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The second major problem was the backend team wasn’t using a proper API framework. They used plain Flask with custom payload validation which they themselves implemented. You can picture it: hundreds of lines of code dedicated to API validation (for dates, for timestamps, for string formats, and so on), most of them untested. No wonder the project was going slow.
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prism
Turn any OpenAPI2/3 and Postman Collection file into an API server with mocking, transformations and validations. (by stoplightio)
prism
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InfluxDB
Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.
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Consolidating the API specification with OpenAPI was a turning point for the project. From that moment we were able to run mock servers to build and test the UI before integrating with the backend, and we were able to validate the backend implementation against the specification. We used prism to run mock servers, and Dredd to validate the server implementation (these days I’d rather use schemathesis).
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It turned out the team just wasn’t aware of REST APIs best practices and standards and didn’t know about the OpenAPI specification. So the first thing I did was to explain what OpenAPI is and how it works. Then we consolidated the API documentation in an OpenAPI specification. This allowed us to be very clear about what to expect from the API.
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Schemathesis
Guarantee flawless API functionality with thorough, high-quality test scenarios generated from your API specification.
Consolidating the API specification with OpenAPI was a turning point for the project. From that moment we were able to run mock servers to build and test the UI before integrating with the backend, and we were able to validate the backend implementation against the specification. We used prism to run mock servers, and Dredd to validate the server implementation (these days I’d rather use schemathesis).
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When it comes to Flask, in particular, there’re plenty of choices. And in fairness, not all frameworks are created equal. You’ve got flasgger, restx (successor of flask-restplus), flask-RESTful, and flask-smorest, to mention a few. How do you choose among those???
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flask-restx
Fork of Flask-RESTPlus: Fully featured framework for fast, easy and documented API development with Flask
When it comes to Flask, in particular, there’re plenty of choices. And in fairness, not all frameworks are created equal. You’ve got flasgger, restx (successor of flask-restplus), flask-RESTful, and flask-smorest, to mention a few. How do you choose among those???
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Sonar
Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.
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When it comes to Flask, in particular, there’re plenty of choices. And in fairness, not all frameworks are created equal. You’ve got flasgger, restx (successor of flask-restplus), flask-RESTful, and flask-smorest, to mention a few. How do you choose among those???
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When it comes to Flask, in particular, there’re plenty of choices. And in fairness, not all frameworks are created equal. You’ve got flasgger, restx (successor of flask-restplus), flask-RESTful, and flask-smorest, to mention a few. How do you choose among those???
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When it comes to Flask, in particular, there’re plenty of choices. And in fairness, not all frameworks are created equal. You’ve got flasgger, restx (successor of flask-restplus), flask-RESTful, and flask-smorest, to mention a few. How do you choose among those???
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Uses a robust data validation library: validating payloads is a complex business. Your data validation library must handle optional and required properties, string formats like ISO dates and UUIDs (both dates and UUIDs are string types in OpenAPI), and strict vs loose type validation (should a string pass as an integer if it can be casted?). Also, in the case of Python, you need to make sure 1 and 0 don’t pass for True and False when it comes to boolean properties. In my experience, the best data validation libraries in the Python ecosystem are pydantic and marshmallow. From the above-mentioned libraries, flasgger and flask-smorest work with marshmallow.
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marshmallow
A lightweight library for converting complex objects to and from simple Python datatypes.
Uses a robust data validation library: validating payloads is a complex business. Your data validation library must handle optional and required properties, string formats like ISO dates and UUIDs (both dates and UUIDs are string types in OpenAPI), and strict vs loose type validation (should a string pass as an integer if it can be casted?). Also, in the case of Python, you need to make sure 1 and 0 don’t pass for True and False when it comes to boolean properties. In my experience, the best data validation libraries in the Python ecosystem are pydantic and marshmallow. From the above-mentioned libraries, flasgger and flask-smorest work with marshmallow.
