Kubernetes Architecture Explained: Breaking Down the Control Plane, Data Plane, and Core Components

This article provides an overview of Kubernetes architecture, focusing on its core components and best practices for architecting Kubernetes clusters. It covers key elements such as the Kubernetes Control Plane, Kubernetes Worker Nodes, Kubernetes Persistent Storage, and Kubernetes Helm. By understanding these components and following best practices, you’ll gain insights into effectively designing and managing Kubernetes clusters to support your containerized applications.

What is Kubernetes?

Kubernetes offers a robust platform for creating, deploying, managing, and scaling application containers across multiple host clusters.

A Kubernetes cluster comprises two distinct planes:

  1. Kubernetes Control Plane: This plane oversees the management of Kubernetes clusters and the workloads they host. It encompasses essential components such as the API Server, Scheduler, and Controller Manager, which collectively coordinate the deployment and execution of containerized applications.
  2. Kubernetes Data Plane: Comprising the machines capable of executing containerized workloads, the data plane is managed by the kubelet, an agent that receives instructions from the control plane. Each node in the cluster falls under this plane, contributing to the collective computing resources available for running applications.

Moreover, Kubernetes environments feature key components essential for effective container orchestration:

  1. Pods: Pods represent the smallest deployable units in Kubernetes and serve as the primary building blocks for managing containerized workloads. Typically, a pod encapsulates one or more containers that collaborate to deliver a specific functionality or microservice, facilitating efficient resource utilization and workload isolation.
  2. Persistent Storage: While local storage on Kubernetes nodes is ephemeral and susceptible to data loss upon pod termination, Kubernetes addresses this challenge through the Persistent Volumes (PV) mechanism. By decoupling storage from individual pods or nodes, PVs enable containerized applications to store data persistently, ensuring data integrity and availability beyond the lifecycle of a pod or node.

Overview of the Kubernetes Architecture

A Kubernetes cluster comprises two primary components: the control plane and the data plane, which consists of machines utilized as compute resources.

  • The control plane houses the components responsible for managing the Kubernetes cluster.
  • On the other hand, worker nodes can be virtual machines (VMs) or physical machines, with each node hosting pods that run one or more containers.

Kubernetes nodes can operate on standard compute instances or cost-effective spot instances. Explore further details in this comprehensive guide to Kubernetes spot instances.

components of kubernetes

Image Source: Kubernetes

Fundamental Components: Kubernetes Control Plane

The control plane comprises essential components such as the API Server, Scheduler, and Controller Manager. Responsible for overseeing the Kubernetes cluster, the control plane facilitates communication with worker nodes. When applications are deployed on Kubernetes, instructions are relayed to the control plane to initiate the containers for these applications. Subsequently, the control plane orchestrates the scheduling of these containers to execute on the nodes within the cluster.

From a technical standpoint, the Kubernetes control plane encompasses a set of processes designed to manage the state of a Kubernetes cluster. It actively gathers information regarding cluster activities and incoming requests, leveraging this data to orchestrate the transition of cluster resources to the desired state. Interaction with individual cluster nodes is facilitated through the kubelet, an agent deployed on each node.

For optimal reliability, it’s recommended to maintain a highly available control plane consisting of a minimum of three control plane nodes, with the components replicated across all three nodes.

The primary parts of the control plane are as follows:

1. API Server

The API Server functions as the primary interface for the Kubernetes control plane, offering an API that serves as both the entry point and the backbone of Kubernetes operations. Its core responsibility lies in managing incoming requests, whether they originate from external sources or internal components. Upon receiving a request, the API Server meticulously evaluates its validity before proceeding to process it accordingly. Users interact with the API Server through various means, including the kubectl command-line interface and tools like kubeadm, as well as through REST calls, enabling seamless communication with the Kubernetes cluster.

2. Scheduler

The Scheduler is a vital component within the Kubernetes architecture, tasked with the critical responsibility of assigning pods to appropriate nodes based on a variety of predefined criteria. These criteria encompass a range of factors, including resource requirements specified by the user, considerations related to affinity and taints or tolerations, priority settings, the availability of persistent volumes (PV), and many others. By adhering to these automated workflows and user-defined conditions, the Scheduler ensures that pods are allocated to nodes in a manner that optimizes resource utilization and meets the specific requirements of the workload.

3. Kubernetes Controller Manager

The Kubernetes Controller Manager serves as a pivotal component within the Kubernetes ecosystem, functioning as a control loop tasked with overseeing and managing the state of a Kubernetes cluster. It continuously monitors the current state of both the cluster itself and the objects residing within it, utilizing this information to orchestrate actions aimed at aligning the cluster with the desired state specified by the cluster operator.

Fundamentally, the controller manager is in charge of a wide range of controllers, each of which is responsible for automating particular tasks at the cluster or pod level. The replication controller, namespace controller, deployment controller, service accounts controller, statefulset controller, and daemonset controller are some of these controllers. The Kubernetes Controller Manager plays a vital role in guaranteeing the seamless and effective operation of the Kubernetes cluster by coordinating the actions of various controllers and enabling automated management and maintenance chores necessary for optimum cluster performance.

4. etcd

Etcd is a distributed key-value database integral to the functioning of a Kubernetes cluster. It serves as a repository for storing critical data pertaining to the state and configuration of the cluster. As a fault-tolerant and distributed database, etcd ensures resilience and reliability in managing essential cluster information.

The data stored in etcd encompasses a wide range of cluster-related information, including configuration settings, cluster membership details, and the current state of various resources and objects within the cluster. This comprehensive storage capability enables etcd to provide the necessary foundation for maintaining consistency and coherence across the Kubernetes cluster.

By leveraging etcd, Kubernetes clusters can achieve robustness and stability, enabling them to effectively manage workload deployment, scaling, and configuration changes while ensuring data integrity and reliability.

5. Cloud Controller Manager

The cloud-controller-manager is a pivotal component in Kubernetes that facilitates seamless integration with cloud platforms by embedding cloud-specific control logic. For instance, it enables interactions with services provided by the cloud provider, such as load balancers. By connecting the Kubernetes cluster with the cloud provider’s API, this component streamlines the management of resources and services within the cluster.

One of its key functions is to abstract the underlying cloud infrastructure, shielding the Kubernetes cluster from the intricacies of individual cloud implementations. This decoupling allows components within the cluster to operate without being tightly coupled to the details of the underlying cloud provider.

Specifically designed for cloud environments, the cloud-controller-manager exclusively runs controllers tailored to the cloud platform being used. In on-premises Kubernetes setups, this component is not necessary. It operates using multiple independent control loops consolidated into a single binary, facilitating efficient management within a single process.

Moreover, the cloud controller-manager enables the scaling of clusters by dynamically adding more nodes on cloud-based virtual machines. It leverages the high availability and load balancing capabilities offered by the cloud provider to enhance the resilience and performance of the Kubernetes cluster.

Core Elements of Kubernetes: Worker Nodes

1. Nodes

Nodes are either physical or virtual machines capable of running pods within a Kubernetes cluster. Each node contributes computing resources and serves as a foundational unit for deploying and managing containerized workloads. Kubernetes clusters have the flexibility to scale up to 5000 nodes, allowing for extensive expansion of computational capacity to accommodate growing demands. To increase a cluster’s capacity, administrators have the option to add additional nodes, thereby enhancing the cluster’s capability to efficiently handle workload requirements.

2. Pods

A pod functions as an individual instance of an application within Kubernetes and is regarded as the smallest entity in the Kubernetes object model. Each pod comprises one or more closely interconnected containers, along with specifications that dictate container execution. For stateful applications, pods can be linked to persistent storage through Kubernetes Persistent Volumes, ensuring that data persists beyond the lifecycle of individual pods.

3. Container Runtime Engine

A container runtime engine is included with every node and is in charge of managing containers. Although Kubernetes supports various runtimes that comply with the Open Container Initiative, such as CRI-O and rkt, Docker is a prominent container runtime engine.

4. Kubelet

Within each node resides a kubelet, a lightweight application capable of communicating with the Kubernetes control plane. The kubelet’s primary responsibility is to ensure that containers defined in the pod configuration are operational on that particular node and to oversee their lifecycle. It executes directives issued by the control plane, ensuring the smooth functioning of containerized workloads.

5. Kube-proxy

Kube-proxy is a network proxy that is present on all compute nodes and helps with Kubernetes networking services. It manages all network interactions both inside and outside the cluster, responding or forwarding data via the operating system’s packet filtering layer.

6. Container Networking

Containers can speak with the host system or with each other thanks to container networking. The Container Networking Interface (CNI), a cooperative project involving Kubernetes, Apache Mesos, Cloud Foundry, Red Hat OpenShift, and other platforms, is frequently used to accomplish this feature.

CNI offers a simple and consistent framework for creating network connectivity inside of containers. You can use the kubelet –network-plugin=cni command-line option to make use of the CNI plugin. After that, while configuring networking for each pod, the kubelet will read configuration files from –cni-conf-dir and apply the CNI settings.

Kubernetes Persistent Storage

The design of a container makes it an immutable entity. All data created throughout the lifetime of a container is lost when it is shut down. For many use situations, sharing and storing information is necessary, even if this stateless feature is perfect for certain applications.

Applications can request and use storage resources by configuring Kubernetes persistent storage. Volumes, which are fundamental parts of a Kubernetes storage design, can be used to accomplish this.

PersistentVolumes (PVs) serve as storage resources specifically designed to provide durable storage for containerized applications within Kubernetes. Each PV represents a persistent storage component within the Kubernetes architecture.

PV resources are part of the cluster but operate independently of pods. This independence ensures that the data stored within PVs remains intact even as the cluster undergoes changes, such as pod deletion and recreation.

There are two primary methods for creating PVs, manual creation and dynamic provisioning. Dynamic provisioning involves the use of PersistentVolumeClaims (PVCs), which specify the details of a resource request. Kubernetes then manages the lifecycle of PVs based on these claims, ensuring seamless storage management within the cluster.

Kubernetes Helm

Kubernetes Helm operates as a package manager within the Kubernetes ecosystem, functioning much like npm does for Node.js and yum does for Linux. Helm facilitates the deployment of charts, which are packaged Kubernetes applications containing pre-configured and versioned resources. Different versions of charts can be deployed using various configuration sets.

Helm plays a crucial role in Kubernetes architecture, enhancing productivity, simplifying deployment processes, and reducing the complexity associated with Kubernetes applications. By leveraging Helm charts, users can effectively manage cloud-native applications and microservices, streamlining the deployment and management of Kubernetes-based workloads.

Top Techniques for Developing Kubernetes Cluster Architectures

Here are some best practices for efficiently managing Kubernetes environments:

  • Keep Kubernetes updated: Regularly update to the latest version of Kubernetes to access new features, improvements, and security patches.
  • Provide training: Invest in training for both developer and operations teams to ensure they understand Kubernetes concepts and best practices.
  • Standardize governance: Establish enterprise-wide governance standards to ensure consistency and alignment across all tools and vendors integrated with Kubernetes.
  • Scan container images: Integrate image scanners into your CI/CD pipeline to detect vulnerabilities and ensure the security of container images.
  • Implement access control: Use role-based access control (RBAC) to enforce the principle of least privilege and zero-trust models, limiting access to Kubernetes resources.
  • Use non-root users: Secure containers by running them with non-root users and setting file systems to read-only where possible.
  • Start with minimal base images: Begin with clean, lightweight base images to reduce the risk of malware and minimize image size.
  • Simplify container design: Aim for single-process containers to simplify orchestration and improve resource utilization.
  • Use descriptive labels: Add descriptive labels to Kubernetes resources to improve visibility and understanding of cluster structure and workflows.
  • Avoid overusing microservices: Consider the appropriate granularity for microservices to avoid unnecessary complexity and overhead.
  • Automate deployments: Automate CI/CD pipelines to streamline Kubernetes deployments and minimize manual intervention.
  • Manage pod lifecycle: Utilize readinessProbe and livenessProbe to manage pod lifecycle and ensure readiness for handling user requests.