Python中的异步方法调用？

Question

我想知道Python 中是否有任何用于异步方法调用的库。如果你能做类似的事情会很棒

@async
def longComputation():
    <code>


token = longComputation()
token.registerCallback(callback_function)
# alternative, polling
while not token.finished():
    doSomethingElse()
    if token.finished():
        result = token.result()

或者异步调用非异步例程

def longComputation()
    <code>

token = asynccall(longComputation())

如果在语言核心中有一个更精细的策略作为本地语言，那就太好了。考虑过吗？

Answer 1

它不在语言核心中，而是一个非常成熟的库，可以满足您的需求，Twisted。它引入了 Deferred 对象，您可以将回调或错误处理程序（“errbacks”）附加到该对象。 Deferred 基本上是一个函数最终会有结果的“承诺”。

Answer 2

类似：

import threading

thr = threading.Thread(target=foo, args=(), kwargs={})
thr.start() # Will run "foo"
....
thr.is_alive() # Will return whether foo is running currently
....
thr.join() # Will wait till "foo" is done

有关详细信息，请参阅https://docs.python.org/library/threading.html 的文档。

Answer 3

有什么理由不使用线程吗？您可以使用threading 类。 使用isAlive() 代替finished() 函数。 result() 函数可以join() 线程并检索结果。并且，如果可以的话，重写run() 和__init__ 函数以调用构造函数中指定的函数并将值保存到类的实例中。

Answer 4

您可以使用 Python 2.6 中添加的multiprocessing module。您可以使用进程池，然后通过以下方式异步获取结果：

apply_async(func[, args[, kwds[, callback]]])

例如：

from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    pool = Pool(processes=1)              # Start a worker processes.
    result = pool.apply_async(f, [10], callback) # Evaluate "f(10)" asynchronously calling callback when finished.

这只是一种选择。这个模块提供了很多功能来实现你想要的。用它来做一个装饰器也很容易。

Answer 5

我的解决办法是：

import threading

class TimeoutError(RuntimeError):
    pass

class AsyncCall(object):
    def __init__(self, fnc, callback = None):
        self.Callable = fnc
        self.Callback = callback

    def __call__(self, *args, **kwargs):
        self.Thread = threading.Thread(target = self.run, name = self.Callable.__name__, args = args, kwargs = kwargs)
        self.Thread.start()
        return self

    def wait(self, timeout = None):
        self.Thread.join(timeout)
        if self.Thread.isAlive():
            raise TimeoutError()
        else:
            return self.Result

    def run(self, *args, **kwargs):
        self.Result = self.Callable(*args, **kwargs)
        if self.Callback:
            self.Callback(self.Result)

class AsyncMethod(object):
    def __init__(self, fnc, callback=None):
        self.Callable = fnc
        self.Callback = callback

    def __call__(self, *args, **kwargs):
        return AsyncCall(self.Callable, self.Callback)(*args, **kwargs)

def Async(fnc = None, callback = None):
    if fnc == None:
        def AddAsyncCallback(fnc):
            return AsyncMethod(fnc, callback)
        return AddAsyncCallback
    else:
        return AsyncMethod(fnc, callback)

并且完全按照要求工作：

@Async
def fnc():
    pass

Answer 6

您可以实现一个装饰器来使您的函数异步，尽管这有点棘手。 multiprocessing 模块充满了小怪癖和看似随意的限制——不过，更有理由将它封装在一个友好的界面后面。

from inspect import getmodule
from multiprocessing import Pool


def async(decorated):
    r'''Wraps a top-level function around an asynchronous dispatcher.

        when the decorated function is called, a task is submitted to a
        process pool, and a future object is returned, providing access to an
        eventual return value.

        The future object has a blocking get() method to access the task
        result: it will return immediately if the job is already done, or block
        until it completes.

        This decorator won't work on methods, due to limitations in Python's
        pickling machinery (in principle methods could be made pickleable, but
        good luck on that).
    '''
    # Keeps the original function visible from the module global namespace,
    # under a name consistent to its __name__ attribute. This is necessary for
    # the multiprocessing pickling machinery to work properly.
    module = getmodule(decorated)
    decorated.__name__ += '_original'
    setattr(module, decorated.__name__, decorated)

    def send(*args, **opts):
        return async.pool.apply_async(decorated, args, opts)

    return send

下面的代码说明了装饰器的用法：

@async
def printsum(uid, values):
    summed = 0
    for value in values:
        summed += value

    print("Worker %i: sum value is %i" % (uid, summed))

    return (uid, summed)


if __name__ == '__main__':
    from random import sample

    # The process pool must be created inside __main__.
    async.pool = Pool(4)

    p = range(0, 1000)
    results = []
    for i in range(4):
        result = printsum(i, sample(p, 100))
        results.append(result)

    for result in results:
        print("Worker %i: sum value is %i" % result.get())

在一个真实的案例中，我会详细说明装饰器，提供一些关闭它以进行调试的方法（同时保持未来的接口到位），或者可能是一种处理异常的工具；但我认为这很好地证明了这个原则。

Answer 7

只是

import threading, time

def f():
    print "f started"
    time.sleep(3)
    print "f finished"

threading.Thread(target=f).start()

Answer 8

你可以使用 eventlet。它可以让你编写看似同步的代码，但让它在网络上异步运行。

这是一个超极小爬虫的例子：

urls = ["http://www.google.com/intl/en_ALL/images/logo.gif",
     "https://wiki.secondlife.com/w/images/secondlife.jpg",
     "http://us.i1.yimg.com/us.yimg.com/i/ww/beta/y3.gif"]

import eventlet
from eventlet.green import urllib2

def fetch(url):

  return urllib2.urlopen(url).read()

pool = eventlet.GreenPool()

for body in pool.imap(fetch, urls):
  print "got body", len(body)

Answer 9

这样的东西对我有用，然后你可以调用该函数，它会将自己分派到一个新线程上。

from thread import start_new_thread

def dowork(asynchronous=True):
    if asynchronous:
        args = (False)
        start_new_thread(dowork,args) #Call itself on a new thread.
    else:
        while True:
            #do something...
            time.sleep(60) #sleep for a minute
    return

Answer 10

从 Python 3.5 开始，您可以将增强的生成器用于异步函数。

import asyncio
import datetime

增强的生成器语法：

@asyncio.coroutine
def display_date(loop):
    end_time = loop.time() + 5.0
    while True:
        print(datetime.datetime.now())
        if (loop.time() + 1.0) >= end_time:
            break
        yield from asyncio.sleep(1)


loop = asyncio.get_event_loop()
# Blocking call which returns when the display_date() coroutine is done
loop.run_until_complete(display_date(loop))
loop.close()

新的async/await 语法：

async def display_date(loop):
    end_time = loop.time() + 5.0
    while True:
        print(datetime.datetime.now())
        if (loop.time() + 1.0) >= end_time:
            break
        await asyncio.sleep(1)


loop = asyncio.get_event_loop()
# Blocking call which returns when the display_date() coroutine is done
loop.run_until_complete(display_date(loop))
loop.close()

Answer 11

您可以使用concurrent.futures（在 Python 3.2 中添加）。

import time
from concurrent.futures import ThreadPoolExecutor


def long_computation(duration):
    for x in range(0, duration):
        print(x)
        time.sleep(1)
    return duration * 2


print('Use polling')
with ThreadPoolExecutor(max_workers=1) as executor:
    future = executor.submit(long_computation, 5)
    while not future.done():
        print('waiting...')
        time.sleep(0.5)

    print(future.result())

print('Use callback')
executor = ThreadPoolExecutor(max_workers=1)
future = executor.submit(long_computation, 5)
future.add_done_callback(lambda f: print(f.result()))

print('waiting for callback')

executor.shutdown(False)  # non-blocking

print('shutdown invoked')

Answer 12

您可以使用进程。如果你想在你的函数中永远运行它（比如网络）：

from multiprocessing import Process
def foo():
    while 1:
        # Do something

p = Process(target = foo)
p.start()

如果您只想运行一次，请这样做：

from multiprocessing import Process
def foo():
    # Do something

p = Process(target = foo)
p.start()
p.join()

Answer 13

2021 年使用 Python 3.9 进行异步调用的原生 Python 方式也适用于 Jupyter / Ipython Kernel

Camabeh 的答案是自 Python 3.3 以来要走的路。

async def display_date(loop):
    end_time = loop.time() + 5.0
    while True:
        print(datetime.datetime.now())
        if (loop.time() + 1.0) >= end_time:
            break
        await asyncio.sleep(1)


loop = asyncio.get_event_loop()
# Blocking call which returns when the display_date() coroutine is done
loop.run_until_complete(display_date(loop))
loop.close()

这将在 Jupyter Notebook / Jupyter Lab 中工作，但会引发错误：

RuntimeError: This event loop is already running

由于 Ipython 使用事件循环，我们需要一种称为嵌套异步循环的东西，它不是 yet implemented in Python。幸运的是有nest_asyncio 来处理这个问题。您需要做的就是：

!pip install nest_asyncio # use ! within Jupyter Notebook, else pip install in shell
import nest_asyncio
nest_asyncio.apply()

（基于this thread）

只有当你调用 loop.close() 时它才会抛出另一个错误，因为它可能是指 Ipython 的主循环。

RuntimeError: Cannot close a running event loop

只要有人回复this github issue，我就会更新这个答案。