What is Kubernetes and how does it work?

Kubernetes is an open-source platform for automated deployment, scaling, and management of containerized applications. It organizes containers into clusters and ensures services run reliably and efficiently. With features like load balancing, self-healing, and rollouts, Kubernetes significantly simplifies the operation of modern applications.

What is Kubernetes?

Kubernetes (K8s) is an open-source system for container orchestration, originally developed by Google and now maintained by the Cloud Native Computing Foundation (CNCF). It manages container applications in distributed environments by automatically starting, scaling, monitoring, and replacing containers as needed.

The architecture written in the Go programming language is based on a master node and multiple worker nodes, with various components like the scheduler responsible for central management tasks. With declarative configurations (such as YAML files), the desired system state is specified, and Kubernetes continuously works to maintain it. The tool is aimed at use both in the cloud and on local computers or in on-premises data centers.

How does Kubernetes work?

Kubernetes is a container orchestration system. This means that the software is not meant to create containers but to manage them. Kubernetes relies on process automation for this purpose. This makes it easier for developers to test, maintain, or release applications. The Kubernetes architecture consists of a clear hierarchy:

• Container: A container holds applications and software environments.
• Pod: This unit in the Kubernetes architecture gathers containers that must collaborate for an application.
• Node: One or more Kubernetes Pods run on a node, which can be either a virtual or a physical machine.
• Cluster: Multiple nodes are combined into a Kubernetes Cluster.

Additionally, the Kubernetes architecture is based on the principle of master and worker nodes. The described nodes serve as worker nodes, which are the controlled parts of the system. They are under the management and control of the Kubernetes master.

A master’s tasks include, for example, distributing pods across nodes. Through continuous monitoring, the master can also intervene if a node fails and directly duplicate it to compensate for the failure. The current state is always compared with the desired state and adjusted if necessary. These operations occur automatically. However, the master also serves as the access point for administrators, who can orchestrate containers through it.

Kubernetes Node

The worker node is a physical or virtual server where one or more containers are active. A runtime environment for the containers is located on the node. Additionally, the so-called Kubelet is active. This is a component that enables communication with the master. The component also starts and stops containers. With cAdvisor, the Kubelet has a service that records resource usage, which is useful for analyses. Finally, there is the Kube-proxy, which the system uses to perform load balancing and to enable network connections via TCP or other protocols.

Kubernetes Master

The master is also a server. To ensure control and monitoring of the nodes, the Controller Manager runs on the master. This component, in turn, combines several processes:

• The Node Controller monitors the nodes and responds if one fails.
• The Replication Controller ensures that the desired number of pods is always running simultaneously. In modern setups, it is largely replaced by ReplicaSets, which are generally managed by deployments.
• The Endpoints Controller manages the endpoint object responsible for connecting services and pods.
• Service Account and Token Controller manage the namespace and create API access tokens.

Alongside the Controller Manager runs a database called etcd. This key-value database stores the configuration of the cluster for which the master is responsible. With the Scheduler component, the master can automate the distribution of pods across nodes. The connection to the node works through the API server integrated into the master. This provides a REST interface and exchanges information with the cluster via JSON. For example, this allows the various controllers to access the nodes.

Are Kubernetes and Docker competitors?

The question of which tool performs better in the Kubernetes vs. Docker comparison doesn’t really come up, since the two are typically used together. Docker (or another container platform like rkt) is responsible for building and running containers—even when Kubernetes is in use. Kubernetes then accesses these containers and manages orchestration and process automation. On its own, Kubernetes cannot create containers.

At most, the real competition exists with Docker Swarm. This tool is designed for Docker orchestration and, like Kubernetes, it works with clusters and provides similar functionality.

What are the advantages of Kubernetes?

Kubernetes impresses with a multitude of advantages that enhance scalability, operational reliability, and efficiency.

✓ Automated scaling: To save costs, Kubernetes can perfectly utilize resources. Instead of keeping currently unnecessary machines running, Kubernetes can release these resources and either use them for other tasks or not use them at all—which can save costs.

✓ High fault tolerance: Through replication and automatic recovery, Kubernetes ensures that applications continue to run even in the event of errors or failures of individual components.

✓ Resource-efficient orchestration: Pods and containers are intelligently distributed across the available nodes, optimizing the use of computing power.

✓ Easy rollout and rollback: New versions of applications can be rolled out with minimal effort. If necessary, a quick rollback to previous versions is also possible.

✓ Platform independence: Kubernetes runs on local servers, in the cloud, or in a Hybrid Cloud; workloads remain portable.

✓ Service discovery and load balancing: Kubernetes automatically detects services within the cluster and evenly distributes traffic without the need for external load balancers.

✓ Efficient management through APIs: A central API allows all cluster components to be managed and automated, even controlled by external tools and CI/CD pipelines.

What is Kubernetes suitable for?

Kubernetes is particularly well-suited for running applications in containers when a scalable and highly available infrastructure is required. Common use cases include:

• Microservice architectures: In practice, K8s is often used to operate microservice architectures, where many small services are developed, tested, and updated independently. Companies rely on Kubernetes to automate both development and production environments and to respond quickly to new requirements.
• CI/CD: Kubernetes is frequently applied in continuous integration and continuous deployment pipelines, enabling automated deployments and reliable version management.
• Multi- and hybrid-cloud: In multi-cloud or hybrid-cloud strategies, Kubernetes allows workloads to be deployed independently of the underlying platform and moved flexibly between providers or data centers.
• Big data and machine learning: Kubernetes is also valuable for big data and machine learning workloads that require many short-lived containers to run in parallel.
• Large platforms: For platforms with a high number of users, Kubernetes is indispensable to automatically manage traffic spikes and maintain reliability.