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Autoscaling service based on queue depth

Prerequisites

Follow this guide to install OpenFunction.

Follow this guide to install a Kafka server named kafka-server and a Topic named sample-topic.

Follow this guide to create a registry credential.

Deployment

To configure the autoscaling demo we will deploy two functions: subscriber which will be used to process messages of the sample-topic queue in Kafka, and the producer, which will be publishing messages.

Build and deploy Producer

Build and push the producer image.

cd producer
docker build -t <your registry name>/v1beta1-autoscaling-producer:latest -f Dockerfile.producer .
docker push <your registry name>/v1beta1-autoscaling-producer:latest

Modify the container image in deploy.yaml:

You can set the NUMBER_OF_PUBLISHERS env and turn up its value appropriately so that the producer can trigger more subscribers in less time.

    spec:
      containers:
        - name: producer
          image: <your registry name>/v1beta1-autoscaling-producer:latest
          imagePullPolicy: Always
          env:
            - name: PUBSUB_NAME
              value: "autoscaling-producer"
            - name: TOPIC_NAME
              value: "sample-topic"
          ports:
            - containerPort: 60034
              name: function-port
              protocol: TCP

Deploy the producer:

kubectl apply -f deploy.yaml

Deploy Subscriber

Modify the spec.image field in subscriber/function-subscribe.yaml to your own container registry address:

apiVersion: core.openfunction.io/v1beta1
kind: Function
metadata:
  name: autoscaling-subscriber
spec:
  image: "<your registry name>/autoscaling-subscriber:v1"

Use the following commands to create these Functions:

kubectl apply -f subscriber/function-subscriber.yaml

Back in the initial terminal now, some 20-30 seconds after the producer starts, you should see the number of subscriber pods being adjusted by Keda based on the number of the sample-topic topic.