Data Validation with Pydantic

Receiving data from clients is a major vulnerability point for any application. A user might send malformed data, incorrect data types, or leave out required fields. Processing such invalid data can lead to bugs, crashes, and security vulnerabilities. Therefore, data validation—the process of ensuring that incoming data is correct, complete, and secure—is not optional; it's a necessity. While you can write manual validation logic (e.g., a series of `if` statements to check for keys and types), this can become tedious, error-prone, and hard to maintain. This is where data validation libraries shine. Pydantic is a popular Python library that uses type annotations to validate data. You define the 'shape' of your expected data as a class, specifying the fields and their types. Pydantic then automatically parses and validates incoming data against that shape. If the data is valid, it returns a clean, type-coerced model object. If it's invalid, it raises a detailed validation error, indicating exactly what was wrong. The FastAPI framework has first-class support for Pydantic. You can simply use your Pydantic model as a type hint in your endpoint's parameters, and FastAPI will automatically handle the request body parsing, validation, and error reporting. This declarative approach makes your code cleaner, more robust, and self-documenting, as the Pydantic model itself clearly defines the API's expected input.