capacity-planning

# Skill: Capacity Planning > **Expertise:** Traffic forecasting, per-pod resource modeling, node pool sizing, cluster capacity headroom, VPA/HPA tuning for growth. ## When to load When planning for growth, validating current cluster headroom, sizing node pools, or preparing for a high-traffic event (sale, launch). ## Traffic Forecasting ```promql # Current RPS baseline (7-day average) avg_over_time( sum(rate(http_requests_total{service="checkout-service"}[5m]))[7d:5m] ) # Peak RPS (7-day p99) quantile_over_time(0.99, sum(rate(http_requests_total{service="checkout-service"}[5m]))[7d:5m] ) # Week-over-week growth rate ( avg_over_time(sum(rate(http_requests_total[5m]))[7d:5m]) / avg_over_time(sum(rate(http_requests_total[5m]))[7d:5m] offset 7d) ) - 1 # e.g. 0.08 = 8% weekly growth → ~3.5× in 6 months ``` ## Per-Pod Resource Modeling ``` Model: what resources does 1 pod consume per RPS unit? Step 1: current pod metrics - pods = 4 (HPA current) - RPS = 200 req/s (avg) - CPU per pod = 320m (avg), 480m (p99) - Memory per pod = 280Mi (avg), 380Mi (peak) Step 2: per-RPS resource cost - CPU per RPS = 320m / (200/4) = 6.4m CPU per RPS - Mem per RPS = 280Mi / (200/4) = 5.6Mi per RPS Step 3: future requirements at 2× traffic (400 RPS) - CPU needed = 400 × 6.4m = 2560m = 2.56 cores - Mem needed = 400 × 5.6Mi = 2240Mi ≈ 2.2Gi - Pods needed (at 70% CPU target) = 2560m / (500m × 0.7) = 7.3 → 8 pods min - Update HPA maxReplicas to accommodate ``` ## C...