Async/await is a powerful programming construct that allows you to write asynchronous code in a synchronous-like style. It was introduced in Python 3.5 as part of the asyncio module and has become a popular choice for writing concurrent and parallel code in Python.

In this post, we’ll take a look at what async/await is and how it works, as well as some of the benefits and drawbacks of using it. We’ll also see some examples of how to use async/await in Python to write efficient and scalable code.

What is async/await?

Async/await is a way of writing asynchronous code that looks and feels like synchronous code. It allows you to write code that can run concurrently with other tasks, without the overhead and complexity of using threads or processes.

To use async/await in Python, you need to use the async and await keywords. An async function is defined using the async def syntax, and it can contain one or more await expressions. An await expression is used to suspend the execution of the async function until the result of a call to an async function is available.

Here’s an example of an async function that uses an await expression to wait for the result of a call to an async function:

import asyncio

async def foo():
    result = await asyncio.sleep(1)
    print(result)

async def main():
    await foo()

asyncio.run(main())

In this example, the foo function uses the await keyword to suspend its execution until the asyncio.sleep function completes. The asyncio.sleep function is an async function that returns after a specified number of seconds, and it’s often used as a placeholder for more complex async operations.

Benefits of async/await

Async/await has several benefits compared to other approaches for writing concurrent and parallel code in Python:

  1. Simplicity: Async/await is easy to understand and use, especially for developers who are familiar with synchronous programming. It allows you to write asynchronous code in a linear, procedural style, without the need for complex control structures or callback functions.

  2. Efficiency: Async/await can lead to more efficient code, as it allows you to use fewer resources and avoid the overhead of using threads or processes. Async functions are implemented using coroutines, which are lightweight and can be scheduled and switched between efficiently.

  3. Scalability: Async/await can help you write scalable code that can handle a large number of concurrent tasks. By using async/await, you can take advantage of Python’s event loop and avoid blocking the main thread, which can lead to better performance and higher throughput.

Drawbacks of async/await

Async/await is not a silver bullet and has some drawbacks that you should be aware of:

  1. Complexity: Async/await can make your code more complex, as it introduces new concepts and syntax that you need to learn and understand. Debugging async code can also be more challenging, as you need to deal with concurrency and the event loop.
  2. Performance: Async/await can lead to lower performance in some cases, especially when used with I/O-bound tasks that involve a lot of context switching between async functions. The overhead of scheduling and switching between coroutines can add up, leading to slower performance compared to other approaches like multithreading or multiprocessing.
  3. Not always the best choice: Async/await is not the best choice for every situation. It’s particularly well-suited for tasks that involve waiting for I/O operations, but it’s not as efficient for CPU-bound tasks that require a lot of computation. In these cases, you might be better off using multiprocessing or multithreading to take advantage of multiple CPU cores
  4. Limited support: Async/await is not supported by all libraries and frameworks in Python, and you might encounter compatibility issues when trying to use it with certain libraries. This can limit the usefulness of async/await and make it more difficult to use in some cases.

When to use async/await

Async/await is a powerful tool for writing concurrent and parallel code in Python, but it’s not always the best choice for every situation. Here are some cases where you might consider using async/await:

  • I/O-bound tasks: Async/await is particularly well-suited for tasks that involve waiting for input/output operations, such as reading from a database, making HTTP requests, or interacting with the file system. By using async/await, you can avoid blocking the main thread and allow other tasks to run concurrently.

  • High-concurrency tasks: Async/await can also be useful for tasks that need to handle a large number of concurrent requests or connections, such as servers or real-time apps. By using async/await, you can take advantage of Python’s event loop and scale your code more easily.

  • CPU-bound tasks: Async/await is not the best choice for tasks that are CPU-bound and require a lot of computation. In these cases, you might be better off using multiprocessing or multithreading to take advantage of multiple CPU cores.

Example: Async HTTP client To see how async/await works in practice, let’s look at an example of an async HTTP client using the aiohttp library. Here’s the code for the client:

import aiohttp

async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()

async def main():
    async with aiohttp.ClientSession() as session:
        html = await fetch(session, "https://www.example.com")
        print(html)

asyncio.run(main())

In this example, the fetch function is an async function that uses the aiohttp library to make an HTTP GET request to a specified URL. It uses the await keyword to suspend its execution until the response is available. The main function is another async function that uses the aiohttp library to create a client session and then calls the fetch function to get the HTML of a website.

You can run this code using the asyncio.run function, which will execute the async function and wait for it to complete.

Conclusion

Async/await is a powerful programming construct that allows you to write asynchronous code in a synchronous-like style. It can lead to more efficient and scalable code, especially for tasks that involve I/O operations or high concurrency. However, it’s not always the best choice for every situation, and you should consider the trade-offs and drawbacks before using it.

I hope this post gave you a good understanding of async/await and how it works in Python. If you have any questions or comments, feel free to leave them below!