Schemas

A schema represent the structure of the data that is exchanged between the client and the server.

In this project, we use Pydantic to define the schemas. Pydantic is a data validation and parsing library that uses Python type annotations to define the schema of the data, it is also used to serialize and deserialize data to and from JSON.

Note

Usually, this are known as Pydantic model. However, as this project implements SQLAlchemy for data management, we want to avoid confusion between SQLAlchemy ORM models, which represent the structure of the database tables, and the Pydantic models. This lead us to chose an alternative naming (recommended in FastAPI documentation).

We grouped in modules the schemas that are related to the same entity. In each module, we can find schemas that have similar use cases for their respective entity.

• Base schema

  Base object from which all other schemas inherit. This is also the one used in case of relation between schemas.

• Create schema

  To create a new entry in the database table. Usually, there is a second one for creation of multiple entries.

• Update schema

  To update an existing entry in the database table.

• Schema named after its entity

  Represents the object that will be returned to the user through the API.