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Apache Kafka (Experimental) Latest

Experimental scaler based on 'segmentio/kafka-go' library. Scale applications based on an Apache Kafka topic or other services that support Kafka protocol.

Availability: v2.12+ Maintainer: Community
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Notice:

  • This is an experimental Kafka scaler based on kafka-go library.

  • This scaler is not fully compatible with the existing Kafka scaler. There are some differences in the configuration and behavior. Please read the documentation carefully before using it.

  • If you are using OAuth authentication, please use the existing Kafka scaler, as this scaler does not yet support OAuth2 authentication.

  • This scaler has support for AWS MSK IAM based authentication.

  • By default, the number of replicas will not exceed:

    • The number of partitions on a topic when a topic is specified;
    • The number of partitions of all topics in the consumer group when no topic is specified;
    • maxReplicaCount specified in ScaledObject/ScaledJob. If not specified, then the default value of maxReplicaCount is taken into account;
    • The number of partitions with non-zero lag if limitToPartitionsWithLag is set to true

    That is, if maxReplicaCount is set more than number of partitions, the scaler won’t scale up to target maxReplicaCount. See allowIdleConsumers below to disable this default behavior.

  • This is so because if there are more number of consumers than the number of partitions in a topic, then extra consumer will have to sit idle.

Trigger Specification

This specification describes the apache-kafka trigger for an Apache Kafka topic.

triggers:
- type: apache-kafka
  metadata:
    bootstrapServers: kafka.svc:9092
    consumerGroup: my-group
    topic: test-topic
    lagThreshold: '5'
    activationLagThreshold: '3'
    offsetResetPolicy: latest
    allowIdleConsumers: false
    scaleToZeroOnInvalidOffset: false
    excludePersistentLag: false
    limitToPartitionsWithLag: false
    ensureEvenDistributionOfPartitions: false
    partitionLimitation: '1,2,10-20,31'
    tls: enable
    sasl: plaintext

Parameter list:

  • bootstrapServers - Comma separated list of Kafka brokers “hostname:port” to connect to for bootstrap.
  • consumerGroup - Name of the consumer group used for checking the offset on the topic and processing the related lag.
  • topic - Name of the topic on which processing the offset lag. (Optional, see note below)
  • lagThreshold - Average target value to trigger scaling actions. (Default: 10, Optional)
  • activationLagThreshold - Target value for activating the scaler. Learn more about activation here. (Default: 0, Optional)
  • offsetResetPolicy - The offset reset policy for the consumer. (Values: latest, earliest, Default: latest, Optional)
  • allowIdleConsumers - When set to true, the number of replicas can exceed the number of partitions on a topic, allowing for idle consumers. (Default: false, Optional)
  • scaleToZeroOnInvalidOffset - This parameter controls what the scaler does when a partition doesn’t have a valid offset. If ‘false’ (the default), the scaler will keep a single consumer for that partition. Otherwise (’true’), the consumers for that partition will be scaled to zero. See the discussion about this parameter.
  • excludePersistentLag - When set to true, the scaler will exclude partition lag for partitions which current offset is the same as the current offset of the previous polling cycle. This parameter is useful to prevent scaling due to partitions which current offset message is unable to be consumed. If false (the default), scaler will include all consumer lag in all partitions as per normal. (Default: false, Optional)
  • limitToPartitionsWithLag - When set to true, the number of replicas will not exceed the number of partitions having non-zero lag. topic must be specified when this parameter is set to true. allowIdleConsumers cannot be true when this parameter is true. (Default: false, Optional)
  • ensureEvenDistributionOfPartitions - When set to true, the scaler will ensure that the number of replicas is even across the topic partitions. (Default: false, Optional)
  • partitionLimitation - Comma separated list of partition ids to scope the scaling on. Allowed patterns are “x,y” and/or ranges “x-y”. If set, the calculation of the lag will only take these ids into account. (Default: All partitions, Optional)
  • sasl - Kafka SASL auth mode. (Values: plaintext, scram_sha256, scram_sha512, gssapi, aws_msk_iam or none, Default: none, Optional). This parameter could also be specified in sasl in TriggerAuthentication
  • tls - To enable SSL auth for Kafka, set this to enable. If not set, TLS for Kafka is not used. (Values: enable, disable, Default: disable, Optional). This parameter could also be specified in tls in TriggerAuthentication

Note:

When topic is unspecified, total offset lag will be calculated with all topics within the consumer group.

  • When there are active consumer instances, all topics includes:
    • Topics the consumer is currently subscribing to;
    • Topics that the consumer group had prior commit history (up to retention period for __consumer_offset, default to 7 days, see KIP-186);
  • When there are no active consumer instances, the feature is still a WIP and as such would highly recommend to specify topic to avoid unexpected behavior. Namely the scaler will not be able to determine the topics it had subscribed to in the past and will not be able to calculate the lag for those topics.

Authentication Parameters

You can use TriggerAuthentication CRD to configure the authenticate by providing sasl.

If TLS is required you should set tls to enable. If required for your Kafka configuration, you may also provide a ca, cert, key and keyPassword. cert and key must be specified together. Another alternative is to specify tls and sasl in ScaledObject instead of tls and sasl in TriggerAuthentication, respectively.

In case of SASL based authentication provide the username and password.

For AWS MSK IAM authentication provide aws_msk_iam as sasl. You don’t need to set username and password in this case. However, you need to enable TLS by setting tls to enable.

Credential based authentication:

SASL:

  • sasl - Kafka SASL auth mode. (Values: plaintext, scram_sha256, scram_sha512, gssapi, aws_msk_iam or none, Default: none, Optional)
  • username - Username used for sasl authentication. (Required if sasl is not none or aws_msk_iam)
  • password - Password used for sasl authentication. (Required if sasl is not none or aws_msk_iam)

TLS:

  • tls - To enable SSL auth for Kafka, set this to enable. If not set, TLS for Kafka is not used. (Values: enable, disable, Default: disable, Optional)
  • ca - Certificate authority file for TLS client authentication. (Optional)
  • cert - Certificate for client authentication. (Optional)
  • key - Key for client authentication. (Optional)
  • keyPassword - If set the keyPassword is used to decrypt the provided key. (Optional)

AWS MSK IAM Specific Configuration:

  • awsRegion - AWS region of your MSK cluster. (Optional, required for AWS MSK IAM authentication)

For authentication, you can also use TriggerAuthentication CRD to configure the authenticate by providing awsAccessKeyID and awsSecretAccessKey or awsRoleArn.

Role based authentication:

  • awsRoleArn - Amazon Resource Names (ARNs) uniquely identify AWS resource.

Credential based authentication:

  • awsAccessKeyID - Id of the user.
  • awsSecretAccessKey - Access key for the user to authenticate with.

New Consumers and Offset Reset Policy

When a new Kafka consumer is created, it must determine its consumer group initial position, i.e. the offset it will start to read from. The position is decided in Kafka consumers via a parameter auto.offset.reset and the possible values to set are latest (Kafka default), and earliest. This parameter in KEDA should be set accordingly. In this initial status, no offset has been committed to Kafka for the consumer group and any request for offset metadata will return an INVALID_OFFSET; so KEDA has to manage the consumer pod’s autoscaling in relation to the offset reset policy that has been specified in the parameters:

  • If the policy is set to earliest (a new consumer wants to replay everything in the topic from its beginning) and no offset is committed, the scaler will return a lag value equal to the last offset in the topic. In the case of a new topic the last offset will be 0, so it will scale the deployment to 0 replicas. If a new message is produced to the topic, KEDA will return the new value of the offset (1), and will scale the deployments to consume the message.
  • If the policy is set to latest (so the new consumer will only consume new messages) and no offset is committed, the scaler will return a negative lag value, and will also tell the HPA to remain active, hence the deployment should have the minimum number of replicas running. This is to allow the consumer to read any new message on the topic, and commit its offset.

New ensureEvenDistributionOfPartitions property support

When scaling a Kafka consumers, you would want to ensure that all partitions are consumed equally. Otherwise some partition would start building up higher lag than others. Prior to introduction of this parameter the Kafka scaler did not factor in the number of partitions on the Kafka topic in its scaling decisions. This is a problem because we could end up in a situation where your consumers would have uneven distribution of partitions. When you have ensureEvenDistributionOfPartitions configuration enabled the scaler would always ensure that the number of partitions are always balanced between the number of consumers that we would be scaling to.

Consider for example a topic with 10 partitions. In this case the ideal consumer count should always be 1,2,5,10. Running any other number of consumers than this set would cause an uneven distribution.

Below are some examples of what the scaling decision would look like. Consider 10 partitions and a lag threshold of 10 as the configuration default.

  • For lag between 1 -> 10 we would be running 1 consumer
  • For lag between 11 -> 20 we would be running 2 consumer
  • For lag between 21 -> 50 we would be running 5 consumers
  • For lag higher than 51 we would be running 10 consumers

Example

Your kafka cluster has no SASL/TLS auth: 

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: kafka-scaledobject
  namespace: default
spec:
  scaleTargetRef:
    name: azure-functions-deployment
  pollingInterval: 30
  triggers:
  - type: kafka
    metadata:
      bootstrapServers: localhost:9092
      consumerGroup: my-group       # Make sure that this consumer group name is the same one as the one that is consuming topics
      topic: test-topic
      # Optional
      lagThreshold: "50"
      offsetResetPolicy: latest

Your kafka cluster turns on SASL/TLS auth:

Method 1: tls and sasl are in TriggerAuthentication 
apiVersion: v1
kind: Secret
metadata:
  name: keda-kafka-secrets
  namespace: default
data:
  sasl: "plaintext"
  username: "admin"
  password: "admin"
  tls: "enable"
  ca: <your ca>
  cert: <your cert>
  key: <your key>
---
apiVersion: keda.sh/v1alpha1
kind: TriggerAuthentication
metadata:
  name: keda-trigger-auth-kafka-credential
  namespace: default
spec:
  secretTargetRef:
  - parameter: sasl
    name: keda-kafka-secrets
    key: sasl
  - parameter: username
    name: keda-kafka-secrets
    key: username
  - parameter: password
    name: keda-kafka-secrets
    key: password
  - parameter: tls
    name: keda-kafka-secrets
    key: tls
  - parameter: ca
    name: keda-kafka-secrets
    key: ca
  - parameter: cert
    name: keda-kafka-secrets
    key: cert
  - parameter: key
    name: keda-kafka-secrets
    key: key
---
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: kafka-scaledobject
  namespace: default
spec:
  scaleTargetRef:
    name: azure-functions-deployment
  pollingInterval: 30
  triggers:
  - type: kafka
    metadata:
      bootstrapServers: localhost:9092
      consumerGroup: my-group       # Make sure that this consumer group name is the same one as the one that is consuming topics
      topic: test-topic
      # Optional
      lagThreshold: "50"
      offsetResetPolicy: latest
    authenticationRef:
      name: keda-trigger-auth-kafka-credential
Method 2: tls and sasl are in ScaledObject 
apiVersion: v1
kind: Secret
metadata:
  name: keda-kafka-secrets
  namespace: default
data:
  username: "admin"
  password: "admin"
  ca: <your ca>
  cert: <your cert>
  key: <your key>
---
apiVersion: keda.sh/v1alpha1
kind: TriggerAuthentication
metadata:
  name: keda-trigger-auth-kafka-credential
  namespace: default
spec:
  secretTargetRef:
  - parameter: username
    name: keda-kafka-secrets
    key: username
  - parameter: password
    name: keda-kafka-secrets
    key: password
  - parameter: ca
    name: keda-kafka-secrets
    key: ca
  - parameter: cert
    name: keda-kafka-secrets
    key: cert
  - parameter: key
    name: keda-kafka-secrets
    key: key
---
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: kafka-scaledobject
  namespace: default
spec:
  scaleTargetRef:
    name: azure-functions-deployment
  pollingInterval: 30
  triggers:
  - type: kafka
    metadata:
      bootstrapServers: localhost:9092
      consumerGroup: my-group       # Make sure that this consumer group name is the same one as the one that is consuming topics
      topic: test-topic
      tls: enable
      sasl: plaintext
      # Optional
      lagThreshold: "50"
      offsetResetPolicy: latest
    authenticationRef:
      name: keda-trigger-auth-kafka-credential

Your AWS MSK has IAM auth: 

apiVersion: v1
kind: Secret
metadata:
  name: keda-kafka-secrets
  namespace: default
data:
  sasl: "aws_msk_iam"
  tls: "enable"
  awsAccessKeyID: <your awsAccessKeyID>
  awsSecretAccessKey: <your awsSecretAccessKey>
---
apiVersion: keda.sh/v1alpha1
kind: TriggerAuthentication
metadata:
  name: keda-trigger-auth-kafka-credential
  namespace: default
spec:
  secretTargetRef:
  - parameter: sasl
    name: keda-kafka-secrets
    key: sasl
  - parameter: tls
    name: keda-kafka-secrets
    key: tls
  - parameter: awsAccessKeyID
    name: keda-kafka-secrets
    key: awsAccessKeyID
  - parameter: awsSecretAccessKey
    name: keda-kafka-secrets
    key: awsSecretAccessKey
---
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: kafka-scaledobject
  namespace: default
spec:
  scaleTargetRef:
    name: azure-functions-deployment
  pollingInterval: 30
  triggers:
  - type: apache-kafka
    metadata:
      bootstrapServers: localhost:9092
      consumerGroup: my-group       # Make sure that this consumer group name is the same one as the one that is consuming topics
      topic: test-topic
      awsRegion: us-east-1         # AWS region of your MSK cluster
      # Optional
      lagThreshold: "50"
      offsetResetPolicy: latest
    authenticationRef:
      name: keda-trigger-auth-kafka-credential