How can I wrap a synchronous function in an async coroutine?

Eventually I found an answer in this thread. The method I was looking for is run_in_executor. This allows a synchronous function to be run asynchronously without blocking an event loop.

In the sleep example I posted above, it might look like this:

import asynciofrom time import sleepasync def sleep_async(loop, delay):    # None uses the default executor (ThreadPoolExecutor)    await loop.run_in_executor(None, sleep, delay)    return 'I slept asynchronously'

Also see the following answer -> How do we call a normal function where a coroutine is expected?

You can use a decorator to wrap the sync version to an async version.

import timefrom functools import wraps, partialdef wrap(func):    @wraps(func)    async def run(*args, loop=None, executor=None, **kwargs):        if loop is None:            loop = asyncio.get_event_loop()        pfunc = partial(func, *args, **kwargs)        return await loop.run_in_executor(executor, pfunc)    return run@wrapdef sleep_async(delay):    time.sleep(delay)    return 'I slept asynchronously'

or use the aioify library

% pip install aioify

then

@aioifydef sleep_async(delay):    pass

Maybe someone will need my solution to this problem. I wrote my own library to solve this, which allows you to make any function asynchronous using a decorator.

To install the library, run this command:

$ pip install awaits

To make any of your functions asynchronous, just add the @awaitable decorator to it, like this:

import timeimport asynciofrom awaits.awaitable import awaitable@awaitabledef sum(a, b):  # heavy load simulation  time.sleep(10)  return a + b

Now you can make sure that your function is really asynchronous coroutine:

print(asyncio.run(sum(2, 2)))

"Under the hood" your function will be executed in the thread pool. This thread pool will not be recreated every time your function is called. A thread pool is created once and accepts new tasks via a queue. This will make your program run faster than using other solutions, because the creation of additional threads is an additional overhead.