hcl VS kubernetes

Terraform HCL (HashiCorp Configuration Language) is an essential language for cloud engineers in 2024, particularly for those involved in infrastructure as code (IaC) practices. HCL is the configuration language used by Terraform, a widely adopted tool that enables engineers to define, provision, and manage cloud infrastructure using a declarative configuration approach. Learning Terraform HCL allows cloud engineers to automate the deployment and lifecycle management of cloud resources across various service providers, ensuring consistency, repeatability, and scalability of cloud environments.

Reminds me of [HCL](https://github.com/hashicorp/hcl), but without all the providers to deploy the config?

HCL: A Go implementation structured configuration language. The native syntax of HCL is inspired by libucl and nginx configurations. It is used to create a structured configuration language that is friendly to humans and machines, mainly for DevOps tools, server configurations, and resource configurations as a Terraform language.

HCL has a JSON representation [1], internally, objects behave that way. so it should be possible to write a Jsonnet wrapper around it. Terraform can currently parse json pipelines too.

[1]: https://github.com/hashicorp/hcl/blob/main/json/spec.md

>https://noyaml.com/

I'm not sure this is the criticism you think it is. Wow, so you basically have to add quotes to get strings in some ambiguous situations?

Yeah sure you could probably improve YAML by getting rid of these weird pitfalls, but that is a minor improvement. The alternative isn't something like TOML, because YAML is optimized for hierarchical configuration. It's every vendor implementing a different syntax such as Hashicorp with their HCL [0].

[0] https://github.com/hashicorp/hcl

The Hashicorp corporation has made a huge impact in providing valuable tools and platforms in the cloud ecosystem. The advantage of using the tools they provide, such as Terraform, Vault, and Packer, is that they all have the same language, Hashicorp Configuration Language (HCL). This means you can easily pick up any of these tools by learning HCL, which is similar to JSON. This approach can be useful when choosing tools to learn or use for a project.

Format is HCL language feature: https://github.com/hashicorp/hcl/blob/main/hclwrite/public.go

Hi, from what I read from HCL Github "HCL is a syntax and API specifically designed for building structured configuration formats.".

When the desired state is relatively simple to define and the mechanism to reach that state is not that important, writing up a declaration of what is needed and letting something/someone else deal with it is the most logical abstraction. This would be like drafting up the architectural draft for your new restaurant and paying a contracting company to actually build it, or writing HTML and letting a web browser render it, or writing a Terraform HCL file and letting the Terraform CLI tool apply it. This is called declarative programming in the software world, and has many advantages (and a few disadvantages!) for cloud infrastructure management.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

If you do kubectl explain deployment than (surprise!) you'll get a description for extensions/v1beta1. Because kubectl explain works the same way, just like kubectl get:

Through the Fast Forward program, we give free services and support to open source projects and the nonprofits that support them. We support many of the world’s top programming languages (like Python, Rust, Ruby, and the wonderful Scratch), foundational technologies (cURL, the Linux kernel, Kubernetes, OpenStreetMap), and projects that make the internet better and more fun for everyone (Inkscape, Mastodon, Electronic Frontier Foundation, Terms of Service; Didn’t Read).

In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.