democratic-csi VS helm

The interface software regarding is not a worry of mine, as democratic-csi does the storage management for me, thus the compatibility it is not limited to the application using the storage per se, as this is handled by Kubernete's CSI drivers, being application-agnostic when utilizing the storage provided.My main worry is not latency, but rather RAM

Check out https://github.com/democratic-csi/democratic-csi

in k3s, i'm using https://github.com/democratic-csi/democratic-csi (was using iSCSI before, now everything is NFS)

Have you checked this out? GitHub democratic-csi I have yet to test this in my @home K8s cluster. It supports iSCSI volume management for FreeNAS, Synology and other CSI backends.

If you ever migrate from docker up to kubernetes, then take a look into democratic-csi. For a modest homelab, it is a valid option (said from somebody who manages a small homelab and plays around with kubernetes).

For persistent storage have you looked into using TrueNAS with a CSI provider with your container orchestrator? I'm assuming your orchestrator is Nomad or Kubernetes.

Yes, Rancher does support FreeNAS, TrueNAS. and Scale using the storage class provider https://github.com/democratic-csi/democratic-csi It's important to remember Rancher is the server. And in this case, you need to ask the question does the k8s cluster that Rancher is managing support this storage class provider? If you are using RKE the answer is Yes.

I use https://github.com/democratic-csi/democratic-csi to mount nfs/iscsi shares (and manage the shares) from my SAN (truenas box).

iSCSI with democratic-csi (https://github.com/democratic-csi/democratic-csi) works great for me on truenas. I use iSCSI for any PVs that don't need to be shared and NFS for anything I'd like to share between different pods (like movies, music).

Storage - democratic-csi looked the most promising, it has worked well so far. I am using zfs-generic-iscsi against an Ubuntu 20.04 storage server. I also tried zfs-generic-nfs and it worked successfully with the caveat of having to deal with NFS file permissions.

Applying Kubernetes manifests individually is problematic because files can get overlooked. Packaging your applications as Helm charts lets you version your manifests and easily repeat deployments into different environments. Helm tracks the state of each deployment as a "release" in your cluster.

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.