SIGRESET)
platforms.signals.ignore(*WORKER_SIGIGNORE)
platforms.set_mp_process_title('celeryd', hostname=hostname)
# This is for Windows and other platforms not supporting
# fork(). Note that init_worker makes sure it's only
# run once per process.
app.loader.init_worker()
app.loader.init_worker_process()
if os.environ.get('FORKED_BY_MULTIPROCESSING'):
# pool did execv after fork
trace.setup_worker_optimizations(app, hostname)
else:
app.set_current() # 这里进行配置
set_default_app(app)
app.finalize()
trace._tasks = app._tasks # enables fast_trace_task optimization.
# rebuild execution handler for all tasks.
from celery.app.trace import build_tracer
for name, task in app.tasks.items():
task.__trace__ = build_tracer(name, task, app.loader, hostname,
app=app)
from celery.worker import state as worker_state
worker_state.reset_state()
signals.worker_process_init.send(sender=None)
配置 Celery 自己
子进程中，具体配置代码位于：celery/app/base.py，我们可以看到 TLS 相关信息。
def set_current(self):
"""Make this the current app for this thread."""
_set_current_app(self)
def _set_current_app(app):
_tls.current_app = app
def _get_current_app():
if default_app is None:
#: creates the global fallback app instance.
from celery.app.base import Celery
set_default_app(Celery(
'default', fixups=[], set_as_current=False,
loader=os.environ.get('CELERY_LOADER') or 'default',
))
return _tls.current_app or default_app
TLS
TLS 定义位于：celery/_state.py
就是各个进程或者线程独立的变量，区别取决于不同实现方式。
class _TLS(threading.local):
#: Apps with the :attr:`~celery.app.base.BaseApp.set_as_current` attribute
#: sets this, so it will always contain the last instantiated app,
#: and is the default app returned by :func:`app_or_default`.
current_app = None
_tls = _TLS()
后续使用
这样后续的使用就可以使用 get_current_app 提出来 Celery 本身，获取应用信息。
具体后续是在 celery/_state.py 做了进一步封装，并且使用，如何使用，我们下文讲解。
if os.environ.get('C_STRICT_APP'): # pragma: no cover
def get_current_app():
"""Return the current app."""
raise RuntimeError('USES CURRENT APP')
elif os.environ.get('C_WARN_APP'): # pragma: no cover
def get_current_app(): # noqa
import traceback
print('-- USES CURRENT_APP', file=sys.stderr) # noqa+
traceback.print_stack(file=sys.stderr)
return _get_current_app()
3.4.3 通知父进程
子进程启动最后，会使用 on_loop_start 来发送一个 WORKER_UP，可以看到是通过管道进行交互。
于是在父进程 ResultHandler . on_process_alive 会响应。
class Worker(_pool.Worker):
"""Pool worker process."""
def on_loop_start(self, pid):
# our version sends a WORKER_UP message when the process is ready
# to accept work, this will tell the parent that the inqueue fd
# is writable.
self.outq.put((WORKER_UP, (pid,)))
父进程启动时候，会设置一个消息响应 函数，这样父进程就知道子进程已经ready，可以给子进程安排工作。
class ResultHandler(_pool.ResultHandler):
"""Handles messages from the pool processes."""
def __init__(self, *args, **kwargs):
self.fileno_to_outq = kwargs.pop('fileno_to_outq')
self.on_process_alive = kwargs.pop('on_process_alive')
super().__init__(*args, **kwargs)
# add our custom message handler
self.state_handlers[WORKER_UP] = self.on_process_alive
3.4.4 正式进入业务逻辑
子进程使用 sys.exit(self.workloop(pid=pid)) 正式进入循环；
代码位置：billiard/pool.py
可以看到，使用 req = wait_for_job() 来监听任务信息，然后运行。
具体堆栈为：
workloop, pool.py:351
__call__, pool.py:292
run, process.py:114
_bootstrap, process.py:327
_main, spawn.py:210
spawn_main, spawn.py:165
<frame not available>
具体代码逻辑如下：
def workloop(self, debug=debug, now=monotonic, pid=None):
pid = pid or os.getpid()
put = self.outq.put
inqW_fd = self.inqW_fd
synqW_fd = self.synqW_fd
maxtasks = self.maxtasks
max_memory_per_child = self.max_memory_per_child or 0
prepare_result = self.prepare_result
wait_for_job = self.wait_for_job
_wait_for_syn = self.wait_for_syn
def wait_for_syn(jid):
i = 0
while 1:
if i > 60:
error('!!!WAIT FOR ACK TIMEOUT: job:%r fd:%r!!!',
jid, self.synq._reader.fileno(), exc_info=1)
req = _wait_for_syn()
if req:
type_, args = req
if type_ == NACK:
return False
assert type_ == ACK
return True
i += 1
completed = 0
try:
while maxtasks is None or (maxtasks and completed < maxtasks):
req = wait_for_job()
if req:
type_, args_ = req
assert type_ == TASK
job, i, fun, args, kwargs = args_
put((ACK, (job, i, now(), pid, synqW_fd)))
if _wait_for_syn:
confirm = wait_for_syn(job)
if not confirm:
continue # received NACK
try:
result = (True, prepare_result(fun(*args, **kwargs)))
except Exception:
result = (False, ExceptionInfo())
try:
put((READY, (job, i, result, inqW_fd)))
except Exception as exc:
_, _, tb = sys.exc_info()
try:
wrapped = MaybeEncodingError(exc, result[1])
einfo = ExceptionInfo((
MaybeEncodingError, wrapped, tb,
))
put((READY, (job, i, (False, einfo), inqW_fd)))
finally:
del(tb)
completed += 1
if max_memory_per_child > 0:
used_kb = mem_rss()
if used_kb <= 0:
error('worker unable to determine memory usage')
if used_kb > 0 and used_kb > max_memory_per_child:
warning(MAXMEM_USED_FMT.format(
used_kb, max_memory_per_child))
return EX_RECYCLE
if maxtasks:
return EX_RECYCLE if completed == maxtasks else EX_FAILURE
return EX_OK
finally:
# Before exiting the worker, we want to ensure that that all
# messages produced by the worker have been consumed by the main
# process. This prevents the worker being terminated prematurely
# and messages being lost.
self._ensure_messages_consumed(completed=completed)
逻辑如下：
+
parent process | child process
+-----------------------------+ |
| SpawnProcess | | +-----------+
| | | | Celery |
| os.getpid()+-----------------+ | | |
| | | | +-----------+
| rhandle +---------------+ | | +---------------+
| Popen | | | | | spawn_main | ^
| + whandle | | | | parent_pid | | 4 +-------------+ |
| | | | | | | self+--------> |SpawnProcess | |
+---+-------------------------+ | | | +---------------> | | +------+------+ |
| | | | | | | fd | | |
| | | | | | +-----------> | ^ | | |
| | get_command_line | | | | | pipe_handle | | | 5 | _bootstrap() |
| | | | | | | +---------------+ | |
| | | | | | | | v +---------+
| v | | | | | ^ | |
| v v | | | | | +---------------------------+
python -c 'from billi|rd.spawn import spawn_main; spawn_main(....)' --billiard-fork ... | | | | | | Worker | |
| + + + | | | | | | | | <---------+
| | | | | | | | | | + | |
| | | | | | | | | | _tls.current_app | |
| | | | | | | | | | | |
| | CreateProcess | | | | | | | +------------+--------------+ |
| | | +---------------------------------+ | | | | |
| | +---------------------------------------+ | | | |
| | | | | |
| | 1 +-----------------+ 2 | | | |
| +----------------------------------------------> | windows kernel | +---------------------+ | | |
| +-----------------+ | | |
| | | |
| | | |
+-----------------------------------------------------------------------------------------------------------+ | |
3 reduction.dump(process_obj, to_child) | 6 __call__ |
+ | |
| +------------------------->+
|
|
+
手机如下：
至此，子进程启动完毕，具体如何运行父进程传来的任务，我们下期进行介绍。
