kubemqctl VS helm

Our message queue is probably redismq, rabbitmq or kubemq, for which we also easily find helm charts.

In terms of resource usage, KubeMQ outshines Kafka with its minimal footprint. The KubeMQ docker container takes up only 30MB of space. Such a small footprint contributes to a fault-tolerant setup and streamlined deployments. Unlike Kafka, adding KubeMQ to a small development Kubernetes environment in a local workstation is straightforward. But at the same time, KubeMQ is scalable enough to be deployed in a hybrid environment running on hundreds of on-premise and cloud-hosted nodes. At the core of this deployment ease is kubemqctl, the command line interface tool for KubeMQ, analogous to Kubernetes’ kubectl.

New-Item -ItemType Directory 'C:\Program Files\kubemqctl' Invoke-WebRequest https://github.com/kubemq-io/kubemqctl/releases/download/latest/kubemqctl.exe -OutFile 'C:\Program Files\kubemqctl\kubemqctl.exe' \$env:Path += ';C:\Program Files\kubemqctl'

helm

Explanation here: https://github.com/helm/helm/issues/12681#issuecomment-19593...

Looks like it's a bug in Helm, but actually isn't Helm's fault, the issue was introduced by Fedora Linux.

Helm (Get from here https://helm.sh/)

It’s also well understood that having a k8s cluster is not enough to make developers able to host their services - you need a devops team to work with them, using tools like delivery pipelines, Helm, kustomize, infra as code, service mesh, ingress, secrets management, key management - the list goes on! Developer Portals like Backstage, Port and Cortex have started to emerge to help manage some of this complexity.

Kubernetes orchestrates deployments and manages resources through yaml configuration files. While Kubernetes supports a wide array of resources and configurations, our aim in this tutorial is to maintain simplicity. For the sake of clarity and ease of understanding, we will use yaml configurations with hardcoded values. This method simplifies the learning process but isn’t ideal for production environments due to the need for manual updates with each new deployment. Although there are methods to streamline and automate this process, such as using Helm charts or bash scripts, we’ll not delve into those techniques to keep the tutorial manageable and avoid fatigue — you might be quite tired by that point!

Helm is a package manager that automates Kubernetes applications' creation, packaging, configuration, and deployment by combining your configuration files into a single reusable package. This eliminates the requirement to create the mentioned Kubernetes resources by ourselves since they have been implemented within the Helm chart. All we need to do is configure it as needed to match our requirements. From the public Helm chart repository, we can get the charts for common software packages like Consul, Jenkins SonarQube, etc. We can also create our own Helm charts for our custom applications so that we don’t need to repeat ourselves and simplify deployments.

We can search for charts https://helm.sh/ . Charts can be pulled(downloaded) and optionally unpacked(untar).

Generally I felt as if I was diving in the deepest of waters without the correct equipement and that was horrifying. Unfortunately to me, I had to dive even deeper before getting equiped with tools like ArgoCD, and k8slens. I had to start working with... HELM.

Within the architecture of Cyclops, a central component is the Helm engine. Helm is very popular within the Kubernetes community; chances are you have already run into it. The popularity of Helm plays to Cyclops's strength because of its straightforward integration.

helm: brew install helm