Code First vs Schema First: Designing APIs with FastAPI

In the world of API development, choosing the right approach for designing your API can significantly impact the development process, maintainability, and overall success of your project. Two popular approaches are Code First and Schema First. FastAPI, a modern, fast (high-performance) web framework for building APIs with Python, can be used effectively with both approaches. This blog post will explore the fundamental concepts of Code First and Schema First, their usage methods, common practices, and best practices when designing APIs with FastAPI.

Table of Contents

  1. Fundamental Concepts
  2. Usage Methods in FastAPI
  3. Common Practices
  4. Best Practices
  5. Conclusion
  6. References

Fundamental Concepts

Code First

The Code First approach involves writing the actual code first and then generating the API schema from the code. In this approach, developers focus on implementing the business logic and API endpoints in the programming language. The API schema, such as OpenAPI, is automatically generated based on the code structure, data models, and route definitions. This approach is more developer-centric as it allows for rapid development and iteration.

Schema First

The Schema First approach starts with defining the API schema, typically using a standard format like OpenAPI. The schema serves as the single source of truth for the API’s structure, endpoints, request/response formats, and data models. Once the schema is defined, the code is then generated based on the schema. This approach is more design-centric and emphasizes upfront planning and clear communication between different stakeholders.

Usage Methods in FastAPI

Code First with FastAPI

In FastAPI, the Code First approach is very straightforward. You define your data models using Python classes (usually with Pydantic), and then you create API endpoints using FastAPI’s decorators. FastAPI will automatically generate the OpenAPI schema based on your code.

Here is a simple example:

from fastapi import FastAPI
from pydantic import BaseModel

# Define a data model
class Item(BaseModel):
    name: str
    price: float

app = FastAPI()

# Define an API endpoint
@app.post("/items/")
def create_item(item: Item):
    return item

In this example, we first define a Item data model using Pydantic. Then we create a POST endpoint /items/ that accepts an Item object as the request body. FastAPI will generate the OpenAPI schema for this API automatically, which you can access at /docs or /redoc endpoints.

Schema First with FastAPI

To use the Schema First approach with FastAPI, you can use tools like openapi-python-client to generate Python code from an existing OpenAPI schema.

First, install the openapi-python-client tool:

pip install openapi-python-client

Then, generate the Python code from an OpenAPI schema file (e.g., openapi.yaml):

openapi-python-client generate --path openapi.yaml

This will generate a Python project with all the necessary code based on the OpenAPI schema. You can then integrate this code into your FastAPI application.

Common Practices

Code First Common Practices

  • Use Pydantic for Data Validation: Pydantic is a powerful library for data validation and serialization in Python. It integrates seamlessly with FastAPI and helps ensure that the data received and sent by your API is in the correct format.
  • Write Clear and Readable Code: Since the code is the source of the API schema, it’s important to write clear and readable code. Use descriptive names for your data models and API endpoints.
  • Iterate Quickly: The Code First approach allows for rapid development and iteration. You can easily make changes to your code and see the impact on the API schema immediately.

Schema First Common Practices

  • Collaborate with Stakeholders: Since the schema is the single source of truth, it’s important to involve all stakeholders (developers, designers, product managers, etc.) in the schema definition process.
  • Use Version Control for the Schema: Keep the OpenAPI schema in version control to track changes and ensure that all team members are working with the same version.
  • Validate the Schema: Before generating the code, make sure to validate the OpenAPI schema to avoid errors in the code generation process.

Best Practices

When to Choose Code First

  • Rapid Prototyping: If you need to quickly build a prototype of your API, the Code First approach is a great choice. You can focus on implementing the core functionality and iterate on it quickly.
  • Small Projects: For small projects with a limited number of endpoints and simple data models, the Code First approach can be more efficient as it requires less upfront planning.
  • Developer-Centric Teams: If your team consists mainly of developers who prefer to work directly with code, the Code First approach may be more suitable.

When to Choose Schema First

  • Large Projects: For large projects with multiple teams and complex APIs, the Schema First approach helps in better organization and communication. It ensures that all teams are on the same page regarding the API’s structure and functionality.
  • Integration with Existing Systems: If your API needs to integrate with existing systems, the Schema First approach can help in aligning the API with the requirements of those systems.
  • Design-Centric Teams: If your team includes designers and product managers who want to have more control over the API design, the Schema First approach is a better fit.

Conclusion

Both Code First and Schema First approaches have their own advantages and disadvantages. The choice between them depends on the specific requirements of your project, the size of your team, and the development process. FastAPI provides excellent support for both approaches, allowing you to choose the one that best suits your needs. By understanding the fundamental concepts, usage methods, common practices, and best practices of both approaches, you can design high-quality APIs with FastAPI more effectively.

References