longhorn VS kubernetes

I'm looking forward to Longhorn[1] taking advantage of this technology.

[1]: https://github.com/longhorn/longhorn

I've been using a 3 nuc (actually Ryzen devices) k3s on SuSE MicroOS https://microos.opensuse.org/ for my homelab for a while, and I really like it. They made some really nice decisions on which parts of k8s to trim down and which Networking / LB / Ingress to use.

The option to use sqlite in place of etcd on an even lighter single node setup makes it super interesting for even lighter weight homelab container environment setups.

I even use it with Longhorn https://longhorn.io/ for shared block storage on the mini cluster.

If anyone uses it with MicroOS, just make sure you switch to kured https://kured.dev/ for the transactional-updates reboot method.

I'd love to compare it against Talos https://www.talos.dev/ but their lack of support for a persistent storage partition (only separate storage device) really hurts most small home / office usage I'd want to try.

Hi,i was wondering the same. Found more information here in this document: https://github.com/longhorn/longhorn/blob/v1.5.x/enhancements/20230103-recurring-snapshot-cleanup.md

Like most people on r/homelab, it started out with Plex. Rough timeline/services below:

0. Got a Synology DS413 with 4x WD Red 3TB drives. Use Playstation Media Server to stream videos from it. Eventually find some Busybox stuff to add various functionality to the NAS, but it had a habit of undoing them periodically, which was frustrating. I also experienced my first and (knock on wood) only drive failure during this time, which concluded without fanfare once the faulty drive was replaced, and the array repaired itself.

1. While teaching self Python as an Electrical Distribution Engineer at a utility, I befriended the IT head, who gave me an ancient (I think Nehalem? Quad-core Xeon) Dell T310. Promptly got more drives, totaling 7, and tried various OS / NAS platforms. I had OpenMediaVault for a while, but got tired of the UI fighting me when I knew how to do things in shell, so I switched to Debian (which it's based on anyway). Moved to MergerFS [0] + SnapRAID [1] for storage management, and Plex for media. I was also tinkering with various Linux stuff on it constantly.

1.1 Got tired of my tinkering breaking things and requiring troubleshooting/fixing (in retrospect, this provided excellent learning), so I installed Proxmox, reinstalled Debian, and made a golden image with everything set up as desired so I could easily revert.

1.2 A friend told me about Docker. I promptly moved Plex over to it, and probably around this time also got the *Arr Stack [2] going.

2. Got a Supermicro X9DRi-LN4F+ in a 2U chassis w/ 12x 3.5" bays. Got faster/bigger CPUs (E5-2680v2), more RAM, more drives, etc. Shifted container management to Docker Compose. Modded the BIOS to allow it to boot from a NVMe drive on a PCIe adapter.

2.1 Shifted to ZFS on Debian. Other than DKMS occasionally losing its mind during kernel upgrades, this worked well.

2.2 Forked [3] some [4] Packer/Ansible projects to suit my needs, made a VM for everything. NAS, Dev, Webserver, Docker host, etc. Other than outgrowing (IMO) MergerFS/SnapRAID, honestly at this point I could have easily stopped, and could to this day revert back to this setup. It was dead reliable and worked extremely well. IIRC I was also playing with Terraform at this time.

2.3 Successfully broke into tech (Associate SRE) as a mid-career shift, due largely (according to the hiring manager) to what I had done with my homelab. Hooray for hobbies paying off.

3. Got a single Dell R620. I think the idea was to install either pfSense or VyOS on it, but that never came to fruition. Networking was from a Unifi USG (their tiny router + firewall + switch) and 8-port switch, with some AC Pro APs.

4. Got two more R620s. Kubernetes all the things. Each one runs Proxmox in a 3-node cluster with two VMs - a control plane, and worker.

4.0.1 Perhaps worth noting here that I thoroughly tested my migration plan via spinning up some VMs in, IIRC, Digital Ocean that mimicked my home setup. I successfully ran it twice, which was good enough for me.

4.1 Played with Ceph via Rook, but a. disliked (and still to this day) running storage for everything out of K8s b. kept getting clock skew between nodes. Someone on Reddit mentioned it was my low-power C-state settings, but since that was saving me something like ~50 watts/node, I didn't want to deal with the higher power/heat. I landed on Longhorn [5] for cluster storage (i.e. anything that wasn't being handled by the ZFS pool), which was fine, but slow. SATA SSDs (used Intel enterprise drives with PLP, if you're wondering) over GBe aren't super fast, but they should be able to exceed 30 MBps.

4.1.1 Again, worth noting that I spent literally a week poring over every bit of Ceph documentation I could find, from the Red Hat stuff to random Wikis and blog posts. It's not something you just jump into, IMO, and most of the horror stories I read boiled down to "you didn't follow the recommended practices."

5. Got a newer Supermicro, an X11SSH-F, thinking that it would save power consumption over the older dual-socket I had for the NAS. It turned out to not make a big difference. For some reason I don't recall, I had a second X9DRi-LN4F+ mobo, so I sold the other one with the faster CPUs, bought some cheaper CPUs for the other one, and bought more drives for it. It's now a backup target that boots up daily to ingest ZFS snapshots. I have 100% on-site backups for everything. Important things (i.e. anything that I can't get from a torrent) are also off-site.

6. Got some Samsung PM863 NVMe SSDs mounted on PCIe adapters for the Dells, and set up Ceph, but this time handled by Proxmox. It's dead easy, and for whatever reason isn't troubled by the same clock skew issues as I had previously. Still in the process of shifting cluster storage from Longhorn, but I have been successfully using Ceph block storage as fast (1 GBe, anyway - a 10G switch is on the horizon) storage for databases.

So specifically, you asked what I do with the hardware. What I do, as far as my family is concerned, is block ads and serve media. On a more useful level, I try things out related to my job, most recently database-related (I moved from SRE to DBRE a year ago). I have MySQL and Postgres running, and am constantly playing with them. Can you actually do a live buffer pool resize in MySQL? (yes) Is XFS actually faster than ext4 for large DROP TABLE operations? (yes, but not by much) Is it faster to shut down a MySQL server and roll back to a previous ZFS snapshot than to rollback a big transaction? (often yes, although obviously a full shutdown has its own problems) Does Postgres suffer from the same write performance issue as MySQL with random PKs like UUIDv4, despite not clustering by default? (yes, but not to the same extent - still enough to matter, and you should use UUIDv7 if you absolutely need them)

I legitimately love this stuff. I could quite easily make do without a fancy enclosed rack and multiple servers, but I like them, so I have them. The fact that it tends to help my professional growth out at the same time is a bonus.

[0]: https://github.com/trapexit/mergerfs

[1]: https://www.snapraid.it

[2]: https://wiki.servarr.com

[3]: https://github.com/stephanGarland/packer-proxmox-templates

[4]: https://github.com/stephanGarland/ansible-initial-server

[5]: https://longhorn.io

Been running k3s for personal projects etc for some time now on a cluster of raspberry pies. Why pies? Were cheap at the time and wanted to play with arm. I don’t think I would suggest them right now. Nucs will be much better value for money.

Some notes:

Using helm and helmfile https://github.com/helmfile/helmfile for deployments. Seems to work pretty nicely and is pretty flexible.

As I’m using a consumer internet provider ingress is done through cloudflare tunnels https://github.com/cloudflare/cloudflare-ingress-controller in order to not have to deal with ip changes and not have to expose ports.

Persistent volumes were my main issue when previously attempting this, and what changed everything for me was longhorn. https://longhorn.io Make sure to backup your volumes.

Really hyped for https://docs.computeblade.com/ xD

But, if you are really wanting high availability, then roll a kubernetes cluster, and run clustered storage such as longhorn.io, or rook/ceph.

I was mainly referring to this one https://github.com/longhorn/longhorn/discussions/5931 but yeah I peeked into that one too. I'm not at my computer at the moment, how do I provide a support bundle?

If it's RWX, Longhorn 1.5.0 will support that. https://github.com/longhorn/longhorn/issues/5143

You have now finally deployed an enterprise-grade Kubernetes cluster with k3s. You can now deploy some work on this cluster. Some components to take note of are for ingress, you already have Traefik installed, longhorn will handle storage and Containerd as the container runtime engine.

I’m joking of course. I’m not really sure about what a faded keyboard says about its owner. What I do know for sure is how important kubectl is to anybody who wants to be a proficient Kubernetes administrator.

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

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.