Horizontal Pod Autoscaler Kubernetes Operator

A few months ago the Kubernetes Operator SDK was released with one of its goals being the conversion of human operational knowledge into code. At Banzai Cloud we’ve been contributors and early adopters of this technology, since it provides a better standardized method of automating our processes and allows us to dramatically ease the lives of our customers. We are building a feature rich enterprise-grade application platform, built for containers on top of Kubernetes, called Pipeline, wherein we endeavour to automate the DevOps experience and the lifecycle of deployments. By default we collect metrics for all deployments done with Pipeline - using Prometheus - and autoscale them. However, our customers bring their own deployments to the platform as well (beside the default, supported ones). This open source component gives them a way to set up autoscaling without having to modify their deployment charts or deploy HPA; we have automated the whole process for them.

Horizontal Autoscaling on custom metrics

In the last post from our scaling series on Kubernetes we discussed how to autoscale Kubernetes deployments.

Kubernetes supports three different kind of autoscalers - cluster, horizontal and vertical.
Autoscaling Kubernetes clusters
Vertical pod autoscaler
Horizontal pod autoscaler

To quickly recap, in order to autoscale, you will need to create a HorizontalPodAutoscaler resource, which must be included in your Helm chart as well. Needless to say that, if we check the official Helm chart repository, most of the available charts don’t support autoscaling without modifications. However, some include the HorizontalPodAutoscaler resource definitions.

tl;dr:

You may not want to, or be able to edit a Helm chart in order to add an autoscaling feature. Nearly all charts supports custom annotations, so we believe it’s a good idea to setup autoscaling just by adding a few 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 a HorizontalPodAutoscaler resource, should you provide the correct autoscale annotations.

Autoscale by annotations

Autoscale annotations can be placed:

directly on Deployment / StatefulSet:

  apiVersion: extensions/v1beta1
  kind: Deployment
  metadata:
    name: example
    labels:
    annotations:
      autoscale/minReplicas: "1"
      autoscale/maxReplicas: "3"
      autoscale/cpu: "70"

or on spec.template.metadata.annotations:

  apiVersion: extensions/v1beta1
  kind: Deployment
  ...
  spec:
    replicas: 3
    template:
      metadata:
        labels:
          ...
        annotations:
          autoscale/minReplicas: "1"
          autoscale/maxReplicas: "3"
          autoscale/cpu: "70"

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

Annotations explained

All annotations must be prefixed with autoscale. It is required that you 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 we can use to specify metrics:

autoscale/cpu: "{targetAverageUtilizationPercentage}" - adds a Resource-type metric for the CPU with targetAverageUtilization set as specified, where targetAverageUtilizationPercentage should be an integer value between [1-100]
autoscale/memory: "{targetAverageValue}" - adds a Resource-type metric for memory with targetAverageValue set as specified, where targetAverageValue is a Quantity.
autoscale.pod/custom_metric_name: "{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, which we explored in detail in our previous post about using HPA with custom metrics.

HPA Operator

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 place to start. Now let’s see how we might add a simple CPU-based autoscale rule for Kafka brokers through the addition of some simple annotations:

Deploy operator

    helm install banzaicloud-stable/hpa-operator

Deploy Kafka chart, with autoscale annotations

    cat > values.yaml <<EOF
    {
        "statefullset": {
           "annotations": {
               "autoscale/minReplicas": "3",
               "autoscale/maxReplicas": "8",
               "autoscale/cpu": "60"
           }
        }
    }
    EOF

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

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!

If you’d like to learn more about Banzai Cloud you should explore other posts on this blog, as well as the Pipeline, Hollowtrees and Bank-Vaults projects on Github, or follow us on LinkedIn and Twitter.

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