REST APIs

Overview Software developers often reach for Python Dataclasses to eliminate the tedious boilerplate of manual `__init__` and `__repr__` methods. While these built-in tools offer a clean, standard-library solution for storing data, they often vanish once a project hits production. This guide explores why frameworks like FastAPI and SQLAlchemy push developers toward Pydantic, and where dataclasses still reign supreme in the development lifecycle. Prerequisites To follow this guide, you should have a solid grasp of Python 3.7+ syntax, specifically decorators and type hinting. Familiarity with REST APIs and Object-Relational Mapping (ORM) concepts will help you understand the structural trade-offs discussed. Key Libraries & Tools * **Dataclasses**: A standard library module that automates class boilerplate. * **Pydantic**: A data validation library that enforces type hints at runtime. * **FastAPI**: A modern web framework built on Pydantic for rapid API development. * **SQLAlchemy**: An SQL toolkit and ORM for mapping Python classes to database tables. Code Walkthrough The Dataclass Foundation Dataclasses provide a minimal footprint for defining data structures. ```python from dataclasses import dataclass @dataclass class Book: title: str author: str pages: int ``` The `@dataclass` decorator automatically generates the initializer and a readable string representation. However, it does not validate that `pages` is actually an integer at runtime. Transitioning to Pydantic for Validation In production APIs, you cannot trust user input. Pydantic extends the dataclass concept by adding strict validation and type coercion. ```python from pydantic import BaseModel, Field class BookRequest(BaseModel): title: str author: str pages: int = Field(gt=0) ``` Unlike standard dataclasses, Pydantic converts a string `"150"` into the integer `150` automatically (type coercion) and throws an error if the value is negative. Syntax Notes Standard dataclasses use the `@dataclass` decorator, whereas Pydantic typically uses inheritance from `BaseModel`. While Pydantic offers its own `@dataclass` decorator for compatibility, it lacks features like `.model_dump()` found in `BaseModel`. Practical Examples Dataclasses are the premier tool for **vibe domain modeling**. When prototyping a complex system, you can quickly sketch out relationships and iterate with ChatGPT without the overhead of database schemas or validation logic. They serve as a high-speed drafting tool before you commit to the rigid structures required by SQLAlchemy. Tips & Gotchas A common mistake is using the same model for both database storage and API responses. Always separate your **Domain Models** (internal data) from your **DTOs** (Data Transfer Objects). Using SQLAlchemy for the database and Pydantic for the API layer ensures that internal IDs or sensitive fields don't accidentally leak into your public JSON responses.