Kyle Stanley <[email protected]> added the comment:
So, here's a prototype implementation of asyncio.ThreadPool that would function
exactly as Yury described, but I'm not convinced about the design. Usually, it
seems preferred to separate the context manager from the rest of the class (as
was done with asyncio.Lock), but I think this one might be simple enough to be
a single class:
class ThreadPool:
def __init__(self, timeout=None):
self.timeout = timeout
self._loop = None
async def __aenter__(self):
self._loop = asyncio.get_running_loop()
# Ensure that ThreadPoolExecutor is being used
self._loop.default_executor = concurrent.futures.ThreadPoolExecutor()
return self
async def __aexit__(self, *args):
await self._loop.shutdown_default_executor(timeout=self.timeout)
def run(self, func, *args, **kwargs):
call = functools.partial(func, *args, **kwargs)
return self._loop.run_in_executor(None, call)
It utilizes the existing lower level event loop API to provide an asynchronous
context manager. I'd say the main advantage is that it's significantly more
user friendly, as there's no loop or executor argument to be specified, and
users can freely pass kwargs to run() thanks to functools.partial().
Additionally, I also included a timeout parameter for shutting down the
ThreadPoolExecutor, which will depend upon GH-16360. This can be used as such:
async def main():
async with asyncio.ThreadPool(timeout=600) as pool:
fut1 = pool.run(func)
fut2 = pool.run(func, arg1, arg2)
fut2 = pool.run(func, arg1, kwarg1=True)
print(await asyncio.gather(fut1, fut2, fut3))
asyncio.run(main())
I don't expect that this would be the final design, but I think it's a decent
prototype to demonstrate the functionality. Thoughts?
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<https://bugs.python.org/issue32309>
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