“GIL”如何影响带有i/o系结任务的Pythonasyncio`run_in

关于Python ayncio 的这个代码示例： run_in_executor

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())

该示例（在注释中）建议使用运行 i/o 系结函式ThreadPoolExecutor，使用ProcessPoolExecutor. 我想通过三个问题来验证我对这背后原因的理解：

这些建议并不是真正的建议，否则事件回圈会阻塞。因此，我们将失去事件编程的主要好处，对吗？
作为单独的执行绪运行 io/bound 任务，需要以下假设：i/o 呼叫将释放 GIL，对吗？因为除此之外，作业系统将无法在事件回圈和这个新的单独执行绪之间进行背景关系切换。
如果第 2 点的答案是肯定的，那么如何确定 i/o 呼叫是否释放了 GIL？

uj5u.com热心网友回复：

如果您在协程中呼叫阻塞（I/O 和 CPU 阻塞）函式而不等待执行程序，则事件回圈将阻塞。在这方面，是的，您不应该允许这种情况发生。

我的建议是，对于每种型别的阻塞代码，它都是一种执行器：将 ProcessPoolExecutor 用于 CPU 系结的东西，将 ThreadPoolExecutor 用于 I/O 系结的东西。

当谈到多执行绪时，Python 将在很短的时间内在执行绪之间切换而不释放 GIL。但是如果一个或多个执行绪有 I/O（或 C 代码），那么 GIL 将被释放，允许解释器花更多时间在需要它的执行绪上。

底线是：

您可以在 executor 中运行任何阻塞代码，它不会阻塞事件回圈。您获得并发性，但可能会或可能不会获得性能。
例如，如果您在 ThreadPoolExecutor 中运行受 CPU 限制的代码，由于 GIL，您将不会从并发中获得性能优势。要获得 CPU 密集型内容的性能，您应该使用 ProcessPoolExecutor。
但是 I/O-bound 可以在 ThreadPoolExecutor 中运行并且您可以获得性能。这里没有必要使用更重的 ProcessPoolExecutor。

我写了一个例子来演示它是如何作业的：

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