image-spec VS PostgreSQL

A buildpack is a software, designed to transform application source code into executable (OCI) images that can run on a variety of cloud platforms. At its core, a buildpack is a directory that includes a specific file named buildpack.toml. This file contains metadata and configuration details that dictate how the buildpack should behave. Buildpacks in simple terms, is a set of standards defining how the different steps that are required to build a compliant container image can be automated. Using those standards, there are projects that have been built round enabling that using an CLI or an API. The most common way of doing that is through the Cloud Native Buildpacks' Pack project. Pack is a CLI command that can run in the same system the developers are using to actually go through creating a Dockerfile.

Eventually, once zstd support gets fully supported, and tiny gzip compression windows are not a limitation, then compressing a full layer would almost certainly have a better ratio over several smaller layers

https://github.com/opencontainers/image-spec/issues/803

Please note that label-schema has been superseded by https://github.com/opencontainers/image-spec/blob/main/annotations.md<^

GitHub Container Registry stores container images within your organization or personal account, and allows you to associate an image with a repository. It currently supports both the Docker Image Manifest V2, Schema 2 and Open Container Initiative (OCI) specifications.

We build all services as containerized workloads, i.e., OCI images - sometimes called Docker images. We deploy these to the Kubernetes product offered by the cloud vendor. Whenever we need some capability, containers are the answer. This insulates our applications from the vendor. In principle, we could switch providers as long as Kubernetes is available.

Build images with anything that makes OCI compliant images, push, and profit.

it's funny that you mention this because it is actually the thing that is next on my agenda for the image, as you can probably see already I bake in OCI image annotations in our image, which is great for including some core pieces of meta data. In addition to this though I will soon be including custom labels for Base64 encoded YAMLs for Kubernetes deployments using this image. I will look at including helm configuration as well. Then it should be just as easy as: $ docker pull registry.gitlab.com/crafty-controller/crafty-4:latest $ docker image inspect registry.gitlab.com/crafty-controller/crafty-4:latest | jq -r ".[].Config.Labels.\"org.arcadiatech.crafty.k8s.deployment\"" | base64 -d | kubectl apply -f -

They still don't do anything really of substance, they're just gateways to their vendor's world - booking systems, payment systems, etc. You learn those as you go along. Yes, as a potential employee, you need to be able to tick those boxes on your CV, but if you understand the underlying technology, it's mostly a matter of booking your own AWS or Azure server for $5-10 a month for a few weeks, and fooling around. (Docker is a bit different in the sense that they were the first to popularize today's de-facto container image standard, the "Docker container", which has since been accepted as a proper standard and renamed to "OCI image format"; but at the end of the day, at this point in time, Docker in itself is still just a company out for the money, and the multi-GB installation of their product can, for the essential functionality part, be replaced by a few hundred lines of Bash code. The cool boys today don't use Docker, they use [Podman(https://podman.io/), which is essentially a much more lightweight drop-in replacement ;-) )

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:

PostgreSQL