What is a Kubernetes Cluster?

A Kubernetes (K8s) cluster is a group of computing nodes, or worker machines, that run containerized applications. Containerization is a software deployment and runtime process that bundles an application’s code with all the files and libraries it needs to run on various infrastructures. Kubernetes is an open-source container orchestration software that can manage, coordinate, and schedule containers at scale. Kubernetes places containers into pods and runs them on nodes. A Kubernetes cluster has, at a minimum, a master node running a container pod and a control plane that manages the cluster. When you deploy Kubernetes, you are essentially running a Kubernetes cluster.

To understand a Kubernetes cluster, you first need to understand the fundamentals of containerization with Kubernetes.

Container

A container is a single application or microservice packaged with its dependencies, runnable as a self-contained environment and application in one. Modern applications often adopt a distributed microservices architecture, where every application includes hundreds or even thousands of discrete software components that run independently. Every component (or microservice) performs a single independent function to enhance code modularity. By creating independent containers for each service, applications can be deployed and distributed across a number of machines. You can scale individual microservice workloads and computation capabilities up or down to maximize application efficiency.

Container orchestration

Kubernetes is open-source container orchestration software for the management of containers at scale. It can schedule, run, start up, and shut down containers and automate management functions. Developers get the benefits of containerization at scale without the administrative overhead.

Pod

A pod is the standard deployable unit under Kubernetes. Pods contain one or more containers and, within the pod, containers share the same system resources such as storage and networking. Each pod is assigned a unique IP address.

Containers within a pod are not isolated. Think of a pod as similar to a virtual machine (VM), with containers similar to applications running on the VM. Pods and groups of pods can be organized by attaching attribute labels to them, such as labeling ‘dev’ or ‘prod’ for the type of environment.

Node

A node is a machine that runs pods. It can be a physical or virtual server, such as an Amazon EC2 instance. The components on a node include:

• Kubelet for node and container management
• Kube-proxy for a network proxy
• Container runtime

A compatible container runtime must be installed on the node to run containers. Kubernetes supports several container runtimes, such as CRI-O and containerd.

Replica set and deployment

A pod is a standalone artifact, and when its node goes down, it does not automatically restart. If a pod, or pods, are grouped into a replica set in Kubernetes, you can designate replica sets that will always be running across nodes. This is critical for scaling up and down and ensuring the continuity of apps and services.

A deployment is the Kubernetes management object for deploying an application, as well as updating or rolling back the app without taking it offline.

Service and ingress

A Kubernetes service exposes a pod or group of pods on the network, through an endpoint, for interactivity that follows standard network communication rules. For public internet traffic access, a Kubernetes ingress is attached to a service, which then links to a pod or pods.

A Kubernetes cluster is a group of one or more nodes with running pods. Within the cluster, the Kubernetes control plane manages nodes and pods.

Control plane components include:

• Kubernetes API server (kube-apiserver) that manages communications within and to the cluster
• Storage (etcd) to record the cluster’s state and configuration
• Scheduler (kube-scheduler) to manage node and subsequent pod Kubernetes resources

Other components include a controller manager for node and job control (kube-controller-manager), and a cloud controller manager for integration with provider-specific public cloud infrastructure (cloud-controller-manager).

Because containers do not have persistent storage, applications need to store data that persists. Pods may also require access to shared data. Persistent volumes can be added to a cluster as storage, referenced within the cluster similarly to a node.

Developers must first download and install Kubernetes on a master node and its worker nodes. They can then deploy the cluster on these physical or virtual machines, locally, in a data center, or in the cloud.

Installation

For a start with Linux virtual machines, on your chosen master node (virtual machine), first install:

• containerd containerization software
• Repository key and code repository of Kubernetes
• Package kubeadm for cluster bootstrapping
• Package kubelet for node coordination
• Package kubectl for the cluster command line

Perform the same process on each of the other designated worker nodes.

Cluster initialization

To initialize a cluster, run the kubeadm init command on the master node. You must add a kube config file and deploy pod networking, typically with a YAML file, before the cluster is ready for work. The kubeadm init command outputs a join command, which can be copied and pasted into the other virtual machine worker nodes’ command lines. This allows each worker node to join the cluster.

Working with Kubernetes

With the Kubernetes UI dashboard, deployers can create and deploy applications on the cluster. For a Kubernetes UI dashboard, run the kubectl proxy command on the master machine, but consider security implications beforehand.

Kubernetes cluster management is the term for managing multiple Kubernetes clusters at scale. As an example, consider a development environment where the team requires test, development, and production clusters that each run across multiple distributed on-site and cloud-based physical and virtual machines.

To manage multiple different types of clusters together, you need to be able to perform cluster operations, such as creation and destruction, in-situ updates, maintenance, reconfiguration, security, cluster data reporting, and so on. Multi-cluster management can be achieved through a combination of Kubernetes services, specialized tools, configurations, and best practices.

When applications grow in complexity and begin to scale across multiple machines, managing containers manually can become difficult and error-prone. Services like a Kubernetes cluster let you run containerized applications across distributed infrastructure. This means you can access the automation and consistency you need through its control plane and worker nodes, providing a standardized system for managing containerized workloads.

There are a number of benefits to using Kubernetes clusters.

Self-healing and automated scaling

Kubernetes clusters automatically monitor active workloads, reconciling any differences between the cluster’s desired state and its actual state. Its control plane components can automatically replace failed containers and reschedule those workloads on healthy worker nodes.

An automated approach to containerized management improves resource utilization in a cluster while also reducing the burden on your operations teams. There is no need to manually restart workloads, with everything performed automatically within the cluster.

Consistency across environments

Kubernetes lets you deploy containerized applications consistently across different production environment systems. As workloads are packed as container images, they will run reliably, regardless of the underlying operating system or infrastructure.

Whether you’re running Kubernetes on-premises in a physical cluster or through a managed cloud provider service, the cluster architecture you deploy remains consistent. Especially in enterprise environments, this reliability can help with better integration with CI/CD pipelines and reducing friction across multiple teams.

Scaled workload orchestration

You can coordinate task assignments through the Kubernetes control plane. The Kubernetes scheduler helps find the optimal placement of pods based on capacity and policy constraints, ensuring balanced distribution of containerized workloads.

Even when running at scale, the built-in orchestration helps to reduce the operational complexity of managing your active systems.

AWS provides cloud services to help you configure, run, and manage your Kubernetes clusters:

• Amazon Elastic Compute Cloud (EC2) helps you provision and run Kubernetes on your choice of instance types.
• Amazon Elastic Kubernetes Service (EKS) helps you start, run, and scale Kubernetes—without needing to provision or manage master instances with a control plane, etc. EKS comes with cluster management tools and integrations with AWS networking and security services.

Get started with Kubernetes clusters on AWS by creating a free account today.