Schema first API design is an approach to designing APIs (Application Programming Interfaces) that begins with defining the data schema first, before writing any code. This is in contrast to code-first API design, where the code is written first and the schema is derived from the code.
In schema first API design, the schema is usually defined using a specific language, such as GraphQL schema language or OpenAPI (formerly known as Swagger) specification. The schema can define the data types, fields, relationships, and other characteristics of the API.
Pros
Clear and explicit API definition: Defining the schema first forces you to think about the structure and types of data that will be exposed through the API, making it easier to communicate the API design to others and ensuring that everyone is on the same page.
Faster and more accurate development: Generating code from the schema can save time and reduce errors, as it ensures that the code implementation matches the API specification. This can also make it easier to update the API as the schema evolves over time.
Better separation of concerns: Separating the API definition from the code implementation can make it easier to iterate on the business logic of your API without the possiblity of introducing breaking changes to your API contracts
Easier API documentation: A well-defined schema can serve as a basis for automatically generating API documentation.
Flexibility: A schema-first approach can enable you to use different programming languages, frameworks, and tools for implementing the API, as long as they can generate code from the schema.
Testability: Having a clear API definition can make it easier to write tests and ensure that the API behaves as expected, especially if the schema is used to generate mock data or tests.
Cons
Upfront investment: Defining the schema can require some upfront investment in terms of time and resources, especially when not familiar with the schema language or tools.
Learning curve: Using a new schema language or tool can require some learning, which may slow down development in the short term.
Over-reliance on automation: Relying too heavily on code generation tools or frameworks can limit your understanding of the underlying code and make it harder to debug issues or optimize performance.