Hi folks, Based on the recent discussions Sven kicked off regarding the complexity of interacting with asyncio from otherwise synchronous code, I came up with an API design that I like inspired by the way background and foreground tasks in the POSIX shell work. My blog post about this design is at http://www.curiousefficiency.org/posts/2015/07/asyncio-background-calls.html, but the essential components are the following two APIs: def run_in_background(target, *, loop=None): """Schedules target as a background task Returns the scheduled task. If target is a future or coroutine, equivalent to asyncio.ensure_future If target is a callable, it is scheduled in the default executor """ ... def run_in_foreground(task, *, loop=None): """Runs event loop in current thread until the given task completes Returns the result of the task. For more complex conditions, combine with asyncio.wait() To include a timeout, combine with asyncio.wait_for() """ ... run_in_background is akin to invoking a shell command with a trailing "&" - it puts the operation into the background, leaving the current thread to move on to the next operation (or wait for input at the REPL). When coroutines are scheduled, they won't start running until you start a foreground task, while callables delegated to the default executor will start running immediately. To actually get the *results* of that task, you have to run it in the foreground of the current thread using run_in_foreground - this is akin to bringing a background process to the foreground of a shell session using "fg". To relate this idea back to some of the examples Sven was discussing, here's how translating some old serialised synchronous code to use those APIs might look in practice: # Serial synchronous data loading def load_and_process_data(): data1 = load_remote_data_set1() data2 = load_remote_data_set2() return process_data(data1, data2) # Parallel asynchronous data loading def load_and_process_data(): future1 = asyncio.run_in_background(load_remote_data_set1_async()) future2 = asyncio.run_in_background(load_remote_data_set2_async()) data1 = asyncio.run_in_foreground(future1) data2 = asyncio.run_in_foreground(future2) return process_data(data1, data2) The application remains fundamentally synchronous, but the asyncio event loop is exploited to obtain some local concurrency in waiting for client IO operations. Regards, Nick. P.S. time.sleep() and asyncio.sleep() are rather handy as standins for blocking and non-blocking IO operations. I wish I'd remembered that earlier :) -- Nick Coghlan | ncoghlan@gmail.com | Brisbane, Australia