What is Kubernetes?

Kubernetes, often shortened to K8s (a numeronym combining the letter K, the eight letters between K and s, and the letter s), is an open-source platform for automating the deployment, scaling, and management of containerized applications. It gives IT teams a consistent way to run applications across on-premises data centers, public cloud, and distributed edge locations without managing every server by hand. Instead of logging into individual machines to start, stop, or update software, teams describe the desired state of an application, and Kubernetes works continuously to keep the running environment matched to that state.

Why Use Kubernetes?

Without an orchestrator, updating an application running across 50 servers means logging into each one, checking it applied correctly, and hoping nothing breaks mid-rollout. A single failed update can mean manually diagnosing and reverting changes under pressure, often outside business hours.

Kubernetes replaces that manual process with a single command that gradually rolls out the update, checks the health of each instance as it goes, and automatically reverts if something fails. For organizations running applications across many locations, whether that is a handful of data centers or hundreds of retail sites, this is the difference between hours of hands-on work and a process that runs unattended in the background.

Kubernetes Benefits

Kubernetes is widely adopted because it handles the repetitive, error-prone parts of running applications at scale so IT teams don't have to. Each capability below solves a specific operational problem rather than simply repeating a general business benefit.

• Self-Healing: Kubernetes continuously monitors container health and automatically restarts, replaces, or reschedules any that fail or stop responding, without human intervention.
• Automated Scaling: Applications can scale up or down automatically based on real-time demand, such as CPU or memory usage, so resources match actual traffic instead of worst-case estimates.
• Automated Rollouts and Rollbacks: Updates are applied incrementally with built-in health checks at each step, and Kubernetes can automatically roll back to the previous working version if a deployment fails.
• Portability and No Vendor Lock-In: Because Kubernetes is an open standard, applications packaged for one Kubernetes environment can generally run on another, whether that is a different cloud provider or on-premises infrastructure.
• Declarative Configuration: Teams define the desired end state of their applications in configuration files, and Kubernetes handles reaching and maintaining that state.
• Load Balancing and Service Discovery: Kubernetes automatically distributes network traffic across healthy application instances and gives each service a stable address, even as individual containers are created or destroyed.
• Storage Orchestration: Kubernetes can automatically mount and manage storage from a range of sources, including local storage, public cloud, and network-attached systems, based on each application's needs.

Kubernetes Architecture Explained

Kubernetes architecture is built around a cluster: one or more machines, called nodes, working together as a single system for running applications. Each node runs the software needed to host containers, and a set of control components decides where those containers run and keeps them healthy.

Nodes are the cluster's building blocks and can be physical servers or virtual machines. Each node runs a kubelet, an agent that communicates with the cluster's control components and manages containers on that node; a container runtime, such as containerd or Docker, that actually runs the containers; and kube-proxy, which handles network communication between pods. A pod is the smallest deployable unit in Kubernetes, typically holding one or more containers that share storage and networking. Pods are scheduled onto nodes based on available resources and are ephemeral by design, meaning Kubernetes can create, destroy, or move them freely as conditions change.

A service gives a set of pods a stable network identity, so other parts of the application can reliably reach them even as individual pods come and go. A namespace, meanwhile, logically divides a single cluster into separate virtual workspaces, which is useful for separating environments like development and production or isolating teams sharing the same infrastructure.

Visualizing Kubernetes: An Overview of the Architecture

At a high level, Kubernetes architecture separates two concerns: deciding what should run and where (the control plane), and actually running it (the nodes). Requests flow into the control plane, which schedules work onto available nodes; the nodes report their status back, and the control plane continuously reconciles the two so the cluster's real state matches the desired state defined by the user.

The diagram above outlines how the control plane, worker nodes, pods, and services fit together within a single cluster.

Kubernetes Control Plane

The control plane is the decision-making layer of a Kubernetes cluster, made up of a small number of distinct components that each handle one job:

• API Server: The entry point for all communication with the cluster, used by both administrators and internal components to read or change cluster state.
• Scheduler: Assigns newly created pods to specific nodes based on available resources, constraints, and workload requirements.
• Controller Manager: Continuously monitors the cluster and takes corrective action to reconcile the actual state with the desired state, such as replacing a failed pod.
• etcd: A distributed data store that holds the cluster's configuration data and current state, acting as the source of truth for everything running in the cluster.

Kubernetes Deployment

A Kubernetes Deployment is a declarative way to manage applications running in the cluster. Rather than manually creating and tracking individual pods, teams define the desired state, including the container image to use, the number of replicas, and the update strategy, in a YAML or JSON configuration file. Kubernetes then creates those pods, keeps the correct number running, and automatically handles rolling updates or rollbacks if something goes wrong. This declarative approach lets Kubernetes Deployments promote consistency and repeatability across environments, from a developer's test cluster to production.

Kubernetes Objects at a Glance

Kubernetes uses a set of standard objects to represent applications and how they run. Understanding these five is usually enough to read most Kubernetes configuration files.

Exploring Kubernetes Container Types and Images

Containers package an application's code, dependencies, and runtime environment into a single, isolated unit that runs consistently across different infrastructure. Within a Kubernetes cluster, most pods run application containers that hold the application code, though some also include sidecar containers that run alongside the main application to handle supporting tasks like logging, monitoring, or network proxying.

A container image is the blueprint used to create a container, built with a tool like Docker and stored in a container registry such as Docker Hub. Kubernetes pulls these images from the registry, schedules them onto nodes as containers, and continuously monitors their health, automatically restarting or rescheduling them if performance or availability drops below defined thresholds.

How to Run Kubernetes Containers

Running a container on Kubernetes generally follows three steps. First, define the container's configuration in a YAML file that specifies the image, resource requirements, and any ports to expose. A simple example for an Nginx web server looks like this:

apiVersion: v1
kind: Pod
metadata:
 name: nginx-pod
spec:
 containers:
 - name: nginx-container
 image: nginx:latest
 ports:
 - containerPort: 80

Second, apply that configuration to the cluster with kubectl apply -f nginx-pod.yaml. Third, monitor the deployed container's status and logs, for example with kubectl logs nginx-pod, to confirm it is running as expected. Behind the scenes, the container runtime handles starting, stopping, and monitoring the container, while tools like Prometheus and Grafana, or the EFK stack (Fluentd, Elasticsearch, Kibana), commonly collect and analyze logs and metrics across a cluster.