如何在Python中替换字符串中的无效unicode字符？(How to replace invalid unicode characters in a string in Python?)

 当你有一些与异步事件循环不兼容的阻塞任务时，你必须把它放在一个单独的线程中。 
 
 如果你将有无限数量的阻塞任​​务，你想使用线程池。 
 
 无论哪种方式，您都希望有一个封装异步任务，阻止来自线程任务的通知。 
 
 最简单的方法是使用像tornado-threadpool这样的预建库。然后，你只需要这样做： 
 
class LongHandler(tornado.web.RequestHandler):
    @thread_pool.in_thread_pool
    def long_time_function(self, callback):
        time.sleep(5)
        callback("foo")
 
 如果你想自己做， 这个要点展示了你必须做的一个例子 - 或者，当然，各种Tornado线程池库的源代码可以作为示例代码。 
 
 只要记住Python GIL的局限性：如果你的后台任务是CPU绑定的（并且在Python中执行大部分工作，而不是在C里扩展，像释放GIL一样），你必须把它放在一个单独的过程。 对Tornado进程池库的快速搜索没有多少好的选择，但是调整线程池代码来处理池代码在Python中通常非常简单。** 
 
 
 *请注意，我并不特别推荐该库; 这只是Google搜索中出现的第一件事情，并且快速浏览它看起来可用且正确。 
 
 **这通常与使用concurrent.futures.ProcessPoolExecutor或具有multiprocessing.Pool替换concurrent.futures.ThreadPoolExecutor一样简单。 唯一的技巧是确保所有的任务参数和返回值都很小并且可以选择。 

When you have some blocking task that doesn't play nice with the async event loop, you have to put it in a separate thread.
 
If you're going to have an unbounded number of blocking tasks, you want to use a thread pool.
 
Either way, you want to have a wrapper async task that blocks on notification from the threaded task.
 
The easiest way to do this is to use a pre-built library like tornado-threadpool.* Then, you just do something like this:
 
class LongHandler(tornado.web.RequestHandler):
    @thread_pool.in_thread_pool
    def long_time_function(self, callback):
        time.sleep(5)
        callback("foo")
 
If you want to do it yourself, this gist shows an example of what you have to do—or, of course, the source code for the various Tornado threadpool libraries can serve as sample code.
 
Just keep in mind the limitations of the Python GIL: If your background task is CPU-bound (and does most of the work in Python, rather than in a C extension that releases the GIL like numpy), you have to put it in a separate process. A quick search for Tornado process pool libraries didn't turn up as many nice options, but adapting thread pool code to process pool code is usually very easy in Python.**
 
 
* Note that I'm not specifically recommending that library; it's just the first thing that came up in a Google search, and it looks usable and correct from a quick glance.
 
** It's often as simple as replacing concurrent.futures.ThreadPoolExecutor with concurrent.futures.ProcessPoolExecutor or multiprocessing.dummy.Pool with multiprocessing.Pool. The only trick is to make sure that all of your task arguments and return values are small and pickleable.