swarmsible VS k3s

We use Docker Swarm for our deployments, so I will answer the questions based on that.

We have built some tooling around setting up and maintaining the swarm using ansible [0]. We also added some Hetzner flavour to that [1] which allows us to automatically spin up completely new clusters in a really short amount of time.

deploy from source repo:

- We use Azure DevOps pipelines that automate deployments based on environment configs living in an encrypted state in Git repos. We use [2] and [3] to make it easier to organize the deployments using `docker stack deploy` under the hood.

keep software up to date:

- We are currently looking into CVE scanners that export into prometheus to give us an idea of what we should update

load balancing:

- depending on the project, Hetzner LB or Cloudflare

handle scaling:

- manually, but i would love to build some autoscaler for swarm that interacts with our tooling [0] and [1]

automate backups:

- docker swarm cronjobs either via jobs with restart condition and a delay or [4]

maintain security:

- Hetzner LB is front facing. Communication is done via encrypted networks inside Hetzner private cloud networks

- [0] https://github.com/neuroforgede/swarmsible

Pretty much the same as our goto for projects at work: Hetzner + Docker (Swarm) with some Ansible to orchestrate things

We have built some automation around cluster management over at https://github.com/neuroforgede/swarmsible.

I used to do everything in ansible, but Docker Stacks are just so much nicer to use.

In any case automation is king. I don't have to remember stuff if I can just look at some IaC Code :).

Our tooling can be found here https://github.com/neuroforgede/swarmsible . It is not yet documented tbh, but most things are pretty straight forward to use if you have used ansible, docker etc already.

Story of one of the projects I am involved in:

We came from Ansible managed deployments of vanilla docker with nginx as single node ingress with another load balancer on top of that.

Worked fine, but HA for containers that are only allowed to exist once in the stack was one thing that caused us headaches.

Then, we had a workshop for Rancher RKE. Looked promising at the start, but operating it became a headache as we didn't have enough people in the project team to maintain it. Certificates expiring was an issue and the fact that you actually kinda had to baby-sit the cluster was a turn off.

We killed the switch to kubernetes.

In the meantime we were toying around with Docker Swarm for smaller scale deployments and inhouse infrastructure. We didn't find anything to not like and are currently moving into that direction.

How we do things in Swarm:

1. Monitoring using an updated Swarmprom stack (https://github.com/neuroforgede/swarmsible/tree/master/envir...)

For anyone interested in this, check out: https://github.com/neuroforgede/swarmsible/blob/master/environments/test/test-swarm/stacks/02_monitoring/README.md

Opinionated meaning it picks, install, patches your CNI/Ingress/Load Balancer/DNS Server/Metrics Server/Monitoring Setup.

k3s is probably most well known as it ships with bunch of preinstall software: https://github.com/k3s-io/k3s so you can just start throwing yaml files at cluster and handling workloads. It's what I use for my homelab.

Paid things I've heard of include OpenStack and SideroLabs. Haven't used personally by SRE coworkers say good things about them.

For self-hosting I've found https://k3s.io to be really good from the SUSE people. Works on basically any Linux distro and makes self-hosting k8s not miserable.

Yes it’s going to depend on which k8s distribution you’re using. We have work in-progress for k3s to natively support nix-snapshotter: https://github.com/k3s-io/k3s/pull/9319

For other distributions, nix-snapshotter works with official containerd releases so it’s just a matter of toml configuration and a systemd unit for nix-snapshotter.

We run Kubernetes outside of NixOS, but yes the NixOS modules provided by the nix-snapshotter certainly make it simple.

K3S: is a lightweight distribution of Kubernetes that is designed for resource-constrained environments. It is an excellent option for running Kubernetes on a virtual machine or cloud server.

I recently purchased a used Lenovo M900 Think Centre (i7 with 32GB RAM) from eBay to expand my mini-homelab, which was just a single Synology DS218+ plugged into my ISP's router (yuck!). Since I've been spending a big chunk of time at work playing around with Kubernetes, I figured that I'd put my skills to the test and run a k3s node on the new server. While I was familiar with k3s before starting this project, I'd never actually run it before, opting for tools like kind (and minikube before that) to run small test clusters for my local development work.

Kubernetes - k3s distribution

I’ve created a local cluster with K3S and installing Windmill could not be simpler with just one chart to configure, which already has sane defaults to get started. For this demo we will also configure workers to passthrough environment variables to our scripts so that they have access to the Kubernetes API server for later.

You should be familiar with Kubernetes and have set up a Kubernetes cluster. I recommend k3s.