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.