Anything better than asyncio.as_completed() and asyncio.wait() to manage execution of large amount of tasks?
Maxime Steisel
maximesteisel at gmail.com
Wed Jul 16 19:09:02 EDT 2014
2014-07-15 14:20 GMT+02:00 Valery Khamenya <khamenya at gmail.com>:
> Hi,
>
> both asyncio.as_completed() and asyncio.wait() work with lists only. No
> generators are accepted. Are there anything similar to those functions that
> pulls Tasks/Futures/coroutines one-by-one and processes them in a limited
> task pool?
Something like this (adapted from as_completed) should do the work:
import asyncio
from concurrent import futures
def parallelize(tasks, *, loop=None, max_workers=5, timeout=None):
loop = loop if loop is not None else asyncio.get_event_loop()
workers = []
pending = set()
done = asyncio.Queue(maxsize=max_workers)
exhausted = False
@asyncio.coroutine
def _worker():
nonlocal exhausted
while not exhausted:
try:
t = next(tasks)
pending.add(t)
yield from t
yield from done.put(t)
pending.remove(t)
except StopIteration:
exhausted = True
def _on_timeout():
for f in workers:
f.cancel()
workers.clear()
#Wake up _wait_for_one()
done.put_nowait(None)
@asyncio.coroutine
def _wait_for_one():
f = yield from done.get()
if f is None:
raise futures.TimeoutError()
return f.result()
workers = [asyncio.async(_worker()) for i in range(max_workers)]
if workers and timeout is not None:
timeout_handle = loop.call_later(timeout, _on_timeout)
while not exhausted or pending or not done.empty():
yield _wait_for_one()
timeout_handle.cancel()
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