A HorizontalPodAutoscaler (HPA for short) automatically updates a workload resource (such as a Deployment or StatefulSet), with the aim of automatically scaling the workload to match demand.
Horizontal scaling means that the response to increased load is to deploy more Pods. This is different from vertical scaling, which for Kubernetes would mean assigning more resources (for example: memory or CPU) to the Pods that are already running for the workload.
If the load decreases, and the number of Pods is above the configured minimum, the HorizontalPodAutoscaler instructs the workload resource (the Deployment, StatefulSet, or other similar resource) to scale back down.
This document walks you through an example of enabling HorizontalPodAutoscaler to automatically manage scale for an example web app. This example workload is Apache httpd running some PHP code.
You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version 1.23. To check the version, enterkubectl version. If you're running an older release of Kubernetes, refer to the version of the documentation for that release (see available documentation versions. To follow this walkthrough, you also need to use a cluster that has a Metrics Server deployed and configured. The Kubernetes Metrics Server collects resource metrics from the kubelets in your cluster, and exposes those metrics through the Kubernetes API, using an APIService to add new kinds of resource that represent metric readings.
To learn how to deploy the Metrics Server, see the metrics-server documentation.
To demonstrate a HorizontalPodAutoscaler, you will first make a custom container image that uses the php-apache image from Docker Hub as its starting point. The Dockerfile is ready-made for you, and has the following content:
FROM php:5-apache
COPY index.php /var/www/html/index.php
RUN chmod a+rx index.php
This code defines a simple index.php page that performs some CPU intensive computations, in order to simulate load in your cluster.
<?php
$x = 0.0001;
for ($i = 0; $i <= 1000000; $i++) {
$x += sqrt($x);
}
echo "OK!";
?>
Once you have made that container image, start a Deployment that runs a container using the image you made, and expose it as a Service using the following manifest:
application/php-apache.yaml apiVersion: apps/v1
kind: Deployment
metadata:
name: php-apache
spec:
selector:
matchLabels:
run: php-apache
replicas: 1
template:
metadata:
labels:
run: php-apache
spec:
containers:
- name: php-apache
image: k8s.gcr.io/hpa-example
ports:
- containerPort: 80
resources:
limits:
cpu: 500m
requests:
cpu: 200m
---
apiVersion: v1
kind: Service
metadata:
name: php-apache
labels:
run: php-apache
spec:
ports:
- port: 80
selector:
run: php-apache
To do so, run the following command:
kubectl apply -f https://k8s.io/examples/application/php-apache.yaml
deployment.apps/php-apache created
service/php-apache created
Now that the server is running, create the autoscaler using kubectl. There is kubectl autoscale subcommand, part of kubectl, that helps you do this.
You will shortly run a command that creates a HorizontalPodAutoscaler that maintains between 1 and 10 replicas of the Pods controlled by the php-apache Deployment that you created in the first step of these instructions.
Roughly speaking, the HPA controller will increase and decrease the number of replicas (by updating the Deployment) to maintain an average CPU utilization across all Pods of 50%. The Deployment then updates the ReplicaSet - this is part of how all Deployments work in Kubernetes - and then the ReplicaSet either adds or removes Pods based on the change to its .spec.
Since each pod requests 200 milli-cores by kubectl run, this means an average CPU usage of 100 milli-cores. See Algorithm details for more details on the algorithm.
Create the HorizontalPodAutoscaler:
kubectl autoscale deployment php-apache --cpu-percent=50 --min=1 --max=10
horizontalpodautoscaler.autoscaling/php-apache autoscaled
You can check the current status of the newly-made HorizontalPodAutoscaler, by running:
# You can use "hpa" or "horizontalpodautoscaler"; either name works OK.
kubectl get hpa
The output is similar to:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 0% / 50% 1 10 1 18s
(if you see other HorizontalPodAutoscalers with different names, that means they already existed, and isn't usually a problem).
Please note that the current CPU consumption is 0% as there are no clients sending requests to the server (the TARGET column shows the average across all the Pods controlled by the corresponding deployment).
Next, see how the autoscaler reacts to increased load. To do this, you'll start a different Pod to act as a client. The container within the client Pod runs in an infinite loop, sending queries to the php-apache service.
# Run this in a separate terminal
# so that the load generation continues and you can carry on with the rest of the steps
kubectl run -i --tty load-generator --rm --image=busybox --restart=Never -- /bin/sh -c "while sleep 0.01; do wget -q -O- http://php-apache; done"
Now run:
# type Ctrl+C to end the watch when you're ready
kubectl get hpa php-apache --watch
Within a minute or so, you should see the higher CPU load; for example:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 305% / 50% 1 10 1 3m
and then, more replicas. For example:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 305% / 50% 1 10 7 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
You should see the replica count matching the figure from the HorizontalPodAutoscaler
NAME READY UP-TO-DATE AVAILABLE AGE
php-apache 7/7 7 7 19m
To finish the example, stop sending the load.
In the terminal where you created the Pod that runs a busybox image, terminate the load generation by typing <Ctrl> + C.
Then verify the result state (after a minute or so):
# type Ctrl+C to end the watch when you're ready
kubectl get hpa php-apache --watch
The output is similar to:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 0% / 50% 1 10 1 11m
and the Deployment also shows that it has scaled down:
kubectl get deployment php-apache
NAME READY UP-TO-DATE AVAILABLE AGE
php-apache 1/1 1 1 27m
Once CPU utilization dropped to 0, the HPA automatically scaled the number of replicas back down to 1.
Autoscaling the replicas may take a few minutes.
You can introduce additional metrics to use when autoscaling the php-apache Deployment by making use of the autoscaling/v2 API version.
First, get the YAML of your HorizontalPodAutoscaler in the autoscaling/v2 form:
kubectl get hpa php-apache -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/v2
kind: HorizontalPodAutoscaler
metadata:
name: php-apache
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: php-apache
minReplicas: 1
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 50
status:
observedGeneration: 1
lastScaleTime: <some-time>
currentReplicas: 1
desiredReplicas: 1
currentMetrics:
- type: Resource
resource:
name: cpu
current:
averageUtilization: 0
averageValue: 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 the metrics.k8s.io API is available.
You can also specify resource metrics in terms of direct values, instead of as percentages of the requested value, by using a target.type of AverageValue instead of Utilization, and setting the corresponding target.averageValue field instead of the target.averageUtilization.
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 support a target type of AverageValue.
Pod metrics are specified using a metric block like this:
type: Pods
pods:
metric:
name: packets-per-second
target:
type: AverageValue
averageValue: 1k
The second alternative metric type is object metrics. These metrics describe a different object in the same namespace, instead of describing Pods. The metrics are not necessarily fetched from the object; they only describe it. Object metrics support target types of both Value and AverageValue. With Value, the target is compared directly to the returned metric from the API. With AverageValue, the value returned from the custom metrics API is divided by the number of Pods before being compared to the target. The following example is the YAML representation of the requests-per-second metric.
type: Object
object:
metric:
name: requests-per-second
describedObject:
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
name: main-route
target:
type: Value
value: 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/v2
kind: HorizontalPodAutoscaler
metadata:
name: php-apache
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: php-apache
minReplicas: 1
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 50
- type: Pods
pods:
metric:
name: packets-per-second
target:
type: AverageValue
averageValue: 1k
- type: Object
object:
metric:
name: requests-per-second
describedObject:
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
name: main-route
target:
type: Value
value: 10k
status:
observedGeneration: 1
lastScaleTime: <some-time>
currentReplicas: 1
desiredReplicas: 1
currentMetrics:
- type: Resource
resource:
name: cpu
current:
averageUtilization: 0
averageValue: 0
- type: Object
object:
metric:
name: requests-per-second
describedObject:
apiVersion: networking.k8s.io/v1beta1
kind: Ingress
name: main-route
current:
value: 10k
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.
Many metrics pipelines allow you to describe metrics either by name or by a set of additional descriptors called labels. For all non-resource metric types (pod, object, and external, described below), you can specify an additional label selector which is passed to your metric pipeline. For instance, if you collect a metric http_requests with the verb label, you can specify the following metric block to scale only on GET requests:
type: Object
object:
metric:
name: http_requests
selector: {matchLabels: {verb: GET}}
This selector uses the same syntax as the full Kubernetes label selectors. The monitoring pipeline determines how to collapse multiple series into a single value, if the name and selector match multiple series. The selector is additive, and cannot select metrics that describe objects that are not the target object (the target pods in the case of the Pods type, and the described object in the case of the Object type).
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 knowledge of your monitoring system; the setup is similar to that required when using custom metrics. External metrics allow you to autoscale your cluster based on any metric available in your monitoring system. Provide a metric block with a name and selector, as above, and use the External metric type instead of Object. If multiple time series are matched by the metricSelector, the sum of their values is used by the HorizontalPodAutoscaler. External metrics support both the Value and AverageValue target types, which function exactly the same as when you use the Object type.
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:
metric:
name: queue_messages_ready
selector:
matchLabels:
queue: "worker_tasks"
target:
type: AverageValue
averageValue: 30
When possible, it's preferable to use the custom metric target types instead of external metrics, since it's easier for cluster administrators to secure the custom metrics API. The external metrics API potentially allows access to any metric, so cluster administrators should take care when exposing it.
When using the autoscaling/v2 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, you 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.
All metrics in the HorizontalPodAutoscaler and metrics APIs are specified using a special whole-number notation known in Kubernetes as a quantity. For example, the quantity 10500m would be written as 10.5 in decimal notation. The metrics APIs will return whole numbers without a suffix when possible, and will generally return quantities in milli-units otherwise. This means you might see your metric value fluctuate between 1 and 1500m, or 1 and 1.5 when written in decimal notation.
Instead of using kubectl autoscale command to create a HorizontalPodAutoscaler imperatively we can use the following manifest to create it declaratively:
application/hpa/php-apache.yaml apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
name: php-apache
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: php-apache
minReplicas: 1
maxReplicas: 10
targetCPUUtilizationPercentage: 50
Then, create the autoscaler by executing the following command:
kubectl create -f https://k8s.io/examples/application/hpa/php-apache.yaml
horizontalpodautoscaler.autoscaling/php-apache created
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https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/