FastAPI Performance Optimization: A Guide to Efficiency Improvement from Code to Deployment

To optimize the performance of FastAPI, it is necessary to systematically advance from five aspects: code, asynchronous processing, database, caching, and deployment. At the code level: prioritize using `async def` to handle I/O-intensive tasks (such as asynchronous database queries), use generators or pagination to reduce memory usage, and leverage parameter validation to filter invalid requests. For asynchronous programming, distinguish task types: use asynchronous frameworks for I/O-intensive tasks, and submit CPU-intensive tasks to a thread pool via `ThreadPoolExecutor`. The core of database optimization includes connection pool reuse, index optimization (to avoid full table scans), batch operations (e.g., `bulk_insert`), and lazy loading. Caching strategies are suitable for frequently accessed data: use in-memory caching with `cachetools` for simple scenarios, and Redis distributed caching for multi-instance deployments. At the deployment end, use Gunicorn + Uvicorn for multi-process/threading, Nginx reverse proxy for static resources, and containerization (Docker) with K8s for elastic scaling. Optimization should first identify bottlenecks, advance step-by-step from code to deployment, prioritize high-cost-effectiveness issues (such as indexes and caching), and continuously monitor and iterate.