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Hello and welcome to this lesson on designing a Kubernetes cluster. In this guide, we will explore the key considerations and best practices for setting up a Kubernetes environment tailored to your specific needs. Before diving in, ask yourself several important questions to determine the requirements and scope of your cluster:
  • What is the primary purpose of the cluster?
    Is it meant for learning, development, testing, or hosting production-grade applications?
  • How mature is your organization’s cloud adoption?
    Do you lean towards a cloud-managed platform or prefer a self-hosted solution?
  • What types of workloads will it support?
    Will you be running a handful of applications or many?
    Are these applications web-based, big data, analytics-oriented, or something else?
  • What network traffic patterns do you anticipate?
    Will the applications see continuous heavy traffic or sporadic bursts?
Your answers will directly influence the design of your Kubernetes cluster.

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Cluster Design Considerations

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Learning Environments

For clusters intended for learning purposes, a simple setup is usually sufficient. You might opt for tools like Minikube or a single-node cluster deployed with kubeadm on local VMs or via cloud services such as GCP or AWS. This setup was demonstrated in our Kubernetes for the Absolute Beginners - Hands-on Tutorial.

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Development & Testing

For development and testing environments, a multi-node cluster with one master and several worker nodes is ideal. Tools like kubeadm perform well in this configuration. Alternatively, managed cloud environments like Google Container Engine (GKE), AWS, or Azure Kubernetes Service (AKS) allow you to quickly provision a multi-node cluster.
The image outlines purposes for using Kubernetes, including education, development, testing, and hosting production applications, with setups on Minikube, GCP, AWS, or AKS.

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Production-Grade Clusters

For production environments, high availability is critical. Deploy a multi-node cluster with multiple master nodes and dedicate them solely to control plane components like the API server, controller manager, scheduler, and ETCD. With kubeadm or managed solutions on GCP, AWS (with COPS), and other platforms, a production cluster can scale impressively—up to 5,000 nodes, 150,000 pods, 300,000 containers, and supporting up to 100 pods per node.
High availability is paramount for production-grade clusters. Ensure that multiple master nodes and strict resource configurations are implemented to handle large-scale deployments.
The image outlines hosting production applications with specifications for high availability clusters on GCP and AWS, detailing node ranges, vCPU, and memory requirements.
For detailed instance sizing and resource guidelines, refer to the official Kubernetes documentation. These baseline recommendations apply whether you deploy on-premises, in virtualized environments like VirtualBox using kubeadm, or in the cloud.

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Storage Options

When selecting storage solutions, align node and disk configurations with workload demands:
  • Use SSD-backed storage for high-performance applications.
  • Consider network-based storage options for scenarios requiring multiple concurrent accesses.
  • Opt for persistent storage volumes when sharing data across multiple pods is necessary.
The image outlines storage features, including SSD-backed high performance, network-based connections, shared volumes, node labeling, and node selectors for application assignment.

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Node and Control Plane Considerations

Nodes in a Kubernetes cluster can be physical or virtual. In this lesson, we focus on a VirtualBox setup with three nodes: one master and two worker nodes. The master node hosts critical control plane components (such as the Kube API Server and ETCD), while the worker nodes run application workloads. Although Kubernetes masters can in some cases run workloads, it is best practice in production environments to reserve them solely for managing the cluster. Tools like kubeadm automatically taint master nodes to prevent workload scheduling. Ensure that all nodes run on 64-bit Linux operating systems. In larger clusters, ETCD can be deployed on dedicated nodes for enhanced high availability.
The image illustrates a Kubernetes architecture with two master nodes, each containing an API server, controller manager, scheduler, and ETCD component.

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Conclusion

Designing a Kubernetes cluster requires careful planning around your intended use case—whether that’s for learning, development, testing, or production environments. It’s important to align your node configurations, control plane setup, and storage solutions with the needs of your applications. For more details and best practices, consult the official Kubernetes Documentation and other trusted resources. In upcoming lessons, we will delve deeper into these topics and walk through provisioning a cluster from scratch. Thank you for following along, and I’ll see you in the next lesson.

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