Ruby on Rails VS PostgreSQL

on April 23, 2024, a PR #51640 was merged into main branch of Ruby On Rails. This PR title is Use Module#include rather than prepend for faster method lookup.

[0] is a my favorite demonstration of it.

[0]: https://github.com/rails/rails/commit/b83965785db1eec019edf1...

Here's a real life example: Imagine a Ruby on Rails app on which a team of developers are working. The code is hosted on GitLab and all the work is coordinated using GitLab issues. In other words: For every commit, there's an associated issue and the issue number acts as a sort of primary key for documentation, time reporting and so forth. This convention has a few advantages, most notably the ability to easily learn more about how, when and by whom features were implemented as well as how this implementation came to be.

Ruby on Rails is regarded as one of the best ruby frameworks. It was the primary language in developing big projects such as Twitter and helped the language boost the community. Often referred to as “Rails,” Ruby on Rails is a web development framework with an MVC control structure and currently running its 6.1 version. The 16-year-old language has dramatically influenced the web development structures and managing databases, web pages, and other components on a web application.

In Rails 7.1, a new option _instance_methods is introduced, allowing developers to opt-out of the automatic generation of instance methods for enums. When enum is defined with _instance_methods: false, Rails will no longer generate methods like pending?, processed?, etc.

Rails isn't super opinionated about database writes, its mostly left up to developers to discover that for relational DBs you do not want to be doing a bunch of small writes all at once.

That said it specifically has tools to address this that started appearing a few years ago https://github.com/rails/rails/pull/35077

The way my team handles it is to stick Kafka in between whats generating the records (for us, a bunch of web scraping workers) and and a consumer that pulls off the Kafka queue and runs an insert when its internal buffer reaches around 50k rows.

Rails is also looking to add some more direct background type work with https://github.com/basecamp/solid_queue but this is still very new - most larger Rails shops are going to be running a second system and a gem called Sidekiq that pulls jobs out of Redis.

I'm looking forward to see what extractions from this will land on rails. For example: https://github.com/rails/rails/issues/50454

Here is what strict_loading does (source):

Kamal is a wonderfully simple way to deploy your applications anywhere. It will also be included by default in Rails 8. Kamal is trivial, but I don’t recommend using it on your development machine.

Here's the GitHub milestone I've based this article on — https://github.com/rails/rails/milestone/87

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.