本文教你如何将FastAPI与Celery结合的应用通过Docker进行容器化部署。首先，介绍了应用的基本结构，包括FastAPI和Celery的任务文件。接着，详细讲解了如何编写Dockerfile和Docker Compose文件，分别用于构建容器镜像和定义服务。最后，通过docker compose up命令一键启动整个应用栈，简化了部署流程。整个过程清晰易懂，适合开发者快速上手。

使用Celery在FastAPI中实现异步任务处理，可有效避免长时间运行函数导致的API超时。通过定义Celery任务并配置Redis作为消息代理，将耗时函数（如计算平方根）放入后台执行。FastAPI提供两个端点：一个用于启动任务并返回任务ID，另一个用于查询任务状态和结果。结合Docker部署，可简化应用管理。

Learn all you need to implement streaming in production using SSE and how to handle streaming errors.

I have been experimenting with FastAPI recently, a Python API framework self-describing as "high performance, easy to learn, fast to code, ready for production".

One of the features I wanted my project to have is to be fully asynchronous, from the app server to the SQL requests. As the API is mostly I/O bound, this would allow it to handle many concurrent requests with a single server process, instead of starting a thread per request, as one commony seen with Flask/Gunicorn.

However, this poses a challenge when it comes to profiling the code and interpreting the results.