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问"GIL“如何影响带有i/o绑定任务的Python异步`run_in_executor`？
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Stack Overflow用户

提问于 2021-12-23 08:23:51

回答 1查看 1K关注 0票数 1

关于Python 示例 run_in_executor的代码

import asyncio
import concurrent.futures

def blocking_io():
    # File operations (such as logging) can block the
    # event loop: run them in a thread pool.
    with open('/dev/urandom', 'rb') as f:
        return f.read(100)

def cpu_bound():
    # CPU-bound operations will block the event loop:
    # in general it is preferable to run them in a
    # process pool.
    return sum(i * i for i in range(10 ** 7))

async def main():
    loop = asyncio.get_running_loop()

    ## Options:

    # 1. Run in the default loop's executor:
    result = await loop.run_in_executor(
        None, blocking_io)
    print('default thread pool', result)

    # 3. Run in a custom process pool:
    with concurrent.futures.ProcessPoolExecutor() as pool:
        result = await loop.run_in_executor(
            pool, cpu_bound)
        print('custom process pool', result)

asyncio.run(main())

示例(注释中)建议使用ThreadPoolExecutor运行i/o绑定函数，使用ProcessPoolExecutor运行cpu绑定函数。我想用以下三个问题来验证我对此背后的原因的理解：

这些建议并不是真正的建议，因为否则事件循环将阻塞。因此，我们将失去事件编程的主要好处，对吗？
将io/绑定任务作为单独的线程运行，需要以下假设: i/o调用将释放GIL，对吗？因为除此之外，os将无法在事件循环和这个新的单独线程之间进行上下文切换。
如果对第2点的回答是肯定的，那么如何确定一个i/o调用是否释放GIL？

multithreading

async-await

python-asyncio

python

回答 1

Stack Overflow用户

回答已采纳

发布于 2021-12-23 10:06:23

这些建议并不是真正的建议，因为否则事件循环将阻塞。因此，我们将失去事件编程的主要好处，对吗？

如果您在协同线中调用阻塞( I/O和CPU阻塞)功能，则事件循环将阻塞，而无需等待执行器。在这方面，是的，你不应该允许这种情况发生。

对于每种类型的阻塞代码，我会说它是一种执行器类型:使用ProcessPoolExecutor进行CPU绑定，对I/O绑定使用ThreadPoolExecutor。

将io/绑定任务作为单独的线程运行，需要以下假设: i/o调用将释放GIL，对吗？因为除此之外，os将无法在事件循环和这个新的单独线程之间进行上下文切换。

当涉及到多线程时，Python将在非常短时间之后在线程之间切换，而不释放GIL。但是，如果一个或多个线程具有I/O (或C代码)，则GIL将被释放，允许解释器在需要它的线程上花费更多的时间。

底线是：

您可以在executor中运行任何阻塞代码，它不会阻止事件循环。您可以获得并发，但可能获得或不可能获得性能。
例如，如果您在ThreadPoolExecutor中运行CPU绑定的代码，由于GIL，您将不会从并发性中获得性能上的好处。要获得CPU绑定的性能，您应该使用ProcessPoolExecutor。
但是I/O绑定可以在ThreadPoolExecutor中运行，这样就可以获得性能。这里没有必要使用更重的ProcessPoolExecutor。

我编写了一个示例来演示它是如何工作的：

import sys
import asyncio
import time
import concurrent.futures
import requests
from contextlib import contextmanager

process_pool = concurrent.futures.ProcessPoolExecutor(2)
thread_pool = concurrent.futures.ThreadPoolExecutor(2)


def io_bound():
    for i in range(3):
        requests.get("https://httpbin.org/delay/0.4")  # I/O blocking
        print(f"I/O bound {i}")
        sys.stdout.flush()


def cpu_bound():
    for i in range(3):
        sum(i * i for i in range(10 ** 7))  # CPU blocking
        print(f"CPU bound {i}")
        sys.stdout.flush()


async def run_as_is(func):
    func()


async def run_in_process(func):
    loop = asyncio.get_event_loop()
    await loop.run_in_executor(process_pool, func)


async def run_in_thread(func):
    loop = asyncio.get_event_loop()
    await loop.run_in_executor(thread_pool, func)


@contextmanager
def print_time():
    start = time.time()
    yield
    finished = time.time() - start
    print(f"Finished in {round(finished, 1)}\n")


async def main():
    print("Wrong due to blocking code in coroutine,")
    print(
        "you get neither performance, nor concurrency (which breaks async nature of the code)"
    )
    print("don't allow this to happen")
    with print_time():
        await asyncio.gather(run_as_is(cpu_bound), run_as_is(io_bound))

    print("CPU bound works concurrently with threads,")
    print("but you gain no performance due to GIL")
    with print_time():
        await asyncio.gather(run_in_thread(cpu_bound), run_in_thread(cpu_bound))

    print("To get perfromance for CPU-bound,")
    print("use process executor")
    with print_time():
        await asyncio.gather(run_in_process(cpu_bound), run_in_process(cpu_bound))

    print("I/O bound will gain benefit from processes as well...")
    with print_time():
        await asyncio.gather(run_in_process(io_bound), run_in_process(io_bound))

    print(
        "... but there's no need in processes since you can use lighter threads for I/O"
    )
    with print_time():
        await asyncio.gather(run_in_thread(io_bound), run_in_thread(io_bound))

    print("Long story short,")
    print("Use processes for CPU bound due to GIL")
    print(
        "and use threads for I/O bound since you benefit from concurrency regardless of GIL"
    )
    with print_time():
        await asyncio.gather(run_in_thread(io_bound), run_in_process(cpu_bound))


if __name__ == "__main__":
    asyncio.run(main())

输出：

Wrong due to blocking code in coroutine,
you get neither performance, nor concurrency (which breaks async nature of the code)
don't allow this to happen
CPU bound 0
CPU bound 1
CPU bound 2
I/O bound 0
I/O bound 1
I/O bound 2
Finished in 5.3

CPU bound works concurrently with threads,
but you gain no performance due to GIL
CPU bound 0
CPU bound 0
CPU bound 1
CPU bound 1
CPU bound 2
CPU bound 2
Finished in 4.6

To get perfromance for CPU-bound,
use process executor
CPU bound 0
CPU bound 0
CPU bound 1
CPU bound 1
CPU bound 2
CPU bound 2
Finished in 2.5

I/O bound will gain benefit from processes as well...
I/O bound 0
I/O bound 0
I/O bound 1
I/O bound 1
I/O bound 2
I/O bound 2
Finished in 3.3

... but there's no need in processes since you can use lighter threads for I/O
I/O bound 0
I/O bound 0
I/O bound 1
I/O bound 1
I/O bound 2
I/O bound 2
Finished in 3.1

Long story short,
Use processes for CPU bound due to GIL
and use threads for I/O bound since you benefit from concurrency regardless of GIL
CPU bound 0
I/O bound 0
CPU bound 1
I/O bound 1
CPU bound 2
I/O bound 2
Finished in 2.9

票数 3

页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持

原文链接：

https://stackoverflow.com/questions/70459437

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