极客教程Python示例，本文介绍 Python 使用生成器代替线程 相关示例。
Python 使用生成器代替线程 问题
你想使用生成器（协程）替代系统线程来实现并发。这个有时又被称为用户级线程或绿色线程。
Python 使用生成器代替线程 解决方案
要使用生成器实现自己的并发，你首先要对生成器函数和 yield 语句有深刻理解。 yield 语句会让一个生成器挂起它的执行，这样就可以编写一个调度器， 将生成器当做某种“任务”并使用任务协作切换来替换它们的执行。 要演示这种思想，考虑下面两个使用简单的 yield 语句的生成器函数：
# Two simple generator functions
def countdown(n):
while n > 0:
print('T-minus', n)
yield
n -= 1
print('Blastoff!')
def countup(n):
x = 0
while x < n:
print('Counting up', x)
yield
x += 1
这些函数在内部使用yield语句，下面是一个实现了简单任务调度器的代码：
from collections import deque
class TaskScheduler:
def __init__(self):
self._task_queue = deque()
def new_task(self, task):
'''
Admit a newly started task to the scheduler
'''
self._task_queue.append(task)
def run(self):
'''
Run until there are no more tasks
'''
while self._task_queue:
task = self._task_queue.popleft()
try:
# Run until the next yield statement
next(task)
self._task_queue.append(task)
except StopIteration:
# Generator is no longer executing
pass
# Example use
sched = TaskScheduler()
sched.new_task(countdown(10))
sched.new_task(countdown(5))
sched.new_task(countup(15))
sched.run()
TaskScheduler 类在一个循环中运行生成器集合——每个都运行到碰到yield语句为止。 运行这个例子，输出如下：
T-minus 10
T-minus 5
Counting up 0
T-minus 9
T-minus 4
Counting up 1
T-minus 8
T-minus 3
Counting up 2
T-minus 7
T-minus 2
...
到此为止，我们实际上已经实现了一个“操作系统”的最小核心部分。 生成器函数就是任务，而yield语句是任务挂起的信号。 调度器循环检查任务列表直到没有任务要执行为止。
实际上，你可能想要使用生成器来实现简单的并发。 那么，在实现actor或网络服务器的时候你可以使用生成器来替代线程的使用。
下面的代码演示了使用生成器来实现一个不依赖线程的actor：
from collections import deque
class ActorScheduler:
def __init__(self):
self._actors = {} # Mapping of names to actors
self._msg_queue = deque() # Message queue
def new_actor(self, name, actor):
'''
Admit a newly started actor to the scheduler and give it a name
'''
self._msg_queue.append((actor,None))
self._actors[name] = actor
def send(self, name, msg):
'''
Send a message to a named actor
'''
actor = self._actors.get(name)
if actor:
self._msg_queue.append((actor,msg))
def run(self):
'''
Run as long as there are pending messages.
'''
while self._msg_queue:
actor, msg = self._msg_queue.popleft()
try:
actor.send(msg)
except StopIteration:
pass
# Example use
if __name__ == '__main__':
def printer():
while True:
msg = yield
print('Got:', msg)
def counter(sched):
while True:
# Receive the current count
n = yield
if n == 0:
break
# Send to the printer task
sched.send('printer', n)
# Send the next count to the counter task (recursive)
sched.send('counter', n-1)
sched = ActorScheduler()
# Create the initial actors
sched.new_actor('printer', printer())
sched.new_actor('counter', counter(sched))
# Send an initial message to the counter to initiate
sched.send('counter', 10000)
sched.run()
完全弄懂这段代码需要更深入的学习，但是关键点在于收集消息的队列。 本质上，调度器在有需要发送的消息时会一直运行着。 计数生成器会给自己发送消息并在一个递归循环中结束。
下面是一个更加高级的例子，演示了使用生成器来实现一个并发网络应用程序：
from collections import deque
from select import select
# This class represents a generic yield event in the scheduler
class YieldEvent:
def handle_yield(self, sched, task):
pass
def handle_resume(self, sched, task):
pass
# Task Scheduler
class Scheduler:
def __init__(self):
self._numtasks = 0 # Total num of tasks
self._ready = deque() # Tasks ready to run
self._read_waiting = {} # Tasks waiting to read
self._write_waiting = {} # Tasks waiting to write
# Poll for I/O events and restart waiting tasks
def _iopoll(self):
rset,wset,eset = select(self._read_waiting,
self._write_waiting,[])
for r in rset:
evt, task = self._read_waiting.pop(r)
evt.handle_resume(self, task)
for w in wset:
evt, task = self._write_waiting.pop(w)
evt.handle_resume(self, task)
def new(self,task):
'''
Add a newly started task to the scheduler
'''
self._ready.append((task, None))
self._numtasks += 1
def add_ready(self, task, msg=None):
'''
Append an already started task to the ready queue.
msg is what to send into the task when it resumes.
'''
self._ready.append((task, msg))
# Add a task to the reading set
def _read_wait(self, fileno, evt, task):
self._read_waiting[fileno] = (evt, task)
# Add a task to the write set
def _write_wait(self, fileno, evt, task):
self._write_waiting[fileno] = (evt, task)
def run(self):
'''
Run the task scheduler until there are no tasks
'''
while self._numtasks:
if not self._ready:
self._iopoll()
task, msg = self._ready.popleft()
try:
# Run the coroutine to the next yield
r = task.send(msg)
if isinstance(r, YieldEvent):
r.handle_yield(self, task)
else:
raise RuntimeError('unrecognized yield event')
except StopIteration:
self._numtasks -= 1
# Example implementation of coroutine-based socket I/O
class ReadSocket(YieldEvent):
def __init__(self, sock, nbytes):
self.sock = sock
self.nbytes = nbytes
def handle_yield(self, sched, task):
sched._read_wait(self.sock.fileno(), self, task)
def handle_resume(self, sched, task):
data = self.sock.recv(self.nbytes)
sched.add_ready(task, data)
class WriteSocket(YieldEvent):
def __init__(self, sock, data):
self.sock = sock
self.data = data
def handle_yield(self, sched, task):
sched._write_wait(self.sock.fileno(), self, task)
def handle_resume(self, sched, task):
nsent = self.sock.send(self.data)
sched.add_ready(task, nsent)
class AcceptSocket(YieldEvent):
def __init__(self, sock):
self.sock = sock
def handle_yield(self, sched, task):
sched._read_wait(self.sock.fileno(), self, task)
def handle_resume(self, sched, task):
r = self.sock.accept()
sched.add_ready(task, r)
# Wrapper around a socket object for use with yield
class Socket(object):
def __init__(self, sock):
self._sock = sock
def recv(self, maxbytes):
return ReadSocket(self._sock, maxbytes)
def send(self, data):
return WriteSocket(self._sock, data)
def accept(self):
return AcceptSocket(self._sock)
def __getattr__(self, name):
return getattr(self._sock, name)
if __name__ == '__main__':
from socket import socket, AF_INET, SOCK_STREAM
import time
# Example of a function involving generators. This should
# be called using line = yield from readline(sock)
def readline(sock):
chars = []
while True:
c = yield sock.recv(1)
if not c:
break
chars.append(c)
if c == b'\n':
break
return b''.join(chars)
# Echo server using generators
class EchoServer:
def __init__(self,addr,sched):
self.sched = sched
sched.new(self.server_loop(addr))
def server_loop(self,addr):
s = Socket(socket(AF_INET,SOCK_STREAM))
s.bind(addr)
s.listen(5)
while True:
c,a = yield s.accept()
print('Got connection from ', a)
self.sched.new(self.client_handler(Socket(c)))
def client_handler(self,client):
while True:
line = yield from readline(client)
if not line:
break
line = b'GOT:' + line
while line:
nsent = yield client.send(line)
line = line[nsent:]
client.close()
print('Client closed')
sched = Scheduler()
EchoServer(('',16000),sched)
sched.run()
这段代码有点复杂。不过，它实现了一个小型的操作系统。 有一个就绪的任务队列，并且还有因I/O休眠的任务等待区域。 还有很多调度器负责在就绪队列和I/O等待区域之间移动任务。
Python 使用生成器代替线程 讨论
在构建基于生成器的并发框架时，通常会使用更常见的yield形式：
def some_generator():
...
result = yield data
...
使用这种形式的yield语句的函数通常被称为“协程”。 通过调度器，yield语句在一个循环中被处理，如下：
f = some_generator()
# Initial result. Is None to start since nothing has been computed
result = None
while True:
try:
data = f.send(result)
result = ... do some calculation ...
except StopIteration:
break
这里的逻辑稍微有点复杂。不过，被传给 send() 的值定义了在yield语句醒来时的返回值。 因此，如果一个yield准备在对之前yield数据的回应中返回结果时，会在下一次 send() 操作返回。 如果一个生成器函数刚开始运行，发送一个None值会让它排在第一个yield语句前面。
除了发送值外，还可以在一个生成器上面执行一个 close() 方法。 它会导致在执行yield语句时抛出一个 GeneratorExit 异常，从而终止执行。 如果进一步设计，一个生成器可以捕获这个异常并执行清理操作。 同样还可以使用生成器的 throw() 方法在yield语句执行时生成一个任意的执行指令。 一个任务调度器可利用它来在运行的生成器中处理错误。
最后一个例子中使用的 yield from 语句被用来实现协程，可以被其它生成器作为子程序或过程来调用。 本质上就是将控制权透明的传输给新的函数。 不像普通的生成器，一个使用 yield from 被调用的函数可以返回一个作为 yield from 语句结果的值。