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Python aiohttp/asyncio - how to process returned data

由 一个人想着一个人 提交于 2019-12-03 02:02:21

Here's an example with concurrent.futures.ProcessPoolExecutor. If it's created without specifying max_workers, the implementation will use os.cpu_count instead. Also note that asyncio.wrap_future is public but undocumented. Alternatively, there's AbstractEventLoop.run_in_executor. 

import asyncio
from concurrent.futures import ProcessPoolExecutor

import aiohttp
import lxml.html


def process_page(html):
    '''Meant for CPU-bound workload'''
    tree = lxml.html.fromstring(html)
    return tree.find('.//title').text


async def fetch_page(url, session):
    '''Meant for IO-bound workload'''
    async with session.get(url, timeout = 15) as res:
      return await res.text()


async def process(url, session, pool):
    html = await fetch_page(url, session)
    return await asyncio.wrap_future(pool.submit(process_page, html))


async def dispatch(urls):
    pool = ProcessPoolExecutor()
    async with aiohttp.ClientSession() as session:
        coros = (process(url, session, pool) for url in urls)
        return await asyncio.gather(*coros)


def main():
    urls = [
      'https://stackoverflow.com/',
      'https://serverfault.com/',
      'https://askubuntu.com/',
      'https://unix.stackexchange.com/'
    ]
    result = asyncio.get_event_loop().run_until_complete(dispatch(urls))
    print(result)

if __name__ == '__main__':
    main()

Your code isn't far from the mark. asyncio.gather returns the results in the order of the arguments, so order is preserved here, but page_content will not be called in order.

A few tweaks:

First of all, you do not need ensure_future here. Creating a Task is only needed if you are trying to have a coroutine outlive its parent, ie if the task has to continue running even though the function that created it is done. Here what you need is instead calling asyncio.gather directly with your coroutines:

async def get_url_data(urls, username, password):
    async with aiohttp.ClientSession(...) as session:
        responses = await asyncio.gather(*(fetch(session, i) for i in urls))
    for i in responses:
        print(i.title.text)
    return responses

But calling this would schedule all the fetch at the same time, and with a high number of URLs, this is far from optimal. Instead you should choose a maximum concurrency and ensure at most X fetches are running at any time. To implement this, you can use a asyncio.Semaphore(20), this semaphore can only be acquired by at most 20 coroutines, so the others will wait to acquire until a spot is available.

CONCURRENCY = 20
TIMEOUT = 15

async def fetch(session, sem, url):
    async with sem:
        async with session.get(url) as response:
            return page_content(await response.text())

async def get_url_data(urls, username, password):
    sem = asyncio.Semaphore(CONCURRENCY)
    async with aiohttp.ClientSession(...) as session:
        responses = await asyncio.gather(*(
            asyncio.wait_for(fetch(session, sem, i), TIMEOUT)
            for i in urls
        ))
    for i in responses:
        print(i.title.text)
    return responses

This way, all the fetches are started immediately, but only 20 of them will be able to acquire the semaphore. The others will block at the first async with instruction and wait until another fetch is done.

I have also replaced the aiohttp.Timeout with the official asyncio equivalent here.

Finally, for the actual processing of the data, if you are limited by CPU time, asyncio will probably not help you much. You will need to use a ProcessPoolExecutor here to parallelise the actual work to another CPU. run_in_executor will probably be of use to.

标签
python
python-asyncio
aiohttp
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