gilded-rose-tdd VS PostgreSQL

In our last episode (https://youtu.be/Uza\_dWsNMUs) we worked out how to save stock items in PostgreSQL (https://www.postgresql.org/) using the JetBrains Exposed library (https://github.com/JetBrains/Exposed). This time we refactor our existing file-based storage, extracting an interface that we can implement with files, in-memory, or with Exposed. For the database version, instead of replacing items in a table when they change, we choose to implement an append-only datastore. This keeps all the old versions of every row, using a query to select the latest versions when we want to see the current state. This has the advantage that we can rebuild the state of our system if things go wrong, and may also be faster than amending when we consider transactions. This is part 62 of an exploration of where a Test Driven Development implementation of the Gilded Rose stock control system might take us in Kotlin. You can see the whole series as a playlist https://www.youtube.com/playlist?list=PL1ssMPpyqociJNwykAOB9\_KEZVW7BW7m2 and the code on GitHub https://github.com/dmcg/gilded-rose-tdd If you like this, you’ll probably like my book Java to Kotlin, A Refactoring Guidebook (http://java-to-kotlin.dev). It's about far more than just the syntax differences between the languages - it shows how to upgrade your thinking to a more functional style. I have some free time between producing videos and working for team Gilded Rose. If you like these videos I'd like to work with you - please get in touch - [email protected]

This is part 60 of an exploration of where a Test Driven Development implementation of the Gilded Rose stock control system might take us in Kotlin. You can see the whole series as a playlist https://www.youtube.com/playlist?list=PL1ssMPpyqociJNwykAOB9_KEZVW7BW7m2 and the code on GitHub https://github.com/dmcg/gilded-rose-tdd

You can see the code on GitHub

Every time I buy a new computer, the first thing I install is the servers for MySql and Postgres, the two most common databases. This way, I can start the databases with a simple command like this:

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).