What is a Kubernetes (K8s) Cluster?
For enterprise developers building and deploying modern applications, managing containerized workloads at scale can present a common challenge. Kubernetes, often abbreviated as K8s, has emerged as the standard for container orchestration. At the core of this powerful open-source system is the Kubernetes cluster, a robust environment designed to automate the deployment, scaling, and management of containerized applications.
Kubernetes clusters defined
A Kubernetes cluster is a set of nodes, or machines, that are grouped together to run containerized applications. It provides a unified and abstracted compute environment, allowing you to deploy and manage your services without having to directly interact with individual servers.
The primary role of a K8s cluster is container orchestration: it automates the complex tasks involved in maintaining application availability, scaling resources based on demand, and rolling out updates with zero downtime.
By managing the entire life cycle of containers, Kubernetes clusters provide the foundational platform that enterprise applications need to be scalable and agile.
Nodes
A node is a worker machine within a Kubernetes cluster, which can be either a virtual machine (VM) from a cloud provider or a physical server in a data center. Each node provides the necessary CPU, memory, and networking resources to run containers. A K8s cluster is composed of a control plane and one or more worker nodes, which collectively provide the cluster's compute capacity.
Pods
In the Kubernetes object model, the smallest and most fundamental deployable unit is a pod. It represents a single instance of an active process within a cluster and encapsulates one or more tightly coupled containers, shared storage resources, and a unique network IP address. While a pod can contain multiple containers, the most common pattern is for a pod to house a single container, creating a 1-to-1 mapping between the pod and the containerized application.
Containers
Containers are lightweight, standalone, and executable software packages that include everything needed to run an application: code, runtime, system tools, system libraries, and settings. This encapsulation helps ensure that the application runs quickly and reliably from one computing environment to another. Because they are portable and efficient, containers are the ideal building blocks for modern, microservices-based applications.
K8s cluster architecture
A Kubernetes cluster architecture consists of two main types of components that can help to create a fault-tolerant system for running applications.
- Control plane: It acts as the brain to manage the clusters.
- Worker nodes: They act as the brawn by providing the runtime environment.
Control plane (master nodes)
The control plane is responsible for maintaining the desired state of the entire cluster. It makes global decisions about scheduling, responds to cluster events, and manages the life cycle of all Kubernetes objects. Key components of the control plane include:
- Backing store (etcd): A consistent and highly-available key-value store used as Kubernetes' backing store for all cluster data.
- API server (kube-apiserver): Serves as the control plane's front end, exposing the Kubernetes API. It manages and validates REST requests, subsequently updating the state of relevant objects within etcd.
- Scheduler (kube-scheduler): The scheduler watches for newly created pods that don't have a node assigned and selects a node for them to run on based on resource availability, policies, and affinity specifications.
- Controller manager (kube-controller-manager) [optional]: This component runs various controller processes that regulate the state of the cluster. For example, controllers exist to handle node failures, maintain the correct number of pods for a deployment, and manage service endpoints.
Worker nodes (compute nodes)
Worker nodes are the machines where your containerized applications actually run. Each node is managed by the control plane and contains the services necessary to run pods. Core components on each worker node include:
- kubelet: An agent that runs on each node in the cluster, the kubelet communicates with the control plane and ensures that the containers described in pod specifications are running and healthy.
- kube-proxy [optional]: This network proxy runs on each node and is responsible for maintaining network rules on nodes. These network rules allow for network communication to your pods from network sessions inside or outside of your cluster.
- Container runtime: This software manages the execution of containers and supports various runtimes, with containerd being a popular option.
Using clusters in K8s
Kubernetes clusters can be highly versatile and address many challenges faced by enterprise development and operations teams.
Containerizing existing applications
Containerizing existing applications
Move legacy applications into containers to improve their portability, scalability, and resource utilization without extensive refactoring.
Building new cloud-native applications
Building new cloud-native applications
Use a K8s cluster as the foundation for microservices-based architectures, enabling independent development, deployment, and scaling of services.
DevOps and CI/CD
DevOps and CI/CD
Automate the build, test, and deployment pipeline by integrating it with a Kubernetes cluster, accelerating release cycles and improving reliability.
Scalability and resilience
Scalability and resilience
Handle variable traffic loads by automatically scaling applications up or down and enable self-healing by automatically restarting or replacing failed containers.
Resource efficiency
Resource efficiency
Improve infrastructure utilization by packing containers more densely onto fewer nodes, which can lead to significant cost savings.
Kubernetes cluster management
While Kubernetes is incredibly powerful, setting up and operating a secure, production-grade K8s cluster involves significant operational overhead. This is where a managed service like Google Kubernetes Engine (GKE) can provide immense value for enterprise teams. GKE can help simplify Kubernetes cluster management by automating many of the complex and time-consuming tasks.
GKE can provide a fully managed control plane, handling its availability, patching, and updates, so your team doesn't have to. It offers features like Autopilot mode, which automates the entire cluster's operational management, including nodes and scaling, to further reduce overhead and optimize resource usage. With GKE, developers can focus on writing code and building applications, while the platform handles the underlying Kubernetes cluster architecture and infrastructure, ensuring it's secure, reliable, and scalable.
Kubernetes clusters learning resources
- Learn more about Kubernetes with GKE by exploring the Google Cloud Skills Boost course.
- Read the official documentation to deploy a cluster and workload.
- Follow the documentation to deploy a containerized app to a GKE cluster.
Related products
While Google Kubernetes Engine (GKE) can provide the core compute and orchestration environment, a production-ready application requires a comprehensive ecosystem of tools for security, observability, networking, and CI/CD. Google Cloud offers a suite of integrated services that can enhance and simplify a Kubernetes-based workflow, allowing enterprise teams to build a secure and efficient software supply chain.
Artifact RegistryArtifact Registry acts as a single, secure, and centralized repository for container images and other build artifacts. It integrates with security scanning to identify vulnerabilities in your images before they are deployed.
Cloud BuildCloud Build provides a fully managed continuous integration and continuous delivery (CI/CD) platform. You can create automated pipelines that build your container images, run tests, push them to Artifact Registry, and then deploy them directly to your GKE clusters.
Cloud Logging and Cloud MonitoringCloud Monitoring automatically collects metrics from the cluster, pods, and containers, while Cloud Logging aggregates logs from all your applications and the Kubernetes components themselves. This can provide critical visibility into the health and performance of your system.
Cloud Service MeshCloud Service Mesh provides a managed service mesh based on Istio. It offers uniform observability, traffic management, and security across all your services without requiring changes to your application code. This simplifies tasks like canary releases, enforcing mTLS encryption between services.
Binary AuthorizationBinary Authorization enforces a policy that only allows trusted, verified container images to be deployed on GKE. This can prevent the deployment of images that haven't passed security scans or were built from an unapproved source.
Cloud RunA fully managed serverless execution environment that enables you to deploy containerized applications quickly and easily. It is well-suited for applications with spiky traffic, event-driven applications, and AI inference and agentic apps.
