How do you implement Kubernetes performance tuning and optimization for high-throughput applications?

Kubernetes performance optimization requires tuning at multiple layers including cluster configuration, resource allocation, networking, and application-specific optimizations.\n\nPerformance Optimization Areas:\n• Node configuration: CPU, memory, and kernel tuning\n• Container runtime: Docker vs containerd optimization\n• Network performance: CNI plugin selection and tuning\n• Storage optimization: Volume types and performance classes\n• Scheduler tuning: Custom scheduling policies\n\nCluster-level Optimizations:\n• etcd performance tuning\n• API server scaling and caching\n• Controller manager optimization\n• kubelet configuration tuning\n• kube-proxy mode selection (iptables vs IPVS)\n\nWorkload Optimizations:\n• Resource requests and limits tuning\n• Quality of Service class selection\n• Pod disruption budgets\n• Topology spread constraints\n• Node affinity and anti-affinity\n\nExample Performance Configuration:\napiVersion: v1\nkind: Pod\nspec:\n containers:\n - name: app\n resources:\n requests:\n cpu: 2000m\n memory: 4Gi\n limits:\n cpu: 4000m\n memory: 8Gi\n topologySpreadConstraints:\n - maxSkew: 1\n topologyKey: kubernetes.io/hostname\n\nBest Practices:\n• Continuous performance monitoring\n• Load testing in staging environments\n• Gradual optimization with measurement\n• Use performance profiling tools\n• Regular performance reviews