How to: Autoscale a Dapr app with KEDA
Dapr, with its building-block API approach, along with the many pub/sub components, makes it easy to write message processing applications. Since Dapr can run in many environments (for example VMs, bare-metal, Cloud or Edge Kubernetes) the autoscaling of Dapr applications is managed by the hosting layer.
For Kubernetes, Dapr integrates with KEDA, an event driven autoscaler for Kubernetes. Many of Dapr’s pub/sub components overlap with the scalers provided by KEDA, so it’s easy to configure your Dapr deployment on Kubernetes to autoscale based on the back pressure using KEDA.
In this guide, you configure a scalable Dapr application, along with the back pressure on Kafka topic. However, you can apply this approach to any pub/sub components offered by Dapr.
NoteIf you’re working with Azure Container Apps, refer to the official Azure documentation for scaling Dapr applications using KEDA scalers.
To install KEDA, follow the Deploying KEDA instructions on the KEDA website.
Install and deploy Kafka
If you don’t have access to a Kafka service, you can install it into your Kubernetes cluster for this example by using Helm:
helm repo add confluentinc https://confluentinc.github.io/cp-helm-charts/ helm repo update kubectl create ns kafka helm install kafka confluentinc/cp-helm-charts -n kafka \ --set cp-schema-registry.enabled=false \ --set cp-kafka-rest.enabled=false \ --set cp-kafka-connect.enabled=false
To check on the status of the Kafka deployment:
kubectl rollout status deployment.apps/kafka-cp-control-center -n kafka kubectl rollout status deployment.apps/kafka-cp-ksql-server -n kafka kubectl rollout status statefulset.apps/kafka-cp-kafka -n kafka kubectl rollout status statefulset.apps/kafka-cp-zookeeper -n kafka
Once installed, deploy the Kafka client and wait until it’s ready:
kubectl apply -n kafka -f deployment/kafka-client.yaml kubectl wait -n kafka --for=condition=ready pod kafka-client --timeout=120s
Create the Kafka topic
Create the topic used in this example (
kubectl -n kafka exec -it kafka-client -- kafka-topics \ --zookeeper kafka-cp-zookeeper-headless:2181 \ --topic demo-topic \ --create \ --partitions 10 \ --replication-factor 3 \ --if-not-exists
The number of topic
partitionsis related to the maximum number of replicas KEDA creates for your deployments.
Deploy a Dapr pub/sub component
Deploy the Dapr Kafka pub/sub component for Kubernetes. Paste the following YAML into a file named
apiVersion: dapr.io/v1alpha1 kind: Component metadata: name: autoscaling-pubsub spec: type: pubsub.kafka version: v1 metadata: - name: brokers value: kafka-cp-kafka.kafka.svc.cluster.local:9092 - name: authRequired value: "false" - name: consumerID value: autoscaling-subscriber
The above YAML defines the pub/sub component that your application subscribes to and that you created earlier (
If you used the Kafka Helm install instructions, you can leave the
brokers value as-is. Otherwise, change this value to the connection string to your Kafka brokers.
autoscaling-subscriber value set for
consumerID. This value is used later to ensure that KEDA and your deployment use the same Kafka partition offset.
Now, deploy the component to the cluster:
kubectl apply -f kafka-pubsub.yaml
Deploy KEDA autoscaler for Kafka
Deploy the KEDA scaling object that:
- Monitors the lag on the specified Kafka topic
- Configures the Kubernetes Horizontal Pod Autoscaler (HPA) to scale your Dapr deployment in and out
Paste the following into a file named
kafka_scaler.yaml, and configure your Dapr deployment in the required place:
apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: subscriber-scaler spec: scaleTargetRef: name: <REPLACE-WITH-DAPR-DEPLOYMENT-NAME> pollingInterval: 15 minReplicaCount: 0 maxReplicaCount: 10 triggers: - type: kafka metadata: topic: demo-topic bootstrapServers: kafka-cp-kafka.kafka.svc.cluster.local:9092 consumerGroup: autoscaling-subscriber lagThreshold: "5"
Let’s review a few metadata values in the file above:
||The Dapr ID of your app defined in the Deployment (The value of the
||The frequency in seconds with which KEDA checks Kafka for current topic partition offset.|
||The minimum number of replicas KEDA creates for your deployment. If your application takes a long time to start, it may be better to set this to
||The maximum number of replicas for your deployment. Given how Kafka partition offset works, you shouldn’t set that value higher than the total number of topic partitions.|
||Should be set to the same topic to which your Dapr deployment subscribed (in this example,
||Should be set to the same broker connection string used in the
||Should be set to the same value as the
ImportantSetting the connection string, topic, and consumer group to the same values for both the Dapr service subscription and the KEDA scaler configuration is critical to ensure the autoscaling works correctly.
Deploy the KEDA scaler to Kubernetes:
kubectl apply -f kafka_scaler.yaml
See the KEDA scaler work
Now that the
ScaledObject KEDA object is configured, your deployment will scale based on the lag of the Kafka topic. Learn more about configuring KEDA for Kafka topics.
As defined in the KEDA scaler manifest, you can now start publishing messages to your Kafka topic
demo-topic and watch the pods autoscale when the lag threshold is higher than
5 topics. Publish messages to the Kafka Dapr component by using the Dapr Publish CLI command.
Was this page helpful?
Glad to hear it! Please tell us how we can improve.
Sorry to hear that. Please tell us how we can improve.