PYTHON ASYNCIO: FUTURE, TASK AND THE EVENT LOOP

from :http://masnun.com/2015/11/20/python-asyncio-future-task-and-the-event-loop.html

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.

Coroutines

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

yield

s, it is paused. When it has a value, it is resumed. When it

return

s, 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.

Python

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import asyncio @asyncio.coroutine def slow_operation(): # yield from suspends execution until # there's some result from asyncio.sleep yield from asyncio.sleep(1) # our task is done, here's the result return 'Future is done!' def got_result(future): print(future.result()) # Our main event loop loop = asyncio.get_event_loop() # We create a task from a coroutine task = loop.create_task(slow_operation()) # Please notify us when the task is complete task.add_done_callback(got_result) # The loop will close when the task has resolved loop.run_until_complete(task)

As you can see already:

@asyncio.coroutine

gets us the default event loop

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:

Python

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import asyncio async def slow_operation(): await asyncio.sleep(1) return 'Future is done!' def got_result(future): print(future.result()) # We have result, so let's stop loop.stop() loop = asyncio.get_event_loop() task = loop.create_task(slow_operation()) task.add_done_callback(got_result) # We run forever loop.run_forever()

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