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How to auto-scale Kafka applications on Kubernetes with KEDA

abhirockzz profile image Abhishek Gupta ・9 min read

This tutorial will demonstrate auto-scaling Kafka based consumer applications on Kubernetes using KEDA which stands for Kubernetes-based Event Driven Autoscaler

KEDA is currently a CNCF Sandbox project

KEDA can drive the scaling of any container in Kubernetes based on the number of events needing to be processed. It is a single-purpose and lightweight component that can be added to any Kubernetes cluster. KEDA works alongside standard Kubernetes components like the Horizontal Pod Autoscaler and can extend functionality without overwriting or duplication.

It has a built-in Kafka scaler which can auto-scale your Kafka consumer applications (traditional Consumer apps, Kafka Streams etc.) based on the consumer offset lag. I will be using Azure Event Hubs as the Kafka broker (although the concepts apply to any Kafka cluster) and Azure Kubernetes Service for the Kubernetes cluster (feel free to use alternatives such as minikube)

Code is available on GitHub

We will go through the following:

  • A quick overview
  • The app and KEDA configuration (mostly YAMLs! to be honest)
  • How to setup KEDA and required Azure services
  • Deploy the solution and watch auto-scaling in action

Overview

Here are the key components:

  • Producer app: This is a simple Go app that produces simulated JSON data to Kafka. It uses the sarama library. You can run this as a Docker container or directly as a Go app (details in an upcoming section)
  • Consumer app: This is another Go app that consumes data from Kafka. To add a bit of variety, it uses the Confluent Go Kafka client. You will be running this as a Kubernetes Deployment (details in an upcoming section)
  • KEDA ScaledObject (which defines the auto-scaling criteria based on Kafka) and other supporting manifests

Pre-requisites

kubectl - https://kubernetes.io/docs/tasks/tools/install-kubectl/

If you choose to use Azure Event Hubs, Azure Kubernetes Service (or both) you will need a Microsoft Azure account. Go ahead and sign up for a free one!

I will be using Helm to install KEDA. Here is the documentation to install Helm - https://helm.sh/docs/intro/install/

For alternative ways (Operator Hub or YAML files) of installing KEDA, take a look at the documentation

Here is how you can set up the required Azure services.

I recommend installing the below services as a part of a single Azure Resource Group which makes it easy to clean up these services

Azure Event Hubs

Azure Event Hubs is a data streaming platform and event ingestion service. It can receive and process millions of events per second. It also provides a Kafka endpoint that can be used by existing Kafka based applications as an alternative to running your own Kafka cluster. Event Hubs supports Apache Kafka protocol 1.0 and later, and works with existing Kafka client applications and other tools in the Kafka ecosystem including Kafka Connect (demonstrated in this blog), MirrorMaker etc.

To set up an Azure Event Hubs cluster, you can choose from a variety of options including the Azure portal, Azure CLI, Azure PowerShell or an ARM template. Once the setup is complete, you will need the connection string (that will be used in subsequent steps) for authenticating to Event Hubs - use this guide to finish this step.

Please ensure that you also create an Event Hub (Kafka topic) to/from which we can send/receive data

Azure Kubernetes Service

Azure Kubernetes Service (AKS) makes it simple to deploy a managed Kubernetes cluster in Azure. It reduces the complexity and operational overhead of managing Kubernetes by offloading much of that responsibility to Azure. Here are examples of how you can set up an AKS cluster using Azure CLI, Azure portal or ARM template

Install KEDA

helm repo add kedacore https://kedacore.github.io/charts
helm repo update

kubectl create namespace keda
helm install keda kedacore/keda --namespace keda
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This will install the KEDA Operator and the KEDA Metrics API server (as separate Deployments)

kubectl get deployment -n keda

NAME                              READY   UP-TO-DATE   AVAILABLE   AGE
keda-operator                     1/1     1            1           1h
keda-operator-metrics-apiserver   1/1     1            1           1h
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To check KEDA Operator logs

kubectl logs -f $(kubectl get pod -l=app=keda-operator -o jsonpath='{.items[0].metadata.name}' -n keda) -n keda
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YAML time!

Let's enjoy introspecting some YAMLs!

Kubernetes Secret

We store Event Hubs connectivity details using a Secret. More on this soon...

apiVersion: v1
kind: Secret
metadata:
  name: eventhub-kafka-credentials
data:
  authMode: <BASE64_AUTH_MODE>
  username: <BASE64_EVENTHUBS_USERNAME>
  password: <BASE64_EVENTHUBS_CONNECTION_STRING>
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ScaledObject

A ScaledObject for the KEDA Kafka scaler

Note the following references:

  • spec.scaleTargetRef.deploymentName points to the name of the Deployment that needs to be scaled
  • the spec.triggers.authenticationRef points to a TriggerAuthentication
apiVersion: keda.k8s.io/v1alpha1
kind: ScaledObject
metadata:
  name: kafka-scaledobject
  namespace: default
  labels:
    deploymentName: kafka-consumer
spec:
  scaleTargetRef:
    deploymentName: kafka-consumer
  pollingInterval: 15
  cooldownPeriod: 100
  maxReplicaCount: 10
  triggers:
    - type: kafka
      metadata:
        bootstrapServers: <EVENTHUBS_NAMESPACE>.servicebus.windows.net:9093
        consumerGroup: <EVENTHUB_CONSUMER_GROUP>
        topic: <EVENTHUB_TOPIC_NAME>
        lagThreshold: "10"
      authenticationRef:
        name: eventhub-kafka-triggerauth
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Consumer Deployment

The Kafka consumer app runs as a Kubernetes Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: kafka-consumer
spec:
  replicas: 1
  selector:
    matchLabels:
      app: kafka-consumer
  template:
    metadata:
      labels:
        app: kafka-consumer
    spec:
      containers:
        - name: kafka-consumer
          image: abhirockzz/kafka-consumer
          imagePullPolicy: IfNotPresent
          env:
            - name: KAFKA_EVENTHUB_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: eventhub-kafka-credentials
                  key: password
            - name: KAFKA_EVENTHUB_ENDPOINT
              value: <EVENTHUBS_NAMESPACE>.windows.net:9093
            - name: KAFKA_EVENTHUB_CONSUMER_GROUP
              value: $Default
            - name: KAFKA_EVENTHUB_TOPIC
              value: <EVENTHUB_TOPIC_NAME>
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Let's look at what the TriggerAuthentication looks like

TriggerAuthentication

It refers to the Secret which was described earlier (named eventhub-kafka-credentials)

apiVersion: keda.k8s.io/v1alpha1
kind: TriggerAuthentication
metadata:
  name: eventhub-kafka-triggerauth
spec:
  secretTargetRef:
    - parameter: authMode
      name: eventhub-kafka-credentials
      key: authMode
    - parameter: username
      name: eventhub-kafka-credentials
      key: username
    - parameter: password
      name: eventhub-kafka-credentials
      key: password
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Deploy

It's time to see things in action

Start by cloning the Git repo:

git clone https://github.com/abhirockzz/keda-eventhubs-kafka
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We will deploy the Secret that contains connection string for your Event Hubs instance. Update the file deploy/1-secret.yaml, where the password attribute contains the base64 encoded value of the Event Hubs connection string e.g. echo -n '<eventhubs-connection-string>' | base64

No need to change the username and authMode. They are just base64 versions of $ConnectionString and sasl_plain respectively

Deploy the Secret

kubectl apply -f deploy/1-secret.yaml
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Update the file deploy/3-consumer.yaml. Check the following references which are a part of spec.containers.env

  • KAFKA_EVENTHUB_ENDPOINT - name of the Event Hubs namespace
  • KAFKA_EVENTHUB_TOPIC - name of the Event Hubs topic

e.g.

          env:
            - name: KAFKA_EVENTHUB_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: eventhub-kafka-credentials
                  key: password
            - name: KAFKA_EVENTHUB_ENDPOINT
              value: my-eventhub.windows.net:9093
            - name: KAFKA_EVENTHUB_CONSUMER_GROUP
              value: $Default
            - name: KAFKA_EVENTHUB_TOPIC
              value: test-topic
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Create the consumer app Deployment

kubectl apply -f deploy/3-consumer.yaml
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You should see the consumer app spin into action (Running state) soon

kubectl get pods -w
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You can check the consumer app logs using kubectl logs -f $(kubectl get pod -l=app=kafka-consumer -o jsonpath='{.items[0].metadata.name}')

Create the TriggerAuthentication followed by the ScaledObject

kubectl apply -f deploy/2-trigger-auth.yaml
kubectl apply -f deploy/4-kafka-scaledobject.yaml
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Check KEDA Operator logs again - you should see that it has reacted to the fact that the ScaledObject was just created

kubectl logs -f $(kubectl get pod -l=app=keda-operator -o jsonpath='{.items[0].metadata.name}' -n keda) -n keda
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Check the consumer Pod - kubectl get pods. You will see that there are no Pods now - this is because KEDA actually scaled down Deployment to zero instances. You can confirm this by checking the Deployment as well kubectl get deployment/kafka-consumer

If you want to change this behavior e.g. have at least one instance of your Deployment running, add minReplicaCount attribute to the ScaledObject (defaults to 0)

From a scaling perspective, KEDA takes care of:

  • scaling your app from 0 to 1 instance depending on the metrics reported by the scaler (Kafka consumer lag in this case)
  • scaling your app from to 1 instance to 0

Rest of the heavy lifting (auto-scaling) is done by the Horizontal Pod Autoscaler (HPA) (created by the controller loop in the KEDA Operator), which is the native resource built into Kubernetes

Test

The stage is set for you to test auto-scaling. In one terminal, keep a watch on consumer Deployment resource

kubectl get deployment -w
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Export environment variables for the producer app - enter the values for KAFKA_EVENTHUB_ENDPOINT, KAFKA_EVENTHUB_CONNECTION_STRING and KAFKA_EVENTHUB_TOPIC

export KAFKA_EVENTHUB_ENDPOINT=<EVENTHUBS_NAMESPACE>.servicebus.windows.net:9093
export KAFKA_EVENTHUB_CONNECTION_STRING="Endpoint=sb://<EVENTHUBS_NAMESPACE>.servicebus.windows.net/;SharedAccessKeyName=RootManageSharedAccessKey;SharedAccessKey=<<EVENTHUBS_ACCESS_KEY>"
export KAFKA_EVENTHUB_TOPIC=<TOPIC_NAME>
export KAFKA_EVENTHUB_USERNAME="\$ConnectionString"
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Build and run the producer application

you can also run the Go app directly if you have Go (1.13 or above) installed - cd producer && go run main.go

docker build -t producer-app producer

docker run --rm -e KAFKA_EVENTHUB_ENDPOINT=$KAFKA_EVENTHUB_ENDPOINT -e KAFKA_EVENTHUB_CONNECTION_STRING=$KAFKA_EVENTHUB_CONNECTION_STRING -e KAFKA_EVENTHUB_TOPIC=$KAFKA_EVENTHUB_TOPIC -e KAFKA_EVENTHUB_USERNAME=$KAFKA_EVENTHUB_USERNAME producer-app
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The will start producing messages and you should see logs similar to this:

Event Hubs broker [<EVENTHUBS_NAMESPACE>.servicebus.windows.net:9093]
Event Hubs topic keda-test
Waiting for ctrl+c
sent message {"time":"Mon Apr 27 09:21:38 2020"} to partition 1 offset 0
sent message {"time":"Mon Apr 27 09:21:39 2020"} to partition 4 offset 0
sent message {"time":"Mon Apr 27 09:21:40 2020"} to partition 2 offset 0
sent message {"time":"Mon Apr 27 09:21:40 2020"} to partition 2 offset 1
sent message {"time":"Mon Apr 27 09:21:41 2020"} to partition 2 offset 2
sent message {"time":"Mon Apr 27 09:21:41 2020"} to partition 1 offset 1
...
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On the other terminal, you should see your application auto-scale as the number of Pods in the Deployment increase.

kafka-consumer   0/5   4     0     33s
kafka-consumer   0/5   4     0     33s
kafka-consumer   0/5   4     0     33s
kafka-consumer   0/5   5     0     33s
kafka-consumer   1/5   5     1     80s
kafka-consumer   2/5   5     2     90s
kafka-consumer   3/5   5     3     101s
kafka-consumer   4/5   5     4     101s
kafka-consumer   5/5   5     5     2m9s
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Please note that the no. of Pods will not increase beyond the no. of partitions for the Event Hubs topic.

The HPA will also reflect the same status

kubectl get hpa/keda-hpa-kafka-consumer

NAME                      REFERENCE                   TARGETS       MINPODS   MAXPODS   REPLICAS   AGE
keda-hpa-kafka-consumer   Deployment/kafka-consumer   10/10 (avg)   1         10        5          3m
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You can stop the producer app now. As the messages get consumed (and offsets are committed), the consumer lag will keep decreasing. At that point, the no. of instances will gradually be scaled down to 0.

Clean-up

To uninstall KEDA:

helm uninstall -n keda keda
kubectl delete -f https://raw.githubusercontent.com/kedacore/keda/master/deploy/crds/keda.k8s.io_scaledobjects_crd.yaml
kubectl delete -f https://raw.githubusercontent.com/kedacore/keda/master/deploy/crds/keda.k8s.io_triggerauthentications_crd.yaml
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To delete the Azure services, just delete the resource group:

az group delete --name <AZURE_RESOURCE_GROUP> --yes --no-wait
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That concludes this blog on auto-scaling a Kafka consumer app deployed to Kubernetes using KEDA! Please join and be a part of the growing KEDA community, check out https://keda.sh/community/ for details

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