我想知道Python中是否有用于异步方法调用的库。如果你能做点什么就太好了
@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就太好了。考虑过这个问题吗?
当前回答
你可以使用过程。如果你想永远运行它，在你的函数中使用while(比如networking):
from multiprocessing import Process
def foo():
while 1:
# Do something
p = Process(target = foo)
p.start()
如果你只想运行一次，可以这样做:
from multiprocessing import Process
def foo():
# Do something
p = Process(target = foo)
p.start()
p.join()
其他回答
你可以使用过程。如果你想永远运行它，在你的函数中使用while(比如networking):
from multiprocessing import Process
def foo():
while 1:
# Do something
p = Process(target = foo)
p.start()
如果你只想运行一次，可以这样做:
from multiprocessing import Process
def foo():
# Do something
p = Process(target = foo)
p.start()
p.join()
你可以使用eventlet。它允许您编写看似同步的代码，但却可以在网络上异步操作。
下面是一个超级小爬虫的例子:
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)
Just
import threading, time
def f():
print "f started"
time.sleep(3)
print "f finished"
threading.Thread(target=f).start()
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)
并完全按要求工作:
@Async
def fnc():
pass
您可以使用Python 2.6中添加的多处理模块。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工具来实现你想要的。此外，它将很容易从这做一个装饰。
