Skip to main content

Using KEDA and Prometheus to scale your k8s workloads

· 8 min read
Djamaile Rahamat

These days, everyone and their grandma are using Kubernetes and one important aspect of Kubernetes is scaling your workloads. With KEDA, it is extremely simple to scale your workloads! Let’s have a look.



Straight from the website of KEDA:

KEDA is a Kubernetes-based Event Driven Autoscaler. With KEDA, you can drive the scaling of any container in Kubernetes based on the number of events needing to be processed.

KEDA provides many 'triggers' on which your application can scale on. For example, Prometheus, PubSub, Postgres and many more. In this blog post we will focus on Prometheus.

Starting up#

First let's spin up a cluster! I am using kind but you are free to use minikube if you prefer that :).

$ kind create cluster

Create the namespace

$ kubectl create ns keda-demo

Switch to the namespace

$ kubectl config set-context --current --namespace=keda-demo

If the cluster is spun up, we can start deploying our Prometheus. For this, I have already written a prometheus manifest so you won’t have to do it.


apiVersion: ClusterRolemetadata:  name: prometheusrules:  - apiGroups: [""]    resources:      - services    verbs: ["get", "list", "watch"]  - nonResourceURLs: ["/metrics"]    verbs: ["get"]---apiVersion: v1kind: ServiceAccountmetadata:  name: keda-demo---apiVersion: ClusterRoleBindingmetadata:  name: prometheusroleRef:  apiGroup:  kind: ClusterRole  name: prometheussubjects:  - kind: ServiceAccount    name: keda-demo    namespace: keda-demo---apiVersion: v1kind: ConfigMapmetadata:  name: prom-conf  labels:    name: prom-confdata:  prometheus.yml: |-    global:      scrape_interval: 5s      evaluation_interval: 5s    scrape_configs:      - job_name: 'go-prom-job'        kubernetes_sd_configs:        - role: service        relabel_configs:        - source_labels: [__meta_kubernetes_service_label_run]          regex: go-prom-app-service          action: keep---apiVersion: apps/v1kind: Deploymentmetadata:  name: prometheus-deploymentspec:  replicas: 1  selector:    matchLabels:      app: prometheus-server  template:    metadata:      labels:        app: prometheus-server    spec:      serviceAccountName: keda-demo      containers:        - name: prometheus          image: prom/prometheus          args:            - "--config.file=/etc/prometheus/prometheus.yml"            - "--storage.tsdb.path=/prometheus/"          ports:            - containerPort: 9090          volumeMounts:            - name: prometheus-config-volume              mountPath: /etc/prometheus/            - name: prometheus-storage-volume              mountPath: /prometheus/      volumes:        - name: prometheus-config-volume          configMap:            defaultMode: 420            name: prom-conf
        - name: prometheus-storage-volume          emptyDir: {}---apiVersion: v1kind: Servicemetadata:  name: prometheus-servicespec:  ports:    - port: 9090      protocol: TCP  selector:    app: prometheus-server

The Prometheus manifest is really simple. Just a Prometheus workload with a clusterrole and a clusterrolebinding. Don't forget to apply the manifest:

$ kubectl apply -f prometheus.yaml

Once the pod is up and running, let's see if it also works:

$ kubectl port-forward svc/prometheus-service 9090

Now visit http://localhost:9090 and you should see the user interface of Prometheus.

Deploying Keda#

We can now deploy the KEDA operator. KEDA provides multiple ways to deploy their operator, but for now we will use the k8s manifest.

$ kubectl apply -f

Now there should be two pods in the namespace keda you can check it with the following command:

$ kubectl get pods -n keda

As you can see there are two pods being spinned up:

on 🤠 kind-kind (keda) Desktop/projects/keda-prometheus ☁️  default🕙[ 07:35:40 ] ❯ kubectl get pods                                                         335msNAME                                      READY   STATUS              RESTARTS   AGEkeda-metrics-apiserver-66b8c68649-2mwf8   0/1     ContainerCreating   0          5skeda-operator-574c6d4769-q9mlc            0/1     ContainerCreating   0          5s

The metrics-apiserver exposes data to the Horizontal Pod Autoscaler, which gets consumed by a deployment. The operator pod activates Kubernetes deployments to scale to and from zero on no events.

Creating the application (Optional)#

The application is a simple go application that increments the metric http_requests when you visit it. This section is optional because you are also free to use my docker image.

in your folder execute the following:

go mod init

Then in your main.go you can put in the following code:

package main
import (    "fmt"    "log"    "net/http"
    ""    "")
type Labels map[string]string
var (    httpRequestsCounter = prometheus.NewCounter(prometheus.CounterOpts{        Name: "http_requests",        Help: "number of http requests",    }))
func init() {    // Metrics have to be registered to be exposed:    prometheus.MustRegister(httpRequestsCounter)}
func main() {    http.Handle("/metrics", promhttp.Handler())    http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) {        defer httpRequestsCounter.Inc()        fmt.Fprintf(w, "Hello, you've requested: %s\n", r.URL.Path)    })    log.Fatal(http.ListenAndServe(":8080", nil))}

Now build the go application with:

$ go mod tidy

Let's then make a simple Dockerfile for it:

FROM golang as build-stage
COPY go.mod /COPY go.sum /COPY main.go /RUN cd / && CGO_ENABLED=0 GOOS=linux go build -a -installsuffix cgo -o go-prom-app
FROM alpineCOPY --from=build-stage /go-prom-app /EXPOSE 8080CMD ["/go-prom-app"]

Only thing left is to build and push the image:

$ docker build -t <your_username>/keda .$ docker push <your_username>/keda

Running the application#

If you don’t have a Docker account or don’t want to use it, that’s fine. You can use my docker image! Let’s get our go application running in our cluster, for that we need some k8s manifests. Not to worry because I already wrote them:


apiVersion: apps/v1kind: Deploymentmetadata:  name: go-prom-app  namespace: keda-demospec:  selector:    matchLabels:      app: go-prom-app  template:    metadata:      labels:        app: go-prom-app    spec:      containers:        - name: go-prom-app          image: djam97/keda          imagePullPolicy: Always          ports:            - containerPort: 8080---apiVersion: v1kind: Servicemetadata:  name: go-prom-app-service  namespace: keda-demo  labels:    run: go-prom-app-servicespec:  ports:    - port: 8080      protocol: TCP  selector:    app: go-prom-app

You can replace the image name with your own image if you prefer that. Let's apply the manifest:

$ kubectl apply -f go-deployment.yaml

If the pod is up verify if it is working

$ kubectl port-forward svc/go-prom-app-service 8080

If you visit http://localhost:8080 you should see Hello, you've requested: /.

Scaling the application#

Now that we have our go application up we can write a manifest that will scale our application. Keda offers many triggers that can scale our application, but of course we will use the Prometheus trigger.

In a new file called scaled-object.yaml add the following content:

apiVersion: Custom CRD provisioned by the Keda operatorkind: ScaledObjectmetadata:  name: prometheus-scaledobjectspec:  scaleTargetRef:    # target our deployment    name: go-prom-app  # Interval to when to query Prometheus  pollingInterval: 15  # The period to wait after the last trigger reported active  # before scaling the deployment back to 1  cooldownPeriod: 30  # min replicas keda will scale to  # if you have an app that has an dependency on pubsub  # this would be a good use case to set it to zero  # why keep your app running if your topic has no messages?  minReplicaCount: 1  # max replicas keda will scale to  maxReplicaCount: 20  advanced:    # HPA config    # Read about it here:    horizontalPodAutoscalerConfig:      behavior:        scaleDown:          stabilizationWindowSeconds: 30          policies:            - type: Percent              value: 50              periodSeconds: 30        scaleUp:          stabilizationWindowSeconds: 0          policies:            - type: Percent              value: 50              periodSeconds: 10  triggers:    - type: prometheus      metadata:        # address where keda can reach our prometheus on        serverAddress: http://prometheus-service.keda-demo.svc.cluster.local:9090        # metric on what we want to scale        metricName: http_requests_total        # if treshold is reached then Keda will scale our deployment        threshold: "100"        query: sum(rate(http_requests[1m]))

Read the yaml manifest and it’s comments to understand what is going on. One important note as well is in advanced.horizontalPodAutoscalerConfig.scaleUp.policies you can see I have specified 50%, that means our pod will scale up with 50% of it’s current amount of pods. 1 -> 2 -> 3 -> 5 -> 8 -> 12 -> 18 -> 20 it will stop at 20 pods because that is the limit we specified.

Let's apply the manifest:

$ kubectl apply -f scaled-object.yaml

This will provision an HPA in your namespace which you can check with:

$ kubectl get hpa

but because this is a custom CRD you can also query the custom CRD with kubectl:

$ kubectl get
NAME                      SCALETARGETKIND      SCALETARGETNAME   MIN   MAX   TRIGGERS     AUTHENTICATION   READY   ACTIVE   FALLBACK   AGEprometheus-scaledobject   apps/v1.Deployment   go-prom-app       1     20    prometheus                    True    False    False      64s

We can see that our prometheus-scaledobject is ready so let’s scale our application! Remember our application scales on the metric http_requests_total and our threshold is only 100 so we should be able reach that threshold. For this we can use a simple tool called hey.

Run the application

$ kubectl port-forward svc/go-prom-app-service 8080

In another terminal watch the pods

$ kubectl get pods -w -n keda-demo

Put load on the application (Do this continuously, until there are 20 pods)

$ hey -n 10000 -m GET http://localhost:8080

It can take a minute before the application actually starts scaling. After a while you should have 20 pods up and running! Now let’s also look at the scale down process. Stop putting load on the application and let’s just watch the pods. This process should go from 20 -> 10 -> 5 - > 2 -> 1. This is basically how KEDA goes to work!

If you like KEDA please check out their docs for more examples and what type of different triggers they provide. Happy auto-scaling!