FastAPI Training: Database Integration

Key Concepts

Integrating databases with FastAPI involves several key concepts:

ORM (Object-Relational Mapping): A technique that lets you query and manipulate data from a database using an object-oriented paradigm.
SQLAlchemy: A popular SQL toolkit and ORM for Python.
Alembic: A database migration tool for SQLAlchemy.
Async Database Connections: Using asynchronous database connections to improve performance.
Data Validation with Pydantic: Ensuring data integrity by validating data before it is stored in the database.

1. ORM (Object-Relational Mapping)

ORM allows you to interact with databases using Python objects rather than writing raw SQL queries. This makes your code more readable and maintainable.

Example:

from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Base = declarative_base()

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String)
    age = Column(Integer)

engine = create_engine('sqlite:///example.db')
Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)
session = Session()

new_user = User(name="John Doe", age=30)
session.add(new_user)
session.commit()

2. SQLAlchemy

SQLAlchemy is a powerful SQL toolkit and ORM for Python. It provides a full suite of well-known enterprise-level persistence patterns, designed for efficient and high-performing database access.

Example:

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine('sqlite:///example.db')
Session = sessionmaker(bind=engine)
session = Session()

# Querying data
users = session.query(User).all()
for user in users:
    print(user.name, user.age)

3. Alembic

Alembic is a database migration tool for SQLAlchemy. It allows you to manage changes to your database schema over time, making it easier to evolve your database schema as your application evolves.

Example:

from alembic import command
from alembic.config import Config

alembic_cfg = Config("alembic.ini")
command.upgrade(alembic_cfg, "head")

4. Async Database Connections

Using asynchronous database connections can significantly improve the performance of your FastAPI application, especially when dealing with I/O-bound operations.

Example:

from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker

engine = create_async_engine('sqlite+aiosqlite:///example.db')
AsyncSession = sessionmaker(engine, expire_on_commit=False, class_=AsyncSession)

async def get_users():
    async with AsyncSession() as session:
        result = await session.execute(select(User))
        return result.scalars().all()

5. Data Validation with Pydantic

Pydantic is a data validation and settings management library for Python. It allows you to define the structure of your data using Pydantic models, ensuring that the data is valid before it is stored in the database.

Example:

from pydantic import BaseModel

class UserCreate(BaseModel):
    name: str
    age: int

user_data = {"name": "John Doe", "age": 30}
user_create = UserCreate(**user_data)

new_user = User(name=user_create.name, age=user_create.age)
session.add(new_user)
session.commit()

Analogies

Think of ORM as a translator that converts your Python code into SQL queries, making it easier to interact with the database. SQLAlchemy is like a toolbox that provides all the necessary tools to work with databases. Alembic is like a version control system for your database schema, allowing you to track changes over time. Async database connections are like having multiple workers handling tasks simultaneously, improving efficiency. Data validation with Pydantic is like having a quality control check before storing data, ensuring it meets the required standards.

By mastering these concepts, you can effectively integrate databases with your FastAPI application, ensuring efficient, maintainable, and scalable data management.