14.3HPA Configuration for Performance

Basic CPU-Based HPA

# hpa-cpu-based.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: checkout-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: checkout-service
  minReplicas: 3
  maxReplicas: 50
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 60   # scale up when avg CPU > 60%
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 30   # react to spikes quickly
      policies:
        - type: Percent
          value: 100      # can double pod count per scaling event
          periodSeconds: 60
        - type: Pods
          value: 5         # or add 5 pods, whichever is larger
          periodSeconds: 60
      selectPolicy: Max
    scaleDown:
      stabilizationWindowSeconds: 300  # wait 5 min before scaling down
      policies:
        - type: Percent
          value: 10       # remove at most 10% of pods per interval
          periodSeconds: 60

Custom Metrics HPA (Requests Per Second)

CPU-based HPA is often too slow for traffic-driven scaling. Custom metrics based on request rate can be more responsive:

# hpa-custom-metrics.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: checkout-hpa-custom
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: checkout-service
  minReplicas: 3
  maxReplicas: 50
  metrics:
    - type: Pods
      pods:
        metric:
          name: http_requests_per_second
        target:
          type: AverageValue
          averageValue: 100   # target 100 req/s per pod
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70