Performance tips by the FastAPI Expert — Marcelo Trylesinski

FastAPI is a modern, fast, web framework for building APIs with Python. As an expert in FastAPI, Marcelo Trylesinski has some valuable tips for optimizing performance when using this framework.

Tip 1: Use async and await

FastAPI fully supports asynchronous programming with Python’s async and await keywords. By using asynchronous programming, you can make your API calls non-blocking, allowing your application to handle a large number of concurrent requests more efficiently.

Tip 2: Leverage caching

Caching can greatly improve the performance of your FastAPI application. By caching the results of expensive operations, such as database queries or external API calls, you can reduce the load on your server and improve response times for the end user.

Tip 3: Optimize database queries

When working with databases in FastAPI, it’s important to optimize your queries to minimize the number of database round trips and to only fetch the data that you actually need. Using tools like query optimization and indexing can help improve the performance of your database queries.

Tip 4: Monitor and profile your application

Monitoring and profiling your FastAPI application can help you identify performance bottlenecks and optimize your code. Tools like Prometheus and Grafana can be used to monitor the performance of your application in real-time, while profiling tools like Py-spy can help you identify areas of your code that are consuming the most CPU time.

Tip 5: Use a production-ready server

When deploying your FastAPI application to production, it’s important to use a production-ready server such as Uvicorn or Hypercorn. These servers are specifically designed to handle high loads and can greatly improve the performance of your application.

In conclusion

By following these performance tips from FastAPI expert Marcelo Trylesinski, you can ensure that your FastAPI application is highly performant and can handle a large number of concurrent requests with ease.