resource-tuning

# Skill: Resource Tuning > **Expertise:** CPU/memory right-sizing, HPA, VPA, KEDA event-driven scaling, namespace quota design. ## When to load When pods are OOMKilled, CPU-throttled, underutilised, or autoscaling isn't working as expected. ## Right-Sizing Methodology ``` 1. Observe → 7-day peak metrics (kubectl top / Prometheus) 2. Set request = average × 1.1 (room for normal variance) 3. Set limit = p99 peak × 1.3 (room for spike without OOM) 4. Verify no throttling with: throttled_cpu_seconds metric 5. Adjust after 2 weeks of production data ``` ```bash # Current resource usage (snapshot) kubectl top pods -n <ns> --sort-by=memory kubectl top pods -n <ns> --sort-by=cpu # Historical usage via Prometheus (7-day p99) # CPU p99: rate(container_cpu_usage_seconds_total{namespace="<ns>",pod=~"my-app-.*"}[5m]) > quantile_over_time(0.99, rate(...[5m])[7d:5m]) # Memory p99: quantile_over_time(0.99, container_memory_working_set_bytes{namespace="<ns>",pod=~"my-app-.*"}[7d:5m]) ``` ## HPA Configuration ```yaml # CPU + Memory HPA (Kubernetes 1.23+) apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: my-service namespace: production spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: my-service minReplicas: 2 maxReplicas: 20 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 # scale out at 70% avg CPU - type: Resource ...