Kubernetes Architecture Explained: Components, Diagram & Working

If you've spent any time with containers, you hear people telling you to put your application "on Kubernetes," as if it were as easy as that. Of course, it is not, not until you understand what is going on beneath the hood. The Kubernetes architecture forms the basis of how applications are deployed, scaled, and maintained in modern systems without any babysitting.

In fact, the architecture of Kubernetes, basically, is a collection of components which are responsible for scheduling, executing and managing containerized applications and workloads in a cluster of machines. From a handful of microservices to an enterprise-grade platform, learning about the architecture of Kubernetes is what separates confident Kubernetes clusters administrators from fighting fires throughout the day.

This article aims to provide you with a comprehensive insight into Kubernetes architecture and its components along with their functionalities and working principles.

What Is Kubernetes Architecture?

Kubernetes architecture refers to the overall structure and design of a Kubernetes cluster – the way it consists of its different components, the way these components interact with each other and with what purpose.

In general, a Kubernetes cluster is divided into two halves:

• Control plane – the "brains" of the cluster that performs all the decisions, such as scheduling, scaling, and reacting to failures.
• Worker nodes – the machines where the execution of the containerized applications takes place.

The above division is important for many reasons and helps make Kubernetes resilient, highly adaptable, and able to run on a diverse set of machines, ranging from a developer's laptop to thousands of servers across multiple data centers.

The architecture of Kubernetes is, by design, a declarative system. Rather than telling the system how to perform a task, it describes what you want it to do and the system is responsible for making that wish a reality.

How Kubernetes Architecture Works

The idea is simple enough , you tell the system what you want, and it figures out how to get there and maintain that state.

In other words, once you make an attempt to create some sort of a configuration (for example, deployment that asks for three replicas of a web application), it will be sent to the API server where it will be validated, stored and will cause the sequence of events to happen, which will be handled by the controllers and the scheduler. The scheduler finds nodes with enough capacity for running containers and then the kubelet starts pulling images and executing them on those nodes.

But once all of this is done, Kubernetes does not stand down. On the contrary, Kubernetes continuously checks whether the current state of the cluster matches the desired one (that was specified in the configuration). In case the pod goes down, node becomes unavailable, or sudden spike in traffic occurs, Kubernetes detects this mismatch and fixes the problem, for instance, by restarting pods, moving them to a different node, or adding replicas if needed And it is this process of continuous reconciliation that is the very core of the Kubernetes architecture.

Kubernetes Architecture Diagram

Picture of the Kubernetes cluster as of two interconnected layers:

Every worker node communicates with the API server on a regular basis, reporting its health and receiving the commands from the control plane. The control plane never runs the application container directly , its only task is performing all the decisions and coordinating different components of the architecture.

This is the diagram you need to memorize once and for all when troubleshooting any issues with a cluster because almost any problem is caused by some failure in communication between these two layers.

Core Components of Kubernetes Architecture

Control Plane Components

The control plane consists of a number of interrelated processes that serve their specific roles:

• kube-apiserver – The entry point to the cluster. Any command that comes from kubectl, a CI/CD pipeline, or any other source is processed by the API server, validated and the corresponding changes are applied to the cluster state.
• etcd – A distributed key-value store that keeps all of the cluster configuration and state information. Losing or damaging this component means losing the cluster's "memory" and that is why its backup is mandatory in a production environment.
• kube-scheduler – Monitors newly created pods that have not yet been assigned a node and decides where they need to run, taking into account the resources and the availability, as well as any affinity rules, taints, and tolerations.
• kube-controller-manager – Runs the various controllers (node controller, replication controller, endpoints controller, etc.), comparing the actual state of the cluster with the desired state and taking all necessary actions to correct any deviations.
• cloud-controller-manager – Bridges Kubernetes and the underlying cloud provider, managing load balancers, storage volumes, and nodes in the cloud environment.

Worker Node Components

Each worker node in the Kubernetes architecture includes the following components:

• Kubelet – An agent responsible for ensuring that the containers described in pod configurations are running properly on this particular node. It communicates directly with the API server.
• Kube-proxy – Manages networking rules on this node, allowing communication to and from the pods both from internal and external sources.
• Container runtime – The software that executes the containers themselves, e.g., containerd or CRI-O. It downloads the required container images and runs them.
• Pods – The smallest units that can be scheduled and deployed on Kubernetes. It is essentially a combination of one or more containers that share the storage, the network, and some configuration information.

Kubernetes Cluster Architecture Explained

Looking at the Kubernetes cluster from an abstract point of view, we can see that Kubernetes cluster architecture is fault-tolerant distributed system architecture.

Usually, in production systems, there are multiple replicas of the control plane component and not just one, which means that there is a high availability in case of failure of one of the components.

The worker nodes, usually, are distributed across several availability zones or physical racks, meaning that a failure in one zone does not mean the collapse of the entire cluster.

Kubernetes cluster architecture implies usage of namespaces, which separates the cluster into different "sub-clusters" logically, which can be used for development, testing and production environments, or just for separation of teams that are working with the same cluster.

It is the hierarchical structure that makes Kubernetes clusters resistant to failures of nodes, network or even an entire availability zone.

Kubernetes Networking Architecture

The architecture of Kubernetes networking follows a number of hard and fast rules that make it very predictable even on a large scale:

1. Each pod gets its own IP address.
2. Pods can communicate with each other across different nodes without the need for NAT.
3. Nodes can communicate with all pods and vice versa.

This is achieved by using a CNI plug-in (Calico, Cilium, Flannel, etc.), which is in charge of actual routing between the pods of a cluster.

Apart from the communication between pods, there are such entities in Kubernetes as Services, which provide a stable IP address and DNS name for a set of pods with volatile IP addresses.

Ingress controllers can be used for HTTP/HTTPS access to the cluster from outside, managing routing, TLS termination, and load balancing.

Having an understanding of all this hierarchy of networks (pod network, service network, and ingress) is crucial for Kubernetes debugging because in most cases, the issue is about some kind of misconfiguration of the network or DNS problems.

Kubernetes Deployment Architecture

The Kubernetes architecture of deployment describes how application updates are rolled out and managed over time. In most cases, it is the Deployment object that creates and maintains required replicas of pods.

Deployments offer a capability of rolling updates by default updating the pods gradually, without downtime. In the event of any problems, it will be easy to revert to the previous version of the deployment.

Apart from the normal rolling updates, almost all teams incorporate methods such as blue-green deployment and canary deployment in order to slowly shift the traffic to a new version while checking for errors.

This flexibility of deployment options is probably one of the key reasons why companies prefer Kubernetes architecture over any other – it turns risky releases into a safe process.

Kubernetes Architecture Workflow

Here is what really happens when you deploy an application:

1. Submit a YAML file, describing your desired state of the cluster via kubectl or any CI/CD pipeline.
2. Your request is validated by the API server and persisted in etcd.
3. Scheduler selects the worker nodes, capable of handling your pods.
4. The kubelet on each selected node instructs the container runtime to download and run the images.
5. Kube-proxy configures the networking rules to allow the access to new pods.
6. Controllers continuously check the actual state of the cluster and apply all corrections.

And this loop works continuously in the background as long as your cluster exists.

Kubernetes Architecture Use Cases

Examples of Kubernetes architecture in practice include the following situations:

• Microservices platforms
• CI/CD pipelines
• Machine learning workloads
• Deployments in multi-cloud or hybrid environments
• Scalable e-commerce platforms

Kubernetes Architecture Best Practices

A couple of best practices separate reliable Kubernetes clusters from fragile ones:

• Run multiple replicas of the control plane for high availability.
• Backup your etcd regularly – it is a single point of failure otherwise.
• Define resource requests and limits for each workload to avoid "noisy neighbor" problems.
• Isolate teams and environments with namespaces and Role-Based Access Control.
• Restrict pod-to-pod communication with network policies as much as possible.
• Continuously monitor the cluster's health with tools like Prometheus and Grafana.

Challenges of Kubernetes Architecture

Despite being very powerful, the architecture of Kubernetes is not without challenges. Here are the most common ones:

• Complexities of networking – resolving CNI and DNS issues may be quite difficult without deep knowledge of Kubernetes architecture.
• Misconfigured resources – the lack of adequate resources may lead to problems with stability or inefficiency in terms of costs.
• A steep learning curve – the number of interacting parts in this ecosystem is overwhelming.
• Frequent upgrades – staying updated with Kubernetes releases is difficult.
• Possible security vulnerabilities – improper RBAC setup or exposed dashboards.

Kubernetes Architecture vs Docker Architecture

There is a lot of confusion around Docker and Kubernetes and it is easy to see why people tend to consider them as competing products, which is incorrect. Docker architecture focuses on building and running individual containers, whereas Kubernetes architecture is used to orchestrate multiple containers on multiple machines to schedule, scale, network and recover them.

In other words, Docker packages and runs your application, whereas Kubernetes schedules and maintains the desired amount of containers with automatic failover. Today, the majority of Kubernetes clusters use container runtimes like containerd or CRI-O rather than Docker itself, but the architecture remains the same.

Benefits of Kubernetes Architecture

• Self-healing – automatically recovers from failures by restarting failed containers and rescheduling pods from unhealthy nodes.
• Horizontal scaling – scales applications in response to changing demands.
• Portability – operates uniformly in on-premises, cloud, or hybrid environments.
• Infrastructure-as-code – defines the state of the infrastructure and applications declaratively and keeps it versioned and repeatable.
• Efficient resource usage – packs workloads into nodes to utilize their resources efficiently.

Frequently Asked Questions

What are the main components of Kubernetes architecture?

There are two essential components of Kubernetes architecture and these include the control plane and the worker nodes. The control plane contains the API server, etcd, scheduler, and controller manager. This component manages the overall operations of the cluster. In contrast, worker nodes contain the kubelet, kube-proxy, container runtime, and pods.

What is the difference between a master node and a worker node?

Master nodes, which are also referred to as the control plane in today’s terminology, control the cluster by scheduling workloads and managing the desired state of the Kubernetes cluster, as well as managing API requests. The worker nodes contain the containerized applications within pods. The worker nodes connect with the control plane using the kubelet.

Is etcd a part of the Kubernetes control plane?

Yes, etcd is definitely part of the Kubernetes control plane. It is basically a distributed key-value storage system and is responsible for storing the cluster configuration, metadata, secrets, and current status in a secure way. The whole Kubernetes depends on etcd for its consistency and recovery of cluster data.

Can Kubernetes run without a container runtime?

No, Kubernetes cannot operate without a container runtime. The container runtime like containerd or CRI-O pulls container images, creates containers, and manages them through their lifecycle. The communication between Kubernetes and the container runtime is done using the Container Runtime Interface (CRI).

Why is the architecture of Kubernetes called self-healing?

Kubernetes is called self-healing because it automatically detects and corrects failures within the cluster. If a pod crashes, becomes unhealthy, or a node fails, Kubernetes controllers replace or reschedule workloads to maintain the desired state. This automated recovery minimizes downtime, improves application availability, and ensures reliable performance without manual intervention.

Conclusion

The Kubernetes architecture is not a magic, it is a well-thought-out system of clearly defined responsibilities working toward a single goal maintaining the desired state of the applications no matter what. Understanding the relationship between the control plane and worker nodes, how the network connects the pods and how applications are deployed in it, is the key to understanding Kubernetes.