Kubernetes
Open-source platform for container orchestration. Automates deployment, scaling and management of containerized applications in clusters.
Kubernetes (K8s) is the de facto standard for container orchestration. Originally from Google and open-sourced in 2014, it now runs a large share of the world’s container workloads. It automates deployment, scaling and operation of containerized apps across clusters. Kubernetes is the “OS of the cloud” – with significant complexity.
What is Kubernetes?
Kubernetes (from Greek “helmsman”, K8s) is an open-source platform for automating deployment, scaling and management of containerized applications. It manages a cluster of nodes (servers) running pods (smallest deployable unit, usually one container). Key concepts: Deployments (desired state), Services (network abstraction for pods), Ingress (HTTP routing), ConfigMaps/Secrets (config), Namespaces (logical separation), Horizontal Pod Autoscaler (auto-scaling). The CNCF stewards the project.
How does Kubernetes work?
Kubernetes is declarative: you describe desired state in YAML (e.g. 3 replicas, 512 MB RAM, port 80). The control plane continuously reconciles actual state with desired state and fixes drift. If a pod dies, it is replaced (self-healing). Under load, the HPA scales pods. Rolling updates replace old with new without downtime. The scheduler places pods on nodes based on resources and constraints.
Practical Examples
Microservices platform: 50 services in a cluster on AWS EKS, each scaling independently, rolling updates via CI/CD.
E-commerce auto-scaling: On Black Friday the cluster scales from 5 to 100 pods on CPU/request metrics, then scales back.
Multi-tenant SaaS: Customers in separate namespaces with resource limits and network policies.
ML pipeline: Training jobs on GPU nodes, results deployed as new images and rolled out with canary deployments.
Typical Use Cases
Microservices: Orchestrate many services with discovery and load balancing
Auto-scaling: Scale on CPU, memory, custom metrics or queue depth
CI/CD: GitOps with ArgoCD or Flux for declarative, versioned deployments
Multi-cloud: Same orchestration across AWS, Azure and GCP
Batch: Jobs and CronJobs for data-heavy batch workloads
Advantages and Disadvantages
Advantages
- Self-healing: Failed containers are replaced and traffic redirected
- Auto-scaling: Horizontal and vertical scaling from metrics
- Rolling updates: Zero-downtime deployments
- Portable: Runs on any cloud, on-prem and locally
- Ecosystem: Helm, Istio, ArgoCD, Prometheus, Grafana and more
Disadvantages
- Complexity: Steep learning curve and many components
- Overhead: For small apps (e.g. under 5 services) K8s can be overkill
- Cost: Managed K8s (EKS, GKE, AKS) and infrastructure add up
- Debugging: Distributed systems on K8s need specific tools and skills
- YAML: Manifests can grow to hundreds of lines
Frequently Asked Questions about Kubernetes
Do I need Kubernetes?
Managed or self-managed Kubernetes?
How do I learn Kubernetes?
Related Terms
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