classification
Title: Allow limiting the number of concurrent tasks in asyncio.as_completed
Type: enhancement Stage:
Components: asyncio Versions: Python 3.7
process
Status: open Resolution:
Dependencies: Superseder:
Assigned To: Nosy List: andybalaam, yselivanov
Priority: normal Keywords:

Created on 2017-06-27 01:05 by andybalaam, last changed 2017-08-17 07:57 by andybalaam.

Pull Requests
URL Status Linked Edit
PR 2424 open andybalaam, 2017-06-27 01:10
Messages (2)
msg296982 - (view) Author: Andy Balaam (andybalaam) * Date: 2017-06-27 01:05
asyncio.as_completed allows us to provide lots of coroutines (or Futures) to schedule, and then deal with the results as soon as they are available, in a loop, or a streaming style.

I propose to allow as_completed to work on very large numbers of coroutines, provided through a generator (rather than a list).  In order to make this practical, we need to limit the number of coroutines that are scheduled simultaneously to a reasonable number.

For tasks that open files or sockets, a reasonable number might be 1000 or fewer.  For other tasks, a much larger number might be reasonable, but we would still like some limit to prevent us running out of memory.

I suggest adding a "limit" argument to as_completed that limits the number of coroutines that it schedules simultaneously.

For me, the key advantage of as_completed (in the proposed modified form) is that it enables a streaming style that looks quite like synchronous code, but is efficient in terms of memory usage (as you'd expect from a streaming style):


#!/usr/bin/env python3

import asyncio
import sys

limit = int(sys.argv[1])

async def double(x):
    await asyncio.sleep(1)
    return x * 2

async def print_doubles():
    coros = (double(x) for x in range(1000000))
    for res in asyncio.as_completed(coros, limit=limit):
        r = await res
        if r % 100000 == 0:
            print(r)

loop = asyncio.get_event_loop()
loop.run_until_complete(print_doubles())
loop.close()


Using my prototype implementation, this runs faster and uses much less memory on my machine when you run it with a limit of 100K instead of 1 million concurrent tasks:

$ /usr/bin/time --format "Memory usage: %MKB\tTime: %e seconds" ./example 1000000
Memory usage: 2234552KB	Time: 97.52 seconds

$ /usr/bin/time --format "Memory usage: %MKB\tTime: %e seconds" ./example 100000
Memory usage: 252732KB	Time: 94.13 seconds

I have been working on an implementation and there is some discussion in my blog posts: http://www.artificialworlds.net/blog/2017/06/12/making-100-million-requests-with-python-aiohttp/ and http://www.artificialworlds.net/blog/2017/06/27/adding-a-concurrency-limit-to-pythons-asyncio-as_completed/

Possibly the most controversial thing about this proposal is the fact that we need to allow passing a generator to as_completed instead of enforcing that it be a list.  This is fundamental to allowing the style I outlined above, but it's possible that we can do better than the blanket allowing of all generators that I did.
msg300401 - (view) Author: Andy Balaam (andybalaam) * Date: 2017-08-17 07:57
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History
Date User Action Args
2017-08-17 07:57:44andybalaamsetmessages: + msg300401
2017-06-27 01:10:51andybalaamsetpull_requests: + pull_request2475
2017-06-27 01:05:22andybalaamcreate