Production-grade container orchestration for modern applications
Kubernetes (K8s) is an open-source platform for automating the deployment, scaling and management of containerized applications. Originally developed by Google.
As the de-facto standard for container orchestration, Kubernetes enables highly available, scalable microservice architectures.
Automatic scaling based on load
Automatic recovery of failed containers
Runs on AWS, Google Cloud, Azure and on-premise
Smallest deployable unit
Network abstraction for pod groups
Declarative updates for pods
HTTP/HTTPS routing to services
Enterprise companies rely on Kubernetes
Everything you need to know about Kubernetes for container orchestration and cloud-native applications
Kubernetes automates container deployment, scaling, and management across clusters of machines, eliminating manual operational overhead. It provides self-healing capabilities by automatically restarting failed containers, replacing unhealthy nodes, and maintaining desired application state without human intervention.
Service discovery and load balancing are built-in, enabling microservices to communicate reliably even as containers move between nodes. Kubernetes handles rolling updates with zero downtime, automatic rollbacks on failure, and horizontal scaling based on resource usage or custom metrics.
Resource optimization includes intelligent scheduling that places containers on nodes based on resource requirements and constraints, while namespace isolation enables multi-tenancy. Configuration management through ConfigMaps and Secrets separates application configuration from container images, improving security and deployment flexibility.
Cloud-native application design follows the twelve-factor app principles with stateless processes, externalized configuration, and horizontal scalability. Applications should handle graceful shutdowns through proper signal handling and health checks for liveness and readiness probes.
Microservices architecture works best with Kubernetes, where each service runs in its own containers with defined resource limits and requests. Data persistence uses persistent volumes rather than local storage, while inter-service communication leverages Kubernetes services and ingress controllers for external access.
Container design includes minimal base images for security, non-root users for safety, and proper logging to stdout/stderr for log aggregation. Applications should be resilient to network partitions, node failures, and resource constraints common in distributed environments.
Kubernetes security involves multiple layers including RBAC (Role-Based Access Control) for API access, pod security policies to enforce security standards, and network policies for micro-segmentation. Container image security includes vulnerability scanning, trusted registries, and admission controllers that prevent insecure deployments.
Secrets management uses Kubernetes Secrets with encryption at rest, while service accounts provide identity for pods with least-privilege access principles. Security contexts define user and group IDs, filesystem permissions, and security capabilities for containers to run with minimal privileges.
Advanced security includes service mesh integration for encrypted inter-service communication, admission webhooks for policy enforcement, and comprehensive audit logging. Regular security scanning, policy validation, and compliance monitoring ensure ongoing security posture in dynamic containerized environments.
Production Kubernetes management involves comprehensive monitoring with Prometheus and Grafana, centralized logging with ELK or Loki stacks, and distributed tracing for microservices debugging. Automated alerting ensures rapid response to issues, while capacity planning prevents resource exhaustion.
GitOps practices enable declarative infrastructure management where cluster state is managed through version control. Helm charts or Kustomize provide templating and configuration management, while CI/CD pipelines automate testing and deployment with proper staging environments.
Operational concerns include backup and disaster recovery strategies, cluster upgrades with minimal downtime, and cost optimization through resource right-sizing and node autoscaling. Multi-cluster management becomes important for high availability, geographic distribution, and environment separation.
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