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 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.
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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)
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As you can see already:
@asyncio.coroutinegets us the default event looploop.create_task(slow_operation())creates a task from the coroutine returned byslow_operation()task.add_done_callback(got_result)adds a callback to our taskloop.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:
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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()
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Here we make the loop run forever and from our callback, we explicitly shut it down when the future has resolved.