What is a Kubernetes Cluster?

A Kubernetes (K8s) cluster is a grouping of nodes that run containerized apps in an efficient, automated, distributed, and scalable manner. K8s clusters allow engineers to orchestrate and monitor containers across multiple physical, virtual, and cloud servers. This decouples the containers from the underlying hardware layer and enables agile and robust deployments.

Even after impressive growth and a surge in popularity the past few years, Kubernetes continues to be one of the most popular topics in the world of application delivery. In fact, RedHat’s 2021 State of Open Source report finding that 85% of IT leaders surveyed indicated “Kubernetes is key” to cloud-native application strategies. Let’s take a closer look at Kubernetes clusters, how they work, and how the right tools can help you secure them.

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The Components of a Kubernetes Cluster

Keeping up with all the terminology in the world of containers can be difficult. Before we go any further, let’s take a minute to answer the “what is a Kubernetes cluster?” question in a bit more detail by reviewing its key components.

Control plane: The control plane is what enables the abstraction that makes K8s so powerful. It is what ensures that the configurations you define for your cluster are automatically implemented. In addition to the kube-controller-manager that runs the cluster, the control plane includes components like kube-apiserver which exposes the K8s API and the kube-scheduler that monitors the health of your cluster and schedules the deployment of pods to nodes based on your configuration.
Workloads: The applications that Kubernetes runs are called workloads. A workload can be a single component or several discrete components working together. Within a K8s cluster, a workload is run across a group of pods.
Pods: Kubernetes pod is one or more containers that share storage and network resources. Pods within a Kubernetes cluster also include a spec that defines how to run the containers.
Nodes: These are the actual resources, like CPU and RAM, which workloads run on top of. The real-world source of these “hardware” resources can be a virtual machine, on-premises physical server, or cloud infrastructure, but regardless of the underlying source nodes are what represent the resources in a K8s cluster.

Combined, these components make up a Kubernetes cluster.

How Does a Kubernetes Cluster Work?

Now that we understand the components of a Kubernetes cluster, we can look at how they work. While the specifics of Kubernetes under the hood can get complex, the basics are easy to conceptualize.

A plaintext YAML file declares the ideal state of a workload, including the container images to be used within pods.
Kubernetes pulls in the container images from a container registry and automatically deploys them across nodes, attempting to efficiently allocate resources and abstracting away the allocation of network and compute resources to pods.
If a change occurs (e.g. some pods become unhealthy) the control plane attempts to automatically restore the ideal state of the workload and this loop repeats abstracting away the complexity of container orchestration

Additionally, a Kubernetes cluster can automatically deploy rolling updates and be configured to scale as needed.

Creating Kubernetes Clusters

When you’re new to K8s, it can be hard to know where to get started. Fortunately, there are several ways to create Kubernetes clusters depending on your desired deployment environment. For example, Azure offers a simple wizard based K8s cluster creation and the AWS platform offers Amazon Elastic Kubernetes Service (EKS) to abstract away the complexity of deployment.

However, if you’re looking to learn and tinker with K8s, one of the best ways to get started is with minikube. After install of minikube and kubectl, a simple minikube start from your system’s terminal can have you up, running, and ready to begin your K8s journey. minikube is also great for developers and engineers looking to test on their local machines.

Benefits of Creating Kubernetes Clusters

At this point, the benefits of Kubernetes clusters should start to become clear. At a high-level the benefit is that K8s clusters abstract away the complexity of container orchestration and resource management. Specifically, benefits of Kubernetes clusters include:

Programmatic orchestration of workloads
Efficient distribution of containers
Self-healing to maintain ideal state
Automatic scaling and updates

Taken together, these benefits lead to more reliable and scalable production applications.

How to Secure Kubernetes Clusters

Of course, when dealing with production applications, you can never overlook security. With Kubernetes, that starts with following container security best practices and configuring the appropriate pod security policies and pod security contexts for your use cases as well as using Kubernetes secrets to store sensitive information.

Additionally, solutions that can improve cluster visibility and enable real-time vulnerability scanning in cloud-native Kubernetes environments can go a long way in protecting your workloads. Check Point CloudGuard was purpose-built to enable full lifecycle security and compliance for container-based workloads.

Specific benefits of CloudGuard include:

Dynamic policies that change with your Kubernetes environment.
Incoming and outgoing Traffic inspection with support for SSL/TLS traffic.
Automatic scanning for vulnerabilities and insecure configurations.
Virtual patching when a vulnerability is detected in a container.
Securing of North-South (on-prem ←→ cloud) traffic flows with IPsec.
Anti-bot protection to mitigate the threat of crypto-miners and other malware.

Next Steps: Onboarding Kubernetes Clusters with CloudGuard

To learn how CloudGuard can secure K8s workloads in multicloud environments, sign up for a demo today. Alternatively, for a technical deep dive on cloud-native security, you’re welcome to check out our free guide to containers and K8s security.