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FastAPI Fundamentals

Build a strong foundation with core FastAPI concepts and patterns.

Advanced Patterns

Master sophisticated API design patterns and optimization techniques.

Deployment & Scaling

Learn to deploy and scale your FastAPI applications effectively.

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Official Documentation

Comprehensive guides and API references

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Source code, issues, and contributions

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Video Tutorials

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FastAPI Quick Start

Get your first FastAPI application running in minutes with this quick start guide.

# Install FastAPI and Uvicorn
pip install fastapi uvicorn

# Create main.py
from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"Hello": "World"}

# Run the server
uvicorn main:app --reload

Visit http://localhost:8000 to see your API in action, and http://localhost:8000/docs for interactive documentation.

Type Safety with Pydantic

FastAPI uses Python type hints and Pydantic models for automatic data validation, serialization, and documentation.

from pydantic import BaseModel
from typing import Optional

class Item(BaseModel):
    name: str
    description: Optional[str] = None
    price: float
    tax: Optional[float] = None

@app.post("/items/")
def create_item(item: Item):
    return item

FastAPI will automatically validate incoming requests and generate API documentation based on your type hints.

Performance Optimization

FastAPI is one of the fastest Python frameworks available. Here's how to maximize performance:

# Use async/await for I/O operations
import asyncio

@app.get("/slow-operation/")
async def slow_operation():
    # Simulate a slow I/O operation
    await asyncio.sleep(1)
    return {"status": "completed"}

# Use Pydantic's ORM mode for database operations
class User(BaseModel):
    id: int
    name: str
    email: str

    class Config:
        orm_mode = True

Other optimizations include using response models, background tasks, and connection pooling.

Containerization with Docker

Containerizing your FastAPI application ensures consistent deployment across environments.

# Dockerfile
FROM python:3.9

WORKDIR /code

COPY ./requirements.txt /code/requirements.txt
RUN pip install --no-cache-dir --upgrade -r requirements.txt

COPY ./app /code/app

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]

# docker-compose.yml for additional services
version: "3.8"
services:
  web:
    build: .
    ports:
      - "8000:80"
    depends_on:
      - db
  db:
    image: postgres:13
    environment:
      - POSTGRES_DB=myapp

Build and run with docker build -t myapp . and docker run -p 8000:80 myapp