Project 1: Asynchronous API with FastAPI

Goal: Build a high-performance, scalable REST API using FastAPI with asynchronous processing, authentication (JWT/OAuth2), database integration (PostgreSQL), and deployment using Docker & AWS Lambda.

🏗️ Project Setup

1️⃣ Prerequisites:

• Python 3.8+
• PostgreSQL (or SQLite for local dev)
• Docker
• AWS Account (for Lambda deployment)
• GitHub (for version control)

🎯 Project Goals & Progress Checklist

✅ Create a REST API with FastAPI

✅ Use Async Programming (asyncio) for improved efficiency

✅ Secure API with JWT Authentication

✅ Integrate a PostgreSQL Database with SQLAlchemy

✅ Dockerize the Application for Deployment

[ ] Deploy to AWS Lambda (using SAM and/or Terraform)

[ ] Implement CI/CD Pipeline for Auto Deployment

📌 Next Steps

1. Optimize API performance (profiling, caching).
2. Extend API with new features (WebSockets, caching with Redis).

☑ Task Breakdown: Project 1 – Async API with FastAPI ☑

✅ Phase 1: Environment Setup

• Create project folder and initialize Git repository
• Set up virtual environment and install packages (fastapi, uvicorn, sqlalchemy, databases, etc.)
• Set up base main.py and a /docs endpoint

Phase 2: Async CRUD API

• Define a SQLAlchemy model for a resource (e.g., Task, User, Note)
• Set up PostgreSQL or SQLite DB and connect with databases package
• Implement basic async CRUD endpoints (GET, POST, PUT, DELETE)

Phase 3: Authentication

• Add JWT-based login and registration routes
• Create middleware or dependencies to protect routes
• Add OAuth2PasswordBearer flow

Phase 4: Docker & Testing

• Write Dockerfile and .dockerignore
• Add pytest test suite for endpoints
• Spin up containers and test locally

Phase 5: Deployment

• Set up deployment scripts or use Terraform
• Deploy to AWS Lambda or EC2 using Docker image
• Monitor logs and test endpoint response from a hosted environment

Phase 6: CI/CD

• Create .github/workflows/deploy.yml
• Automate testing and deployment to your staging/production environment