As developers, we are constantly seeking ways to optimize our applications to handle more requests, faster. Today, I'm excited to share the results of a benchmark I conducted on FastAPI, a modern, fast (high-performance) web framework for building APIs with Python. This benchmark sheds light on the stark differences between synchronous and asynchronous endpoints, and how leveraging asynchronous programming can significantly boost your application's performance.

The Benchmark Setup:
To provide a comprehensive comparison, I set up two types of endpoints in FastAPI: one synchronous and one asynchronous. Both endpoints simulate a delay of one second to mimic I/O-bound operations. I then used Apache Benchmark (ab) to measure the requests per second (RPS) under different server configurations.

Key Findings:

1. Asynchronous Triumphs: The asynchronous endpoint consistently outperformed its synchronous counterpart by a wide margin. With a concurrency level of 10,000 and 10,000 total requests, the asynchronous endpoint achieved an impressive 7,593.36 RPS, while the synchronous endpoint managed only 155.80 RPS.

2. Worker Impact: Increasing the number of workers with Gunicorn had a positive effect on both types of endpoints. However, the asynchronous endpoint saw a more dramatic improvement, further highlighting the benefits of asynchronous processing.

3. Real-World Implications: These results are particularly relevant for applications that rely on I/O-bound operations, such as database queries, file I/O, or network requests. By adopting an asynchronous approach, developers can ensure their applications are not only faster but also more scalable.

Conclusion:
The benchmark clearly demonstrates the power of asynchronous programming in FastAPI. For developers looking to build high-performance APIs, embracing asynchronous endpoints is a no-brainer. Not only does it lead to better performance, but it also allows for more efficient resource utilization, which is crucial for modern, scalable applications.

If you're intrigued by these results and want to explore how you can apply asynchronous programming in your FastAPI projects, I encourage you to check out the full benchmark details on my GitHub repository. Let's push the boundaries of what's possible with FastAPI and Python!

GitHub Repository: fastapi-deployment-benchmark

Happy coding, and may your APIs be fast and your responses swift!