Faster time-to-market with API-first

This page summarizes the projects mentioned and recommended in the original post on dev.to

Judoscale - Save 47% on cloud hosting with autoscaling that just works
Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.
judoscale.com
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InfluxDB high-performance time series database
Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.
influxdata.com
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  1. Flask

    The Python micro framework for building web applications.

    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.

  2. Judoscale

    Save 47% on cloud hosting with autoscaling that just works. Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.

    Judoscale logo
  3. prism

    Turn any OpenAPI2/3 and Postman Collection file into an API server with mocking, transformations and validations. (by stoplightio)

    prism

  4. dredd

    Discontinued Language-agnostic HTTP API Testing Tool

    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).

  5. OpenAPI-Specification

    The OpenAPI Specification Repository

    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.

  6. Schemathesis

    Supercharge your API testing, catch bugs, and ensure compliance

    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).

  7. flasgger

    Easy OpenAPI specs and Swagger UI for your Flask API

    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???

  8. 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???

  9. InfluxDB

    InfluxDB high-performance time series database. Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.

    InfluxDB logo
  10. 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???

  11. flask-restful

    Simple framework for creating REST APIs

    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???

  12. flask-smorest

    DB agnostic framework to build auto-documented REST APIs with Flask and marshmallow

    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???

  13. pydantic

    Data validation using Python type hints

    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.

  14. 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.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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