K3s Raspberry Pi 4 Cluster Architecture

The integration of K3s on Raspberry Pi 4 represents a pivotal shift in the accessibility of container orchestration, transforming a series of credit-card-sized single-board computers into a fully functional, production-grade Kubernetes cluster. This synergy allows for the creation of a home lab environment that provides the same architectural capabilities as enterprise cloud environments but at a fraction of the cost. Historically, Raspberry Pi devices were viewed primarily as educational tools or toys for hobbyists, but they have evolved into legitimate computing platforms capable of sustaining complex workloads. When paired with K3s—a lightweight Kubernetes distribution engineered by Rancher (now a part of SUSE)—these devices can support home automation, edge computing, and experimental software development.

K3s is specifically designed to operate in resource-constrained environments. It achieves this by stripping away legacy cloud-provider code and features that are rarely utilized outside of massive cloud clusters, while retaining all the essential components required for a fully operational Kubernetes experience. This makes it uniquely suited for devices with limited RAM and CPU, allowing users to deploy real-world applications, databases, and dashboards on hardware that can fit in a small shed, on a desk, or at the extreme edge of a network. By leveraging K3s on Raspberry Pi 4, users can move from theoretical learning to practical application, building real clusters with real tools while maintaining a low-cost budget.

The Architectural Philosophy of K3s

K3s is not merely a "small" version of Kubernetes; it is a highly optimized redistribution. The primary objective of the K3s project is to provide a lightweight footprint that does not compromise the API compatibility of Kubernetes. For the end user, this means that any application designed to run on a standard Kubernetes cluster (K8s) will function identically on K3s.

The impact of this design is most visible in the resource consumption. K3s is engineered to run efficiently on devices with as little as 1GB to 4GB of RAM. This allows the Raspberry Pi 4, which is available in various RAM configurations, to act as a stable host for containerized workloads. By reducing the overhead of the control plane, K3s frees up more system memory for the actual pods and services the user intends to deploy. This makes the platform portable and scalable, enabling a seamless transition from a single-node setup for learning to a multi-node cluster for edge workloads.

Hardware Requirements and Component Selection

While K3s is lightweight, the physical limitations of the Raspberry Pi 4 must be addressed to ensure cluster stability. The hardware selection directly impacts the reliability of the control plane and the performance of the worker nodes.

Single Node Minimum Specifications

For those beginning with a single-node setup, the following minimum requirements must be met to ensure the system can boot and run basic pods.

The impact of choosing the recommended over the minimum is significant. For example, using a Pi 4 with 4GB or 8GB of RAM provides the necessary "breathing room" for workloads, preventing the node from crashing when deploying memory-intensive applications like databases.

Three-Node Cluster Configuration

To experience the true power of Kubernetes—including high availability and workload distribution—a multi-node cluster is required. A standard recommended setup involves one Master node (Control Plane) and two Worker nodes.

• Master Node: Raspberry Pi 4 with 4GB RAM. This node manages the cluster state and schedules pods.
• Worker Node 1: Raspberry Pi 4 with 4GB RAM. This node executes the containerized workloads.
• Worker Node 2: Raspberry Pi 4 with 4GB RAM. This node provides redundancy and additional compute capacity.

The networking for this cluster should be centralized through a home router or switch. The Master node communicates with Worker nodes via the K3s Agent, creating a cohesive unit where the Master directs the actions of the workers.

Comprehensive Shopping List for Cluster Deployment

To avoid the common pitfalls of underpowered hardware, the following components are essential for a stable three-node cluster:

• Raspberry Pi 4 (4GB recommended) x3: The 4GB model is the baseline for stability. If the user intends to run heavy applications or databases, the 8GB model is advised.
• microSD cards (32GB A2 rated) x3: The A2 rating is critical as it ensures faster random I/O, which reduces the latency of the operating system and K3s. For production environments, booting from an SSD via USB 3.0 is the superior choice.
• USB-C power supplies (5V 3A) x3: Underpowered supplies are a leading cause of random crashes in Raspberry Pi clusters. Only official or certified PSUs should be used.
• Ethernet cables (Cat6) x3: Gigabit networking is essential for cluster traffic. WiFi is discouraged due to latency and instability.

Installation of the Control Plane

Rancher simplifies the deployment process by providing a single installation script that automates the complex process of setting up a Kubernetes environment.

The Installation Process

To begin, the user must SSH into the designated master node. The following command is executed to install K3s as the server:

bash curl -sfL https://get.k3s.io | sh -

This single command triggers a sequence of critical operations:
- It downloads the necessary K3s binary.
- It installs K3s as a systemd service to ensure it starts automatically upon boot.
- It initializes the control plane components.
- It generates the required TLS certificates for secure communication.
- It creates the kubeconfig file used for cluster management.

On a Raspberry Pi 4, this process typically takes between 1 and 2 minutes.

Post-Installation Verification

Once the script completes, the user must verify that the service is running correctly.

To check the status of the systemd service:
bash sudo systemctl status k3s
The output should indicate that the service is active (running).

To verify that the node is registered and ready within the cluster:
bash sudo k3s kubectl get nodes
The output should display the node name, a status of Ready, and the role as control-plane,master.

Node Token Retrieval

For worker nodes to join the cluster, they must authenticate with the master. This is done using a unique node token. The token can be retrieved using the following command:

bash sudo cat /var/lib/rancher/k3s/server/node-token
This token must be saved securely, as it is the primary security mechanism for adding new members to the cluster.

K3s Configuration and Customization

K3s provides a variety of configuration flags that can be passed during installation to optimize the cluster for the Raspberry Pi's hardware constraints.

Custom Installation Options

Users can use environment variables to modify the installation. For example, to disable Traefik if a different ingress controller (like Nginx) is preferred:

bash curl -sfL https://get.k3s.io | INSTALL_K3S_EXEC="--disable traefik" sh -

To install a specific version of K3s for compatibility reasons:

bash curl -sfL https://get.k3s.io | INSTALL_K3S_VERSION="v1.28.5+k3s1" sh -

To bind K3s to a specific network interface and advertise a specific address:

bash curl -sfL https://get.k3s.io | INSTALL_K3S_EXEC="--bind-address 192.168.1.100 --advertise-address 192.168.1.100" sh -

To use an external database instead of the embedded etcd, which is beneficial for high-availability (HA) setups using PostgreSQL or MySQL:

bash curl -sfL https://get.k3s.io | INSTALL_K3S_EXEC="--datastore-endpoint='postgres://user:pass@host:5432/k3s'" sh -

Resource Optimization Flags

For users operating on the most constrained Raspberry Pi hardware, the following flags can be used to reduce memory usage:

• --disable servicelb: Disables the built-in load balancer.
• --disable traefik: Disables the built-in ingress.
• --disable local-storage: Disables the local path provisioner.
• --kubelet-arg="max-pods=50": Limits the number of pods per node to prevent memory exhaustion.

Expanding the Cluster with Worker Nodes

Once the control plane is stable, the cluster can be scaled by adding worker nodes. These nodes provide the actual compute resources where the application pods will run.

Joining Worker Nodes to the Master

The user must SSH into the worker node and set the necessary environment variables. The following variables are required:

bash K3S_MASTER_IP="192.168.1.100" K3S_TOKEN="K10xxxxxxxxxxxxxxxxxxxxxxxxxxxxx::server:xxxxxxxxxxxxx"

After defining these variables, the user installs K3s as an agent (worker) rather than a server.

Best Practices for Cluster Stability

Running a Kubernetes cluster on single-board computers requires a disciplined approach to resource management and operational hygiene.

Hardware and Infrastructure Best Practices

To ensure the cluster does not suffer from random crashes or performance degradation, the following rules should be observed:

• Use Raspberry Pi 4 with at least 4GB RAM for control plane nodes to avoid memory pressure.
• Boot from a USB SSD instead of a microSD card for production workloads to improve I/O speed and longevity.
• Use quality power supplies rated at 5V 3A minimum to prevent voltage drops.
• Connect via Gigabit Ethernet rather than WiFi to eliminate network jitter and instability.
• Implement active cooling (fans) for sustained workloads to prevent thermal throttling.

Installation and Configuration Best Practices

The software configuration must be optimized before and during the K3s installation:

• Enable cgroups for memory before installing K3s to allow the kernel to manage container resources.
• Disable swap permanently on all nodes to ensure Kubernetes can accurately manage memory.
• Use static IP addresses for all cluster nodes to prevent the control plane from losing track of workers.
• Document the node token securely to avoid the need for re-installation.
• Start with a single master node before attempting to add worker nodes.

Resource and Workload Management

Kubernetes can easily consume all available RAM on a Raspberry Pi if not managed.

• Always set resource requests and limits on deployments to prevent a single pod from consuming all node resources.
• Use alpine-based container images when possible to keep the image size small and memory footprint low.
• Disable unused K3s components (such as Traefik or ServiceLB) to save overhead.
• Limit the maximum number of pods per node based on available memory.
• Configure eviction thresholds to prevent node instability when memory runs low.

Operational and Networking Best Practices

Ongoing maintenance is required to keep the cluster healthy.

• Back up the /var/lib/rancher/k3s/server/ directory regularly to allow for rapid recovery of the control plane.
• Monitor node resources using lightweight tools like htop or k9s.
• Use node affinity to spread workloads evenly across the cluster.
• Keep K3s updated by re-running the installation script:
  bash curl -sfL https://get.k3s.io | sh -
• Label nodes to facilitate easier workload scheduling.

For networking, the following strategies are recommended:

• Use Flannel (the K3s default) for simplicity, or Calico if complex network policies are required.
• Consider MetalLB for bare-metal load balancing.
• Enable BBR TCP congestion control to improve network throughput.
• Assign dedicated VLANs for cluster traffic to isolate it from other home network traffic.

Monitoring and Observability

A cluster without monitoring is a black box. For Raspberry Pi clusters, it is essential to use lightweight monitoring that does not consume the resources it is meant to monitor.

One option for visualization, alerting, and incident management is OneUptime. This can be deployed using a Helm chart:

bash helm repo add oneuptime https://helm.oneuptime.com helm install oneuptime-agent oneuptime/oneuptime-agent \ --set apiKey=YOUR_API_KEY \ --set endpoint=https://oneuptime.com

This allows the user to send cluster metrics to a centralized dashboard for better visibility into the health of the Raspberry Pi nodes.

Conclusion

The implementation of K3s on Raspberry Pi 4 is an exercise in balancing the power of enterprise-grade orchestration with the physical constraints of embedded hardware. The success of such a cluster depends not only on the installation of the software but on the meticulous selection of hardware and the application of strict operational best practices. By utilizing the Raspberry Pi 4's 4GB or 8GB RAM configurations and ensuring stable power and networking via Gigabit Ethernet and official PSUs, users can create a robust environment for edge computing.

The architectural impact of K3s is the democratization of Kubernetes. By stripping away the unnecessary bloat of cloud-provider integrations, Rancher has enabled a scenario where a three-node cluster can be managed as a cohesive unit, providing a low-cost entry point for learning and deployment. The ability to customize the installation—disabling Traefik, limiting pod counts, and utilizing external datastores—ensures that the cluster can be tuned to the specific needs of the user. Ultimately, the combination of K3s and Raspberry Pi 4 proves that high-level orchestration does not require massive server racks, but rather a smart application of lightweight software on efficient, scalable hardware.

Sources

1. OneUptime
2. KevsRobots