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Autoscaling a Dapr app with KEDA
Dapr, with its modular building-block approach, along with the 10+ different pub/sub components, make it easy to write message processing applications. Since Dapr can run in many environments (e.g. VM, bare-metal, Cloud, or Edge) the autoscaling of Dapr applications is managed by the hosting later.
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.
This how-to walks through the configuration of 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.
To install KEDA, follow the Deploying KEDA instructions on the KEDA website.
Install Kafka (optional)
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
When done, also 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
Next, create the topic which is used in this example (for example
The number of topic partitions is related to the maximum number of replicas KEDA creates for your deployments
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
Deploy a Dapr Pub/Sub component
Next, we’ll 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, the
demo-topic we created above. If you used the Kafka Helm install instructions above you can leave the
brokers value as is. Otherwise, change this to the connection string to your Kafka brokers.
Also notice the
autoscaling-subscriber value set for
consumerID which is used later to make sure 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
Next, we will deploy the KEDA scaling object that monitors the lag on the specified Kafka topic and 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"
A few things to review here in the above file:
scaleTargetRefsection in the
spec:is the Dapr ID of your app defined in the Deployment (The value of the
pollingIntervalis the frequency in seconds with which KEDA checks Kafka for current topic partition offset
minReplicaCountis the minimum number of replicas KEDA creates for your deployment. (Note, if your application takes a long time to start it may be better to set that to
1to ensure at least one replica of your deployment is always running. Otherwise, set that to
0and KEDA creates the first replica for you)
maxReplicaCountis 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
topicin the Kafka
metadatasection which should be set to the same topic to which your Dapr deployment subscribe (In this example
- Similarly the
bootstrapServersshould be set to the same broker connection string used in the
consumerGroupshould be set to the same value as the
Note: setting 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.
Next, deploy the KEDA scaler to Kubernetes:
kubectl apply -f kafka_scaler.yaml
Now, that the
ScaledObject KEDA object is configured, your deployment will scale based on the lag of the Kafka topic. More information on configuring KEDA for Kafka topics is available here.
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, as we have defined in the KEDA scaler manifest. You can publish messages to the Kafka Dapr component by using the Dapr Publish CLI command