Home
custom-metrics-api
LICENSETable of Contents
k8s-prom-hpa
Autoscaling is an approach to automatically scale up or down workloads based on the resource usage.
Autoscaling in Kubernetes has two dimensions: the Cluster Autoscaler that deals with node scaling
operations and the Horizontal Pod Autoscaler that automatically scales the number of pods in a
deployment or replica set. The Cluster Autoscaling together with Horizontal Pod Autoscaler can be used
to dynamically adjust the computing power as well as the level of parallelism that your system needs to meet SLAs.
While the Cluster Autoscaler is highly dependent on the underling capabilities of the cloud provider
that's hosting your cluster, the HPA can operate independently of your IaaS/PaaS provider.
The Horizontal Pod Autoscaler feature was first introduced in Kubernetes v1.1 and
has evolved a lot since then. Version 1 of the HPA scaled pods based on
observed CPU utilization and later on based on memory usage.
In Kubernetes 1.6 a new API Custom Metrics API was introduced that enables HPA access to arbitrary metrics.
And Kubernetes 1.7 introduced the aggregation layer that allows 3rd party applications to extend the
Kubernetes API by registering themselves as API add-ons.
The Custom Metrics API along with the aggregation layer made it possible for monitoring systems
like Prometheus to expose application-specific metrics to the HPA controller.
The Horizontal Pod Autoscaler is implemented as a control loop that periodically queries
the Resource Metrics API for core metrics like CPU/memory and the Custom Metrics API for application-specific metrics.
What follows is a step-by-step guide on configuring HPA v2 for Kubernetes 1.9 or later.
You will install the Metrics Server add-on that supplies the core metrics and then you'll use a demo
app to showcase pod autoscaling based on CPU and memory usage. In the second part of the guide you will
deploy Prometheus and a custom API server. You will register the custom API server with the
aggregator layer and then configure HPA with custom metrics supplied by the demo application.
Before you begin you need to install Go 1.8 or later and clone the k8s-prom-hpa repo in your GOPATH:
cd $GOPATH
git clone https://github.com/stefanprodan/k8s-prom-hpa
Setting up the Metrics Server
The Kubernetes Metrics Server
is a cluster-wide aggregator of resource usage data and is the successor of Heapster.
The metrics server collects CPU and memory usage for nodes and pods by pooling data from the kubernetes.summary_api.
The summary API is a memory-efficient API for passing data from Kubelet/cAdvisor to the metrics server.
If in the first version of HPA you would need Heapster to provide CPU and memory metrics, in
HPA v2 and Kubernetes 1.8 only the metrics server is required with the
horizontal-pod-autoscaler-use-rest-clients switched on.
The HPA rest client is enabled by default in Kubernetes 1.9.
GKE 1.9 comes with the Metrics Server pre-installed.
Deploy the Metrics Server in the kube-system namespace:
kubectl create -f ./metrics-server
After one minute the metric-server starts reporting CPU and memory usage for nodes and pods.
View nodes metrics:
kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes" | jq .
View pods metrics:
kubectl get --raw "/apis/metrics.k8s.io/v1beta1/pods" | jq .
Auto Scaling based on CPU and memory usage
You will use a small Golang-based web app to test the Horizontal Pod Autoscaler (HPA).
Deploy podinfo to the default namespace:
kubectl create -f ./podinfo/podinfo-svc.yaml,./podinfo/podinfo-dep.yaml
Access podinfo with the NodePort service at http://<K8S_PUBLIC_IP>:31198.
Next define a HPA that maintains a minimum of two replicas and scales up to ten
if the CPU average is over 80% or if the memory goes over 200Mi:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: podinfo
spec:
scaleTargetRef:
apiVersion: extensions/v1beta1
kind: Deployment
name: podinfo
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
targetAverageUtilization: 80
- type: Resource
resource:
name: memory
targetAverageValue: 200Mi
Create the HPA:
kubectl create -f ./podinfo/podinfo-hpa.yaml
After a couple of seconds the HPA controller contacts the metrics server and then fetches the CPU
and memory usage:
kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
podinfo Deployment/podinfo 2826240 / 200Mi, 15% / 80% 2 10 2 5m
In order to increase the CPU usage, run a load test with rakyll/hey:
#install hey
go get -u github.com/rakyll/hey
#do 10K requests
hey -n 10000 -q 10 -c 5 http://<K8S_PUBLIC_IP>:31198/
You can monitor the HPA events with:
$ kubectl describe hpa
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulRescale 7m horizontal-pod-autoscaler New size: 4; reason: cpu resource utilization (percentage of request) above target
Normal SuccessfulRescale 3m horizontal-pod-autoscaler New size: 8; reason: cpu resource utilization (percentage of request) above target
Remove podinfo for the moment. You will deploy it again later on in this tutorial:
kubectl delete -f ./podinfo/podinfo-hpa.yaml,./podinfo/podinfo-dep.yaml,./podinfo/podinfo-svc.yaml
Setting up a Custom Metrics Server
In order to scale based on custom metrics you need to have two components.
One component that collects metrics from your applications and stores them the Prometheus time series database.
And a second component that extends the Kubernetes custom metrics API with the metrics supplied by the collect, the k8s-prometheus-adapter.
You will deploy Prometheus and the adapter in a dedicated namespace.
Create the monitoring namespace:
kubectl create -f ./namespaces.yaml
Deploy Prometheus v2 in the monitoring namespace:
If you are deploying to GKE you might get an error saying: Error from server (Forbidden): error when creating
This will help you resolve that issue: RBAC on GKE
kubectl create -f ./prometheus
Generate the TLS certificates needed by the Prometheus adapter:
make certs
Deploy the Prometheus custom metrics API adapter:
kubectl create -f ./custom-metrics-api
List the custom metrics provided by Prometheus:
kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1" | jq .
Get the FS usage for all the pods in the monitoring namespace:
kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/namespaces/monitoring/pods/*/fs_usage_bytes" | jq .
Auto Scaling based on custom metrics
Create podinfo NodePort service and deployment in the default namespace:
kubectl create -f ./podinfo/podinfo-svc.yaml,./podinfo/podinfo-dep.yaml
The podinfo app exposes a custom metric named http_requests_total.
The Prometheus adapter removes the _total suffix and marks the metric as a counter metric.
Get the total requests per second from the custom metrics API:
kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/*/http_requests" | jq .
{
"kind": "MetricValueList",
"apiVersion": "custom.metrics.k8s.io/v1beta1",
"metadata": {
"selfLink": "/apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/%2A/http_requests"
},
"items": [
{
"describedObject": {
"kind": "Pod",
"namespace": "default",
"name": "podinfo-6b86c8ccc9-kv5g9",
"apiVersion": "/__internal"
},
"metricName": "http_requests",
"timestamp": "2018-01-10T16:49:07Z",
"value": "901m"
},
{
"describedObject": {
"kind": "Pod",
"namespace": "default",
"name": "podinfo-6b86c8ccc9-nm7bl",
"apiVersion": "/__internal"
},
"metricName": "http_requests",
"timestamp": "2018-01-10T16:49:07Z",
"value": "898m"
}
]
}
The m represents milli-units, so for example, 901m means 901 milli-requests.
Create a HPA that will scale up the podinfo deployment if the number of requests goes over 10 per second:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: podinfo
spec:
scaleTargetRef:
apiVersion: extensions/v1beta1
kind: Deployment
name: podinfo
minReplicas: 2
maxReplicas: 10
metrics:
- type: Pods
pods:
metricName: http_requests
targetAverageValue: 10
Deploy the podinfo HPA in the default namespace:
kubectl create -f ./podinfo/podinfo-hpa-custom.yaml
After a couple of seconds the HPA fetches the http_requests value from the metrics API:
kubectl get hpa
NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
podinfo Deployment/podinfo 899m / 10 2 10 2 1m
Apply some load on the podinfo service with 25 requests per second:
#install hey
go get -u github.com/rakyll/hey
#do 10K requests rate limited at 25 QPS
hey -n 10000 -q 5 -c 5 http://<K8S-IP>:31198/healthz
After a few minutes the HPA begins to scale up the deployment:
kubectl describe hpa
Name: podinfo
Namespace: default
Reference: Deployment/podinfo
Metrics: ( current / target )
"http_requests" on pods: 9059m / 10
Min replicas: 2
Max replicas: 10
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulRescale 2m horizontal-pod-autoscaler New size: 3; reason: pods metric http_requests above target
At the current rate of requests per second the deployment will never get to the max value of 10 pods.
Three replicas are enough to keep the RPS under 10 per each pod.
After the load tests finishes, the HPA down scales the deployment to it's initial replicas:
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulRescale 5m horizontal-pod-autoscaler New size: 3; reason: pods metric http_requests above target
Normal SuccessfulRescale 21s horizontal-pod-autoscaler New size: 2; reason: All metrics below target
You may have noticed that the autoscaler doesn't react immediately to usage spikes.
By default the metrics sync happens once every 30 seconds and scaling up/down can
only happen if there was no rescaling within the last 3-5 minutes.
In this way, the HPA prevents rapid execution of conflicting decisions and gives time for the
Cluster Autoscaler to kick in.
Conclusions
Not all systems can meet their SLAs by relying on CPU/memory usage metrics alone, most web and mobile
backends require autoscaling based on requests per second to handle any traffic bursts.
For ETL apps, auto scaling could be triggered by the job queue length exceeding some threshold and so on.
By instrumenting your applications with Prometheus and exposing the right metrics for autoscaling you can
fine tune your apps to better handle bursts and ensure high availability.