Master FastAPI - Build Modern APIs
Learn to build high-performance REST APIs with FastAPI and deploy them to Cloudflare Workers
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🎯 What You'll Learn
Learn to build high-performance REST APIs with FastAPI and deploy them to Cloudflare Workers
Understand ASGI architecture and how FastAPI works
Build REST APIs with automatic documentation
Implement data validation with Pydantic models
Handle path parameters, query parameters, and request bodies
Deploy FastAPI applications to Cloudflare Workers
Connect to Cloudflare services (KV, D1, R2)
🧠 Core Concepts
Your First FastAPI Application
FastAPI is a modern, fast web framework for building APIs with Python. It uses Python type hints for automatic validation and documentation generation.
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():
return {"message": "Hello, FastAPI!"}
@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
return {"item_id": item_id, "q": q}⚠️ Common Pitfall: Always install FastAPI with 'pip install fastapi[standard]' to get all necessary dependencies including uvicorn server.
📚 Related Resources:
Request Validation with Pydantic
Pydantic models provide automatic request validation, serialization, and API schema generation. Define your data structure once and FastAPI handles the rest.
from fastapi import FastAPI
from pydantic import BaseModel, Field
app = FastAPI()
class Item(BaseModel):
name: str = Field(..., min_length=1)
price: float = Field(..., gt=0)
quantity: int = 1
@app.post("/items")
def create_item(item: Item):
total = item.price * item.quantity
return {"item": item, "total": total}⚠️ Common Pitfall: Pydantic v2 uses model_dump() instead of dict(). Check your version with 'pydantic.__version__'.
📚 Related Resources:
FastAPI on Cloudflare Workers
Deploy FastAPI to Cloudflare's global edge network. Access environment variables and bindings through req.scope['env'].
from workers import WorkerEntrypoint
from fastapi import FastAPI, Request
import asgi
class Default(WorkerEntrypoint):
async def fetch(self, request):
return await asgi.fetch(app, request, self.env)
app = FastAPI()
@app.get("/")
async def root():
return {"message": "Running on Workers!"}
@app.get("/kv/{key}")
async def get_kv(key: str, req: Request):
kv = req.scope["env"].MY_KV
value = await kv.get(key)
return {"key": key, "value": value}⚠️ Common Pitfall: Use req.scope['env'] to access bindings, NOT os.environ. Bindings must be defined in wrangler.toml.
📚 Related Resources:
🛠️ Hands-On Projects
Task API - CRUD Operations
Build a simple task management API with create, read, update, and delete operations using in-memory storage.
Skills:
Blog API with KV Storage
Create a blog API that persists data to Cloudflare Workers KV. Include pagination and search functionality.
Skills:
Full-Stack API with D1
Build a production-ready API with D1 SQLite database, including migrations, relationships, and authentication.
Skills:
📝 Test Your Knowledge
📚 Additional Resources
✅ Best Practices
Always use type hints for automatic validation and documentation
Use Pydantic models for all request and response data
Implement async/await for I/O-bound operations
Use dependency injection for reusable logic
Don't use synchronous blocking operations in async functions
Don't skip error handling - always validate user input
Don't use 'fastapi dev' in production - use 'fastapi run'
Test your API with the automatic /docs endpoint during development