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 , 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 offered by Dapr.

    To install KEDA, follow the Deploying KEDA instructions on the KEDA website.

    If you don’t have access to a Kafka service, you can install it into your Kubernetes cluster for this example by using Helm:

    To check on the status of the Kafka deployment:

    2. kubectl rollout status deployment.apps/kafka-cp-ksql-server -n kafka
    3. kubectl rollout status statefulset.apps/kafka-cp-kafka -n kafka

    Next, create the topic which is used in this example (for example demo-topic):

    1. kubectl -n kafka exec -it kafka-client -- kafka-topics \
    2.         --zookeeper kafka-cp-zookeeper-headless:2181 \
    3.         --topic demo-topic \
    4.         --create \
    5.         --partitions 10 \
    6.         --replication-factor 3 \
    7.         --if-not-exists

    Next, we’ll deploy the Dapr Kafka pub/sub component for Kubernetes. Paste the following YAML into a file named kafka-pubsub.yaml:

    The above YAML defines the pub/sub component that your application subscribes to, the 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 .

    Now, deploy the component to the cluster:

      Paste the following into a file named kafka_scaler.yaml, and configure your Dapr deployment in the required place:

      A few things to review here in the above file:

      • name in the scaleTargetRef section in the spec: is the Dapr ID of your app defined in the Deployment (The value of the dapr.io/id annotation)
      • pollingInterval is the frequency in seconds with which KEDA checks Kafka for current topic partition offset
      • minReplicaCount is 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 1 to ensure at least one replica of your deployment is always running. Otherwise, set that to and KEDA creates the first replica for you)
      • maxReplicaCount is 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
      • topic in the Kafka metadata section which should be set to the same topic to which your Dapr deployment subscribe (In this example demo-topic)
      • Similarly the bootstrapServers should be set to the same broker connection string used in the kafka-pubsub.yaml file
      • The consumerGroup should be set to the same value as the consumerID in the kafka-pubsub.yaml file

      Next, deploy the KEDA scaler to Kubernetes:

        All done!

        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 .