hpa-operator

README

⚠️ DEPRECATION NOTICE ⚠️

This operator and the helm chart are deprecated and no longer actively maintained.

Horizontal Pod Autoscaler operator

You may not want nor can edit a Helm chart just to add an autoscaling feature. Nearly all charts supports custom annotations so we believe that it would be a good idea to be able to setup autoscaling just by adding some simple annotations to your deployment.

We have open sourced a Horizontal Pod Autoscaler operator. This operator watches for your Deployment or StatefulSet and automatically creates an HorizontalPodAutoscaler resource, should you provide the correct autoscale annotations.

• Horizontal Pod Autoscaler operator
• Horizontal Pod Autoscaler operator Helm chart

Autoscale by annotations

Autoscale annotations can be placed:

• directly on Deployment / StatefulSet:

 apiVersion: extensions/v1beta1
 kind: Deployment
 metadata:
   name: example
   labels:
   annotations:
     hpa.autoscaling.banzaicloud.io/minReplicas: "1"
     hpa.autoscaling.banzaicloud.io/maxReplicas: "3"
     cpu.hpa.autoscaling.banzaicloud.io/targetAverageUtilization: "70"

• or on spec.template.metadata.annotations:

 apiVersion: extensions/v1beta1
 kind: Deployment
 ...
 spec:
   replicas: 3
   template:
     metadata:
       labels:
         ...
       annotations:
           hpa.autoscaling.banzaicloud.io/minReplicas: "1"
           hpa.autoscaling.banzaicloud.io/maxReplicas: "3"
           cpu.hpa.autoscaling.banzaicloud.io/targetAverageUtilization: "70"

The Horizontal Pod Autoscaler operator takes care of creating, deleting, updating HPA, with other words keeping in sync with your deployment annotations.

Annotations explained

All annotations must contain the autoscaling.banzaicloud.io prefix. It is required to specify minReplicas/maxReplicas and at least one metric to be used for autoscale. You can add Resource type metrics for cpu & memory and Pods type metrics. Let's see what kind of annotations can be used to specify metrics:

• cpu.hpa.autoscaling.banzaicloud.io/targetAverageUtilization: "{targetAverageUtilizationPercentage}" - adds a Resource type metric for cpu with targetAverageUtilizationPercentage set as specified, where targetAverageUtilizationPercentage should be an int value between [1-100]

• cpu.hpa.autoscaling.banzaicloud.io/targetAverageValue: "{targetAverageValue}" - adds a Resource type metric for cpu with targetAverageValue set as specified, where targetAverageValue is a Quantity.

• memory.hpa.autoscaling.banzaicloud.io/targetAverageUtilization: "{targetAverageUtilizationPercentage}" - adds a Resource type metric for memory with targetAverageUtilizationPercentage set as specified, where targetAverageUtilizationPercentage should be an int value between [1-100]

• memory.hpa.autoscaling.banzaicloud.io/targetAverageValue: "{targetAverageValue}" - adds a Resource type metric for memory with targetAverageValue set as specified, where targetAverageValue is a Quantity.

• pod.hpa.autoscaling.banzaicloud.io/customMetricName: "{targetAverageValue}" - adds a Pods type metric with targetAverageValue set as specified, where targetAverageValue is a Quantity.

To use custom metrics from Prometheus, you have to deploy Prometheus Adapter and Metrics Server, explained in detail in our previous post about using HPA with custom metrics

Custom metrics from version 0.1.5

From version 0.1.5 we have removed support for Pod type custom metrics and added support for Prometheus backed custom metrics exposed by Kube Metrics Adapter. To setup HPA based on Prometheus one has to setup the following deployment annotations:

prometheus.customMetricName.hpa.autoscaling.banzaicloud.io/query: "sum({kubernetes_pod_name=~"^YOUR_POD_NAME.*",__name__=~"YOUR_PROMETHUES_METRICNAME"})" prometheus.customMetricName.hpa.autoscaling.banzaicloud.io/targetValue: "{targetValue}" prometheus.customMetricName.hpa.autoscaling.banzaicloud.io/targetAverageValue: "{targetAverageValue}"

The query should be a syntactically correct Prometheus query. Pay attention to select only metrics related to your Deployment / Pod / Service. You should specify either targetValue or targetAverageValue, in which case metric value is averaged with current replica count.

Quick usage example

Let's pick Kafka as an example chart, from our curated list of Banzai Cloud Helm charts. The Kafka chart by default doesn't contains any HPA resources, however it allows specifying Pod annotations as params so it's a good example to start with. Now let's see how you can add a simple cpu based autoscale rule for Kafka brokers by adding some simple annotations:

1. Deploy operator

 helm repo add banzaicloud-stable https://kubernetes-charts.banzaicloud.com
 helm install hpa-operator banzaicloud-stable/hpa-operator

1. Deploy Kafka chart, with autoscale annotations

 cat > values.yaml <<EOF
 {
     "statefullset": {
        "annotations": {
             hpa.autoscaling.banzaicloud.io/minReplicas: "3"
             hpa.autoscaling.banzaicloud.io/maxReplicas: "8"
             cpu.hpa.autoscaling.banzaicloud.io/targetAverageUtilization: "60"
        }
     }
 }
 EOF

 helm install -f values.yaml banzaicloud-stable/kafka

1. Check if HPA is created

 kubectl get hpa

 NAME      REFERENCE           TARGETS           MINPODS   MAXPODS   REPLICAS   AGE
 kafka     StatefulSet/kafka   3% / 60%          3         8         1          1m

Happy Autoscaling!