如何将asyncio与现有的阻止库一起使用? [英] How to use asyncio with existing blocking library?

问题描述

我几乎没有阻塞功能foo，bar，并且我无法更改它们(某些我不控制的内部库.与一个或多个网络服务进行了交谈).我如何使用它作为异步?例如.我将不会执行以下操作.

I have few blocking functions foo, bar and I can't change those (Some internal library I don't control. Talks to one or more network services). How do I use it as async?. E.g. I wan't to do the following.

results = []
for inp in inps:
    val = foo(inp)
    result = bar(val)
    results.append(result)

这将是低效率的，因为我在等待第一个输入时可以调用foo作为第二个输入，而对于bar则可以相同.我如何包装它们以便它们可与asyncio一起使用(即新的async，await语法)?

This will be inefficient as I can call foo for the second input while I am waiting for the first and same for bar. How do I wrap them such that they are usable with asyncio (i.e new async, await syntax)?

让我们假设函数是可重入的.也就是说，当先前的foo正在处理时，可以再次调用foo.

Lets assume the functions are re-entrant. i.e it is fine to call foo again when already a previous foo is processing.

更新

使用可重用装饰器扩展答案.例如，点击此处.

Extending answer with reusable decorator. Click here for example.

def run_in_executor(f):
    @functools.wraps(f)
    def inner(*args, **kwargs):
        loop = asyncio.get_running_loop()
        return loop.run_in_executor(None, functools.partial(f, *args, **kwargs))

    return inner

推荐答案

此处有(两个)问题:首先，如何异步运行阻塞代码，其次，如何并行运行异步代码(asyncio是单个-threaded，因此GIL仍然适用，因此不是真正并发，但我离题了.

There are (sort of) two questions here: first, how to run blocking code asynchronously, and second, how to run async code in parallel (asyncio is single-threaded, so the GIL still applies, so it isn't truly concurrent, but I digress).

可以使用asyncio.ensure_future创建并行任务，如此处.

Parallel tasks can be created using asyncio.ensure_future, as documented here.

要运行同步代码，您将需要

To run synchronous code, you will need to run the blocking code in an executor. Example:

import concurrent.futures
import asyncio
import time

def blocking(delay):
    time.sleep(delay)
    print('Completed.')

async def non_blocking(loop, executor):
    # Run three of the blocking tasks concurrently. asyncio.wait will
    # automatically wrap these in Tasks. If you want explicit access
    # to the tasks themselves, use asyncio.ensure_future, or add a
    # "done, pending = asyncio.wait..." assignment
    await asyncio.wait(
        fs={
            # Returns after delay=12 seconds
            loop.run_in_executor(executor, blocking, 12),

            # Returns after delay=14 seconds
            loop.run_in_executor(executor, blocking, 14),

            # Returns after delay=16 seconds
            loop.run_in_executor(executor, blocking, 16)
        },
        return_when=asyncio.ALL_COMPLETED
    )

loop = asyncio.get_event_loop()
executor = concurrent.futures.ThreadPoolExecutor(max_workers=5)
loop.run_until_complete(non_blocking(loop, executor))

如果要使用for循环安排这些任务(如您的示例)，则有几种不同的策略，但是基本方法是使用for循环(或列表)安排任务理解等)，请通过asyncio.wait等待它们，然后 then 检索结果.示例:

If you want to schedule these tasks using a for loop (as in your example), you have several different strategies, but the underlying approach is to schedule the tasks using the for loop (or list comprehension, etc), await them with asyncio.wait, and then retrieve the results. Example:

done, pending = await asyncio.wait(
    fs=[loop.run_in_executor(executor, blocking_foo, *args) for args in inps],
    return_when=asyncio.ALL_COMPLETED
)

# Note that any errors raise during the above will be raised here; to
# handle errors you will need to call task.exception() and check if it
# is not None before calling task.result()
results = [task.result() for task in done]

这篇关于如何将asyncio与现有的阻止库一起使用?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！