swagger-ui VS PostgreSQL

Swagger is a fantastic open-source toolset that's perfect for developing and describing RESTful APIs. It offers you a user-friendly interface to understand a service's capabilities without needing to dig into the code. It even provides a way for you to interact directly with the API, which means you get to test its functionality.

The API had an OpenAPI endpoint built with Swagger where I could download a JSON specification file for the API.

Swagger is a widely used tool for documenting and testing APIs.

Swagger is an open-source software framework that implements the OpenAPI Specification—an API description format for REST APIs. The OpenAPI Specification defines a standard, language-agnostic interface to HTTP APIs, enabling both humans and computers to discover and understand the capabilities of the API.

Mocking and Testing: Once the API design is complete, developers create mock APIs to simulate the behavior of the actual services. This early testing phase helps identify any issues or mismatches between design and implementation before substantial development efforts are invested. Tools like Postman or Swagger are invaluable for API testing and validation.

import fp from "fastify-plugin"; import swagger from "@fastify/swagger"; export default fp(async (fastify) => { fastify.register(swagger, { openapi: { info: { tags: [ { name: "drinks", description: "Drink-related endpoints", externalDocs: { description: "Find out more", url: "http://swagger.io", }, }, ], }, }, }); });

A very requested feature for Sharkio was the auto-generation of OpenAPI documentation using the recorded HTTP requests - to create standardized documentation.

Automation QA is a lot of the same duties as manual QA, but instead of writing and executing the test plans manually, we create and update automated tests that can run those validations programmatically, for example by using Selenium to automatically fill out and submit forms for a web application, or using Postman and/or Swagger to generate an API conversation test.

Data Handling: Utilizes Windmill for data pipelines, with a primary database powered by PostgreSQL. Auxiliary data storage is handled by MongoDB, with Redis for caching to optimize performance

PostgreSQL

The LMS Moodle Operator serves as a meta-operator, orchestrating the deployment and management of Moodle instances in Kubernetes. It handles the entire stack required to run Moodle, including components like Postgres, Keydb, NFS-Ganesha, and Moodle itself. Each of these components has its own Kubernetes Operator, ensuring seamless integration and management.

Another key feature of txtai is being able to quickly move from prototyping to production. This article will demonstrate how txtai can integrate with Postgres, a powerful, production-ready and open source object-relational database system. After txtai persists content to Postgres, we'll show it can be directly queried with SQL from any Postgres client

SQL (Structured Query Language) databases are relational databases. They organize data into tables with rows and columns, and they use SQL for querying and managing data. Examples include MySQL, PostgreSQL, and SQLite.

Node.js, MySQL and PostgreSQL servers installed on your machine

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).

I've used MySQL for years. But recently, I found myself working PostgreSQL and simple things like dumping and restoring a database are different enough that I decided to document the process. It's straightforward enough once I knew how.

A running Rails application needs a database to connect to. You may already have your database of choice installed, but if not, I recommend PostgreSQL, or Postgres for short. On a Mac, probably the easiest way to install it is with Posrgres.app. Another option, the one I prefer, is to use Homebrew. With Homebrew installed, this command will install PostgreSQL version 16 along with libpq: