Horizontal Pod Autoscaler

Horizontal Pod Autoscaler

Using the Kubernetes Horizontal Pod Autoscaler feature (HPA), you can configure your cluster to automatically scale the services it’s running up or down.

Why Use Horizontal Pod Autoscaler?

Using HPA, you can automatically scale the number of pods within a replication controller, deployment, or replica set up or down. HPA automatically scales the number of pods that are running for maximum efficiency. Factors that affect the number of pods include:

A minimum and maximum number of pods allowed to run, as defined by the user.
Observed CPU/memory use, as reported in resource metrics.
Custom metrics provided by third-party metrics application like Prometheus, Datadog, etc.HPA improves your services by:
Releasing hardware resources that would otherwise be wasted by an excessive number of pods.
Increase/decrease performance as needed to accomplish service level agreements.

How HPA Works

HPA is implemented as a control loop, with a period controlled by the kube-controller-manager flags below:

Flag	Default	Description
`—horizontal-pod-autoscaler-sync-period`	`30s`	How often HPA audits resource/custom metrics in a deployment.
`—horizontal-pod-autoscaler-downscale-delay`	`5m0s`	Following completion of a downscale operation, how long HPA must wait before launching another downscale operations.
`—horizontal-pod-autoscaler-upscale-delay`	`3m0s`	Following completion of an upscale operation, how long HPA must wait before launching another upscale operation.

For full documentation on HPA, refer to the Kubernetes Documentation.

Horizontal Pod Autoscaler API Objects

HPA is an API resource in the Kubernetes autoscaling API group. The current stable version is autoscaling/v1, which only includes support for CPU autoscaling. To get additional support for scaling based on memory and custom metrics, use the beta version instead: autoscaling/v2beta1.

For more information about the HPA API object, see the HPA GitHub Readme.

kubectl Commands

You can create, manage, and delete HPAs using kubectl:

Creating HPA
- With manifest: kubectl —kubeconfig=kube_configxxx.yml create -f <HPA_MANIFEST>
- Without manifest (Just support CPU): kubectl autoscale deployment hello-world —min=2 —max=5 —cpu-percent=50
Getting HPA info
- Basic: kubectl —kubeconfig=kube_configxxx.yml get hpa hello-world
- Detailed description: kubectl —kubeconfig=kube_configxxx.yml describe hpa hello-world
Deleting HPA
- kubectl —kubeconfig=kube_configxxx.yml delete hpa hello-world

HPA Manifest Definition Example

The following snippet demonstrates use of different directives in an HPA manifest. See the list below the sample to understand the purpose of each directive.

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: hello-world
spec:
  scaleTargetRef:
    apiVersion: extensions/v1beta1
    kind: Deployment
    name: hello-world
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 50
  - type: Resource
    resource:
      name: memory
      targetAverageValue: 100Mi

Directive	Description
`apiVersion: autoscaling/v2beta1`	The version of the Kubernetes `autoscaling` API group in use. This example manifest uses the beta version, so scaling by CPU and memory is enabled.
`name: hello-world`	Indicates that HPA is performing autoscaling for the `hello-word` deployment.
`minReplicas: 1`	Indicates that the minimum number of replicas running can’t go below 1.
`maxReplicas: 10`	Indicates the maximum number of replicas in the deployment can’t go above 10.
`targetAverageUtilization: 50`	Indicates the deployment will scale pods up when the average running pod uses more than 50% of its requested CPU.
`targetAverageValue: 100Mi`	Indicates the deployment will scale pods up when the average running pod uses more that 100Mi of memory.

Installation

Before you can use HPA in your Kubernetes cluster, you must fulfill some requirements.

Requirements

Be sure that your Kubernetes cluster services are running with these flags at minimum:

kube-api: requestheader-client-ca-file
kubelet: read-only-port at 10255
kube-controller: Optional, just needed if distinct values than default are required.
- horizontal-pod-autoscaler-downscale-delay: "5m0s"
- horizontal-pod-autoscaler-upscale-delay: "3m0s"
- horizontal-pod-autoscaler-sync-period: "30s"For an RKE Kubernetes cluster definition, add this snippet in the services section. To add this snippet using the Rancher v2.0 UI, open the Clusters view and select Ellipsis (…) > Edit for the cluster in which you want to use HPA. Then, from Cluster Options, click Edit as YAML. Add the following snippet to the services section:

services:
...
  kube-api:
    extra_args:
      requestheader-client-ca-file: "/etc/kubernetes/ssl/kube-ca.pem"
  kube-controller:
    extra_args:
      horizontal-pod-autoscaler-downscale-delay: "5m0s"
      horizontal-pod-autoscaler-upscale-delay: "1m0s"
      horizontal-pod-autoscaler-sync-period: "30s"
  kubelet:
    extra_args:
      read-only-port: 10255

Once the Kubernetes cluster is configured and deployed, you can deploy metrics services.

Note: kubectl command samples in the sections that follow were tested in a cluster running Rancher v2.0.6 and Kubernetes v1.10.1.

Configuring HPA to Scale Using Resource Metrics

To create HPA resources based on resource metrics such as CPU and memory use, you need to deploy the metrics-server package in the kube-system namespace of your Kubernetes cluster. This deployment allows HPA to consume the metrics.k8s.io API.

Prerequisite: You must be running kubectl 1.8 or later.

Connect to your Kubernetes cluster using kubectl.
Clone the GitHub metrics-server repo:

  git clone https://github.com/kubernetes-incubator/metrics-server

Install the metrics-server package.

  kubectl --kubeconfig=kube_configxxx.yml create  -f metrics-server/deploy/1.8+/

Check that metrics-server is running properly. Check the service pod and logs in the kube-system namespace.
- Check the service pod for a status of running. Enter the following command:

kubectl --kubeconfig=kube_configxxx.yml  get  pods -n kube-system

Then check for the status of running.

NAME                                  READY     STATUS    RESTARTS   AGE
...
metrics-server-6fbfb84cdd-t2fk9       1/1       Running   0          8h
...

Check the service logs for service availability. Enter the following command:

# kubectl --kubeconfig=kube_configxxx.yml -n kube-system logs metrics-server-6fbfb84cdd-t2fk9

Then review the log to confirm that that the metrics-server package is running.

Metrics Server Log Output

I0723 08:09:56.193136       1 heapster.go:71] /metrics-server --source=kubernetes.summary_api:''
I0723 08:09:56.193574       1 heapster.go:72] Metrics Server version v0.2.1
I0723 08:09:56.194480       1 configs.go:61] Using Kubernetes client with master "https://10.43.0.1:443" and version
I0723 08:09:56.194501       1 configs.go:62] Using kubelet port 10255
I0723 08:09:56.198612       1 heapster.go:128] Starting with Metric Sink
I0723 08:09:56.780114       1 serving.go:308] Generated self-signed cert (apiserver.local.config/certificates/apiserver.crt, apiserver.local.config/certificates/apiserver.key)
I0723 08:09:57.391518       1 heapster.go:101] Starting Heapster API server...
[restful] 2018/07/23 08:09:57 log.go:33: [restful/swagger] listing is available at https:///swaggerapi
[restful] 2018/07/23 08:09:57 log.go:33: [restful/swagger] https:///swaggerui/ is mapped to folder /swagger-ui/
I0723 08:09:57.394080       1 serve.go:85] Serving securely on 0.0.0.0:443

Check that the metrics api is accessible from kubectl.
- If you are accessing the cluster directly, enter your Server URL in the kubectl config in the following format: https://<K8s_URL>:6443.

# kubectl --kubeconfig=kube_configxxx.yml  get  --raw /apis/metrics.k8s.io/v1beta1

If the the API is working correctly, you should receive output similar to the output below.

{"kind":"APIResourceList","apiVersion":"v1","groupVersion":"metrics.k8s.io/v1beta1","resources":[{"name":"nodes","singularName":"","namespaced":false,"kind":"NodeMetrics","verbs":["get","list"]},{"name":"pods","singularName":"","namespaced":true,"kind":"PodMetrics","verbs":["get","list"]}]}

If you are accessing the cluster through Rancher, enter your Server URL in the kubectl config in the following format: https://<RANCHER_URL>/k8s/clusters/<CLUSTER_ID>. Add the suffix /k8s/clusters/<CLUSTER_ID> to API path.

# kubectl --kubeconfig=kube_configxxx.yml  get  --raw /k8s/clusters/<CLUSTER_ID>/apis/metrics.k8s.io/v1beta1

If the the API is working correctly, you should receive output similar to the output below.

{"kind":"APIResourceList","apiVersion":"v1","groupVersion":"metrics.k8s.io/v1beta1","resources":[{"name":"nodes","singularName":"","namespaced":false,"kind":"NodeMetrics","verbs":["get","list"]},{"name":"pods","singularName":"","namespaced":true,"kind":"PodMetrics","verbs":["get","list"]}]}

Configuring HPA to Scale Using Custom Metrics (Prometheus)

You can also configure HPA to autoscale based on custom metrics provided by third-party software. The most common use case for autoscaling using third-party software is based on application-level metrics (i.e., HTTP requests per second). HPA uses the custom.metrics.k8s.io API to consume these metrics. This API is enabled by deploying a custom metrics adapter for the metrics collection solution.

For this example, we are going to use Prometheus. We are beginning with the following assumptions:

Prometheus is deployed in the cluster.
Prometheus is configured correctly and collecting proper metrics from pods, nodes, namespaces, etc.
Prometheus is exposed at the following URL and port: http://prometheus.mycompany.io:80Prometheus is available for deployment in the Rancher v2.0 catalog. Deploy it from Rancher catalog if it isn’t already running in your cluster.

For HPA to use custom metrics from Prometheus, package k8s-prometheus-adapter is required in the kube-system namespace of your cluster. To install k8s-prometheus-adapter, we are using the Helm chart available at banzai-charts.

Initialize Helm in your cluster.

  # kubectl --kubeconfig=kube_configxxx.yml -n kube-system create serviceaccount tiller
  kubectl --kubeconfig=kube_configxxx.yml create clusterrolebinding tiller --clusterrole cluster-admin --serviceaccount=kube-system:tiller
  helm init --service-account tiller

Clone the banzai-charts repo from GitHub:

  # git clone https://github.com/banzaicloud/banzai-charts

Install the prometheus-adapter chart, specifying the Prometheus URL and port number.

  helm install --name prometheus-adapter banzai-charts/prometheus-adapter \
  --set prometheus.url="http://prometheus.mycompany.io",prometheus.port="80" --namespace kube-system

Check that prometheus-adapter is running properly. Check the service pod and logs in the kube-system namespace.
- Check that the service pod is Running. Enter the following command.

# kubectl --kubeconfig=kube_configxxx.yml  get  pods -n kube-system

From the resulting output, look for a status of Running.

NAME                                  READY     STATUS    RESTARTS   AGE
...
prometheus-adapter-prometheus-adapter-568674d97f-hbzfx   1/1       Running   0          7h
...

Check the service logs to make sure the service is running correctly by entering the command that follows.

# kubectl logs prometheus-adapter-prometheus-adapter-568674d97f-hbzfx -n kube-system

Then review the log output to confirm the service is running.

Prometheus Adaptor Logs

...
I0724 10:18:45.696679       1 round_trippers.go:436] GET https://10.43.0.1:443/api/v1/namespaces/default/pods?labelSelector=app%3Dhello-world 200 OK in 2 milliseconds
I0724 10:18:45.696695       1 round_trippers.go:442] Response Headers:
I0724 10:18:45.696699       1 round_trippers.go:445]     Date: Tue, 24 Jul 2018 10:18:45 GMT
I0724 10:18:45.696703       1 round_trippers.go:445]     Content-Type: application/json
I0724 10:18:45.696706       1 round_trippers.go:445]     Content-Length: 2581
I0724 10:18:45.696766       1 request.go:836] Response Body: {"kind":"PodList","apiVersion":"v1","metadata":{"selfLink":"/api/v1/namespaces/default/pods","resourceVersion":"6237"},"items":[{"metadata":{"name":"hello-world-54764dfbf8-q6l82","generateName":"hello-world-54764dfbf8-","namespace":"default","selfLink":"/api/v1/namespaces/default/pods/hello-world-54764dfbf8-q6l82","uid":"484cb929-8f29-11e8-99d2-067cac34e79c","resourceVersion":"4066","creationTimestamp":"2018-07-24T10:06:50Z","labels":{"app":"hello-world","pod-template-hash":"1032089694"},"annotations":{"cni.projectcalico.org/podIP":"10.42.0.7/32"},"ownerReferences":[{"apiVersion":"extensions/v1beta1","kind":"ReplicaSet","name":"hello-world-54764dfbf8","uid":"4849b9b1-8f29-11e8-99d2-067cac34e79c","controller":true,"blockOwnerDeletion":true}]},"spec":{"volumes":[{"name":"default-token-ncvts","secret":{"secretName":"default-token-ncvts","defaultMode":420}}],"containers":[{"name":"hello-world","image":"rancher/hello-world","ports":[{"containerPort":80,"protocol":"TCP"}],"resources":{"requests":{"cpu":"500m","memory":"64Mi"}},"volumeMounts":[{"name":"default-token-ncvts","readOnly":true,"mountPath":"/var/run/secrets/kubernetes.io/serviceaccount"}],"terminationMessagePath":"/dev/termination-log","terminationMessagePolicy":"File","imagePullPolicy":"Always"}],"restartPolicy":"Always","terminationGracePeriodSeconds":30,"dnsPolicy":"ClusterFirst","serviceAccountName":"default","serviceAccount":"default","nodeName":"34.220.18.140","securityContext":{},"schedulerName":"default-scheduler","tolerations":[{"key":"node.kubernetes.io/not-ready","operator":"Exists","effect":"NoExecute","tolerationSeconds":300},{"key":"node.kubernetes.io/unreachable","operator":"Exists","effect":"NoExecute","tolerationSeconds":300}]},"status":{"phase":"Running","conditions":[{"type":"Initialized","status":"True","lastProbeTime":null,"lastTransitionTime":"2018-07-24T10:06:50Z"},{"type":"Ready","status":"True","lastProbeTime":null,"lastTransitionTime":"2018-07-24T10:06:54Z"},{"type":"PodScheduled","status":"True","lastProbeTime":null,"lastTransitionTime":"2018-07-24T10:06:50Z"}],"hostIP":"34.220.18.140","podIP":"10.42.0.7","startTime":"2018-07-24T10:06:50Z","containerStatuses":[{"name":"hello-world","state":{"running":{"startedAt":"2018-07-24T10:06:54Z"}},"lastState":{},"ready":true,"restartCount":0,"image":"rancher/hello-world:latest","imageID":"docker-pullable://rancher/hello-world@sha256:4b1559cb4b57ca36fa2b313a3c7dde774801aa3a2047930d94e11a45168bc053","containerID":"docker://cce4df5fc0408f03d4adf82c90de222f64c302bf7a04be1c82d584ec31530773"}],"qosClass":"Burstable"}}]}
I0724 10:18:45.699525       1 api.go:74] GET http://prometheus-server.prometheus.34.220.18.140.xip.io/api/v1/query?query=sum%28rate%28container_fs_read_seconds_total%7Bpod_name%3D%22hello-world-54764dfbf8-q6l82%22%2Ccontainer_name%21%3D%22POD%22%2Cnamespace%3D%22default%22%7D%5B5m%5D%29%29+by+%28pod_name%29&time=1532427525.697 200 OK
I0724 10:18:45.699620       1 api.go:93] Response Body: {"status":"success","data":{"resultType":"vector","result":[{"metric":{"pod_name":"hello-world-54764dfbf8-q6l82"},"value":[1532427525.697,"0"]}]}}
I0724 10:18:45.699939       1 wrap.go:42] GET /apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/%2A/fs_read?labelSelector=app%3Dhello-world: (12.431262ms) 200 [[kube-controller-manager/v1.10.1 (linux/amd64) kubernetes/d4ab475/system:serviceaccount:kube-system:horizontal-pod-autoscaler] 10.42.0.0:24268]
I0724 10:18:51.727845       1 request.go:836] Request Body: {"kind":"SubjectAccessReview","apiVersion":"authorization.k8s.io/v1beta1","metadata":{"creationTimestamp":null},"spec":{"nonResourceAttributes":{"path":"/","verb":"get"},"user":"system:anonymous","group":["system:unauthenticated"]},"status":{"allowed":false}}
...

Check that the metrics API is accessible from kubectl.
- If you are accessing the cluster directly, enter your Server URL in the kubectl config in the following format: https://<Kubernetes_URL>:6443.

# kubectl --kubeconfig=kube_configxxx.yml  get  --raw /apis/custom.metrics.k8s.io/v1beta1

If the API is accessible, you should receive output that’s similar to what follows.

API Response

{"kind":"APIResourceList","apiVersion":"v1","groupVersion":"custom.metrics.k8s.io/v1beta1","resources":[{"name":"pods/fs_usage_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_rss","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_cpu_period","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_cfs_throttled","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_io_time","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_read","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_sector_writes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_user","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/last_seen","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/tasks_state","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_cpu_quota","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/start_time_seconds","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_limit_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_write","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_cache","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_usage_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_cfs_periods","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_cfs_throttled_periods","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_reads_merged","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_working_set_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/network_udp_usage","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_inodes_free","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_inodes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_io_time_weighted","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_failures","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_swap","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_cpu_shares","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_memory_swap_limit_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_usage","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_io_current","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_writes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_failcnt","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_reads","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_writes_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_writes_merged","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/network_tcp_usage","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_max_usage_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_memory_limit_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_memory_reservation_limit_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_load_average_10s","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_system","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_reads_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_sector_reads","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]}]}

If you are accessing the cluster through Rancher, enter your Server URL in the kubectl config in the following format: https://<RANCHER_URL>/k8s/clusters/<CLUSTER_ID>. Add the suffix /k8s/clusters/<CLUSTER_ID> to API path.

# kubectl --kubeconfig=kube_configxxx.yml  get  --raw /k8s/clusters/<CLUSTER_ID>/apis/custom.metrics.k8s.io/v1beta1

If the API is accessible, you should receive output that’s similar to what follows.

API Response

{"kind":"APIResourceList","apiVersion":"v1","groupVersion":"custom.metrics.k8s.io/v1beta1","resources":[{"name":"pods/fs_usage_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_rss","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_cpu_period","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_cfs_throttled","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_io_time","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_read","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_sector_writes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_user","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/last_seen","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/tasks_state","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_cpu_quota","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/start_time_seconds","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_limit_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_write","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_cache","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_usage_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_cfs_periods","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_cfs_throttled_periods","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_reads_merged","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_working_set_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/network_udp_usage","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_inodes_free","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_inodes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_io_time_weighted","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_failures","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_swap","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_cpu_shares","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_memory_swap_limit_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_usage","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_io_current","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_writes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_failcnt","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_reads","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_writes_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_writes_merged","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/network_tcp_usage","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/memory_max_usage_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_memory_limit_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/spec_memory_reservation_limit_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_load_average_10s","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/cpu_system","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_reads_bytes","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]},{"name":"pods/fs_sector_reads","singularName":"","namespaced":true,"kind":"MetricValueList","verbs":["get"]}]}

Assigning Additional Required Roles to Your HPA

By default, HPA reads resource and custom metrics with the user system:anonymous. Assign system:anonymous the the view-resource-metrics and view-custom-metrics in the ClusterRole and ClusterRoleBindings manifests. These roles are used to access metrics.

To do it, follow these steps:

Configure kubectl to connect to your cluster.
Copy the ClusterRole and ClusterRoleBinding manifest for the type of metrics you’re using for your HPA.

Resource Metrics: ApiGroups resource.metrics.k8s.io

    apiVersion: rbac.authorization.k8s.io/v1
    kind: ClusterRole
    metadata:
      name: view-resource-metrics
    rules:
    - apiGroups:
        - metrics.k8s.io
      resources:
        - pods
        - nodes
      verbs:
        - get
        - list
        - watch
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: ClusterRoleBinding
    metadata:
      name: view-resource-metrics
    roleRef:
      apiGroup: rbac.authorization.k8s.io
      kind: ClusterRole
      name: view-resource-metrics
    subjects:
      - apiGroup: rbac.authorization.k8s.io
        kind: User
        name: system:anonymous

Custom Metrics: ApiGroups custom.metrics.k8s.io

  apiVersion: rbac.authorization.k8s.io/v1
  kind: ClusterRole
  metadata:
    name: view-custom-metrics
  rules:
  - apiGroups:
      - custom.metrics.k8s.io
    resources:
      - "*"
    verbs:
      - get
      - list
      - watch
  ---
  apiVersion: rbac.authorization.k8s.io/v1
  kind: ClusterRoleBinding
  metadata:
    name: view-custom-metrics
  roleRef:
    apiGroup: rbac.authorization.k8s.io
    kind: ClusterRole
    name: view-custom-metrics
  subjects:
    - apiGroup: rbac.authorization.k8s.io
      kind: User
      name: system:anonymous

Create them in your cluster using one of the follow commands, depending on the metrics you’re using.

  # kubectl --kubeconfig=kube_configxxx.yml create  -f <RESOURCE_METRICS_MANIFEST>
  # kubectl --kubeconfig=kube_configxxx.yml create  -f <CUSTOM_METRICS_MANIFEST>

Testing HPAs with a Service Deployment

For HPA to work correctly, service deployments should have resources request definitions for containers. Follow this hello-world example to test if HPA is working correctly.

Configure kubectl to connect to your Kubernetes cluster.
Copy the hello-world deployment manifest below.

Hello World Manifest

  apiVersion: apps/v1beta2
          kind: Deployment
          metadata:
            labels:
              app: hello-world
            name: hello-world
            namespace: default
          spec:
            replicas: 1
            selector:
              matchLabels:
                app: hello-world
            strategy:
              rollingUpdate:
                maxSurge: 1
                maxUnavailable: 0
              type: RollingUpdate
            template:
              metadata:
                labels:
                  app: hello-world
              spec:
                containers:
                - image: rancher/hello-world
                  imagePullPolicy: Always
                  name: hello-world
                  resources:
                    requests:
                      cpu: 500m
                      memory: 64Mi
                  ports:
                  - containerPort: 80
                    protocol: TCP
                restartPolicy: Always
          ---
          apiVersion: v1
          kind: Service
          metadata:
            name: hello-world
            namespace: default
          spec:
            ports:
            - port: 80
              protocol: TCP
              targetPort: 80
            selector:
              app: hello-world

Deploy it to your cluster.

# kubectl --kubeconfig=kube_configxxx.yml create  -f <HELLO_WORLD_MANIFEST>

Copy one of the HPAs below based on the metric type you’re using:

Hello World HPA: Resource Metrics

 apiVersion: autoscaling/v2beta1
        kind: HorizontalPodAutoscaler
        metadata:
          name: hello-world
          namespace: default
        spec:
          scaleTargetRef:
            apiVersion: extensions/v1beta1
            kind: Deployment
            name: hello-world
          minReplicas: 1
          maxReplicas: 10
          metrics:
          - type: Resource
            resource:
              name: cpu
              targetAverageUtilization: 50
          - type: Resource
            resource:
              name: memory
              targetAverageValue: 1000Mi

Hello World HPA: Custom Metrics

apiVersion: autoscaling/v2beta1
  kind: HorizontalPodAutoscaler
  metadata:
    name: hello-world
    namespace: default
  spec:
    scaleTargetRef:
      apiVersion: extensions/v1beta1
      kind: Deployment
      name: hello-world
    minReplicas: 1
    maxReplicas: 10
    metrics:
    - type: Resource
      resource:
        name: cpu
        targetAverageUtilization: 50
    - type: Resource
      resource:
        name: memory
        targetAverageValue: 100Mi
    - type: Pods
      pods:
        metricName: cpu_system
        targetAverageValue: 20m

View the HPA info and description. Confirm that metric data is shown.

Resource Metrics

Enter the following command.

# kubectl --kubeconfig=kube_configxxx.yml  get  hpa

You should receive the output that follows:

NAME          REFERENCE                TARGETS                     MINPODS   MAXPODS   REPLICAS   AGE
hello-world   Deployment/hello-world   1253376 / 100Mi, 0% / 50%   1         10        1          6m
    # kubectl --kubeconfig=kube_configxxx.yml  describe    hpa
Name:                                                  hello-world
Namespace:                                             default
Labels:                                                <none>
Annotations:                                           <none>
CreationTimestamp:                                     Mon, 23 Jul 2018 20:21:16 +0200
Reference:                                             Deployment/hello-world
Metrics:                                               ( current / target )
  resource memory on pods:                             1253376 / 100Mi
  resource cpu on pods  (as a percentage of request):  0% (0) / 50%
Min replicas:                                          1
Max replicas:                                          10
Conditions:
  Type            Status  Reason              Message
  ----            ------  ------              -------
  AbleToScale     True    ReadyForNewScale    the last scale time was sufficiently old as to warrant a new scale
  ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from memory resource
  ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
Events:           <none>

Custom Metrics

Enter the following command.

# kubectl --kubeconfig=kube_configxxx.yml  describe    hpa

You should receive the output that follows.

Name:                                                  hello-world
Namespace:                                             default
Labels:                                                <none>
Annotations:                                           <none>
CreationTimestamp:                                     Tue, 24 Jul 2018 18:36:28 +0200
Reference:                                             Deployment/hello-world
Metrics:                                               ( current / target )
  resource memory on pods:                             3514368 / 100Mi
  "cpu_system" on pods:                                0 / 20m
  resource cpu on pods  (as a percentage of request):  0% (0) / 50%
Min replicas:                                          1
Max replicas:                                          10
Conditions:
  Type            Status  Reason              Message
  ----            ------  ------              -------
  AbleToScale     True    ReadyForNewScale    the last scale time was sufficiently old as to warrant a new scale
  ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from memory resource
  ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
Events:           <none>

Generate a load for the service to test that your pods autoscale as intended. You can use any load-testing tool (Hey, Gatling, etc.), but we’re using Hey.
Test that pod autoscaling works as intended.To Test Autoscaling Using Resource Metrics:

Upscale to 2 Pods: CPU Usage Up to Target

Use your load testing tool to to scale up to two pods based on CPU Usage.

View your HPA.

# kubectl --kubeconfig=kube_configxxx.yml  describe    hpa

You should receive output similar to what follows.

Name:                                                  hello-world
Namespace:                                             default
Labels:                                                <none>
Annotations:                                           <none>
CreationTimestamp:                                     Mon, 23 Jul 2018 22:22:04 +0200
Reference:                                             Deployment/hello-world
Metrics:                                               ( current / target )
  resource memory on pods:                             10928128 / 100Mi
  resource cpu on pods  (as a percentage of request):  56% (280m) / 50%
Min replicas:                                          1
Max replicas:                                          10
Conditions:
  Type            Status  Reason              Message
  ----            ------  ------              -------
  AbleToScale     True    SucceededRescale    the HPA controller was able to update the target scale to 2
  ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
  ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
Events:
  Type    Reason             Age   From                       Message
  ----    ------             ----  ----                       -------
  Normal  SuccessfulRescale  13s   horizontal-pod-autoscaler  New size: 2; reason: cpu resource utilization (percentage of request) above target

Enter the following command to confirm you’ve scaled to two pods.

kubectl --kubeconfig=kube_configxxx.yml  get  pods

You should receive output similar to what follows:

  NAME                                                     READY     STATUS    RESTARTS   AGE
  hello-world-54764dfbf8-k8ph2                             1/1       Running   0          1m
  hello-world-54764dfbf8-q6l4v                             1/1       Running   0          3h

Upscale to 3 pods: CPU Usage Up to Target

Use your load testing tool to upspace to 3 pods based on CPU usage with horizontal-pod-autoscaler-upscale-delay set to 3 minutes.

Enter the following command.

# kubectl --kubeconfig=kube_configxxx.yml  describe    hpa

You should receive output similar to what follows

  Name:                                                  hello-world
  Namespace:                                             default
  Labels:                                                <none>
  Annotations:                                           <none>
  CreationTimestamp:                                     Mon, 23 Jul 2018 22:22:04 +0200
  Reference:                                             Deployment/hello-world
  Metrics:                                               ( current / target )
    resource memory on pods:                             9424896 / 100Mi
    resource cpu on pods  (as a percentage of request):  66% (333m) / 50%
  Min replicas:                                          1
  Max replicas:                                          10
  Conditions:
    Type            Status  Reason              Message
    ----            ------  ------              -------
    AbleToScale     True    SucceededRescale    the HPA controller was able to update the target scale to 3
    ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
    ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
  Events:
    Type    Reason             Age   From                       Message
    ----    ------             ----  ----                       -------
    Normal  SuccessfulRescale  4m    horizontal-pod-autoscaler  New size: 2; reason: cpu resource utilization (percentage of request) above target
    Normal  SuccessfulRescale  16s   horizontal-pod-autoscaler  New size: 3; reason: cpu resource utilization (percentage of request) above target

Enter the following command to confirm three pods are running.

# kubectl --kubeconfig=kube_configxxx.yml  get  pods

You should receive output similar to what follows.

  NAME                                                     READY     STATUS    RESTARTS   AGE
  hello-world-54764dfbf8-f46kh                             0/1       Running   0          1m
  hello-world-54764dfbf8-k8ph2                             1/1       Running   0          5m
  hello-world-54764dfbf8-q6l4v                             1/1       Running   0          3h

Downscale to 1 Pod: All Metrics Below Target

Use your load testing to to scale down to 1 pod when all metrics are below target for horizontal-pod-autoscaler-downscale-delay (5 minutes by default).

Enter the following command.

# kubectl --kubeconfig=kube_configxxx.yml  describe    hpa

You should receive output similar to what follows.

  Name:                                                  hello-world
  Namespace:                                             default
  Labels:                                                <none>
  Annotations:                                           <none>
  CreationTimestamp:                                     Mon, 23 Jul 2018 22:22:04 +0200
  Reference:                                             Deployment/hello-world
  Metrics:                                               ( current / target )
    resource memory on pods:                             10070016 / 100Mi
    resource cpu on pods  (as a percentage of request):  0% (0) / 50%
  Min replicas:                                          1
  Max replicas:                                          10
  Conditions:
    Type            Status  Reason              Message
    ----            ------  ------              -------
    AbleToScale     True    SucceededRescale    the HPA controller was able to update the target scale to 1
    ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from memory resource
    ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
  Events:
    Type    Reason             Age   From                       Message
    ----    ------             ----  ----                       -------
    Normal  SuccessfulRescale  10m   horizontal-pod-autoscaler  New size: 2; reason: cpu resource utilization (percentage of request) above target
    Normal  SuccessfulRescale  6m    horizontal-pod-autoscaler  New size: 3; reason: cpu resource utilization (percentage of request) above target
    Normal  SuccessfulRescale  1s    horizontal-pod-autoscaler  New size: 1; reason: All metrics below target

To Test Autoscaling Using Custom Metrics:

Upscale to 2 Pods: CPU Usage Up to Target

Use your load testing tool to upscale two pods based on CPU usage.

Enter the following command.

# kubectl --kubeconfig=kube_configxxx.yml  describe    hpa

You should receive output similar to what follows.

Name:                                                  hello-world
Namespace:                                             default
Labels:                                                <none>
Annotations:                                           <none>
CreationTimestamp:                                     Tue, 24 Jul 2018 18:01:11 +0200
Reference:                                             Deployment/hello-world
Metrics:                                               ( current / target )
  resource memory on pods:                             8159232 / 100Mi
  "cpu_system" on pods:                                7m / 20m
  resource cpu on pods  (as a percentage of request):  64% (321m) / 50%
Min replicas:                                          1
Max replicas:                                          10
Conditions:
  Type            Status  Reason              Message
  ----            ------  ------              -------
  AbleToScale     True    SucceededRescale    the HPA controller was able to update the target scale to 2
  ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
  ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
Events:
  Type    Reason             Age   From                       Message
  ----    ------             ----  ----                       -------
  Normal  SuccessfulRescale  16s   horizontal-pod-autoscaler  New size: 2; reason: cpu resource utilization (percentage of request) above target

Enter the following command to confirm two pods are running.

# kubectl --kubeconfig=kube_configxxx.yml  get  pods

You should receive output similar to what follows.

    NAME                           READY     STATUS    RESTARTS   AGE
    hello-world-54764dfbf8-5pfdr   1/1       Running   0          3s
    hello-world-54764dfbf8-q6l82   1/1       Running   0          6h

Upscale to 3 Pods: CPU Usage Up to Target

Use your load testing tool to scale up to three pods when the cpu_system usage limit is up to target.

Enter the following command.

# kubectl --kubeconfig=kube_configxxx.yml  describe    hpa

You should receive output similar to what follows:

  Name:                                                  hello-world
  Namespace:                                             default
  Labels:                                                <none>
  Annotations:                                           <none>
  CreationTimestamp:                                     Tue, 24 Jul 2018 18:01:11 +0200
  Reference:                                             Deployment/hello-world
  Metrics:                                               ( current / target )
    resource memory on pods:                             8374272 / 100Mi
    "cpu_system" on pods:                                27m / 20m
    resource cpu on pods  (as a percentage of request):  71% (357m) / 50%
  Min replicas:                                          1
  Max replicas:                                          10
  Conditions:
    Type            Status  Reason              Message
    ----            ------  ------              -------
    AbleToScale     True    SucceededRescale    the HPA controller was able to update the target scale to 3
    ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
    ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
  Events:
    Type    Reason             Age   From                       Message
    ----    ------             ----  ----                       -------
    Normal  SuccessfulRescale  3m    horizontal-pod-autoscaler  New size: 2; reason: cpu resource utilization (percentage of request) above target
    Normal  SuccessfulRescale  3s    horizontal-pod-autoscaler  New size: 3; reason: pods metric cpu_system above target

Enter the following command to confirm three pods are running.

# kubectl --kubeconfig=kube_configxxx.yml  get  pods

You should receive output similar to what follows:

  # kubectl --kubeconfig=kube_configxxx.yml  get  pods
  NAME                           READY     STATUS    RESTARTS   AGE
  hello-world-54764dfbf8-5pfdr   1/1       Running   0          3m
  hello-world-54764dfbf8-m2hrl   1/1       Running   0          1s
  hello-world-54764dfbf8-q6l82   1/1       Running   0          6h

Upscale to 4 Pods: CPU Usage Up to Target

Use your load testing tool to upscale to four pods based on CPU usage. horizontal-pod-autoscaler-upscale-delay is set to three minutes by default.

Enter the following command.

# kubectl --kubeconfig=kube_configxxx.yml  describe    hpa

You should receive output similar to what follows.

  Name:                                                  hello-world
  Namespace:                                             default
  Labels:                                                <none>
  Annotations:                                           <none>
  CreationTimestamp:                                     Tue, 24 Jul 2018 18:01:11 +0200
  Reference:                                             Deployment/hello-world
  Metrics:                                               ( current / target )
    resource memory on pods:                             8374272 / 100Mi
    "cpu_system" on pods:                                27m / 20m
    resource cpu on pods  (as a percentage of request):  71% (357m) / 50%
  Min replicas:                                          1
  Max replicas:                                          10
  Conditions:
    Type            Status  Reason              Message
    ----            ------  ------              -------
    AbleToScale     True    SucceededRescale    the HPA controller was able to update the target scale to 3
    ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from cpu resource utilization (percentage of request)
    ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
  Events:
    Type    Reason             Age   From                       Message
    ----    ------             ----  ----                       -------
    Normal  SuccessfulRescale  5m    horizontal-pod-autoscaler  New size: 2; reason: cpu resource utilization (percentage of request) above target
    Normal  SuccessfulRescale  3m    horizontal-pod-autoscaler  New size: 3; reason: pods metric cpu_system above target
    Normal  SuccessfulRescale  4s    horizontal-pod-autoscaler  New size: 4; reason: cpu resource utilization (percentage of request) above target

Enter the following command to confirm four pods are running.

# kubectl --kubeconfig=kube_configxxx.yml  get  pods

You should receive output similar to what follows.

  NAME                           READY     STATUS    RESTARTS   AGE
  hello-world-54764dfbf8-2p9xb   1/1       Running   0          5m
  hello-world-54764dfbf8-5pfdr   1/1       Running   0          2m
  hello-world-54764dfbf8-m2hrl   1/1       Running   0          1s
  hello-world-54764dfbf8-q6l82   1/1       Running   0          6h

Downscale to 1 Pod: All Metrics Below Target

Use your load testing tool to scale down to one pod when all metrics below target for horizontal-pod-autoscaler-downscale-delay.

Enter the following command.

# kubectl --kubeconfig=kube_configxxx.yml  describe    hpa

You should receive similar output to what follows.

    Name:                                                  hello-world
    Namespace:                                             default
    Labels:                                                <none>
    Annotations:                                           <none>
    CreationTimestamp:                                     Tue, 24 Jul 2018 18:01:11 +0200
    Reference:                                             Deployment/hello-world
    Metrics:                                               ( current / target )
      resource memory on pods:                             8101888 / 100Mi
      "cpu_system" on pods:                                8m / 20m
      resource cpu on pods  (as a percentage of request):  0% (0) / 50%
    Min replicas:                                          1
    Max replicas:                                          10
    Conditions:
      Type            Status  Reason              Message
      ----            ------  ------              -------
      AbleToScale     True    SucceededRescale    the HPA controller was able to update the target scale to 1
      ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from memory resource
      ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
    Events:
      Type    Reason             Age   From                       Message
      ----    ------             ----  ----                       -------
      Normal  SuccessfulRescale  10m    horizontal-pod-autoscaler  New size: 2; reason: cpu resource utilization (percentage of request) above target
      Normal  SuccessfulRescale  8m    horizontal-pod-autoscaler  New size: 3; reason: pods metric cpu_system above target
      Normal  SuccessfulRescale  5m    horizontal-pod-autoscaler  New size: 4; reason: cpu resource utilization (percentage of request) above target
      Normal   SuccessfulRescale             13s               horizontal-pod-autoscaler  New size: 1; reason: All metrics below target

Enter the following command to confirm a single pods is running.

    # kubectl --kubeconfig=kube_configxxx.yml  get  pods

You should receive output similar to what follows.

NAME                           READY     STATUS    RESTARTS   AGE
hello-world-54764dfbf8-q6l82   1/1       Running   0          6h

Conclusion

Horizontal Pod Autoscaling is a great way to automate the number of pod you have deployed for maximum efficency. You can use it to accomodate deployment scale to real service load and to meet service level agreements.

By adjusting the horizontal-pod-autoscaler-downscale-delay and horizontal-pod-autoscaler-upscale-delay flag values, you can adjust the time needed before kube-controller scales your pods up or down.

We’ve demonstrated how to setup an HPA based on custom metrics provided by Prometheus. We used the cpu_system metric as an example, but you can use other metrics that monitor service performance, like http_request_number, http_response_time, etc.

Note:To facilitate HPA use, we are working to integrate metric-server as an addon on RKE cluster deployments. This feature is included in RKE v0.1.9-rc2 for testing, but is not officially supported as of yet. It would be supported at rke v0.1.9.