Kubernetes has evolved into the industry standard for orchestrating containerized applications. In this article, we break down the architecture of a Kubernetes cluster using practical examples, and code snippets. Whether you’re a beginner or an experienced engineer, you’ll gain clarity on the roles of control plane (historically “master”) and worker nodes, the nuances of multi-control-plane deployments, and updated OpenShift configurations. Additionally, we’ll explore how CloudCasa can simplify Kubernetes backup, disaster recovery, and workload mobility.
What is a Kubernetes Cluster?
A Kubernetes cluster is a set of machines working together to run containerized applications. At its core, a cluster is divided into two key parts:
- Control Plane (formerly Master Nodes): These nodes manage the overall state of the cluster, handle API requests, and perform scheduling.
- Worker Nodes: These nodes run the actual application workloads (containers), hosting the pods that encapsulate your applications.
The Role of Clusters in Container Orchestration
Kubernetes clusters form the backbone of container orchestration. They allow you to deploy, manage, and scale applications in a consistent and automated way. Containers package applications with their dependencies, and the cluster ensures that they run reliably regardless of where they are deployed.
Nodes vs. Servers in Kubernetes
A common question is: “Are nodes and servers the same thing in Kubernetes?”
While they may seem similar, there is a subtle distinction. In Kubernetes, a node is any machine—physical or virtual—that participates in the cluster. A server generally refers to a machine running services. In Kubernetes, every node is a server configured to run specific roles (either the control plane or application workloads). So, while all nodes are servers, not all servers are configured to be part of a Kubernetes cluster.
Example: Simple Pod Deployment
To illustrate, here’s a basic YAML file for deploying a simple pod:
This YAML file describes a pod named hello-world running an Nginx container. In a Kubernetes cluster, the control plane schedules this pod on one of the worker nodes.
Kubernetes Cluster Architecture: Control Plane and Worker Nodes
Understanding the roles within a Kubernetes cluster is crucial for managing and scaling your applications. Let’s dive into the responsibilities of control plane nodes and worker nodes.
Control Plane Nodes in Kubernetes
Control plane nodes (historically called master nodes) are the brain of the Kubernetes cluster. They perform several key functions:
- Managing Cluster State: They continuously monitor the cluster to ensure the actual state matches the desired state defined in configuration files.
- Handling API Requests: All interactions—such as deployments, scaling operations, and updates—pass through the Kubernetes API server running on the control plane.
- Scheduling Workloads: The scheduler on the control plane assigns pods to appropriate worker nodes based on resource availability, policies, and constraints.
Multi-Control-Plane Setups
A common question is: “Can we have multiple master nodes in Kubernetes?”
The answer is yes. In production environments, a multi-control-plane (or multi-master) configuration is best practice. This setup provides high availability and fault tolerance. If one control plane node fails, the remaining nodes continue to manage the cluster, ensuring that API requests, scheduling, and state management remain uninterrupted.
Clusters with Only Control Plane Nodes
You might wonder: “How is a cluster with only control plane nodes called?”
A cluster running only control plane nodes is usually called a management cluster or control plane cluster. This setup is primarily used for managing other clusters or for monitoring purposes—not for running application workloads. In specialized scenarios or training environments, you might deploy such a cluster to focus solely on the control plane’s behavior.
Example: Simulated Control Plane Health Check
Below is a shell script snippet that simulates checking the health of control plane nodes:
This script lists all nodes labeled as control plane nodes and prints their health conditions.
Worker Nodes in Kubernetes
Worker nodes are where your applications actually run. They host pods and containers that execute your workloads.
Responsibilities of Worker Nodes
- Running Application Workloads: Worker nodes execute the containerized applications as defined in your deployment configurations.
- Resource Allocation: They manage the allocation of CPU, memory, and storage resources to running containers.
- Networking: Worker nodes handle network communication between containers, services, and the external environment.
How Kubernetes Distributes Workloads
The control plane’s scheduler ensures that workloads are distributed intelligently across worker nodes. When a new pod is created, the scheduler evaluates each node’s available resources and any scheduling constraints (such as node selectors, taints, or affinities) to determine the best fit.
Example: Node Selector for Pod Scheduling
Node selectors allow you to target pods to specific nodes. Consider this YAML snippet:
Here, the pod specialized-pod is scheduled only on nodes labeled with disktype: ssd.
Example: Using Taints and Tolerations
Sometimes you need to restrict which nodes can run certain pods. Kubernetes uses taints and tolerations to enforce this:
This configuration allows the tolerant-pod to run on nodes that have the matching taint.
Kubernetes Cluster Deployment Variations
Kubernetes clusters can be deployed in various configurations to suit different use cases, especially when using platforms like OpenShift.
Can You Have One Control Plane and One Worker in OpenShift?
Red Hat OpenShift is an enterprise Kubernetes distribution that builds on standard Kubernetes but adds additional developer and operational tools.
Minimum OpenShift Cluster Requirements
For production environments, OpenShift recommends multiple control plane nodes for high availability. However, for development, testing, or small-scale deployments, you can run an OpenShift cluster with a single control plane node and one worker node. This minimal configuration allows you to experiment with OpenShift features without the resource overhead of a full-scale cluster.
When a Single Control Plane-Worker Setup Might Be Useful
- Development Environments: Developers can test OpenShift features on a simplified setup.
- Proof of Concepts (POCs): A single-node deployment may be sufficient to demonstrate core functionalities.
- Resource Constraints: In scenarios where resources are limited, a minimal control plane-worker configuration can be used effectively.
Updated Local Development Tool: OpenShift Local
Historically, Minishift was used to run an OpenShift 3.x cluster locally, but it has been discontinued. For OpenShift 4.x, the recommended local development tool is now OpenShift Local (formerly known as CodeReady Containers or CRC). OpenShift Local provides a lightweight, single-node OpenShift cluster that runs on your laptop, simulating the core features of a full OpenShift environment. Here’s a quick command to start OpenShift Local:
This command starts an OpenShift Local instance with 4GB of memory and 2 CPUs—ideal for development and testing.
CloudCasa: Backup, Recovery, and Application Mobility for Kubernetes
As Kubernetes deployments grow in complexity, so does the need for robust backup and disaster recovery strategies. CloudCasa provides a comprehensive solution for protecting Kubernetes clusters and ensuring seamless application mobility.
Kubernetes Backup & Recovery
Why Backup and Disaster Recovery Are Critical
Kubernetes clusters manage critical applications, so any disruption can lead to significant downtime or data loss. A reliable backup and disaster recovery strategy ensures that:
- Data is Protected: Regular backups safeguard against data corruption, accidental deletions, or malicious attacks.
- Downtime is Minimized: Quick restoration of clusters reduces the impact of outages.
- Compliance Requirements are Met: Many industries require robust data protection measures to comply with regulations.
How CloudCasa Enhances Backup and Restoration
CloudCasa offers an easy-to-use platform that integrates directly with your Kubernetes clusters. Its features include:
- Automated Backups: Schedule and automate backups of Kubernetes resources (including persistent volumes and configuration data).
- Point-in-Time Recovery: Restore clusters or specific workloads to a precise state prior to an incident.
- Comprehensive Data Coverage: Back up not only application data but also the state and configuration of the cluster.
Example: CloudCasaBackup Workflow
Imagine you have a Kubernetes deployment running critical applications. With CloudCasa, you can schedule daily backups and quickly restore if needed. Here’s a policy scheduling example to illustrate the idea:
This setup ensures that key cluster resources are backed up automatically, reducing the risk of data loss.
Application and Workload Mobility
Migrating applications between clusters or across different cloud environments presents unique challenges. CloudCasaaddresses these with a focus on workload mobility.
Challenges in Moving Kubernetes Applications
- Configuration Differences: Clusters might have differing network policies, storage classes, or security configurations.
- Data Migration: Persistent data must move seamlessly along with the application.
- Downtime Minimization: Effective migration strategies are needed to reduce service interruptions.
CloudCasa’s Approach
CloudCasa supports both containerized workloads and KubeVirt virtual machines, enabling:
- Seamless Cluster Migrations: Move workloads from on-premises to cloud environments, or between cloud providers.
- Hybrid Cloud Deployments: Facilitate the distribution of workloads across multiple clouds.
- Rapid Disaster Recovery: Quickly restore applications on a backup cluster following an outage.
For organizations looking to modernize their infrastructure, CloudCasa provides a reliable backup and migration pathway, ensuring that data and applications remain protected and portable.
Conclusion & Key Takeaways
In this article, we explored the intricacies of Kubernetes cluster architecture with updates for 2025. Here are the key points:
- Kubernetes Clusters: These are composed of a control plane (formerly called master nodes) and worker nodes. The control plane manages the cluster’s state and scheduling, while worker nodes run application workloads.
- Control Plane Nodes: They are responsible for managing the overall cluster state, handling API requests, and scheduling. Multi-control-plane setups provide high availability in production environments.
- Worker Nodes: They execute your containerized applications and are managed by the control plane’s scheduler. Techniques like node selectors, taints, and tolerations help distribute workloads efficiently.
- OpenShift Deployments: OpenShift 4.x follows the same basic principles but comes with enterprise enhancements. For local development, OpenShift Local (the successor to the discontinued Minishift) is the recommended tool.
- CloudCasa: A modern solution for Kubernetes backup, disaster recovery, and workload mobility. CloudCasa enables automated backups, quick restorations, and seamless migration of applications, ensuring high resilience and flexibility for your deployments.
By staying current with best practices—such as using updated terminology (control plane instead of master), adopting multi-control-plane setups, and leveraging modern tools like OpenShift Local and CloudCasa—you can ensure that your Kubernetes clusters are robust, scalable, and ready for the challenges of 2025 and beyond.
Call-to-Action
Ready to modernize your Kubernetes management? Try CloudCasa today for comprehensive Kubernetes backup, disaster recovery, and workload mobility solutions. Empower your infrastructure with the latest tools and best practices for a resilient, future-proof deployment.
Additional Examples and Code Snippets
To further solidify your understanding, here are more detailed examples and practical code snippets that illustrate key Kubernetes concepts.
Example: Deploying a Multi-Container Pod
A pod can run multiple containers if needed. Here’s an example that runs an application container alongside a helper container:
In this configuration, app-container serves the main application, while sidecar-container handles logging tasks.
Example: Horizontal Pod Autoscaling
Kubernetes supports horizontal pod autoscaling to adjust the number of pods based on demand. Here’s a sample configuration:
This configuration automatically adjusts the number of pods in the webapp-deployment based on CPU usage.
Best Practices for Kubernetes Cluster Management
Here are some tips to keep your clusters running smoothly:
- Regular Backups: Use automated backup solutions like CloudCasa to safeguard data and configurations.
- Monitoring and Logging: Implement monitoring tools (such as Prometheus and Grafana) to track cluster performance and centralized logging.
- Security Practices: Regularly update your cluster components, apply security patches, and enforce network policies.
- Resource Management: Monitor resource usage and set appropriate requests and limits to avoid overcommitment.
- Disaster Recovery Drills: Regularly test backup and restore processes to ensure they work as expected during an outage.
Recap: Control Plane vs. Worker Node Roles
- Control Plane Nodes: These manage the overall state of your Kubernetes cluster, handling API requests and scheduling. In production, multi-control-plane setups are critical for high availability.
- Worker Nodes: They run the application containers. Kubernetes uses sophisticated scheduling—such as node selectors, taints, and tolerations—to distribute workloads efficiently across available nodes.
How CloudCasa Enhances Your Kubernetes Experience
CloudCasa isn’t just a backup tool—it’s a comprehensive solution for managing Kubernetes clusters. With features that include automated backups, point-in-time recovery, and seamless workload mobility (even supporting KubeVirt virtual machines), CloudCasa helps you protect critical data and enables rapid migration across clusters or cloud providers.
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Final Thoughts
Kubernetes continues to drive modern application deployment, and staying current with its best practices is essential. By understanding the roles of the control plane and worker nodes, adopting updated deployment strategies (like using OpenShift Local for local development), and integrating robust backup and recovery solutions like CloudCasa, you can ensure your Kubernetes clusters are ready for the challenges of 2025 and beyond.
Embrace modern tooling and best practices to build a resilient, scalable infrastructure that meets today’s demands and is prepared for future growth.
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Note: This article is for educational purposes. For the most current configuration recommendations, consult the official Kubernetes documentation and Red Hat OpenShift documentation.
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By integrating the latest updates and corrections, this article now provides an accurate and comprehensive guide to Kubernetes cluster architecture for 2025—covering everything from control plane roles to advanced backup and disaster recovery strategies with CloudCasa.
Pawel Staniec