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。如果你能做像

这样的事情会很棒

@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())

在语言核心中拥有更加精致的策略是很棒的。这是考虑过吗？

Answer 1

类似的东西：

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

有关详情，请参阅https://docs.python.org/2/library/threading.html#module-threading上的文档;此代码也适用于Python 3。

Answer 2

您可以使用Python 2.6中添加的multiprocessing module。您可以使用进程池，然后以异步方式获取结果：

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

E.g：

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。该模块提供了许多设施来实现您的需求。从这个角度来制作装饰也很容易。

Answer 3

从Python 3.5开始，您可以将增强型生成器用于异步功能。

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 4

它不是语言核心，而是一个非常成熟的库，可以做你想要的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。延迟基本上是一个“承诺”，函数最终会产生结果。

Answer 5

你可以实现一个装饰器来使你的函数异步，虽然这有点棘手。 multiprocessing模块充满了小怪癖和看似随意的限制 - 更有理由将它封装在一个友好的界面之后。

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())

在一个真实世界的案例中，我会在装饰器上详细说明，提供一些方法来关闭它以进行调试（同时保持未来的接口），或者可能是处理异常的工具;但我认为这足以证明这一原则。

Answer 6

只需

import threading, time

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

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

Answer 7

我的解决方案是：

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)

完全按照要求行事：

@Async
def fnc():
    pass

Answer 8

您可以使用eventlet。它允许您编写看似同步代码的内容，但让它通过网络异步操作。

以下是超级最小抓取工具的示例：

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，它会将自己调度到一个新的线程上。

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

有没有理由不使用线程？您可以使用threading课程。 而不是finished()函数使用isAlive()。 result()函数可以join()线程并检索结果。并且，如果可以，请覆盖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 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

2021 年异步调用的原生 Python 方式，Python 3.9 也适用于 Jupyter/Ipython 内核

Camabeh 的答案是自 Python 3.3 以来要走的路。

<块引用>

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 可以解决这个问题。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）

仅当您调用 loop.close() 时，它才会抛出另一个错误，因为它可能指的是 Ipython 的主循环。

RuntimeError: Cannot close a running event loop

我会在有人回复 this github issue 后立即更新此答案。

Answer 13

您可以使用流程。如果你想永远地运行它，请使用while（如网络）功能：

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，请执行以下操作：

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

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