Python asyncio: Future, Task and the Event Loop

Event Loop

On any platform, when we want to do something asynchronously, it usually involves an event loop. An event loop is a loop that can register tasks to be executed, execute them, delay or even cancel them and handle different events related to these operations. Generally, we schedule multiple async functions to the event loop. The loop runs one function, while that function waits for IO, it pauses it and runs another. When the first function completes IO, it is resumed. Thus two or more functions can co-operatively run together. This the main goal of an event loop.

The event loop can also pass resource intensive functions to a thread pool for processing. The internals of the event loop is quite complex and we don’t need to worry much about it right away. We just need to remember that the event loop is the mechanism through which we can schedule our async functions and get them executed.

Futures / Tasks

If you are into Javascript too, you probably know about Promise. In Python we have similar concepts – Future/Task. A Future is an object that is supposed to have a result in the future. A Task is a subclass of Future that wraps a coroutine. When the coroutine finishes, the result of the Task is realized.


We discussed Coroutines in our last blog post. It’s a way of pausing a function and returning a series of values periodically. A coroutine can pause the execution of the function by using the yield yield from or await (python 3.5+) keywords in an expression. The function is paused until the yield statement actually gets a value.

Fitting Event Loop and Future/Task Together

It’s simple. We need an event loop and we need to register our future/task objects with the event loop. The loop will schedule and run them. We can add callbacks to our future/task objects so that we can be notified when a future has it’s results.

Very often we choose to use coroutines for our work. We wrap a coroutine in Future and get a Task object. When a coroutine yields, it is paused. When it has a value, it is resumed. When it returns, the Task has completed and gets a value. Any associated callback is run. If the coroutine raises an exception, the Task fails and not resolved.

So let’s move ahead and see example codes.

As you can see already:

  • @asyncio.coroutine declares it as a coroutine
  • loop.create_task(slow_operation()) creates a task from the coroutine returned by slow_operation()
  • task.add_done_callback(got_result) adds a callback to our task
  • loop.run_until_complete(task) runs the event loop until the task is realized. As soon as it has value, the loop terminates

The run_until_complete function is a nice way to manage the loop. Of course we could do this:

Here we make the loop run forever and from our callback, we explicitly shut it down when the future has resolved.

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