FastAPI Expert

Production-ready FastAPI knowledge covering basic API development to planet-scale deployment.

Quick Start

Create a basic FastAPI application:

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello World"}

# Run with: uvicorn main:app --reload

Core Topics

1. Database Operations

See: references/database.md

• SQLModel integration (recommended ORM)
• CRUD operations with dependency injection
• Async database operations
• Connection pooling
• Migrations with Alembic
• Neon Serverless PostgreSQL setup

2. Security & Authentication

See: references/security.md

• OAuth2 with password flow
• JWT token-based authentication
• Password hashing with Argon2
• OAuth2 scopes for permissions
• CORS configuration
• Rate limiting
• API key authentication
• Security best practices

3. Deployment & Scalability

See: references/deployment.md

• Docker containerization
• Kubernetes deployment
• Production server configuration (Uvicorn + Gunicorn)
• Horizontal pod autoscaling
• Performance monitoring with Prometheus
• Caching strategies with Redis
• Platform-specific guides (Vercel, AWS, GCP)

4. Advanced Features

See: references/advanced.md

• Dependency injection patterns
• Custom middleware
• WebSocket support
• Background tasks
• Request/response models with validation
• Streaming responses
• File uploads
• Testing strategies
• Event handlers

Project Templates

Use the provided production-ready templates in assets/:

FastAPI Project Structure

assets/project-template/
├── main.py           # Application entry point
├── config.py         # Settings management
├── database.py       # Database setup
├── models.py         # SQLModel models
└── auth.py           # Authentication logic

Copy the template to start a new project:

cp -r assets/project-template/* your-project/

Docker Deployment

Use assets/Dockerfile for containerizing your application with multi-stage builds and security best practices.

Kubernetes Deployment

Use assets/kubernetes-deployment.yaml for deploying to Kubernetes with:

• Deployment with 3 replicas
• Service with LoadBalancer
• Horizontal Pod Autoscaler
• Health and readiness probes

Common Patterns

Database CRUD with Session Dependency

from typing import Annotated
from fastapi import Depends
from sqlmodel import Session, select

SessionDep = Annotated[Session, Depends(get_session)]

@app.get("/users/{user_id}")
def get_user(user_id: int, session: SessionDep):
    user = session.get(User, user_id)
    if not user:
        raise HTTPException(status_code=404, detail="User not found")
    return user

Protected Routes with Authentication

from typing import Annotated
from fastapi import Depends

CurrentUser = Annotated[User, Depends(get_current_active_user)]

@app.get("/users/me")
async def read_users_me(current_user: CurrentUser):
    return current_user

Background Tasks

from fastapi import BackgroundTasks

@app.post("/send-email/")
async def send_email(email: str, background_tasks: BackgroundTasks):
    background_tasks.add_task(send_email_task, email)
    return {"message": "Email queued"}

Best Practices Checklist

Development

• Use type hints everywhere
• Implement request/response models with Pydantic
• Use dependency injection for shared logic
• Add proper error handling with HTTPException
• Use async/await for I/O operations

Security

• Hash passwords with Argon2
• Use JWT for authentication
• Implement OAuth2 scopes for authorization
• Configure CORS properly
• Store secrets in environment variables
• Enable HTTPS in production

Database

• Use SQLModel for ORM
• Implement connection pooling
• Use migrations (Alembic)
• Leverage dependency injection for sessions
• Add database indexes for performance

Deployment

• Multi-stage Dockerfile
• Non-root container user
• Health check endpoints
• Resource limits in Kubernetes
• Horizontal pod autoscaling
• Prometheus metrics
• Structured logging

Scalability Strategies

Async Operations

Always use async def for endpoints that perform I/O:

@app.get("/users/")
async def get_users():
    users = await fetch_from_db()
    return users

Caching

Implement Redis caching for frequently accessed data:

@cache(expire=600)
async def expensive_operation():
    # Heavy computation
    return result

Background Processing

Offload long-running tasks:

background_tasks.add_task(process_data, data)

Connection Pooling

Configure database connection pools:

engine = create_engine(
    DATABASE_URL,
    pool_size=20,
    max_overflow=10,
    pool_timeout=30
)

Troubleshooting

Performance Issues

1. Enable Prometheus metrics to identify bottlenecks
2. Use async operations for all I/O
3. Implement caching with Redis
4. Optimize database queries (indexes, eager loading)
5. Enable GZip compression

Authentication Errors

1. Verify JWT secret key matches
2. Check token expiration time
3. Ensure password hashing is consistent
4. Validate CORS configuration

Database Connection Issues

1. Check connection string format
2. Verify connection pool settings
3. Test database reachability
4. Review firewall rules

Production Deployment Flow

1. Develop locally with auto-reload
2. Test with TestClient and pytest
3. Build Docker image
4. Push to container registry
5. Deploy to Kubernetes cluster
6. Monitor with Prometheus/Grafana
7. Scale with HPA based on metrics

Example: Complete CRUD API

from fastapi import FastAPI, Depends, HTTPException
from sqlmodel import Field, Session, SQLModel, create_engine, select
from typing import Annotated

# Database setup
engine = create_engine("sqlite:///database.db")

def get_session():
    with Session(engine) as session:
        yield session

SessionDep = Annotated[Session, Depends(get_session)]

# Model
class Item(SQLModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    title: str
    description: str | None = None

# App
app = FastAPI()

@app.on_event("startup")
def on_startup():
    SQLModel.metadata.create_all(engine)

# CRUD endpoints
@app.post("/items/", response_model=Item)
def create_item(item: Item, session: SessionDep):
    session.add(item)
    session.commit()
    session.refresh(item)
    return item

@app.get("/items/", response_model=list[Item])
def read_items(session: SessionDep, skip: int = 0, limit: int = 100):
    items = session.exec(select(Item).offset(skip).limit(limit)).all()
    return items

@app.get("/items/{item_id}", response_model=Item)
def read_item(item_id: int, session: SessionDep):
    item = session.get(Item, item_id)
    if not item:
        raise HTTPException(status_code=404, detail="Item not found")
    return item

@app.patch("/items/{item_id}", response_model=Item)
def update_item(item_id: int, item_update: Item, session: SessionDep):
    db_item = session.get(Item, item_id)
    if not db_item:
        raise HTTPException(status_code=404, detail="Item not found")

    item_data = item_update.model_dump(exclude_unset=True)
    for key, value in item_data.items():
        setattr(db_item, key, value)

    session.add(db_item)
    session.commit()
    session.refresh(db_item)
    return db_item

@app.delete("/items/{item_id}")
def delete_item(item_id: int, session: SessionDep):
    item = session.get(Item, item_id)
    if not item:
        raise HTTPException(status_code=404, detail="Item not found")

    session.delete(item)
    session.commit()
    return {"ok": True}

This skill provides everything needed to build production-ready FastAPI applications from basic CRUD to planet-scale deployments.