How to limit concurrency with Python asyncio?

Question

Let\'s assume we have a bunch of links to download and each of the link may take a different amount of time to download. And I\'m allowed to download using utmost 3 connecti

Answer 1

If I'm not mistaken you're searching for asyncio.Semaphore. Example of usage:

import asyncio
from random import randint


async def download(code):
    wait_time = randint(1, 3)
    print('downloading {} will take {} second(s)'.format(code, wait_time))
    await asyncio.sleep(wait_time)  # I/O, context will switch to main function
    print('downloaded {}'.format(code))


sem = asyncio.Semaphore(3)


async def safe_download(i):
    async with sem:  # semaphore limits num of simultaneous downloads
        return await download(i)


async def main():
    tasks = [
        asyncio.ensure_future(safe_download(i))  # creating task starts coroutine
        for i
        in range(9)
    ]
    await asyncio.gather(*tasks)  # await moment all downloads done


if __name__ ==  '__main__':
    loop = asyncio.get_event_loop()
    try:
        loop.run_until_complete(main())
    finally:
        loop.run_until_complete(loop.shutdown_asyncgens())
        loop.close()

Output:

downloading 0 will take 3 second(s)
downloading 1 will take 3 second(s)
downloading 2 will take 1 second(s)
downloaded 2
downloading 3 will take 3 second(s)
downloaded 1
downloaded 0
downloading 4 will take 2 second(s)
downloading 5 will take 1 second(s)
downloaded 5
downloaded 3
downloading 6 will take 3 second(s)
downloading 7 will take 1 second(s)
downloaded 4
downloading 8 will take 2 second(s)
downloaded 7
downloaded 8
downloaded 6

Example of async downloading with aiohttp can be found here.

Answer 2

Before reading the rest of this answer, please note that the idiomatic way of limiting the number of parallel tasks this with asyncio is using asyncio.Semaphore, as shown in Mikhail's answer and elegantly abstracted in Andrei's answer. This answer contains working, but a bit more complicated ways of achieving the same. I am leaving the answer because in some cases this approach can have advantages over a semaphore, specifically when the work to be done is very large or unbounded, and you cannot create all the coroutines in advance. In that case the second (queue-based) solution is this answer is what you want. But in most regular situations, such as parallel download through aiohttp, you should use a semaphore instead.

---

You basically need a fixed-size pool of download tasks. asyncio doesn't come with a pre-made task pool, but it is easy to create one: simply keep a set of tasks and don't allow it to grow past the limit. Although the question states your reluctance to go down that route, the code ends up much more elegant:

async def download(code):
    wait_time = randint(1, 3)
    print('downloading {} will take {} second(s)'.format(code, wait_time))
    await asyncio.sleep(wait_time)  # I/O, context will switch to main function
    print('downloaded {}'.format(code))

async def main(loop):
    no_concurrent = 3
    dltasks = set()
    i = 0
    while i < 9:
        if len(dltasks) >= no_concurrent:
            # Wait for some download to finish before adding a new one
            _done, dltasks = await asyncio.wait(
                dltasks, return_when=asyncio.FIRST_COMPLETED)
        dltasks.add(loop.create_task(download(i)))
        i += 1
    # Wait for the remaining downloads to finish
    await asyncio.wait(dltasks)

An alternative is to create a fixed number of coroutines doing the downloading, much like a fixed-size thread pool, and feed them work using an asyncio.Queue. This removes the need to manually limit the number of downloads, which will be automatically limited by the number of coroutines invoking download():

# download() defined as above

async def download_worker(q):
    while True:
        code = await q.get()
        await download(code)
        q.task_done()

async def main(loop):
    q = asyncio.Queue()
    workers = [loop.create_task(download_worker(q)) for _ in range(3)]
    i = 0
    while i < 9:
        await q.put(i)
        i += 1
    await q.join()  # wait for all tasks to be processed
    for worker in workers:
        worker.cancel()
    await asyncio.gather(*workers, return_exceptions=True)

As for your other question, the obvious choice would be aiohttp.

Answer 3

I used Mikhails answer and ended up with this little gem

async def gather_with_concurrency(n, *tasks):
    semaphore = asyncio.Semaphore(n)

    async def sem_task(task):
        async with semaphore:
            return await task
    return await asyncio.gather(*(sem_task(task) for task in tasks))

Which you would run instead of normal gather

await gather_with_concurrency(100, *my_coroutines)

Answer 4

The asyncio-pool library does exactly what you need.

https://pypi.org/project/asyncio-pool/

LIST_OF_URLS = ("http://www.google.com", "......")

pool = AioPool(size=3)
await pool.map(your_download_coroutine, LIST_OF_URLS)

Answer 5

Small Update: It's no longer necessary to create the loop. I tweaked the code below. Just cleans things up slightly.

# download(code) is the same

async def main():
    no_concurrent = 3
    dltasks = set()
    for i in range(9):
        if len(dltasks) >= no_concurrent:
            # Wait for some download to finish before adding a new one
            _done, dltasks = await asyncio.wait(dltasks, return_when=asyncio.FIRST_COMPLETED)
        dltasks.add(asyncio.create_task(download(i)))
    # Wait for the remaining downloads to finish
    await asyncio.wait(dltasks)

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