Automatic Scaling of Apinizer Environments

You can configure horizontal auto-scaling operations for pods, optimize resource usage by automatically increasing and decreasing pod count according to application demands, and configure scaling policies and thresholds.

This document explains how to configure horizontal auto-scaling (horizontal auto-scaling) operations for pods.

This horizontal scaling feature optimizes resource usage by automatically increasing and decreasing pod count according to application demands.

To use the scaling feature in your Kubernetes cluster, a metric-server must be installed. If it is not present, you can click here for installation.

Configuration

Create yaml file:

sudo vi hpa-autoscaling.yaml

Edit NAMESPACE and DEPLOYMENT_NAME fields in yaml according to your application.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: apinizer-hpa
  namespace: <NAMESPACE>
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: <DEPLOYMENT_NAME>
  minReplicas: 1
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
    - type: Resource
      resource:
        name: memory
        target:
          type: Utilization
          averageUtilization: 70
  behavior:
    scaleDown:
      selectPolicy: Max
      policies:
        - type: Pods
          value: 1
          periodSeconds: 60
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
        - type: Pods
          value: 1
          periodSeconds: 60
      selectPolicy: Max

Apply yaml file:

kubectl apply -f manager-hpa-autoscaling.yaml

Configuration Parameters

Field	Description
scaleTargetRef	This field specifies which deployment scaling will work for. Example: manager.
minReplicas	Minimum number of pods that should exist. Example: 2.
maxReplicas	Maximum number of pods that can exist in the system. Example: 10.
averageUtilization	In this field, you can enter a percentage value for cpu and memory usage. It will create pods when it exceeds the specified level.
scaleDown	When it falls below the target usage level, it will reduce pods by the given value amount every 60 seconds from the pods it newly created.
scaleUp	When it exceeds the target usage level, it is checked for 60 seconds, which is the stabilizationWindowSeconds value specified. If the condition is met, one pod is added to your cluster every 60 seconds.

To disable scaleDown feature, change it to “selectPolicy: Disabled”.