12.12 使用生成器代替线程

## 问题 You want to implement concurrency using generators (coroutines) as an alternative tosystem threads. This is sometimes known as user-level threading or green threading. ## 解决方案 To implement your own concurrency using generators, you first need a fundamentalinsight concerning generator functions and the yield statement. Specifically, the fun‐damental behavior of yield is that it causes a generator to suspend its execution. Bysuspending execution, it is possible to write a scheduler that treats generators as a kindof “task” and alternates their execution using a kind of cooperative task switching.To illustrate this idea, consider the following two generator functions using a simpleyield: # Two simple generator functionsdef countdown(n): > while n > 0:print(‘T-minus', n)yieldn -= 1> print(‘Blastoff!') def countup(n): x = 0while x < n: > print(‘Counting up', x)yieldx += 1 These functions probably look a bit funny using yield all by itself. However, considerthe following code that implements a simple task scheduler: 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 statementnext(task)self._task_queue.append(task) except StopIteration:# Generator is no longer executingpass # Example usesched = TaskScheduler()sched.new_task(countdown(10))sched.new_task(countdown(5))sched.new_task(countup(15))sched.run() In this code, the TaskScheduler class runs a collection of generators in a round-robinmanner—each one running until they reach a yield statement. For the sample, theoutput will be as follows: T-minus 10T-minus 5Counting up 0T-minus 9T-minus 4Counting up 1T-minus 8T-minus 3Counting up 2T-minus 7T-minus 2... At this point, you’ve essentially implemented the tiny core of an “operating system” ifyou will. Generator functions are the tasks and the yield statement is how tasks signalthat they want to suspend. The scheduler simply cycles over the tasks until none are leftexecuting.In practice, you probably wouldn’t use generators to implement concurrency for some‐thing as simple as shown. Instead, you might use generators to replace the use of threadswhen implementing actors (see Recipe 12.10) or network servers. The following code illustrates the use of generators to implement a thread-free versionof actors: from collections import deque class ActorScheduler:def __init__(self):self._actors = { } # Mapping of names to actorsself._msg_queue = deque() # Message queuedef 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] = actordef 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 useif __name__ == ‘__main__': > def printer():while True:msg = yieldprint(‘Got:', msg)def counter(sched):while True:> # Receive the current countn = yieldif n == 0: > > break > # Send to the printer tasksched.send(‘printer', n)# Send the next count to the counter task (recursive) > sched.send(‘counter', n-1) > sched = ActorScheduler()# Create the initial actorssched.new_actor(‘printer', printer())sched.new_actor(‘counter', counter(sched)) > # Send an initial message to the counter to initiatesched.send(‘counter', 10000)sched.run() The execution of this code might take a bit of study, but the key is the queue of pendingmessages. Essentially, the scheduler runs as long as there are messages to deliver. Aremarkable feature is that the counter generator sends messages to itself and ends upin a recursive cycle not bound by Python’s recursion limit.Here is an advanced example showing the use of generators to implement a concurrentnetwork application: from collections import dequefrom select import select # This class represents a generic yield event in the schedulerclass YieldEvent: > def handle_yield(self, sched, task):passdef handle_resume(self, sched, task):pass # Task Schedulerclass Scheduler: > def __init__(self):self._numtasks = 0 # Total num of tasksself._ready = deque() # Tasks ready to runself._read_waiting = {} # Tasks waiting to readself._write_waiting = {} # Tasks waiting to write> # Poll for I/O events and restart waiting tasksdef _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 setdef _read_wait(self, fileno, evt, task): > > self._read_waiting[fileno] = (evt, task) > # Add a task to the write setdef _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 yieldr = 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/Oclass ReadSocket(YieldEvent): > def __init__(self, sock, nbytes):self.sock = sockself.nbytes = nbytesdef 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 = sockself.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 = sockdef 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 yieldclass Socket(object): > def __init__(self, sock):self._sock = sockdef 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_STREAMimport 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 generatorsclass EchoServer: > def __init__(self,addr,sched):self.sched = schedsched.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:' + linewhile line: > > nsent = yield client.send(line)line = line[nsent:] > client.close()print(‘Client closed') sched = Scheduler()EchoServer((‘',16000),sched)sched.run() This code will undoubtedly require a certain amount of careful study. However, it isessentially implementing a small operating system. There is a queue of tasks ready torun and there are waiting areas for tasks sleeping for I/O. Much of the scheduler involvesmoving tasks between the ready queue and the I/O waiting area. ## 讨论 When building generator-based concurrency frameworks, it is most common to workwith the more general form of yield: def some_generator():...result = yield data... Functions that use yield in this manner are more generally referred to as “coroutines.”Within a scheduler, the yield statement gets handled in a loop as follows: f = some_generator() # Initial result. Is None to start since nothing has been computedresult = Nonewhile True: > try:data = f.send(result)result = ... do some calculation ...except StopIteration:break The logic concerning the result is a bit convoluted. However, the value passed to send()defines what gets returned when the yield statement wakes back up. So, if a yield isgoing to return a result in response to data that was previously yielded, it gets returnedon the next send() operation. If a generator function has just started, sending in a valueof None simply makes it advance to the first yield statement.In addition to sending in values, it is also possible to execute a close() method on agenerator. This causes a silent GeneratorExit exception to be raised at the yield state‐ment, which stops execution. If desired, a generator can catch this exception and per‐form cleanup actions. It’s also possible to use the throw() method of a generator to raisean arbitrary execution at the yield statement. A task scheduler might use this to com‐municate errors into running generators.The yield from statement used in the last example is used to implement coroutinesthat serve as subroutines or procedures to be called from other generators. Essentially,control transparently transfers to the new function. Unlike normal generators, a func‐tion that is called using yield from can return a value that becomes the result of theyield from statement. More information about yield from can be found in PEP 380.Finally, if programming with generators, it is important to stress that there are somemajor limitations. In particular, you get none of the benefits that threads provide. Forinstance, if you execute any code that is CPU bound or which blocks for I/O, it willsuspend the entire task scheduler until the completion of that operation. To work aroundthis, your only real option is to delegate the operation to a separate thread or processwhere it can run independently. Another limitation is that most Python libraries havenot been written to work well with generator-based threading. If you take this approach,you may find that you need to write replacements for many standard library functions.As basic background on coroutines and the techniques utilized in this recipe, see PEP342 and “A Curious Course on Coroutines and Concurrency”.PEP 3156 also has a modern take on asynchronous I/O involving coroutines. In practice,it is extremelyunlikely that you will write a low-level coroutine scheduler yourself.However, ideas surrounding coroutines are the basis for many popular libraries, in‐cluding gevent, greenlet, Stackless Python, and similar projects.