Experimental KEDA support for Knative Event Sources Autoscaling

This component is ALPHA

STATUS	Experimental
Sponsoring WG	Eventing

Warning: Still under development. Not meant for production deployment.

Design

To enable KEDA Autoscaling of Knative Event Sources (and other components in the future) there is a separate controller implemented, ie. no hard dependency in Knative. This controller watches CustomResourcesDefinitions resources in the cluster. If the newly installed CRD is supported by this controller, it creates a new dynamic controller which watches custom objects of kind specified by the CRD.

Currently there is support for Kafka Source. We also have experimental support for RabbitMQ Broker and Redis Stream Source.

Annotations

User can enable and configure autoscaling on a particular Source or Broker by a set of annotations.

metadata:
  annotations:
    autoscaling.knative.dev/class: keda.autoscaling.knative.dev
    autoscaling.knative.dev/minScale: "0"
    autoscaling.knative.dev/maxScale: "5"
    keda.autoscaling.knative.dev/pollingInterval: "30"
    keda.autoscaling.knative.dev/cooldownPeriod: "30"

    # Kafka Source
    keda.autoscaling.knative.dev/kafkaLagThreshold: "10"

    # Redis Stream Source
    keda.autoscaling.knative.dev/redisStreamPendingEntriesCount: "5"

• autoscaling.knative.dev/class: keda.autoscaling.knative.dev - needs to be specified on a Source to enable KEDA autoscaling
• autoscaling.knative.dev/minScale - minimum number of replicas to scale down to. Default: 0
• autoscaling.knative.dev/maxScale - maximum number of replicas to scale out to. Default: 50
• keda.autoscaling.knative.dev/pollingInterval - interval in seconds KEDA uses to poll metrics. Default: 30
• keda.autoscaling.knative.dev/cooldownPeriod - period of time in seconds KEDA waits until it scales down. Default: 300
• keda.autoscaling.knative.dev/kafkaLagThreshold - only for Kafka Source, refers to the lag on the current consumer group that's used for scaling (1<->N). Default: 10
• keda.autoscaling.knative.dev/kafkaActivationLagThreshold - only for Kafka Source, refers to the lag on the current consumer group that's used for activation (0<->1). Default: 0
• keda.autoscaling.knative.dev/rabbitMQQueueLength - only for RabbitMQ broker, refers to the target value for number of messages in a RabbitMQ brokers trigger queue: 1
• keda.autoscaling.knative.dev/redisStreamPendingEntriesCount - only for Redis Stream Source, refers to the target value for number of entries in the Pending Entries List for the specified consumer group in the Redis Stream. Default: 5

HOW TO

Install KEDA v2

It is needed to install KEDA v2, which is using different namespace for it's CRDs (keda.k8s.io -> keda.sh). KEDA v1 is not supported.

To install KEDA, please follow installation instructions.

Confirm there are 2 pods running in keda namespace:

$ kubectl get pods -n keda
NAME                                      READY   STATUS    RESTARTS   AGE
keda-metrics-apiserver-7cf7765dc8-k9lnc   1/1     Running   0          5m2s
keda-operator-55658855fc-rc9rb            1/1     Running   0          5m3s

Install eventing-autoscaler-keda Controller

export KO_DOCKER_REPO=...
ko apply -f config/

Confirm there is a pod of eventing-autoscaler-keda controller running in knative-eventing namespace:

$ kubectl get pod -n knative-eventing -l app=eventing-autoscaler-keda-controller
NAME                                                   READY   STATUS    RESTARTS   AGE
eventing-autoscaler-keda-controller-69bf565cb8-r5922   1/1     Running   0          3m7s

Example of Kafka Source autoscaled by KEDA

1. Set up Kafka Cluster, eg. use Strimzi operator

2. Install Knative Serving and Eventing

3. Install Knative Eventing Kafka Source

4. Create KafkaSource resource, with annotation autoscaling.knative.dev/class: keda.autoscaling.knative.dev. There are other KEDA related annotations, see the example:

apiVersion: sources.knative.dev/v1alpha1
kind: KafkaSource
metadata:
  name: kafka-source
  namespace: default
  annotations:
    autoscaling.knative.dev/class: keda.autoscaling.knative.dev
    autoscaling.knative.dev/minScale: "0"
    autoscaling.knative.dev/maxScale: "5"
    keda.autoscaling.knative.dev/pollingInterval: "30"
    keda.autoscaling.knative.dev/cooldownPeriod: "30"
    keda.autoscaling.knative.dev/kafkaLagThreshold: "10"
spec:
  consumerGroup: knative-group
  bootstrapServers:
    - my-cluster-kafka-bootstrap.openshift-operators:9092
  topics:
    - my-topic
  sink:
    ref:
      apiVersion: serving.knative.dev/v1
      kind: Service
      name: event-display

5. Check that ScaledObject was created for this KafkaSource:

$ kubectl get scaledobjects
NAME                                      SCALETARGETKIND      SCALETARGETNAME                                                 TRIGGERS   AUTHENTICATION   READY   ACTIVE   AGE
so-f87369e5-c320-4f44-b23a-8c535a523e3a   apps/v1.Deployment   kafkasource-kafka-source-f87369e5-c320-4f44-b23a-8c535a523e3a   kafka                       True    False     6m5s

Example of RabbitMQ Broker autoscaled by KEDA

1. Install Knative Serving and Eventing

2. Install RabbitMQ Broker

3. Install a Broker / Trigger and sources as directed in the above guide.

4. Enable the autoscaler by applying the KEDA patch:

kubectl patch broker default --type merge --patch '{"metadata": {"annotations": {"autoscaling.knative.dev/class": "keda.autoscaling.knative.dev"}}}'

5. Check that the scaled resources were created and are ready

vaikas-a01:eventing-autoscaler-keda vaikas$ kubectl get triggerauthentications
NAME                   PODIDENTITY   SECRET                  ENV
default-trigger-auth                 default-broker-rabbit
vaikas-a01:eventing-autoscaler-keda vaikas$ kubectl get scaledobjects
NAME           SCALETARGETKIND      SCALETARGETNAME           TRIGGERS   AUTHENTICATION         READY   ACTIVE   AGE
ping-trigger   apps/v1.Deployment   ping-trigger-dispatcher   rabbitmq   default-trigger-auth   True    True     14m