k3s Health Dashboard

A Streamlit dashboard for monitoring a k3s (or any Kubernetes) cluster — nodes, pods, deployments, and metrics in one place.

Architecture

app/
  main.py          FastAPI backend (REST API)
  cache.py         Streamlit cache wrappers (TTL-based)
  k8s_client.py    Kubernetes API client & data helpers
  config.py        Config loaded from environment variables
  routers/         FastAPI route handlers
app.py             Streamlit frontend entry point
k3s-dashboard.yaml Kubernetes deployment manifest

Prerequisites

• Python 3.10+
• A running Kubernetes cluster with a valid kubeconfig at ~/.kube/config
• (Optional) metrics-server installed on the cluster for CPU/memory usage

Local setup

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Copy the example environment file and edit as needed:

cp .env .env.local

Key environment variables:

Variable	Default	Description
KUBECONFIG	~/.kube/config	Path to kubeconfig
IN_CLUSTER	(unset)	Set to true when running as a pod
CACHE_TTL	15	Seconds between Kubernetes API calls
METRICS_ENABLED	true	Set false if metrics-server absent

Running locally

Streamlit dashboard:

streamlit run app.py

Opens at http://localhost:8501.

FastAPI backend only:

uvicorn app.main:app --reload

API docs at http://localhost:8000/docs.

Running tests

pytest

Deploying to Kubernetes

1. Build and push the Docker image:

docker build -t your-registry/k3s-dashboard:latest .
docker push your-registry/k3s-dashboard:latest

2. Update the image name in k3s-dashboard.yaml, then apply:

kubectl apply -f k3s-dashboard.yaml

The manifest creates a ServiceAccount with read-only ClusterRole access to nodes, pods, namespaces, and metrics.