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Rainy21
87 lines
2.1 KiB
Markdown
87 lines
2.1 KiB
Markdown
# k3s Health Dashboard
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A Streamlit dashboard for monitoring a k3s (or any Kubernetes) cluster — nodes, pods, deployments, and metrics in one place.
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## Architecture
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```
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app/
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main.py FastAPI backend (REST API)
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cache.py Streamlit cache wrappers (TTL-based)
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k8s_client.py Kubernetes API client & data helpers
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config.py Config loaded from environment variables
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routers/ FastAPI route handlers
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app.py Streamlit frontend entry point
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k3s-dashboard.yaml Kubernetes deployment manifest
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```
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## Prerequisites
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- Python 3.10+
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- A running Kubernetes cluster with a valid kubeconfig at `~/.kube/config`
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- (Optional) `metrics-server` installed on the cluster for CPU/memory usage
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## Local setup
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```bash
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python -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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```
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Copy the example environment file and edit as needed:
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```bash
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cp .env .env.local
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```
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Key environment variables:
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| Variable | Default | Description |
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|-------------------|-----------------------|--------------------------------------|
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| `KUBECONFIG` | `~/.kube/config` | Path to kubeconfig |
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| `IN_CLUSTER` | (unset) | Set to `true` when running as a pod |
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| `CACHE_TTL` | `15` | Seconds between Kubernetes API calls |
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| `METRICS_ENABLED` | `true` | Set `false` if metrics-server absent |
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## Running locally
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**Streamlit dashboard:**
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```bash
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streamlit run app.py
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```
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Opens at `http://localhost:8501`.
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**FastAPI backend only:**
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```bash
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uvicorn app.main:app --reload
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```
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API docs at `http://localhost:8000/docs`.
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## Running tests
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```bash
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pytest
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```
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## Deploying to Kubernetes
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1. Build and push the Docker image:
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```bash
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docker build -t your-registry/k3s-dashboard:latest .
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docker push your-registry/k3s-dashboard:latest
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```
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2. Update the image name in `k3s-dashboard.yaml`, then apply:
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```bash
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kubectl apply -f k3s-dashboard.yaml
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```
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The manifest creates a `ServiceAccount` with read-only `ClusterRole` access to nodes, pods, namespaces, and metrics.
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