namespace: autoscaling-demo

d breaks stateful assumptions.

• CA Node Group Alignment: CA scales node pools, not individual nodes. Node groups must have matching labels, taints, and instance types to avoid scheduling deadlocks.

Code Examples

HPA with Custom Metric (Prometheus Adapter)

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: web-frontend-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-frontend
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Pods
    pods:
      metric:
        name: http_requests_per_second
      target:
        type: AverageValue
        averageValue: "500"
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 45
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60
    scaleDown:
      stabilizationWindowSeconds: 150
      policies:
      - type: Percent
        value: 20
        periodSeconds: 120

VPA in Recommendation Mode

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: web-frontend-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-frontend
  updatePolicy:
    updateMode: "Off" # Recommendation only
  resourcePolicy:
    containerPolicies:
    - containerName: "*"
      minAllowed:
        cpu: "100m"
        memory: "128Mi"
      maxAllowed:
        cpu: "2"
        memory: "2Gi"

KEDA ScaledObject for Kafka

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: kafka-consumer-scaledobject
spec:
  scaleTargetRef:
    name: kafka-consumer
  pollingInterval: 15
  cooldownPeriod: 30
  minReplicaCount: 2
  maxReplicaCount: 50
  triggers:
  - type: kafka
    metadata:
      bootstrapServers: kafka-broker:9092
      consumerGroup: processing-group
      topic: events
      lagThreshold: "100"

Pitfall Guide

1. Missing or Misaligned Resource Requests

HPA calculates utilization as (current usage / request) * 100. Without requests, HPA defaults to 0, triggering immediate scale-up to maxReplicas. Misaligned requests cause premature scaling or scheduler starvation. Always define requests; use VPA recommendations to calibrate.

2. Aggressive Scale-Up Thresholds

Setting targetAverageValue too low creates artificial saturation. If a pod handles 500 req/s comfortably, targeting 200 req/s forces unnecessary replica creation. Calibrate thresholds against load testing data, not theoretical capacity.

3. Ignoring Pod Startup Latency

Scale-up requests new pods, but initialization (image pull, init containers, health checks) adds 10-40s. If the HPA targets a metric that spikes faster than startup time, traffic hits unready pods. Use readinessGates and align stabilizationWindowSeconds with actual boot time.

4. VPA Auto Mode in Production

VPA Auto recreates pods to apply new resource requests. This breaks in-memory caches, active connections, and stateful assumptions. Use Off or Initial mode. Apply recommendations manually or via GitOps pipelines.

5. Cluster Autoscaler Node Group Misconfiguration

CA scales node groups, not individual nodes. If node groups lack proper labels, taints, or instance type diversity, pending pods remain unschedulable. CA will not provision nodes that violate affinity rules or exceed cluster resource limits. Verify node group tags and scheduling constraints.

6. Custom Metric Pipeline Bottlenecks

Prometheus scrape intervals, adapter latency, and metric cardinality directly impact HPA responsiveness. A 30s scrape interval + 15s adapter delay = 45s feedback lag. Align scrape intervals with workload volatility. Avoid high-cardinality labels in scaling metrics.

7. Scale-Down Thrashing Without Stabilization

Default 300s scale-down delay is often disabled, causing rapid pod eviction during traffic dips. Subsequent spikes recreate pods, wasting cold-start time. Enforce scaleDown.stabilizationWindowSeconds and monitor kubectl get hpa events to detect oscillation.

Best Practices from Production

• Right-size before autoscaling. VPA recommendations eliminate guesswork.
• Use custom metrics for I/O-bound workloads. CPU is a lagging indicator.
• Align stabilization windows with actual startup/shutdown times.
• Test scaling behavior under controlled load injection before production rollout.
• Monitor kube_pod_status_phase and scheduler_pending_pods to detect CA bottlenecks.
• Never disable PDBs during scaling events. They prevent cascading failures.

Production Bundle

Action Checklist

• Audit resource requests: Ensure every container defines requests.cpu and requests.memory
• Deploy VPA in Off mode: Collect 7-14 days of usage data before applying recommendations
• Select scaling metrics: Replace CPU with request latency, queue depth, or connection count where applicable
• Configure stabilization windows: Set scale-up to 30-60s, scale-down to 120-180s based on workload characteristics
• Verify Cluster Autoscaler node groups: Confirm labels, taints, and instance types match scheduling constraints
• Implement PodDisruptionBudgets: Define minAvailable or maxUnavailable to prevent mass evictions
• Load-test scaling behavior: Inject traffic spikes and measure scale-up latency, pod readiness, and cost impact
• Monitor scaling events: Track HPA/CA metrics in dashboards and alert on oscillation or pending pods

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
Stateful web service with predictable traffic	HPA + Custom Metrics (Prometheus)	Aligns scaling with actual request load, reduces idle replicas	-25% waste
Async job processor with bursty queues	KEDA + Queue Depth Trigger	Event-driven scaling eliminates polling overhead and cold starts	-35% waste
Variable memory footprint workloads	VPA (Recommendation) + HPA	Right-sizes requests, prevents OOMKills and scheduler starvation	-20% waste
Multi-tenant cluster with strict SLOs	HPA + PDB + CA + Stabilization Windows	Prevents cascading failures, maintains capacity during scale-down	Neutral (stability gain)
Low-traffic internal tools	Static provisioning + VPA (Off)	Autoscaling overhead exceeds benefit; right-sizing suffices	-15% waste

Configuration Template

# namespace: autoscaling-demo
apiVersion: apps/v1
kind: Deployment
metadata:
  name: demo-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: demo-app
  template:
    metadata:
      labels:
        app: demo-app
    spec:
      containers:
      - name: app
        image: myregistry/demo-app:latest
        resources:
          requests:
            cpu: "250m"
            memory: "256Mi"
          limits:
            cpu: "500m"
            memory: "512Mi"
---
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: demo-app-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: demo-app
  updatePolicy:
    updateMode: "Off"
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: demo-app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: demo-app
  minReplicas: 3
  maxReplicas: 15
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 45
    scaleDown:
      stabilizationWindowSeconds: 150
      policies:
      - type: Percent
        value: 25
        periodSeconds: 60
---
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: demo-app-pdb
spec:
  minAvailable: 2
  selector:
    matchLabels:
      app: demo-app

Quick Start Guide

1. Apply resource requests: Ensure all deployments define resources.requests. Deploy VPA in Off mode and collect metrics for 7 days.
2. Create HPA manifest: Use the template above. Replace scaleTargetRef with your deployment name. Adjust minReplicas, maxReplicas, and metric thresholds based on load test data.
3. Deploy PDB: Apply the PodDisruptionBudget to prevent scale-down from evicting all replicas simultaneously.
4. Verify scaling behavior: Run kubectl get hpa -w and inject traffic using hey or k6. Confirm replica count increases within 45-60s and stabilizes.
5. Monitor and tune: Check kubectl describe hpa <name> for metric readings and scaling events. Adjust stabilization windows and thresholds if oscillation occurs. Enable Cluster Autoscaler node group tagging if pods remain pending.