How to Set Up Horizontal Pod Autoscaling in Kubernetes

Base de Conhecimento

Horizontal Pod Autoscaling (HPA) automatically adjusts the number of running Pod replicas based on observed CPU, memory, or custom metrics — scaling up under load and back down when demand drops, without manual intervention.

Prerequisites

A working Kubernetes cluster with the Metrics Server installed
An existing Deployment to scale

Step 1 — Install the Metrics Server (If Not Already Present)

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

The Metrics Server collects resource usage data that HPA relies on to make scaling decisions.

Step 2 — Verify Metrics Are Available

kubectl top nodes
kubectl top pods

Step 3 — Ensure Your Deployment Has Resource Requests Defined

spec:
  containers:
  - name: my-app
    image: my-app:latest
    resources:
      requests:
        cpu: 100m
        memory: 128Mi
      limits:
        cpu: 500m
        memory: 512Mi

HPA calculates scaling decisions as a percentage of the requests value — without this defined, autoscaling based on CPU/memory won't function correctly.

Step 4 — Create an HPA Resource

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

kubectl apply -f hpa.yaml

This configuration keeps average CPU utilization around 70%, scaling between 2 and 10 replicas as needed.

Step 5 — Create via Command Line (Alternative, Quicker)

kubectl autoscale deployment my-app --cpu-percent=70 --min=2 --max=10

Step 6 — Monitor Autoscaling in Action

kubectl get hpa my-app-hpa --watch

Shows current CPU utilization vs target, and current replica count, updating live.

Testing Autoscaling with a Load Generator

kubectl run load-test --image=busybox --restart=Never -- /bin/sh -c \
  "while true; do wget -q -O- http://my-app-service; done"

Generates continuous load against your service to observe the HPA scale up in real time; delete this test Pod once done (kubectl delete pod load-test).

Scaling on Memory Instead of (or Alongside) CPU

metrics:
- type: Resource
  resource:
    name: memory
    target:
      type: Utilization
      averageUtilization: 80

Common Errors

HPA shows "unknown" for current metrics — the Metrics Server isn't installed or isn't functioning correctly; verify with kubectl top pods first.

HPA never scales despite high load — verify resource requests are actually defined on the target Deployment's containers; this is the single most common oversight.

FAQ

Does HPA also scale the underlying VPS/node resources?
No — HPA only scales the number of Pod replicas within existing node capacity; scaling the nodes themselves (Cluster Autoscaler) is a separate, more advanced mechanism not typically applicable to a fixed set of VPS instances.

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