I’m currently struggling with upgrading some Postgres DBs on my home-k3s and I’m seriously considering throwing it all away since it’s such a hassle.
So, how do you handle DBs? K8s? Just a regular daemon?
I just run one mariadb container via docker-compose that all my other services use as their database.
version: "2"
services:
mariadb:
image: lscr.io/linuxserver/mariadb:latest
container_name: mariadb
environment:
- TZ=####/####
- PUID=###
- PGID=###
- MYSQL_ROOT_PASSWORD==############
volumes:
- /docker/mariadb:/config
ports:
- 3306:3306
restart: unless-stopped
Off-topic but I don’t really get the appeal in running Kubernetes (or similar technologies) in a homelab. Unless it’s something you want to learn for work of course.
I’m running kubernetes simply because the other options are worse.
Proxmox takes to many resources.
Docker Compose caused countless issues for me when running multiple services (especially network related).
Bare metal is annoying, because you’re forced to keep all the services in lockstep, dependency wise.
I’m using kubernetes at with, the overhead is rather small (with k3s) and mostly it’s working pretty great.
As a bonus, you can just join multiple machines to the cluster and have work spread out over them.
I don’t like Docker as a company, the networking seems unnecessarily obtuse to me, and k3s is a smaller version of k8s, which is here to stay in my opinion (has a bigger learning curve though), and will help me in my career. Those would be my reasons, but if someone doesn’t have a use for k3s I suppose there’s not much of a point, considering everything is still written for docker
That, and you have to take into account each person’s available hardware and resources.
I have an under powered 10 year old desktop, a resonably specd 5 year old laptop with a busted screen, and 8 Raspberry Pi’s (3s and 4s). And can’t currently afford better hardware.Sometimes clustering those Pi’s makes sense.
You can use whatever you have to hand.
That’s a great point I hadn’t considered tbh! And that learning new technologies even if there is no “purpose” to it can be… fun! :)
Are we talking database schema migrations or migrating a database between Postgres instances?
If it’s the former, the pattern is usually to run them in init containers or Jobs but I have been wanting to try out SchemaHero for a while which is a tool to orchestrate it and looks pretty neat.
ETA: Thought I was replying to your below comment but Memmy deleted it the first time for some reason, my bad.
It’s about PostgreSQL upgrade.
The “pattern” there is to either dump and reinsert the entire DB or upgrade by having two installations (old and new version), which doesn’t exactly work well in k8s. It’s possible, but seems hacky
I can’t think of any situation other than maybe wanting to get better indexing or changing the storage engine that I would need to re-create and re-insert that way so I’m not sure if you have a constraint that necessitates that or not but now I’m curious and I am always curious to find new or better methods so why do you do it that way?
At home to upgrade Postgres I would just make a temporary copy the data directory as a backup and then just change the version of the container and if it’s needed run pg_upgrade as jobs in kubernetes.
In a work environment there is more likely to be clustering involved so the upgrade path depends on that but it’s similar but there really isn’t a need to re-create the data, the new version starts with the same PVCs using whatever rollout strategy applies. Major version upgrades can sometimes require extra steps but the engine is almost always backwards compatible at least several versions.
I’ve always used this docker image to do pg upgrades. It runs pg_upgrade to recreate the system tables and copy the user tables (which normally don’t have any storage changes). It does require that the database isn’t running during the upgrade so you’re going to have a bit of downtime. Make sure you redo any changes to any configuration files, especially pg_hba.conf
I’m a big fan of the zalando postgres operator. A lot of the critical features you’d want in production databases are handled and very nicely abstracted.
Did they get it working with multi arch setups? I have a few pi’s in my cluster and last time I looked at using that it wasn’t ready for arm64
I’m not sure, actually. My personal cluster is all x86 so I’m not usually that aware of the multiarch stuff. 😬
I have a single database server because I can’t afford two servers with high storage. The servers that need access to it connect over wireguard VPN. This is slow as f**k don’t do that.
