Comparing the Quality of Flask and FastAPI: A Code Analysis Perspective
In the world of open-source software (OSS), the engineers behind famous projects are undoubtedly elite. But even well-maintained codebases grow, and growth has its cost. This article presents a comparative analysis of two popular web frameworks, Flask and FastAPI, using an open-source Python static analyzer called pyscn.
Quality Metrics: A Tale of Two Frameworks
By running pyscn on both frameworks, we can measure structural quality aspects such as function complexity, class tangling, code duplication, and overall health score. These metrics provide insights into the quality of the codebases and help us understand the trade-offs each framework has made.
Health Score and Architecture
Despite being mature codebases, both Flask and FastAPI scored above 94% in architecture quality. However, their health scores were modest (C and B), which is common in large, evolved projects. Delving deeper into the individual metrics, we can see that both frameworks exhibit low coupling, minimal duplication, almost no dead code, and other signs of well-maintained codebases.
Complexity: The Cost of Features
FastAPI's higher complexity can be attributed to its automatic OpenAPI schema generation feature, a functionality that Flask does not include in its core. On the other hand, Flask has its own complex spots, but they serve different purposes, such as CLI app discovery.
Coupling: The OpenAPI Tax
FastAPI's higher coupling is a deliberate choice to build OpenAPI specs programmatically. Flask, on the other hand, keeps features in extensions, resulting in lower coupling.
Circular Dependencies: A Surprise Win for FastAPI
FastAPI's clean dependency graph reflects lessons learned from predecessors, making deep refactoring easier. Flask, with its 14-year-old circular dependency, is a product of organic growth, which, while not ideal, is stable.
Duplication: The Cost of Compatibility
FastAPI's duplication mostly supports both Pydantic v1 and v2, while Flask has minimal duplication due to its smaller surface area.
Implications for North East India and Beyond
This analysis offers valuable insights for developers in North East India and across India. By understanding the trade-offs between Flask and FastAPI, developers can make informed decisions when choosing a web framework for their projects. Additionally, tools like pyscn can help identify areas for improvement in any codebase, promoting better coding practices and maintainable software.
Looking Forward
While both Flask and FastAPI are well-maintained, even the best-maintained codebases have room for improvement. Tools like pyscn help developers identify patterns that may need revisiting, such as circular dependencies, gradual coupling, and other potential issues. By staying aware of these areas, developers can continuously improve the quality of their codebases and build even better software for the future.