# A conceptual overview of `asyncio`

This HOWTO article seeks to help you build a sturdy mental model of how `asyncio` fundamentally works, helping you understand the how and why behind the recommended patterns.

You might be curious about some key `asyncio` concepts. By the end of this article, you’ll be able to comfortably answer these questions:

- What’s happening behind the scenes when an object is awaited?

- How does `asyncio` differentiate between a task which doesn’t need CPU time (such as a network request or file read) as opposed to a task that does (such as computing n-factorial)?

- How to write an asynchronous variant of an operation, such as an async sleep or database request.

Se även

- The guide that inspired this HOWTO article, by Alexander Nordin.

- This in-depth YouTube tutorial series on `asyncio` created by Python core team member, Łukasz Langa.

- 500 Lines or Less: A Web Crawler With asyncio Coroutines by A. Jesse Jiryu Davis and Guido van Rossum.

## A conceptual overview part 1: the high-level

In part 1, we’ll cover the main, high-level building blocks of `asyncio`: the event loop, coroutine functions, coroutine objects, tasks, and `await`.

### Event loop

Everything in `asyncio` happens relative to the event loop. It’s the star of the show, but prefers to work behind the scenes, managing and coordinating resources. It’s like an orchestra conductor. Some power is explicitly granted to it, but a lot of its ability to get things done comes from the respect and cooperation of its band members.

In more technical terms, the event loop contains a collection of jobs to be run. Some jobs are added directly by you, and some indirectly by `asyncio`. The event loop takes a job from its backlog of work and invokes it (or ”gives it control”), similar to calling a function, and then that job runs. Once it pauses or completes, it returns control to the event loop. The event loop will then select another job from its pool and invoke it. You can roughly think of the collection of jobs as a queue: jobs are added and then processed one at a time, generally (but not always) in order. This process repeats indefinitely, with the event loop cycling endlessly onwards. If there are no more jobs pending execution, the event loop is smart enough to rest and avoid needlessly wasting CPU cycles, and will come back when there’s more work to be done, such as when I/O operations complete or timers expire.

Effective execution relies on jobs sharing well and cooperating; a greedy job could hog control and leave the other jobs to starve, rendering the overall event loop approach rather useless.

    import asyncio

    # This creates an event loop and indefinitely cycles through
    # its collection of jobs.
    event_loop = asyncio.new_event_loop()
    event_loop.run_forever()

### Asynchronous functions and coroutines

This is a basic, boring Python function:

    def hello_printer():
        print(
            "Hi, I am a lowly, simple printer, though I have all I "
            "need in life -- \nfresh paper and my dearly beloved octopus "
            "partner in crime."
        )

Calling a regular function invokes its logic or body:

    >>> hello_printer()
    Hi, I am a lowly, simple printer, though I have all I need in life --
    fresh paper and my dearly beloved octopus partner in crime.

The async def, as opposed to just a plain `def`, makes this an asynchronous function (or ”coroutine function”). Calling it creates and returns a coroutine object.

    async def loudmouth_penguin(magic_number: int):
        print(
         "I am a super special talking penguin. Far cooler than that printer. "
         f"By the way, my lucky number is: {magic_number}."
        )

Calling the async function, `loudmouth_penguin`, does not execute the print statement; instead, it creates a coroutine object:

    >>> loudmouth_penguin(magic_number=3)
    <coroutine object loudmouth_penguin at 0x104ed2740>

The terms ”coroutine function” and ”coroutine object” are often conflated as coroutine. That can be confusing! In this article, coroutine specifically refers to a coroutine object, or more precisely, an instance of `types.CoroutineType` (native coroutine). Note that coroutines can also exist as instances of `collections.abc.Coroutine` – a distinction that matters for type checking.

A coroutine represents the function’s body or logic. A coroutine has to be explicitly started; again, merely creating the coroutine does not start it. Notably, the coroutine can be paused and resumed at various points within the function’s body. That pausing and resuming ability is what allows for asynchronous behavior!

Coroutines and coroutine functions were built by leveraging the functionality of generators and generator functions. Recall, a generator function is a function that `yield`s, like this one:

    def get_random_number():
        # This would be a bad random number generator!
        print("Hi")
        yield 1
        print("Hello")
        yield 7
        print("Howdy")
        yield 4
        ...

Similar to a coroutine function, calling a generator function does not run it. Instead, it creates a generator object:

    >>> get_random_number()
    <generator object get_random_number at 0x1048671c0>

You can proceed to the next `yield` of a generator by using the built-in function `next()`. In other words, the generator runs, then pauses. For example:

    >>> generator = get_random_number()
    >>> next(generator)
    Hi
    1
    >>> next(generator)
    Hello
    7

### Tasks

Roughly speaking, tasks are coroutines (not coroutine functions) tied to an event loop. A task also maintains a list of callback functions whose importance will become clear in a moment when we discuss `await`.

Creating a task automatically schedules it for execution (by adding a callback to run it in the event loop’s to-do list, that is, collection of jobs). The recommended way to create tasks is via `asyncio.create_task()`.

`asyncio` automatically associates tasks with the event loop for you. This automatic association was purposely designed into `asyncio` for the sake of simplicity. Without it, you’d have to keep track of the event loop object and pass it to any coroutine function that wants to create tasks, adding redundant clutter to your code.

    coroutine = loudmouth_penguin(magic_number=5)
    # This creates a Task object and schedules its execution via the event loop.
    task = asyncio.create_task(coroutine)

Earlier, we manually created the event loop and set it to run forever. In practice, it’s recommended to use (and common to see) `asyncio.run()`, which takes care of managing the event loop and ensuring the provided coroutine finishes before advancing. For example, many async programs follow this setup:

    import asyncio

    async def main():
        # Perform all sorts of wacky, wild asynchronous things...
        ...

    if __name__ == "__main__":
        asyncio.run(main())
        # The program will not reach the following print statement until the
        # coroutine main() finishes.
        print("coroutine main() is done!")

It’s important to be aware that the task itself is not added to the event loop, only a callback to the task is. This matters if the task object you created is garbage collected before it’s called by the event loop. For example, consider this program:

     1async def hello():
     2    print("hello!")
     3
     4async def main():
     5    asyncio.create_task(hello())
     6    # Other asynchronous instructions which run for a while
     7    # and cede control to the event loop...
     8    ...
     9
    10asyncio.run(main())

Because there’s no reference to the task object created on line 5, it might be garbage collected before the event loop invokes it. Later instructions in the coroutine `main()` hand control back to the event loop so it can invoke other jobs. When the event loop eventually tries to run the task, it might fail and discover the task object does not exist! This can also happen even if a coroutine keeps a reference to a task but completes before that task finishes. When the coroutine exits, local variables go out of scope and may be subject to garbage collection. In practice, `asyncio` and Python’s garbage collector work pretty hard to ensure this sort of thing doesn’t happen. But that’s no reason to be reckless!

### await

`await` is a Python keyword that’s commonly used in one of two different ways:

    await task
    await coroutine

In a crucial way, the behavior of `await` depends on the type of object being awaited.

#### Awaiting tasks

Awaiting a task will cede control from the current task or coroutine to the event loop. In the process of relinquishing control, a few important things happen. We’ll use the following code example to illustrate:

    async def plant_a_tree():
        dig_the_hole_task = asyncio.create_task(dig_the_hole())
        await dig_the_hole_task

        # Other instructions associated with planting a tree.
        ...

In this example, imagine the event loop has passed control to the start of the coroutine `plant_a_tree()`. As seen above, the coroutine creates a task and then awaits it. The `await dig_the_hole_task` instruction adds a callback (which will resume `plant_a_tree()`) to the `dig_the_hole_task` object’s list of callbacks. And then, the instruction cedes control to the event loop. Some time later, the event loop will pass control to `dig_the_hole_task` and the task will finish whatever it needs to do. Once the task finishes, it will add its various callbacks to the event loop, in this case, a call to resume `plant_a_tree()`.

Generally speaking, when the awaited task finishes (`dig_the_hole_task`), the original task or coroutine (`plant_a_tree()`) is added back to the event loop’s to-do list to be resumed.

This is a basic, yet reliable mental model. In practice, the control handoffs are slightly more complex, but not by much. In part 2, we’ll walk through the details that make this possible.

#### Awaiting coroutines

Unlike tasks, awaiting a coroutine does not hand control back to the event loop! Wrapping a coroutine in a task first, then awaiting that would cede control. The behavior of `await coroutine` is effectively the same as invoking a regular, synchronous Python function. Consider this program:

    import asyncio

    async def coro_a():
       print("I am coro_a(). Hi!")

    async def coro_b():
       print("I am coro_b(). I sure hope no one hogs the event loop...")

    async def main():
       task_b = asyncio.create_task(coro_b())
       num_repeats = 3
       for _ in range(num_repeats):
          await coro_a()
       await task_b

    asyncio.run(main())

The first statement in the coroutine `main()` creates `task_b` and schedules it for execution via the event loop. Then, `coro_a()` is repeatedly awaited. Control never cedes to the event loop, which is why we see the output of all three `coro_a()` invocations before `coro_b()`’s output:

    I am coro_a(). Hi!
    I am coro_a(). Hi!
    I am coro_a(). Hi!
    I am coro_b(). I sure hope no one hogs the event loop...

If we change `await coro_a()` to `await asyncio.create_task(coro_a())`, the behavior changes. The coroutine `main()` cedes control to the event loop with that statement. The event loop then proceeds through its backlog of work, calling `task_b` and then the task which wraps `coro_a()` before resuming the coroutine `main()`.

    I am coro_b(). I sure hope no one hogs the event loop...
    I am coro_a(). Hi!
    I am coro_a(). Hi!
    I am coro_a(). Hi!

This behavior of `await coroutine` can trip a lot of people up! That example highlights how using only `await coroutine` could unintentionally hog control from other tasks and effectively stall the event loop. `asyncio.run()` can help you detect such occurrences via the `debug=True` flag, which enables debug mode. Among other things, it will log any coroutines that monopolize execution for 100ms or longer.

The design intentionally trades off some conceptual clarity around usage of `await` for improved performance. Each time a task is awaited, control needs to be passed all the way up the call stack to the event loop. Then, the event loop needs to manage its internal state and work through its processing logic to resume the next job. That might sound minor, but in a large program with many `await`s, that overhead can add up to a non-negligible performance drag.

## A conceptual overview part 2: the nuts and bolts

Part 2 goes into detail on the mechanisms `asyncio` uses to manage control flow. This is where the magic happens. You’ll come away from this section knowing what `await` does behind the scenes and how to make your own asynchronous operators.

### The inner workings of coroutines

`asyncio` leverages four components of Python to pass around control.

`coroutine.send(arg)` is the method used to start or resume a coroutine. If the coroutine was paused and is now being resumed, the argument `arg` will be sent in as the return value of the `yield` statement which originally paused it. If the coroutine is being used for the first time (as opposed to being resumed), `arg` must be `None`.

     1class Rock:
     2    def __await__(self):
     3        value_sent_in = yield 7
     4        print(f"Rock.__await__ resuming with value: {value_sent_in}.")
     5        return value_sent_in
     6
     7async def main():
     8    print("Beginning coroutine main().")
     9    rock = Rock()
    10    print("Awaiting rock...")
    11    value_from_rock = await rock
    12    print(f"Coroutine received value: {value_from_rock} from rock.")
    13    return 23
    14
    15coroutine = main()
    16intermediate_result = coroutine.send(None)
    17print(f"Coroutine paused and returned intermediate value: {intermediate_result}.")
    18
    19print(f"Resuming coroutine and sending in value: 42.")
    20try:
    21    coroutine.send(42)
    22except StopIteration as e:
    23    returned_value = e.value
    24print(f"Coroutine main() finished and provided value: {returned_value}.")

yield, as usual, pauses execution and returns control to the caller. In the example above, the `yield`, on line 3, is called by `... = await rock` on line 11. More broadly speaking, `await` calls the `__await__()` method of the given object. `await` also does one more very special thing: it propagates (or ”passes along”) any `yield`s it receives up the call chain. In this case, that’s back to `... = coroutine.send(None)` on line 16.

The coroutine is resumed via the `coroutine.send(42)` call on line 21. The coroutine picks back up from where it `yield`ed (or paused) on line 3 and executes the remaining statements in its body. When a coroutine finishes, it raises a `StopIteration` exception with the return value attached in the `value` attribute.

That snippet produces this output:

    Beginning coroutine main().
    Awaiting rock...
    Coroutine paused and returned intermediate value: 7.
    Resuming coroutine and sending in value: 42.
    Rock.__await__ resuming with value: 42.
    Coroutine received value: 42 from rock.
    Coroutine main() finished and provided value: 23.

It’s worth pausing for a moment here and making sure you followed the various ways that control flow and values were passed. A lot of important ideas were covered and it’s worth ensuring your understanding is firm.

The only way to yield (or effectively cede control) from a coroutine is to `await` an object that `yield`s in its `__await__` method. That might sound odd to you. You might be thinking:

> 1. What about a `yield` directly within the coroutine function? The coroutine function becomes an async generator function, a different beast entirely.

> 2. What about a yield from within the coroutine function to a (plain) generator? That causes the error: `SyntaxError: yield from not allowed in a coroutine.` This was intentionally designed for the sake of simplicity – mandating only one way of using coroutines. Despite that, `yield from` and `await` effectively do the same thing. Initially `yield` was barred as well, but was re-accepted to allow for async generators.

### Futures

A future is an object meant to represent a computation’s status and result. The term is a nod to the idea of something still to come or not yet happened, and the object is a way to keep an eye on that something.

A future has a few important attributes. One is its state, which can be either ”pending”, ”cancelled”, or ”done”. Another is its result, which is set when the state transitions to done. Unlike a coroutine, a future does not represent the actual computation to be done; instead, it represents the status and result of that computation, kind of like a status light (red, yellow, or green) or indicator.

`asyncio.Task` subclasses `asyncio.Future` in order to gain these various capabilities. The prior section said tasks store a list of callbacks, which wasn’t entirely accurate. It’s actually the `Future` class that implements this logic, which `Task` inherits.

Futures may also be used directly (not via tasks). Tasks mark themselves as done when their coroutine is complete. Futures are much more versatile and will be marked as done when you say so. In this way, they’re the flexible interface for you to make your own conditions for waiting and resuming.

### A homemade asyncio.sleep

We’ll go through an example of how you could leverage a future to create your own variant of asynchronous sleep (`async_sleep`) which mimics `asyncio.sleep()`.

This snippet registers a few tasks with the event loop and then awaits the task created by `asyncio.create_task`, which wraps the `async_sleep(3)` coroutine. We want that task to finish only after three seconds have elapsed, but without preventing other tasks from running.

    async def other_work():
        print("I like work. Work work.")

    async def main():
        # Add a few other tasks to the event loop, so there's something
        # to do while asynchronously sleeping.
        work_tasks = [
            asyncio.create_task(other_work()),
            asyncio.create_task(other_work()),
            asyncio.create_task(other_work())
        ]
        print(
            "Beginning asynchronous sleep at time: "
            f"{datetime.datetime.now().strftime("%H:%M:%S")}."
        )
        await asyncio.create_task(async_sleep(3))
        print(
            "Done asynchronous sleep at time: "
            f"{datetime.datetime.now().strftime("%H:%M:%S")}."
        )
        # asyncio.gather effectively awaits each task in the collection.
        await asyncio.gather(*work_tasks)

Below, we use a future to enable custom control over when that task will be marked as done. If `future.set_result()` (the method responsible for marking that future as done) is never called, then this task will never finish. We’ve also enlisted the help of another task, which we’ll see in a moment, that will monitor how much time has elapsed and, accordingly, call `future.set_result()`.

    async def async_sleep(seconds: float):
        future = asyncio.Future()
        time_to_wake = time.time() + seconds
        # Add the watcher-task to the event loop.
        watcher_task = asyncio.create_task(_sleep_watcher(future, time_to_wake))
        # Block until the future is marked as done.
        await future

Below, we use a rather bare `YieldToEventLoop()` object to `yield` from its `__await__` method, ceding control to the event loop. This is effectively the same as calling `asyncio.sleep(0)`, but this approach offers more clarity, not to mention it’s somewhat cheating to use `asyncio.sleep` when showcasing how to implement it!

As usual, the event loop cycles through its tasks, giving them control and receiving control back when they pause or finish. The `watcher_task`, which runs the coroutine `_sleep_watcher(...)`, will be invoked once per full cycle of the event loop. On each resumption, it’ll check the time and if not enough has elapsed, then it’ll pause once again and hand control back to the event loop. Once enough time has elapsed, `_sleep_watcher(...)` marks the future as done and completes by exiting its infinite `while` loop. Given this helper task is only invoked once per cycle of the event loop, you’d be correct to note that this asynchronous sleep will sleep at least three seconds, rather than exactly three seconds. Note this is also true of `asyncio.sleep`.

    class YieldToEventLoop:
        def __await__(self):
            yield

    async def _sleep_watcher(future, time_to_wake):
        while True:
            if time.time() >= time_to_wake:
                # This marks the future as done.
                future.set_result(None)
                break
            else:
                await YieldToEventLoop()

Here is the full program’s output:

    $ python custom-async-sleep.py
    Beginning asynchronous sleep at time: 14:52:22.
    I like work. Work work.
    I like work. Work work.
    I like work. Work work.
    Done asynchronous sleep at time: 14:52:25.

You might feel this implementation of asynchronous sleep was unnecessarily convoluted. And, well, it was. The example was meant to showcase the versatility of futures with a simple example that could be mimicked for more complex needs. For reference, you could implement it without futures, like so:

    async def simpler_async_sleep(seconds):
        time_to_wake = time.time() + seconds
        while True:
            if time.time() >= time_to_wake:
                return
            else:
                await YieldToEventLoop()

But that’s all for now. Hopefully you’re ready to more confidently dive into some async programming or check out advanced topics in the `rest of the documentation`.