Autoscaling
Autoscaling is a feature in which the cluster is capable of increasing or decreasing the number of pods as the demand for service response need it.
Module
The Horizontal Pod Autoscaler automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization.
Overview
At the end of this module, you will :
Learn the format of a YAML Autoscale file
Learn how to manage a Autoscale
Learn the composition of a Autoscale
Prerequisites
Create the directory data/autoscaling
in your home folder to manage the YAML file needed in this module.
mkdir ~/data/autoscaling
mkdir ~/data/autoscaling/metrics-server
Create
Looks up a Deployment, ReplicaSet, or ReplicationController by name and creates an autoscaler that uses the given resource as a reference. An autoscaler can automatically increase or decrease number of pods deployed within the system as needed.
Horizontal Pod Autoscaler automatically scales the number of pods in a deployment or replica set based on observed CPU, Memory or Custom Metrics utilization depending the API version used.
The Kubernetes basic autoscaling architecture can be schematized like this :

The create command can directly ask the API resource to create an HorizontalPodAutoscaler in command line or create an HorizontalPodAutoscaler object based on a yaml file definition.
Deploy metrics-server on Digital Ocean cluster
By default, metrics-server, the tool that will pull the metrics, is not installed on Digital Ocean cluster, to enable autoscaling, we need to install it.
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: system:aggregated-metrics-reader
labels:
rbac.authorization.k8s.io/aggregate-to-view: "true"
rbac.authorization.k8s.io/aggregate-to-edit: "true"
rbac.authorization.k8s.io/aggregate-to-admin: "true"
rules:
- apiGroups: ["metrics.k8s.io"]
resources: ["pods", "nodes"]
verbs: ["get", "list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: metrics-server:system:auth-delegator
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: system:auth-delegator
subjects:
- kind: ServiceAccount
name: metrics-server
namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: metrics-server-auth-reader
namespace: kube-system
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: Role
name: extension-apiserver-authentication-reader
subjects:
- kind: ServiceAccount
name: metrics-server
namespace: kube-system
---
apiVersion: apiregistration.k8s.io/v1beta1
kind: APIService
metadata:
name: v1beta1.metrics.k8s.io
spec:
service:
name: metrics-server
namespace: kube-system
group: metrics.k8s.io
version: v1beta1
insecureSkipTLSVerify: true
groupPriorityMinimum: 100
versionPriority: 100
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: metrics-server
namespace: kube-system
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: metrics-server
namespace: kube-system
labels:
k8s-app: metrics-server
spec:
selector:
matchLabels:
k8s-app: metrics-server
template:
metadata:
name: metrics-server
labels:
k8s-app: metrics-server
spec:
serviceAccountName: metrics-server
volumes:
# mount in tmp so we can safely use from-scratch images and/or read-only containers
- name: tmp-dir
emptyDir: {}
containers:
- name: metrics-server
image: k8s.gcr.io/metrics-server-amd64:v0.3.6
args:
- --cert-dir=/tmp
- --secure-port=4443
- --kubelet-insecure-tls
- --kubelet-preferred-address-types=InternalIP,ExternalIP,Hostname
ports:
- name: main-port
containerPort: 4443
protocol: TCP
securityContext:
readOnlyRootFilesystem: true
runAsNonRoot: true
runAsUser: 1000
imagePullPolicy: Always
volumeMounts:
- name: tmp-dir
mountPath: /tmp
nodeSelector:
beta.kubernetes.io/os: linux
---
apiVersion: v1
kind: Service
metadata:
name: metrics-server
namespace: kube-system
labels:
kubernetes.io/name: "Metrics-server"
kubernetes.io/cluster-service: "true"
spec:
selector:
k8s-app: metrics-server
ports:
- port: 443
protocol: TCP
targetPort: main-port
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: system:metrics-server
rules:
- apiGroups:
- ""
resources:
- pods
- nodes
- nodes/stats
- namespaces
- configmaps
verbs:
- get
- list
- watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: system:metrics-server
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: system:metrics-server
subjects:
- kind: ServiceAccount
name: metrics-server
namespace: kube-system
kubectl apply -f ~/data/autoscaling/metrics-server/metrics-server.yaml
Exercise n°1
Run a sample app based on a webserver to expose it on port 80.
Create an Horizontal Pod Autoscaler to automatically scale the Deployment if the CPU usage is above 50%.
apiVersion: apps/v1
kind: Deployment
metadata:
name: php-apache
labels:
app: php-apache
spec:
replicas: 3
selector:
matchLabels:
app: php-apache
template:
metadata:
labels:
app: php-apache
spec:
containers:
- name: php-apache
image: k8s.gcr.io/hpa-example
resources:
requests:
cpu: "200m"
limits:
cpu: "300m"
ports:
- name: php-apache
containerPort: 80
---
apiVersion: v1
kind: Service
metadata:
name: php-apache
spec:
type: ClusterIP
selector:
app: php-apache
ports:
- name: php-apache
port: 80
protocol: TCP
targetPort: php-apache
kubectl apply -f data/autoscaling/01_php-apache.yaml
# version kubectl <1.17
kubectl run php-apache --image=k8s.gcr.io/hpa-example --requests=cpu=200m --limits=cpu=300m --expose --port=80
# Create an Horizontal Pod Autoscaler based on the CPU usage
kubectl autoscale deployment php-apache --cpu-percent=50 --min=3 --max=10
Exercise n°2
Run a sample nginx application exposing port 8080
Create an Horizontal Pod Autoscaler to automatically scale the Deployment if the memory is above 80%.
apiVersion: apps/v1
kind: Deployment
metadata:
name: nginx
labels:
app: nginx
spec:
replicas: 1
selector:
matchLabels:
app: nginx
template:
metadata:
labels:
app: nginx
spec:
containers:
- name: nginx
image: nginx
resources:
requests:
memory: "500Mi"
limits:
memory: "1Gi"
ports:
- name: nginx
containerPort: 8080
---
apiVersion: v1
kind: Service
metadata:
name: nginx
spec:
type: ClusterIP
selector:
app: nginx
ports:
- name: nginx
port: 8080
protocol: TCP
targetPort: nginx
kubectl apply -f data/autoscaling/02_deployment.yaml
# version kubectl <1.17
kubectl run nginx --image=nginx --requests=memory=500m --limits=memory=1G --expose --port=8080
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: nginx
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: nginx
minReplicas: 1
maxReplicas: 5
metrics:
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 80
kubectl apply -f data/autoscaling/01_hpa.yaml
Get
The get command list the object asked. It could be a single object or a list of multiple objects comma separated. This command is useful to get the status of each object. The output can be formatted to only display some information based on some json search or external tools like tr
, sort
, uniq
.
The default output display some useful information about each services :
Name : the name for the newly created object
Reference : the object managed by the autoscaler, like Pod name, a Deployment name ...
Targets : the metrics defined to autoscale the referenced resource
Minpods : the lower limit for the number of pods that can be set by the autoscaler
Maxpods : the upper limit for the number of pods that can be set by the autoscaler
Replicas : the current replicas number
Age : the age of the object from his creation
Exercise n°1
Get the current HorizontalPodAutoscaler resources in the default namespace.
kubectl get hpa
Exercise n°2
Stress the Pod created in the previous section and check the HorizontalPoMindAutoscaler associated.
# Connect to the Pod
kubectl run -it load-generator --image=busybox /bin/sh
# Run a loop bash command in the container to stress the CPU
while true; do wget -q -O- http://php-apache.default.svc.cluster.local; done
# Check the Horizontal Pod Autoscaler status
kubectl get hpa
Longer Execution Times
The autoscaling can take more than 2 minutes to run. Please be patient. Do not close the window or cancel the operation.
Exercise n°3
Stop to stress the Pod previously created and check that the autoscaler come back to normal.
kubectl get hpa
Describe
Once an object is running, it is inevitably a need to debug problems or check the configuration deployed.
The describe command display a lot of configuration information about the Horizontal Pod Autoscaler (labels, annotations, etc.) and the scale policy (selector, type, number of pods, ...).
This command is really useful to introspect and debug an object deployed in a cluster.
Exercise n°1
Describe one of the existing Autoscaler in the default namespace.
kubectl describe horizontalpodautoscaler php-apache
Explain
Kubernetes come with a lot of documentation about his objects and the available options in each one. Those information can be fin easily in command line or in the official Kubernetes documentation.
The explain command allows to directly ask the API resource via the command line tools to display information about each Kubernetes objects and their architecture.
Exercise n°1
Get the documentation of a specific field of a resource.
kubectl explain hpa.spec
Add the --recursive flag to display all of the fields at once without descriptions.
Delete
The delete command delete resources by filenames, stdin, resources and names, or by resources and label selector.
Be careful on the deletion of an autoscaling object, this can have effects in the availability of the services associated.
Note that the delete command does NOT do resource version checks, so if someone submits an update to a resource right when you submit a delete, their update will be lost along with the rest of the resource.
Exercise n°1
Delete the previous autoscaling group created in command line.
# Delete the HorizontalPodAutoscaler
kubectl delete hpa php-apache
# Delete the Pods
kubectl delete deployment php-apache load-generator
# Delete the Services
kubectl delete service php-apache
Module exercise
The purpose of this section is to manage each steps of the lifecycle of an application to better understand each concepts of the Kubernetes course.
The main objective in this module is to understand how to dynamically and automatically manage the number of Pods needed to handle the workload.
For more information about the application used all along the course, please refer to the Exercise App > Voting App link in the left panel.
Based on the principles explain in this module, try by your own to handle this steps. The development of a yaml file is recommended.
The file developed has to be stored in this directory : ~/data/votingapp/10_autoscaling
Manage the HorizontalPodAutoscaler of the worker Pods to :
Ensure that the worker has minimum one Pods
Ensure that the worker has maximum five Pods
Ensure that the Pods is autoscaled when the CPU is above 80%.
External documentation
Those documentations can help you to go further in this topic :
Kubernetes official documentation on Horizontal Pod Auto Scaling (HPA)
Kubernetes official documentation walkthrough HPA
Kubernetes official documentation of autoscale API
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