What is a Kubernetes Cluster?

A Kubernetes cluster is a group of computing resources, such as servers or virtual machines, managed by the Kubernetes container orchestration system. Kubernetes is an open-source containerization platform that makes deploying and scaling application containers simple and easy. It provides an easy-to-use API for managing containers across different environments and infrastructures. A well-orchestrated Kubernetes environment also needs reliable data protection. If you're already leveraging Kubernetes or considering doing so in the near future, Veeam's native backup solutions can help keep your clusters and critical workloads protected.

Components of a Kubernetes Cluster

A Kubernetes cluster consists of two node types: master nodes and worker nodes. Master nodes are the control plane components responsible for managing the overall state of the cluster, such as maintaining the desired state of the cluster, scheduling and deploying applications and managing the cluster’s networking. In a highly available Kubernetes cluster, there can be multiple master nodes for fault tolerance.

Worker nodes, on the other hand, are the compute resources where the actual containerized applications run. They host pods, which contain at least one container, and are the smallest units you can deploy in Kubernetes. The worker nodes communicate with the master node to ensure everything is running as intended.

Kubernetes also has two types of components: control plane components and node components. Control plane components consist of the following.

• Kube-apiserver: The API server is the primary control mechanism of the Kubernetes control plane. It provides access to the Kubernetes API and processes requests and operations to update the cluster’s state.
• Kube-scheduler: This component is responsible for placing pods onto nodes based on resource requirements, Quality of Service, requirements and other constraints. It takes into account node capacity, affinity/anti-affinity rules and other factors to make scheduling decisions.
• Kube-controller-manager: This component runs controller processes, which manage the overall state of the cluster. Some examples of controllers include the Replication Controller, which ensures the desired number of replicas for a specified application is running, and the Service Controller, which manages load balancing for applications.
• etcd: This is a distributed, consistent key-value store used by Kubernetes to store all data required to manage the cluster, including configuration data and the overall state of the system.

Individual nodes, on the other hand, involve the components listed below.

• Kubelet: This agent runs on each worker node, ensuring containers are running in pods and reporting the status of the node and its running pods to the control plane. It communicates with the API server to ensure the desired state of the application is maintained.
• Kube-proxy: This component is a network proxy that runs on each worker node and enables service abstraction by maintaining network rules and ensuring traffic is properly forwarded to the appropriate pods.
• Container runtime: This software is responsible for running containers, such as Docker, containerd or CRI-O. Kubernetes supports different container runtimes through the Container Runtime Interface.

Both categories, combined within a cluster, work together to provide a consistent and reliable platform for deploying and managing containerized applications. More specifically, Kubernetes clusters provide the necessary abstraction and automation to manage containerized applications at scale, enabling developers to focus on code and operations teams to manage infrastructure more efficiently.

How Do You Work With a Kubernetes Cluster?

Kubernetes is set up, configured and maintained almost entirely with the Kubernetes API. This API exposes system functionality and enables the management of clusters and nodes programmatically. There are several ways to work with the Kubernetes APIs, including:

• kubectl: The primary way to interact with the Kubernetes API is by using the kubectl command-line tool. It translates your commands into API calls and sends them to the API server.
• RESTful API calls: You can directly make RESTful API calls to the Kubernetes API server using tools such as curl, wget or any programming language that supports HTTP requests. To authenticate and authorize API calls, you’ll need to use the appropriate credentials and tokens.
• Client libraries: Kubernetes provides client libraries for various programming languages, such as Go, Python, Java and JavaScript. These libraries offer a more convenient and idiomatic way to interact with the Kubernetes API in your preferred language.

In Kubernetes, the desired state is a declarative representation of the expected state of the resources within the cluster. When updating pods, deployments or devices, the state is defined using a YAML or JSON configuration file. Kubernetes then works to reconcile the actual state of the resources with the one that’s specified in the configuration file.

The desired state includes information, such as the container image, the number of replicas for a deployment and the environment variables for pods. It also includes the type of load balancing used for a service. Kubernetes controllers continuously monitor these files to make adjustments to match the desired state of the container.

This declarative approach provides several benefits, including Kuberenetes’ ability to self-heal when a pod crashes or a node becomes unreachable. Kubernetes automatically takes corrective actions to restore the desired state. It also helps keep cluster resources in line with familiar version control workflows, which makes rollbacks a breeze.

How to Set Up a Kubernetes Cluster

While it may seem complicated at first, Kubernetes provides powerful abstractions and a consistent API that makes life much easier when it comes to managing applications at scale. That said, setting up a Kubernetes cluster is unique as far as infrastructure goes, so it’s helpful to gain an overview of the process. Before diving in, however, it’s helpful to understand the deployment requirements.

Kubernetes can run on a variety of hardware and VM configurations. The exact requirements depend on the scale and resource demands of your applications. However, for a minimal cluster, each node should have at least 2 CPU cores and 2GB of RAM, a stable and performant network connection and sufficient storage in the form of local storage, a NAS or a cloud-based storage option, such as Amazon EBS, Google Persistent Disk and Azure Disk.

Set up the Kubernetes Cluster

Kubernetes clusters can be deployed in nearly any environment, including on-premises, in a public cloud or using a managed Kubernetes service, such as Google Kubernetes Engine, Amazon Elastic Kubernetes Service or Azure Kubernetes Service. Going the managed route simplifies the process, although self-managing Kubernetes offers more control over the infrastructure.

Setup requires installing kubectl, which is the command-line tool that interacts with the Kubernetes API. It connects via HTTP, and it's installed from a local machine and configured to connect to a cluster.

Define Application Components

Containerized applications in Kubernetes are described using declarative YAML or JSON configuration files, which define the desired state of the application components. The main components include:

• Pods: The smallest deployable units that run one or more containers
• Services: Define how to expose your application to the network or other parts of the cluster
• Deployments: Define the desired state of the application and manage rolling updates
• ConfigMaps and secrets: Store configuration data and sensitive information separately from the container image

Deploy the Application

Clusters are deployed using kubectl to apply the configuration files, which instructs Kubernetes to create the necessary resources to achieve the desired state of your application. Meanwhile, the actual configuration requires quite a bit of fine-tuning.

• Configure the Control Plane: Set up the master node(s) with the necessary components, including kube-apiserver, etcd, kube-scheduler, kube-controller-manager. In a highly available setup, you’ll need multiple master nodes behind a load balancer.
• Configure worker nodes: Set up the worker nodes with the necessary components, including kubelet, kube-proxy and the container runtime. You may need to configure the container runtime, such as Docker or containerd, based on your preference.
• Configure networking: Kubernetes supports various networking solutions, such as Calico, Flannel or Weave. Choose the one that best fits your needs and follow the provider’s documentation for configuration.
• Configure storage: Set up the storage classes and provisions according to your chosen storage solution. This includes configuring local volumes, network-attached storage or cloud-based storage services. You’ll also want to deploy a reliable backup solution.
• Configure role-based access control: Define roles and permissions for users and applications to access the Kubernetes API and resources securely.
• Configure monitoring and logging: Set up monitoring tools, such as Prometheus and Grafana, to collect metrics and logs.
• Configure security: Implement security best practices, such as securing the Kubernetes API server, encrypting secrets and enabling network policies to isolate namespaces.

Keep in mind, these are high-level steps for deploying and configuring a Kubernetes cluster. The exact process will vary, depending on your chosen deployment option, infrastructure and specific requirements.

While Kubernetes has a learning curve, its powerful abstractions and tools make it easier to manage containerized applications at scale. With practice and experience, you’ll find that working with Kubernetes becomes more intuitive over time. There are also plenty of resources available online to help you learn and master this powerful containerization technology.

Kubernetes Cluster Management

For monitoring, Kubernetes provides built-in tools to help maintain the cluster’s health and performance. Meanwhile, there are also external and third-party tools and platforms that provide advanced monitoring, logging and alerting.

Kubernetes makes it easy to scale applications up or down based on demand, either manually or automatically, using the Horizontal Pods Autoscaler, also referred to as HPA. You can ensure the longevity of your application's performance by keeping it secure and updated. You can perform rolling updates of your application with zero downtime by updating the Deployment configuration.

How to Get Started With Veeam

Embarking on your Kubernetes journey may seem daunting, but with the right approach and resources, you can quickly become proficient. Start by exploring common solutions and best practices within the Kubernetes ecosystem. Consider your organization’s specific needs and how Kubernetes, coupled with Veeam’s native backup solution, can help address them.

When you're ready to get started, take a look at Veeam's Kasten K10, a next-generation native backup solution designed and engineered specifically for Kubernetes. Register the community version for free, and start building a backup strategy today. Remember, a well-orchestrated Kubernetes environment is incomplete without reliable data protection. Make Veeam your trusted companion on your containerization adventure with Kubernetes.