14.2k6 Test for HPA Validation

// k6-container-scaling-test.js
// Verify Kubernetes HPA responds correctly to traffic spikes
import http from 'k6/http';
import { check, sleep } from 'k6';
import { Trend } from 'k6/metrics';

const scalingLatency = new Trend('scaling_response_time', true);

export const options = {
  scenarios: {
    spike: {
      executor: 'ramping-arrival-rate',
      startRate: 10,
      timeUnit: '1s',
      preAllocatedVUs: 50,
      maxVUs: 500,
      stages: [
        { duration: '1m', target: 10 },    // baseline
        { duration: '30s', target: 200 },   // sudden spike
        { duration: '5m', target: 200 },    // sustain spike (HPA should scale)
        { duration: '30s', target: 10 },    // drop back
        { duration: '5m', target: 10 },     // verify scale-down
      ],
    },
  },
  thresholds: {
    // Even during spike, 95th percentile should stay under 2s
    // (once HPA has scaled, which may take 1-2 minutes)
    http_req_duration: ['p(95)<2000'],
    http_req_failed: ['rate<0.05'],
  },
};

export default function () {
  const res = http.get('https://app.example.com/api/heavy-computation');
  scalingLatency.add(res.timings.duration);

  check(res, {
    'status is 200': (r) => r.status === 200,
    'no 503 (service unavailable)': (r) => r.status !== 503,
  });
}

What to Monitor During the Test

While k6 runs, monitor the Kubernetes cluster in a parallel terminal or dashboard:

# Watch pod count change in real-time
kubectl get pods -l app=my-service -w

# Watch HPA status
kubectl get hpa my-service-hpa -w

# Check HPA events for scaling decisions
kubectl describe hpa my-service-hpa

Expected Timeline

T+0:00  - 10 req/s, 3 pods (baseline)
T+1:00  - Spike to 200 req/s, latency increases immediately
T+1:30  - HPA detects CPU > target, begins scaling
T+2:00  - New pods scheduled, pulling images
T+2:30  - New pods running, latency begins to decrease
T+3:00  - Full scale-up complete (e.g., 15 pods), latency normalized
T+6:30  - Traffic drops to 10 req/s
T+7:00  - HPA begins scale-down (cooldown period)
T+11:30 - Scale-down complete, back to 3 pods

Critical question: What happens to users during T+1:00 to T+3:00 (the scaling gap)? This is where you discover if your HPA configuration is adequate.