Nice! At first, I thought that implementation would be trivial, but upon inspection it's actually educational! Perhaps interactive environments like ipython and jupyter-notebook could benefit from this library.
Cheers, d. On 4 May 2018 at 15:45, Alice Heaton <alicem...@loveisanalogue.info> wrote: > Hello, > > I have been working on miniasync, a small library build on top of > asyncio to facilitate the occasional use of async code in otherwise > synchronous applications. > > A typical use case is an otherwise synchronous application, which at > some point wants to send notifications to multiple online services. > miniasync makes it easier to implement this asynchronously without > having to write asyncio boilerplate. > > http://miniasync.readthedocs.io > > asyncio is vast, powerful and flexible. But there is a learning curve - > you need to understand the event loop api (other async languages like > Javascript don't expose that), how to create it and run tasks on it, > which means you need to understand about Tasks and Futures, etc. The > flexibility is great, and gives Python developers a lot of scope for > implementing advanced applications, but it's a put off for simple use cases. > > miniasync exposes a single function, "miniasync.run", which takes a list > of coroutine objects, creates a local event loop, runs all the > co-routines until they're complete, and returns their result in the > order they were defined: > > import aiofiles > import miniasync > > async def get_file_content(filename): > async with aiofiles.open(filename, mode='r') as f: > return await f.read() > > results = miniasync.run( > get_file_content('file1.txt'), > get_file_content('file2.txt'), > ) > > assert results == [ > '<the content of file1.txt>', > '<the content of file2.txt>' > ] > > This is similar to using a combination of gather and run_until_complete, > but as a single step and without explicit reference to the loop. This > also differs from gather in that: > > - Instead of saying you either want exceptions raised or returned, you > need to list explicitly exceptions you want returned, all other > exceptions are raised (explicit is better than implicit); > - Unhandled exceptions cause all other tasks to be cancelled. > > miniasync.run always creates a new loop (so you can nest invocations of > miniasync.run, and rely on each invocation only executing it's > parameters). For the cases where you need the loop before running (eg. > for creating a asyncio.Queue object to be shared amongst your > co-routines), miniasync also exposes a context manager that creates a > loop and lets you run co-routines on it: > > import asyncio > import miniasync > > async def coro1(q): > q.put_nowait('world') > return 'hello' > > async def coro2(q): > return await q.get() > > with miniasync.loop() as loop: > q = asyncio.Queue() > results = loop.run( > coro1(q), > coro2(q) > ) > > assert results == ['hello', 'world'] > > You can pip install miniasync to try out, or read the docs on > readthedocs (as above). > > For now miniasync covers what was my main issue with running simple > async code. Other things I have in mind for the future is a simple > interface for running multiple http requests (based on top of aiohttp). > > I'm keen to hear about other issues that could be simplified for simple > use cases. > > :) > Alice > _______________________________________________ > Async-sig mailing list > Async-sig@python.org > https://mail.python.org/mailman/listinfo/async-sig > Code of Conduct: https://www.python.org/psf/codeofconduct/ _______________________________________________ Async-sig mailing list Async-sig@python.org https://mail.python.org/mailman/listinfo/async-sig Code of Conduct: https://www.python.org/psf/codeofconduct/
