What is k8s?

k8s is short for Kubernetes, an open‑source platform that helps you run many containers (small, lightweight pieces of software) across a group of computers. Think of it as a manager that makes sure each container is in the right place, has the resources it needs, and stays running even if something fails.

Let's break it down

  • Container: A tiny, portable package that holds an app and everything it needs to run.
  • Node: A single computer (physical or virtual) that runs containers.
  • Cluster: A collection of nodes that work together, managed by Kubernetes.
  • Pod: The smallest unit Kubernetes controls; it can hold one or more tightly‑coupled containers.
  • Control Plane: The brain of the cluster; it decides where pods go, monitors health, and handles scaling.
  • API Server: The entry point where you tell Kubernetes what you want (e.g., “run 3 copies of this app”).

Why does it matter?

Kubernetes automates the hard parts of running many containers: placement, scaling, updates, and recovery. This means developers can focus on writing code, while operations get reliability, efficient use of hardware, and the ability to handle traffic spikes without manual intervention.

Where is it used?

  • Cloud providers (Google Cloud GKE, Amazon EKS, Azure AKS) offer managed Kubernetes services.
  • Companies run it on‑premises for private data centers.
  • It powers micro‑service architectures, CI/CD pipelines, big data processing, and edge computing devices.
  • Any scenario that needs to run many containerized apps reliably at scale can benefit.

Good things about it

  • Portability: Works the same on any cloud or on‑premises environment.
  • Self‑healing: Restarts failed containers, replaces unhealthy nodes automatically.
  • Scalability: Adds or removes pods based on demand with simple commands or metrics.
  • Extensibility: Plugins and custom resources let you add new features (e.g., networking, storage).
  • Large ecosystem: Lots of tools, tutorials, and community support.

Not-so-good things

  • Complexity: Learning the concepts and commands can be steep for beginners.
  • Resource overhead: The control plane and extra components consume CPU and memory.
  • Operational burden: Managing upgrades, security patches, and cluster configuration requires expertise.
  • Debugging difficulty: Issues can span multiple layers (pods, nodes, network), making troubleshooting harder.
  • Not a silver bullet: Simple applications may not need the full power of Kubernetes, adding unnecessary complexity.