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Horizontal Pod Autoscaler Walkthrough

Horizontal Pod Autoscaler automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization (or, with beta support, on some other, application-provided metrics).

This document walks you through an example of enabling Horizontal Pod Autoscaler for the php-apache server. For more information on how Horizontal Pod Autoscaler behaves, see the Horizontal Pod Autoscaler user guide.

Before you begin

This example requires a running Kubernetes cluster and kubectl, version 1.2 or later. Heapster monitoring needs to be deployed in the cluster as Horizontal Pod Autoscaler uses it to collect metrics (if you followed getting started on GCE guide, heapster monitoring will be turned-on by default).

To specify multiple resource metrics for a Horizontal Pod Autoscaler, you must have a Kubernetes cluster and kubectl at version 1.6 or later. Furthermore, in order to make use of custom metrics, your cluster must be able to communicate with the API server providing the custom metrics API. Finally, to use metrics not related to any Kubernetes object you must have a Kubernetes cluster at version 1.10 or later, and you must be able to communicate with the API server that provides the external metrics API. See the Horizontal Pod Autoscaler user guide for more details.

Run & expose php-apache server

To demonstrate Horizontal Pod Autoscaler we will use a custom docker image based on the php-apache image. The Dockerfile has the following content:

FROM php:5-apache
ADD index.php /var/www/html/index.php
RUN chmod a+rx index.php

It defines an index.php page which performs some CPU intensive computations:

<?php
  $x = 0.0001;
  for ($i = 0; $i <= 1000000; $i++) {
    $x += sqrt($x);
  }
  echo "OK!";
?>

First, we will start a deployment running the image and expose it as a service:

$ kubectl run php-apache --image=k8s.gcr.io/hpa-example --requests=cpu=200m --expose --port=80
service "php-apache" created
deployment "php-apache" created

Create Horizontal Pod Autoscaler

Now that the server is running, we will create the autoscaler using kubectl autoscale. The following command will create a Horizontal Pod Autoscaler that maintains between 1 and 10 replicas of the Pods controlled by the php-apache deployment we created in the first step of these instructions. Roughly speaking, HPA will increase and decrease the number of replicas (via the deployment) to maintain an average CPU utilization across all Pods of 50% (since each pod requests 200 milli-cores by kubectl run, this means average CPU usage of 100 milli-cores). See here for more details on the algorithm.

$ kubectl autoscale deployment php-apache --cpu-percent=50 --min=1 --max=10
deployment "php-apache" autoscaled

We may check the current status of autoscaler by running:

$ kubectl get hpa
NAME         REFERENCE                     TARGET    MINPODS   MAXPODS   REPLICAS   AGE
php-apache   Deployment/php-apache/scale   0% / 50%  1         10        1          18s

Please note that the current CPU consumption is 0% as we are not sending any requests to the server (the CURRENT column shows the average across all the pods controlled by the corresponding deployment).

Increase load

Now, we will see how the autoscaler reacts to increased load. We will start a container, and send an infinite loop of queries to the php-apache service (please run it in a different terminal):

$ kubectl run -i --tty load-generator --image=busybox /bin/sh

Hit enter for command prompt

$ while true; do wget -q -O- http://php-apache.default.svc.cluster.local; done

Within a minute or so, we should see the higher CPU load by executing:

$ kubectl get hpa
NAME         REFERENCE                     TARGET      CURRENT   MINPODS   MAXPODS   REPLICAS   AGE
php-apache   Deployment/php-apache/scale   305% / 50%  305%      1         10        1          3m

Here, CPU consumption has increased to 305% of the request. As a result, the deployment was resized to 7 replicas:

$ kubectl get deployment php-apache
NAME         DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
php-apache   7         7         7            7           19m

Note Sometimes it may take a few minutes to stabilize the number of replicas. Since the amount of load is not controlled in any way it may happen that the final number of replicas will differ from this example.

Stop load

We will finish our example by stopping the user load.

In the terminal where we created the container with busybox image, terminate the load generation by typing <Ctrl> + C.

Then we will verify the result state (after a minute or so):

$ kubectl get hpa
NAME         REFERENCE                     TARGET       MINPODS   MAXPODS   REPLICAS   AGE
php-apache   Deployment/php-apache/scale   0% / 50%     1         10        1          11m

$ kubectl get deployment php-apache
NAME         DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
php-apache   1         1         1            1           27m

Here CPU utilization dropped to 0, and so HPA autoscaled the number of replicas back down to 1.

Note autoscaling the replicas may take a few minutes.

Autoscaling on multiple metrics and custom metrics

You can introduce additional metrics to use when autoscaling the php-apache Deployment by making use of the autoscaling/v2beta1 API version.

First, get the YAML of your HorizontalPodAutoscaler in the autoscaling/v2beta1 form:

$ kubectl get hpa.v2beta1.autoscaling -o yaml > /tmp/hpa-v2.yaml

Open the /tmp/hpa-v2.yaml file in an editor, and you should see YAML which looks like this:

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: php-apache
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: php-apache
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 50
status:
  observedGeneration: 1
  lastScaleTime: <some-time>
  currentReplicas: 1
  desiredReplicas: 1
  currentMetrics:
  - type: Resource
    resource:
      name: cpu
      currentAverageUtilization: 0
      currentAverageValue: 0

Notice that the targetCPUUtilizationPercentage field has been replaced with an array called metrics. The CPU utilization metric is a resource metric, since it is represented as a percentage of a resource specified on pod containers. Notice that you can specify other resource metrics besides CPU. By default, the only other supported resource metric is memory. These resources do not change names from cluster to cluster, and should always be available, as long as Heapster is deployed.

You can also specify resource metrics in terms of direct values, instead of as percentages of the requested value. To do so, use the targetAverageValue field instead of the targetAverageUtilization field.

There are two other types of metrics, both of which are considered custom metrics: pod metrics and object metrics. These metrics may have names which are cluster specific, and require a more advanced cluster monitoring setup.

The first of these alternative metric types is pod metrics. These metrics describe pods, and are averaged together across pods and compared with a target value to determine the replica count. They work much like resource metrics, except that they only have the targetAverageValue field.

Pod metrics are specified using a metric block like this:

type: Pods
pods:
  metricName: packets-per-second
  targetAverageValue: 1k

The second alternative metric type is object metrics. These metrics describe a different object in the same namespace, instead of describing pods. Note that the metrics are not fetched from the object – they simply describe it. Object metrics do not involve averaging, and look like this:

type: Object
object:
  metricName: requests-per-second
  target:
    apiVersion: extensions/v1beta1
    kind: Ingress
    name: main-route
  targetValue: 2k

If you provide multiple such metric blocks, the HorizontalPodAutoscaler will consider each metric in turn. The HorizontalPodAutoscaler will calculate proposed replica counts for each metric, and then choose the one with the highest replica count.

For example, if you had your monitoring system collecting metrics about network traffic, you could update the definition above using kubectl edit to look like this:

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: php-apache
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: php-apache
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 50
  - type: Pods
    pods:
      metricName: packets-per-second
      targetAverageValue: 1k
  - type: Object
    object:
      metricName: requests-per-second
      target:
        apiVersion: extensions/v1beta1
        kind: Ingress
        name: main-route
      targetValue: 10k
status:
  observedGeneration: 1
  lastScaleTime: <some-time>
  currentReplicas: 1
  desiredReplicas: 1
  currentMetrics:
  - type: Resource
    resource:
      name: cpu
      currentAverageUtilization: 0
      currentAverageValue: 0

Then, your HorizontalPodAutoscaler would attempt to ensure that each pod was consuming roughly 50% of its requested CPU, serving 1000 packets per second, and that all pods behind the main-route Ingress were serving a total of 10000 requests per second.

Applications running on Kubernetes may need to autoscale based on metrics that don’t have an obvious relationship to any object in the Kubernetes cluster, such as metrics describing a hosted service with no direct correlation to Kubernetes namespaces. In Kubernetes 1.10 and later, you can address this use case with external metrics.

Using external metrics requires a certain level of knowledge of your monitoring system, and it requires a cluster monitoring setup similar to one required for using custom metrics. With external metrics, you can autoscale based on any metric available in your monitoring system by providing a metricName field in your HorizontalPodAutoscaler manifest. Additionally you can use a metricSelector field to limit which metrics’ time series you want to use for autoscaling. If multiple time series are matched by metricSelector, the sum of their values is used by the HorizontalPodAutoscaler.

For example if your application processes tasks from a hosted queue service, you could add the following section to your HorizontalPodAutoscaler manifest to specify that you need one worker per 30 outstanding tasks.

- type: External
  external:
    metricName: queue_messages_ready
    metricSelector:
      matchLabels:
        queue: worker_tasks
    targetAverageValue: 30

If your metric describes work or resources that can be divided between autoscaled pods the targetAverageValue field describes how much of that work each pod can handle. Instead of using the targetAverageValue field, you could use the targetValue to define a desired value of your external metric.

Appendix: Horizontal Pod Autoscaler Status Conditions

When using the autoscaling/v2beta1 form of the HorizontalPodAutoscaler, you will be able to see status conditions set by Kubernetes on the HorizontalPodAutoscaler. These status conditions indicate whether or not the HorizontalPodAutoscaler is able to scale, and whether or not it is currently restricted in any way.

The conditions appear in the status.conditions field. To see the conditions affecting a HorizontalPodAutoscaler, we can use kubectl describe hpa:

$ kubectl describe hpa cm-test
Name:                           cm-test
Namespace:                      prom
Labels:                         <none>
Annotations:                    <none>
CreationTimestamp:              Fri, 16 Jun 2017 18:09:22 +0000
Reference:                      ReplicationController/cm-test
Metrics:                        ( current / target )
  "http_requests" on pods:      66m / 500m
Min replicas:                   1
Max replicas:                   4
ReplicationController pods:     1 current / 1 desired
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 pods metric http_requests
  ScalingLimited        False   DesiredWithinRange      the desired replica count is within the acceptable range
Events:

For this HorizontalPodAutoscaler, we can see several conditions in a healthy state. The first, AbleToScale, indicates whether or not the HPA is able to fetch and update scales, as well as whether or not any backoff-related conditions would prevent scaling. The second, ScalingActive, indicates whether or not the HPA is enabled (i.e. the replica count of the target is not zero) and is able to calculate desired scales. When it is False, it generally indicates problems with fetching metrics. Finally, the last condition, ScalingLimited, indicates that the desired scale was capped by the maximum or minimum of the HorizontalPodAutoscaler. This is an indication that you may wish to raise or lower the minimum or maximum replica count constraints on your HorizontalPodAutoscaler.

Appendix: Other possible scenarios

Creating the autoscaler declaratively

Instead of using kubectl autoscale command to create a HorizontalPodAutoscaler imperatively we can use the following file to create it declaratively:

hpa-php-apache.yaml docs/tasks/run-application 
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: php-apache
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: php-apache
  minReplicas: 1
  maxReplicas: 10
  targetCPUUtilizationPercentage: 50

We will create the autoscaler by executing the following command:

$ kubectl create -f https://k8s.io/docs/tasks/run-application/hpa-php-apache.yaml
horizontalpodautoscaler "php-apache" created

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