azure-k3s-cluster (WIP)

A (WIP) dynamically resizable k3s cluster for Azure, based on my azure-docker-swarm-cluster project.

What

This is an Azure Resource Manager template that automatically deploys a k3s cluster atop Ubuntu 20.04. This cluster has a single master VM and a VM scaleset for workers/agents, plus required network infrastructure.

The template defaults to deploying B-Series VMs (B1ls) with the smallest possible managed disk size (S4, 32GB). It also deploys (and mounts) an Azure File Share on all machines with (very) permissive access at /srv, which makes it quite easy to run stateful services.

The key aspect of this template is that you can add and remove agents at will simply by resizing the VM scaleset, which is very handy when running the node pool as spot instances - the cluster comes with a few (very simple) helper scripts that allow nodes to join and leave the cluster as they are created/destroyed, and the k3s scheduler will redeploy pods as needed.

Why

This was originally built as a Docker Swarm template, and even though Azure has a perfectly serviceable Kubernetes managed service, I enjoy the challenge of building my own stuff and fine-tuning it.

k3s is a breath of fresh air, and an opportunity to play around with a simpler, slimmer version of Kubernetes--and break it to see what happens.

Also, a lot of the ARM templating involved (for metrics, managed identities, etc.) lacked comprehensive samples when I started the project, so this was also a way for me to provide a fully working example that other people can learn from.

Roadmap

`Makefile` commands

make keys - generates an SSH key for provisioning
make deploy-storage - deploys shared storage
make params - generates ARM template parameters
make deploy-compute - deploys cluster resources and pre-provisions Docker on all machines
make view-deployment - view deployment progress
make list-agents - lists all agent VMs
make scale-agents-<number> - scales the agent VM scale set to <number> instances, i.e., make scale-10 will resize it (up or down) to 10 VMs
make stop-agents - stops all agents
make start-agents - starts all agents
make reimage-agents-parallel - nukes and paves all agents
make reimage-agents-serial - reimages all agents in sequence
make chaos-monkey - restarts all agents in random order
make proxy - opens an SSH session to master0 and sets up TCP forwarding to localhost
make tail-helper - opens an SSH session to master0 and tails the k3s-helper log
make list-endpoints - list DNS aliases
make destroy-cluster - destroys the entire cluster

Recommended Sequence

az login
make keys
make deploy-storage
make params
make deploy-compute
make view-deployment
# Go to the Azure portal and check the deployment progress

# Clean up after we're done working
make destroy-cluster

Requirements

Python
The Azure CLI (pip install -U -r requirements.txt will install it)
GNU make (you can just read through the Makefile and type the commands yourself)

Internals

master0 runs a very simple HTTP server (only accessible inside the cluster) that provides tokens for new VMs to join the cluster and an endpoint for them to signal that they're leaving. That server also cleans up the node table once agents are gone.

Upon provisioning, all agents try to obtain a token and join the cluster. Upon rebooting, they signal they're leaving the cluster and re-join it again.

This is done in the simplest possible way, by using cloud-init to bootstrap a few helper scripts that are invoked upon shutdown and (re)boot. Check the YAML files for details.

Provisioning Flow

To avoid using VM extensions (which are nice, but opaque to most people used to using cloud-init) and to ensure each fresh deployment runs the latest Docker version, VMs are provisioned using customData in their respective ARM templates.

cloud-init files and SSH keys are then packed into the JSON parameters file and submitted as a single provisioning transaction, and upon first boot Ubuntu takes the cloud-init file and provisions the machine accordingly.

See azure-docker-swarm-cluster for more details.

Disclaimers

Keep in mind that this was written for conciseness and ease of experimentation -- look to AKS for a production service.

lstiebel/azure-k3s-cluster