调用 thread.join() 会阻塞异步上下文中的事件循环吗？

Question

我正在使用 aiohttp 实现 Web API，使用 gunicorn 部署并启用 UVloop --worker-class aiohttp.GunicornUVLoopWebWorker。因此，我的代码始终在异步上下文中运行。我的想法是在处理请求时实现并行作业以获得更好的性能。

我没有使用asyncio，因为我想要Parallelism，而不是Concurrency。

我知道 python 中的multiprocessing 和GIL problem。但加入流程也适用于我的问题。

这是一个例子：

from aiohttp.web import middleware

@middleware
async def context_init(request, handler):
    request.context = {}
    request.context['threads'] = []
    ret = await handler(request)
    for thread in request.context['threads']:
        thread.join()
    return ret

考虑到thread.join() 或process.join() 阻塞了当前线程，这将阻塞事件循环（据我所知）。如何异步加入？我想要的可以形象地表示为：await thread.join() 或await process.join()。

更新：

感谢@user4815162342，我能够为我的项目编写正确的代码：

中间件：

from aiohttp.web import middleware
from util.process_session import ProcessSession

@middleware
async def context_init(request, handler):
    request.context = {}
    request.context['process_session'] = ProcessSession()
    request.context['processes'] = {}
    ret = await handler(request)
    await request.context['process_session'].wait_for_all()
    return ret

实用程序：

import asyncio
import concurrent.futures
from functools import partial

class ProcessSession():

    def __init__(self):
        self.loop = asyncio.get_running_loop()
        self.pool = concurrent.futures.ProcessPoolExecutor()
        self.futures = []

    async def wait_for_all(self):
        await asyncio.wait(self.futures)

    def add_process(self, f, *args, **kwargs):
        ret = self.loop.run_in_executor(self.pool, partial(f, *args, **kwargs))
        self.futures.append(ret)
        return ret

class ProcessBase():

    def __init__(self, process_session, f, *args, **kwargs):
        self.future = process_session.add_process(f, *args, **kwargs)

    async def wait(self):
        await asyncio.wait([self.future])
        return self.future.result()

Answer 1

回答您的问题：是的，它确实阻塞了事件循环。

我发现ThreadPoolExecutor 在这种情况下效果很好。

from util.process_session import ProcessSession
from concurrent.futures.thread import ThreadPoolExecutor
import asyncio

from aiohttp.web import middleware

@middleware
async def context_init(request, handler):
    request.context = {}
    request.context['threads'] = []
    ret = await handler(request)
    with ThreadPoolExecutor(1) as executor:
           await asyncio.get_event_loop().run_in_executor(executor, 
           functools.partial(join_threads, data={
             'threads': request.context['threads']
           }))
    return ret

def join_threads(threads):
    for t in threads:
        t.join()

Answer 2

我找到了使用multiprocesses 的解决方案。可以使用Pool 来完成。标准库提供了一些“异步”方法（它并不是真正的异步，它只是将进程的初始化与进程的输出分开）：apply_async

使用简单的async wrapper，我成功实现了我想要的：

from multiprocessing import Pool
from async_converter import sync_to_async
import asyncio

def f(x):
    i = 0
    while i < 10000000 * x:
       i = i + 1
    print("Finished: " + str(x))
    return i


async def run():
    print("Started with run")
    with Pool(processes=4) as pool:         # start 4 worker processes
        result1 = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously
        result2 = pool.apply_async(f, (2,))
        res1 = await sync_to_async(result1.get)()
        print(res1)
        res2 = await sync_to_async(result2.get)()
        print(res2)

async def dummy(output):
    print(output)

async def main():
    # Schedule three calls *concurrently*:
    await asyncio.gather(
        run(),
        dummy("Nice"),
        dummy("Async"),
        dummy("Loop"),
        dummy("Perfect"),
        dummy("Dummy1"),
        dummy("Dummy2"),
        dummy("Dummy3"),
        dummy("Dummy4"),
        dummy("Dummy5"),
        dummy("Dummy6"),
        dummy("Dummy7"),
        dummy("Dummy8"),
        dummy("Dummy9"),
        dummy("Dummy10"),
    )

loop = asyncio.get_event_loop()
loop.run_until_complete(main())
loop.close()

输出：

Perfect
Dummy6
Nice
Dummy1
Dummy7
Started with run
Dummy2
Dummy8
Dummy3
Dummy9
Async
Dummy4
Dummy10
Loop
Dummy5
Finished: 2
Finished: 10
100000000
20000000

与 asyncio 的并行性:)