Question

我想达到与

相同的效果

# Code 1
from multiprocessing.pool import ThreadPool as Pool
from time import sleep, time

def square(a):
    print('start', a)
    sleep(a)
    print('end', a)
    return a * a

def main():
    p = Pool(2)
    queue = list(range(4))
    start = time()
    results = p.map(square, queue)
    print(results)
    print(time() - start)

if __name__ == "__main__":
    main()

具有类似异步功能

# Code 2
from multiprocessing.pool import ThreadPool as Pool
from time import sleep, time
import asyncio

async def square(a):
    print('start', a)
    sleep(a)  # await asyncio.sleep same effect
    print('end', a)
    return a * a

async def main():
    p = Pool(2)
    queue = list(range(4))
    start = time()
    results = p.map_async(square, queue)
    results = results.get()
    results = [await result for result in results]
    print(results)
    print(time() - start)

if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())
    loop.close()

当前代码1需要4秒钟，代码2需要6秒钟，这意味着它不是并行运行的。并行运行多个异步功能的正确和最干净的方法是什么？

最好与python 3.6兼容。谢谢！

Answer 1

df <- data.frame(size = round(rnorm(30, 39, 2)), pop = sample(c("kuopio", "tampere"), 30, replace = T), height = sample(c("short", "tall"), 30, replace = T)) tapply(df$size, INDEX = df[c(3, 2)], mean, na.rm=T) # df[c(3, 2)] refers to height and pop columns of df respectively pop height kuopio tampere short 39 39.57143 tall 41 39.22222与map_async()中的“异步”不同-如果使用async def方法馈送它，则它实际上不会运行，而是立即返回协程实例（请尝试在不使用async def的情况下调用此方法。然后，您await逐一await进入4个协程，这等于顺序执行，并以6秒结束。

请参见以下示例：

from time import time
import asyncio
from asyncio.locks import Semaphore

semaphore = Semaphore(2)


async def square(a):
    async with semaphore:
        print('start', a)
        await asyncio.sleep(a)
        print('end', a)
        return a * a


async def main():
    start = time()
    tasks = []
    for a in range(4):
        tasks.append(asyncio.ensure_future(square(a)))
    await asyncio.wait(tasks)
    print([t.result() for t in tasks])
    print(time() - start)


if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())
    loop.close()

Semaphore的行为类似于ThreadPool，它只允许2个并发协程进入async with semaphore:块。

如何在Python 3中正确使用异步等待与线程池

1 个答案: