Modern web applications are expected to handle unpredictable traffic patterns while maintaining performance and reliability. Kubernetes addresses this challenge through native autoscaling capabilities, and at the heart of pod-level scaling lies the Horizontal Pod Autoscaler (HPA). HPA dynamically adjusts the number of running pods based on real-time resource consumption or custom metrics, ensuring optimal performance without manual intervention.

What Is Kubernetes Horizontal Pod Autoscaler?

The Horizontal Pod Autoscaler automatically increases or decreases the number of pod replicas in a Deployment, ReplicaSet, or StatefulSet based on observed metrics such as CPU utilization, memory usage, or custom application metrics.

Instead of overprovisioning resources or manually scaling during traffic spikes, HPA allows applications to scale horizontally, ensuring elasticity and cost efficiency.

How HPA Works Internally

HPA operates as a Kubernetes control loop and follows this general workflow:

1. Metrics Collection Metrics are gathered from:
   • Metrics Server (CPU, memory)
   • Custom or external metrics providers (Prometheus, cloud monitoring APIs)
2. Metric Evaluation
   HPA compares current metrics against defined target thresholds.

     3. Scaling Decision
         If the metrics exceed or fall below the target, Kubernetes calculates the desired number of replicas.

     4. Replica Adjustment
         The controller updates the replica count on the target resource.

This loop runs approximately every 15 seconds, allowing near-real-time responsiveness to workload changes.

Prerequisites for Using HPA

• A running Kubernetes cluster
• Metrics Server installed and functioning
• Resource requests defined for pods
• A scalable workload (Deployment, ReplicaSet, or StatefulSet)

Without resource requests, Kubernetes cannot compute utilization percentages, and HPA will not function correctly.

Basic HPA Configuration (CPU-Based Scaling)

The simplest HPA configuration scales pods based on CPU utilization.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
     name: web-app-hpa
spec: scaleTargetRef:
     apiVersion: apps/v1
kind: Deployment
name: web-app
minReplicas: 2
maxReplicas: 10
metrics:
– type: Resource
  resource:
   name: cpu
   target:
    type: Utilization
    averageUtilization: 70

Here, Pods scale when average CPU usage exceeds 70% and the application runs between 2 and 10 replicas.

Scaling Based on Memory Usage

While CPU-based scaling is common, memory-based scaling is equally useful for applications with high memory pressure.

metrics:
– type: Resource
   resource:
      name: memory
      target:
         type: Utilization
         averageUtilization: 75

Memory-based HPA requires careful tuning, as memory does not decrease as predictably as CPU.

Custom Metrics and Application-Level Autoscaling

For advanced use cases, HPA can scale using custom metrics, such as:

• • Requests per second
  • Queue length
  • Active sessions
  • Application latency

This is commonly implemented using Prometheus and a custom metrics adapter. Example (external metric):

metrics:
– type: External external:
metric:
name: http_requests_per_second target:
type: Value value: 100

This approach enables autoscaling based on real business or application performance indicators rather than raw infrastructure metrics.

Stabilization and Scaling Behavior.

Kubernetes allows fine-grained control over scaling behavior to prevent flapping or aggressive scaling.

behavior:
scaleUp: stabilizationWindowSeconds: 60 policies:
– type: Percent value: 50
periodSeconds: 60 scaleDown:
stabilizationWindowSeconds: 300

These settings provide:

• Limit rapid scale-up
• Delay scale-down to avoid premature pod termination

Best Practices for Production Deployments

• Always define CPU and memory requests
• Set realistic min and max replicas
• Use stabilization windows to avoid oscillation
• Monitor scaling events and pod startup times
• Test autoscaling under load using tools like Locust or k6
• Prefer application-level metrics when possible

Common Pitfalls to Avoid

• Missing or incorrect resource requests
• Overly aggressive scaling thresholds
• Scaling stateful or startup-heavy workloads without tuning
• Relying solely on CPU for non-CPU-bound applications

When Should You Use HPA?

HPA is ideal for:

• Web applications with variable traffic
• APIs and microservices
• SaaS platforms and multi-tenant environments
• Cloud-native workloads requiring elasticity

It may not be suitable for workloads with long initialization times or strict state dependencies unless carefully designed.

Conclusion

Kubernetes Horizontal Pod Autoscaler is a foundational building block for scalable, resilient web applications. By leveraging real-time metrics and declarative policies, HPA enables platforms to respond dynamically to load while maintaining cost efficiency and performance.

When combined with proper observability, resource planning, and application-aware metrics, HPA becomes a powerful mechanism for operating modern cloud-native applications at scale.