本文所剖析的tornado源码版本为4.4.2
ioloop是tornado的关键,是他的最底层。
ioloop就是对I/O多路复用的封装,它实现了一个单例,将这个单例保存在IOLoop._instance中
ioloop实现了Reactor模型,将所有要处理的I/O事件注册到一个中心I/O多路复用器上,同时主线程/进程阻塞在多路复用器上;一旦有I/O事件到来或是准备就绪(文件描述符或socket可读、写),多路复用器返回并将事先注册的相应I/O事件分发到对应的处理器中。
另外,ioloop还被用来集中运行回调函数以及集中处理定时任务。
一 准备知识:
1 首先我们要了解Reactor模型
2 其次,我们要了解I/O多路复用,由于本文假设系统为Linux,所以要了解epoll以及Python中的select模块
3 IOLoop类是Configurable类的子类,而Configurable类是一个工厂类,讲解在这。
二 创建IOLoop实例
来看IOLoop,它的父类是Configurable类,也就是说:IOLoop是一个直属配置子类
class IOLoop(Configurable):
......
这里就要结合Configurable类进行讲解:
def __new__(cls, *args, **kwargs)
'''
解析出impl对象
1 cls是直属配置子类时,impl就是该直属配置子类的'执行类对象'
2 cls是从属配置子类时,impl就是该从属配置子类自身
然后实例化一个impl实例对象
运行其initialize方法,并传入合并后的参数
返回该impl实例对象
'''
base = cls.configurable_base()
init_kwargs = {}
if cls is base:
impl = cls.configured_class()
if base.__impl_kwargs:
init_kwargs.update(base.__impl_kwargs)
else:
impl = cls
init_kwargs.update(kwargs)
instance = super(Configurable, cls).__new__(impl)
instance.initialize(*args, **init_kwargs)
return instance
Configurable中的__new__方法
1 首先实例化一个该直属配置子类的'执行类对象',也就是调用该类的configurable_default方法并返回赋值给impl:
@classmethod
def configurable_default(cls):
if hasattr(select, "epoll"): # 因为我们假设我们的系统为Linux,且支持epoll,所以这里为True
from tornado.platform.epoll import EPollIOLoop
return EPollIOLoop
if hasattr(select, "kqueue"):
# Python 2.6+ on BSD or Mac
from tornado.platform.kqueue import KQueueIOLoop
return KQueueIOLoop
from tornado.platform.select import SelectIOLoop
return SelectIOLoop
2 也就是impl是EPollIOLoop类对象,然后实例化该对象,运行其initialize方法
class EPollIOLoop(PollIOLoop): # 该类只有这么短短的几句,可见主要的方法是在其父类PollIOLoop中实现。
def initialize(self, **kwargs):
super(EPollIOLoop, self).initialize(impl=select.epoll(), **kwargs) # 执行了父类PollIOLoop的initialize方法,并将select.epoll()传入
来看一看PollIOLoop.initialize(EPollIOLoop(),impl=select.epoll())干了些啥:
class PollIOLoop(IOLoop): # 从属配置子类 def initialize(self, impl, time_func=None, **kwargs):
super(PollIOLoop, self).initialize(**kwargs) # 调用IOLoop的initialize方法
self._impl = impl # self._impl = select.epoll()
if hasattr(self._impl, 'fileno'): # 文件描述符的close_on_exec属性
set_close_exec(self._impl.fileno())
self.time_func = time_func or time.time
self._handlers = {} # 文件描述符对应的fileno()作为key,(文件描述符对象,处理函数)作为value
self._events = {} # 用来存储epoll_obj.poll()返回的事件,也就是哪个fd发生了什么事件{(fd1, event1), (fd2, event2)……}
self._callbacks = []
self._callback_lock = threading.Lock() # 添加线程锁
self._timeouts = [] # 存储定时任务
self._cancellations = 0
self._running = False
self._stopped = False
self._closing = False
self._thread_ident = None # 获得当前线程标识符
self._blocking_signal_threshold = None
self._timeout_counter = itertools.count() # Create a pipe that we send bogus data to when we want to wake
# the I/O loop when it is idle
self._waker = Waker()
self.add_handler(self._waker.fileno(),
lambda fd, events: self._waker.consume(),
self.READ)
首先调用了IOLoop.initialize(self,**kwargs)方法:
def initialize(self, make_current=None):
if make_current is None:
if IOLoop.current(instance=False) is None:
self.make_current()
elif make_current:
if IOLoop.current(instance=False) is not None:
raise RuntimeError("current IOLoop already exists")
self.make_current()
@staticmethod
def current(instance=True):
current = getattr(IOLoop._current, "instance", None)
if current is None and instance:
return IOLoop.instance()
return current def make_current(self):
IOLoop._current.instance = self
我们可以看到IOLoop.initialize()主要是对线程做了一些支持和操作。
3 返回该实例
三 剖析PollIOLoop
1 处理I/O事件以及其对应handler的相关属性以及方法
使用self._handlers用来存储fd与handler的对应关系,文件描述符对应的fileno()作为key,元组(文件描述符对象,处理函数)作为value
self._events 用来存储epoll_obj.poll()返回的事件,也就是哪个fd发生了什么事件{(fd1, event1), (fd2, event2)……}
add_handler方法用来添加handler
update_handle方法用来更新handler
remove_handler方法用来移除handler
def add_handler(self, fd, handler, events):
# 向epoll中注册事件 , 并在self._handlers[fd]中为该文件描述符添加相应处理函数
fd, obj = self.split_fd(fd) # fd.fileno(),fd
self._handlers[fd] = (obj, stack_context.wrap(handler))
self._impl.register(fd, events | self.ERROR) def update_handler(self, fd, events):
fd, obj = self.split_fd(fd)
self._impl.modify(fd, events | self.ERROR) def remove_handler(self, fd):
fd, obj = self.split_fd(fd)
self._handlers.pop(fd, None)
self._events.pop(fd, None)
try:
self._impl.unregister(fd)
except Exception:
gen_log.debug("Error deleting fd from IOLoop", exc_info=True)
2 处理回调函数的相关属性以及方法
self._callbacks用来存储回调函数
add_callback方法用来直接添加回调函数
add_future方法用来间接的添加回调函数,future对象详解在这
def add_callback(self, callback, *args, **kwargs):
# 因为Python的GIL的限制,导致Python线程并不算高效。加上tornado实现了多进程 + 协程的模式,所以我们略过源码中的部分线程相关的一些操作
if self._closing:
return
self._callbacks.append(functools.partial(stack_context.wrap(callback), *args, **kwargs))
def add_future(self, future, callback):
# 为future对象添加经过包装后的回调函数,该回调函数会在future对象被set_done后添加至_callbacks中
assert is_future(future)
callback = stack_context.wrap(callback)
future.add_done_callback(
lambda future: self.add_callback(callback, future))
3 处理定时任务的相关属性以及方法
self._timeouts用来存储定时任务
self.add_timeout用来添加定时任务(self.call_later self.call_at都是间接调用了该方法)
def add_timeout(self, deadline, callback, *args, **kwargs):
"""
``deadline``可能是一个数字,表示相对于当前时间的时间(与“IOLoop.time”通常为“time.time”相同的大小),或者是datetime.timedelta对象。
自从Tornado 4.0以来,`call_later`是一个比较方便的替代方案,因为它不需要timedelta对象。 """
if isinstance(deadline, numbers.Real):
return self.call_at(deadline, callback, *args, **kwargs)
elif isinstance(deadline, datetime.timedelta):
return self.call_at(self.time() + timedelta_to_seconds(deadline),
callback, *args, **kwargs)
else:
raise TypeError("Unsupported deadline %r" % deadline)
4 启动io多路复用器
启动也一般就意味着开始循环,那么循环什么呢?
1 运行回调函数
2 运行时间已到的定时任务
3 当某个文件描述法发生事件时,运行该事件对应的handler
使用start方法启动ioloop,看一下其简化版(去除线程相关,以及一些相对不重要的细节):
def start(self):
try:
while True:
callbacks = self._callbacks
self._callbacks = []
due_timeouts = []
# 将时间已到的定时任务放置到due_timeouts中,过程省略
for callback in callbacks: # 执行callback
self._run_callback(callback)
for timeout in due_timeouts: # 执行定时任务
if timeout.callback is not None:
self._run_callback(timeout.callback)
callbacks = callback = due_timeouts = timeout = None # 释放内存
# 根据情况设置poll_timeout的值,过程省略
if not self._running: # 终止ioloop运行时,在执行完了callback后结束循环
break
try:
event_pairs = self._impl.poll(poll_timeout)
except Exception as e:
if errno_from_exception(e) == errno.EINTR: # 系统调用被信号处理函数中断,进行下一次循环
continue
else:
raise
self._events.update(event_pairs)
while self._events:
fd, events = self._events.popitem() # 获取一个fd以及对应事件
try:
fd_obj, handler_func = self._handlers[fd] # 获取该fd对应的事件处理函数
handler_func(fd_obj, events) # 运行该事件处理函数
except (OSError, IOError) as e:
if errno_from_exception(e) == errno.EPIPE: # 当客户端关闭连接时会产生EPIPE错误
pass
# 其他异常处理已经省略
fd_obj = handler_func = None # 释放内存空间
def start(self):
if self._running:
raise RuntimeError("IOLoop is already running")
self._setup_logging()
if self._stopped:
self._stopped = False
return
old_current = getattr(IOLoop._current, "instance", None)
IOLoop._current.instance = self
self._thread_ident = thread.get_ident() # 获得当前线程标识符
self._running = True
old_wakeup_fd = None
if hasattr(signal, 'set_wakeup_fd') and os.name == 'posix':
# 需要Python2.6及以上版本,类UNIX系统,set_wake_up_fd存在。在windows系统上运行会崩溃
try:
old_wakeup_fd = signal.set_wakeup_fd(self._waker.write_fileno())
if old_wakeup_fd != -1:
# Already set, restore previous value. This is a little racy,
# but there's no clean get_wakeup_fd and in real use the
# IOLoop is just started once at the beginning.
signal.set_wakeup_fd(old_wakeup_fd)
old_wakeup_fd = None
except ValueError:
# Non-main thread, or the previous value of wakeup_fd
# is no longer valid.
old_wakeup_fd = None try:
while True:
# 防止多线程模型时产生脏数据
with self._callback_lock:
callbacks = self._callbacks
self._callbacks = [] due_timeouts = []
if self._timeouts: # 将时间已到的定时任务放置到due_timeouts中
now = self.time()
while self._timeouts:
if self._timeouts[0].callback is None:
heapq.heappop(self._timeouts)
self._cancellations -= 1
elif self._timeouts[0].deadline <= now:
due_timeouts.append(heapq.heappop(self._timeouts))
else:
break
if (self._cancellations > 512 and
self._cancellations > (len(self._timeouts) >> 1)):
self._cancellations = 0
self._timeouts = [x for x in self._timeouts
if x.callback is not None]
heapq.heapify(self._timeouts) for callback in callbacks: # 执行callbacks
self._run_callback(callback)
for timeout in due_timeouts: # 执行timeout_callback
if timeout.callback is not None:
self._run_callback(timeout.callback)
# 释放内存
callbacks = callback = due_timeouts = timeout = None if self._callbacks: # 如果在执行callbacks 或者 timeouts的过程中,他们执行了add_callbacks ,那么这时:self._callbacks就非空了,
# 为了尽快的执行其中的callbacks,我们需要将poll_timeout 设置为0,这样我们就不需要等待fd事件发生,尽快运行callbacks了
poll_timeout = 0.0
elif self._timeouts:
# If there are any timeouts, schedule the first one.
# Use self.time() instead of 'now' to account for time
# spent running callbacks.
poll_timeout = self._timeouts[0].deadline - self.time()
poll_timeout = max(0, min(poll_timeout, _POLL_TIMEOUT))
else:
# 如果没有回调函数也没有定时任务,我们就使用默认值
poll_timeout = _POLL_TIMEOUT if not self._running: # 终止ioloop运行时,在执行完了callback后结束循环
break if self._blocking_signal_threshold is not None:
# clear alarm so it doesn't fire while poll is waiting for
# events.
signal.setitimer(signal.ITIMER_REAL, 0, 0) try:
event_pairs = self._impl.poll(poll_timeout)
except Exception as e:
# http://blog.csdn.net/benkaoya/article/details/17262053 解释EINTR是什么。系统调用被信号处理函数中断,进行下一次循环
if errno_from_exception(e) == errno.EINTR:
continue
else:
raise if self._blocking_signal_threshold is not None:
signal.setitimer(signal.ITIMER_REAL,
self._blocking_signal_threshold, 0) # 从一组待处理的fds中一次弹出一个fd并运行其处理程序。
# 由于该处理程序可能会对其他文件描述符执行操作,因此可能会重新调用此IOLoop来修改self._events
self._events.update(event_pairs)
while self._events:
fd, events = self._events.popitem() # 获取一个fd以及对应事件
try:
fd_obj, handler_func = self._handlers[fd] # 获取该fd对应的事件处理函数
handler_func(fd_obj, events) # 运行该事件处理函数
except (OSError, IOError) as e:
if errno_from_exception(e) == errno.EPIPE:
# 当客户端关闭连接时会产生EPIPE错误
pass
else:
self.handle_callback_exception(self._handlers.get(fd))
except Exception:
self.handle_callback_exception(self._handlers.get(fd))
# 释放内存空间
fd_obj = handler_func = None finally:
# reset the stopped flag so another start/stop pair can be issued
self._stopped = False
if self._blocking_signal_threshold is not None:
signal.setitimer(signal.ITIMER_REAL, 0, 0)
IOLoop._current.instance = old_current
if old_wakeup_fd is not None:
signal.set_wakeup_fd(old_wakeup_fd)
start完整版
5 关闭io多路复用器
def close(self, all_fds=False):
with self._callback_lock:
self._closing = True
self.remove_handler(self._waker.fileno())
if all_fds: # 该参数若为True,则表示会关闭所有文件描述符
for fd, handler in self._handlers.values():
self.close_fd(fd)
self._waker.close()
self._impl.close()
self._callbacks = None
self._timeouts = None
四 参考
https://zhu327.github.io/2016/06/14/tornado%E4%BB%A3%E7%A0%81%E9%98%85%E8%AF%BB%E7%AC%94%E8%AE%B0-ioloop/
https://www.zhihu.com/question/20021164
http://*.com/questions/12179271/meaning-of-classmethod-and-staticmethod-for-beginner/12179752#12179752
http://blog.csdn.net/benkaoya/article/details/17262053