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Async crash course

New to asynchronous programming in Python? Fear not! This crash course will get you up and running with async and how it impacts building web applications.

If you're already comfortable with async, feel free to skip to the tutorial.

We'll keep this short, to the point, and focused on using async in Bocadillo. To learn more, see Resources for aspiring experts at the bottom of this page.


  • Defining an async function: use async def instead of def.
  • Calling an async function: use the await keyword inside an async function: value = await func().
  • CPU-bound operations: use the starlette.concurrency.run_in_threadpool helper.
  • Async libraries: check out awesome-asyncio or do your own research!


Synchronous function

Also known as a regular function, this is just a standard Python function defined using the def syntax:

def get_attendees():
    return ["John", "Mary", "Isabella"]

Asynchronous function

Also known as a coroutine function, an asynchronous function returns a coroutine and is defined using the async def syntax:

async def get_attendees():
    return ["John", "Mary", "Isabella"]


An awaitable is an object which can be used in an await expression. The term "awaitable" is really not much more than a syntax-level definition.


The return value of an asynchronous function is a particular kind of awaitable known as a coroutine.

Coroutines are first-class citizens in Python. There's even a built-in type for them! Here, take a look:

async def get_attendees():
    return ["John", "Mary", "Isabella"]

coro = get_attendees()
print(type(coro))  # <class 'coroutine'>

Working with coroutines


Consider the following asynchronous function:

async def get_items():
    print("Getting items…")
    return [1, 2, 3]

Let's call it:

items = get_items()

At this point, items is a coroutine:

print(type(items))  # <class 'coroutine'>

Have you noticed that "Getting items…" has not been printed yet? This is because get_items() has not even run yet!

"But then", you say, "how do I run the coroutine and get its result?"

Good question. Here's an inaccurate but pragmatic answer*:

  1. Make sure you are in an asynchronous function, i.e. one defined with async def.
  2. Use the await syntax.

Here's what it looks like:

async def main():  # <- example async function
    items = await get_items()  # <- call another async function with `await`
    print(items)  # [1, 2, 3]

*If you're curious to know the more accurate answer, you'll find a lot of useful information in the asyncio documentation, e.g. in Coroutines and Tasks.

"But then", you say, "how am I supposed to run main()?!"

Well, you don't, because…

Async is the interface

As an async web framework, Bocadillo provides an asynchronous runtime and takes care of running coroutines for you. This allows you to write async/await code without worrying about who runs it and how.

If that sounds confusing, take a look at the following "Hello, World" application:

from bocadillo import App, configure

app = App()

async def hello(req, res):
    res.text = "Hello, world!"

This code doesn't care about how the hello function is actually run. You don't even need to know anything about asyncio or how it works.

You just need to use the async def syntax on the view, and Bocadillo will do the heavy lifting to handle requests in a concurrent fashion. It's magic. ✨


To sum up, here's the gist of what you need to know when working with asynchronous functions and coroutines in Bocadillo:

  • Define an asynchronous function* using async def func(): ....
  • Call an asynchronous function* and get its result using value = await func().
  • Bocadillo provides the asynchronous runtime so you can focus on writing async code instead of worrying about how it should run.

*A view, an error handler, a hook, an HTTP middleware callback, etc.

Common patterns

Here are a few patterns you may find useful while working with async code.

Executing CPU-bound operations

In Python, async relies on cooperative multitasking. This means that if a function puts high load on the CPU without using await, other coroutines won't be able to run in the meantime, and you'll lose concurrency.

In a web context in particular, this means that clients need to wait for said function to terminate before they can get their request processed.

To solve this issue, you can use Starlette's run_in_threadpool helper. It will run the function in a separate thread to ensure that the main thread (where coroutines are run) does not get blocked.

Here's an example of running an expensive CPU-bound operation (sorting random numbers) in a view using run_in_threadpool:

Not found: /home/travis/build/bocadilloproject/bocadillo/docs/guide/snippets/work_check.py

Try it out for yourself:

  1. Start the server: uvicorn app:app
  2. Create two new terminal sessions.
  3. In the first terminal, run the following to start sorting 10^7 random numbers:
curl http://localhost:8000/work/7
  1. In the second terminal, run the following while the first terminal is still waiting for a response:
curl http://localhost:8000/check

You should be able to get OK responses in the second terminal even though the server is still sorting numbers. This is because the sort happens in a separate thread — concurrency is preserved! 🎉

Converting a regular function to an asynchronous function

If you're given a regular function and you need to convert it to an asynchronous function, you can just write an async wrapper:

from somelib import compute

async def compute_async(*args, **kwargs):
    return compute(*args, **kwargs)

Note: if compute is CPU-bound, wrapping it in an async function won't magically prevent it from blocking the main thread — you need to use run_in_threadpool as well:

from starlette.concurrency import run_in_threadpool
from somelib import compute

async def compute_async(*args, **kwargs):
    return await run_in_threadpool(compute, *args, **kwargs)

This can be simplified using functools.partial:

from functools import partial
from starlette.concurrency import run_in_threadpool
from somelib import compute

compute_async = partial(run_in_threadpool, compute)

Finding async libraries to replace synchronous ones

One of the caveats associated to async is that everything needs to be asynchronous (a.k.a. non-blocking), or you may block the main thread and lose concurrency.

For this reason, you will need to use async equivalents of your favorite libraries if they don't support async natively.

For example:

You can check out awesome-asyncio, Libraries that work with async/await or do your own research to find async libraries. The ecosystem is ever evolving — be on the lookout!

Resources for aspiring experts

If you're curious to learn more about async in Python — the history, the implementation, the ecosystem — here are a few resources we think you'll find useful.




  • aio-libs - The set of asyncio-based libraries built with high quality.
  • awesome-asyncio - A curated list of awesome Python asyncio frameworks, libraries, software and resources.