Autoscaling using datadog as a external metrics provider

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Since Kubernetes v1.2 we can autoscale an application based on metrics like CPU provided by the metrics-server. As of Kubernetes v1.6, it is possible to autoscale off of custom metrics and later on, starting Kubernetes v1.10, we can autoscale using any metric from outside the cluster, like the ones collected by datadog

To be able to configure an HPA object to use datadog's metrics we will have to enable the metrics provider. If we are using it's helm chart to deploy the Cluster Agent we will have to set the following values:

  appKey: abcd...
  apiKey: efgh...

  enabled: true
    enabled: true

Once the Cluster Agent have been upgraded and the metrics provider is available:

$ kubectl get pods -n datadog
NAME                                          READY   STATUS    RESTARTS   AGE
datadog-2pc4x                                 3/3     Running   0          7m13s
datadog-cluster-agent-6cc7cf55d6-6946r        1/1     Running   1          120m
datadog-kube-state-metrics-699964c777-wrkwz   1/1     Running   0          100m
datadog-xk79r                                 3/3     Running   0          6m49s
$ kubectl get apiservice | grep datadog        datadog/datadog-cluster-agent-metrics-api   True        100m

We can now start pushing HPA objects with references to external metrics, for example:

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
  name: ampa
    apiVersion: apps/v1
    kind: Deployment
    name: ampa
  minReplicas: 2
  maxReplicas: 22
    - type: External
        metricName: kubernetes.kubelet.cpu.usage
            cluster_name: example
        targetAverageValue: 50

The important setting that controls how the HPA behaves are:

  • metricName: Defines the metrics we are going to use to autoscale, it can be any metric that the Cluster Agent retrieves, for example kubernetes_state.deployment.replicas_available or kubernetes.kubelet.cpu.usage
  • metricsSelector.matchLabels: We can specify a set of tags we are going to use to filter the metric
  • targetValue or targetAverageValue: We are setting the desired value of the metric that will be used to scale up and down the objecy defined under spec.scaleTargetRef. If we use targetValue, it's values is the absolute value. For targetAverageValue we are defining the individual value for each replica (so it divides the value by the number of replicas)

Posted on 10/01/2022