Chapter 5. Kafka configuration

After a new custom resource type is added to your cluster by installing a CRD, you can create instances of the resource based on its specification.

The custom resources for AMQ Streams components have common configuration properties, which are defined under spec.

In this fragment from a Kafka topic custom resource, the apiVersion and kind properties identify the associated CRD. The spec property shows configuration that defines the number of partitions and replicas for the topic.

Kafka topic custom resource

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 1
  replicas: 1
  # ...

There are many additional configuration options that can be incorporated into a YAML definition, some common and some specific to a particular component.

Some of the configuration options common to resources are described here. Security and metrics collection might also be adopted where applicable.

Example YAML showing common configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  bootstrapServers: my-cluster-kafka-bootstrap:9092
  resources:
    requests:
      cpu: 12
      memory: 64Gi
    limits:
      cpu: 12
      memory: 64Gi
  logging:
    type: inline
    loggers:
      connect.root.logger.level: "INFO"
  readinessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  jvmOptions:
    "-Xmx": "2g"
    "-Xms": "2g"
  template:
    pod:
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                      - fast-network
  # ...

A kafka cluster comprises one or more brokers. For producers and consumers to be able to access topics within the brokers, Kafka configuration must define how data is stored in the cluster, and how the data is accessed. You can configure a Kafka cluster to run with multiple broker nodes across racks.

If you are using OAuth 2.0 for token-based authentication, you can configure listeners to use the authorization server.

Example YAML showing Kafka configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    listeners:
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
      - name: external1
        port: 9094
        type: route
        tls: true
        authentication:
          type: tls
    # ...
    storage:
      type: persistent-claim
      size: 10000Gi
    # ...
    rack:
      topologyKey: topology.kubernetes.io/zone
    # ...

To set up MirrorMaker, a source and target (destination) Kafka cluster must be running.

You can use AMQ Streams with MirrorMaker 2.0, although the earlier version of MirrorMaker continues to be supported.

MirrorMaker 2.0

MirrorMaker 2.0 is based on the Kafka Connect framework, connectors managing the transfer of data between clusters.

MirrorMaker 2.0 uses:

Cluster configuration

You can use MirrorMaker 2.0 in active/passive or active/active cluster configurations.

You configure a KafkaMirrorMaker2 custom resource to define the Kafka Connect deployment, including the connection details of the source and target clusters, and then run a set of MirrorMaker 2.0 connectors to make the connection.

Topic configuration is automatically synchronized between the source and target clusters according to the topics defined in the KafkaMirrorMaker2 custom resource. Configuration changes are propagated to remote topics so that new topics and partitions are detected and created. Topic replication is defined using regular expression patterns to whitelist or blacklist topics.

The following MirrorMaker 2.0 connectors and related internal topics help manage the transfer and synchronization of data between the clusters.

Figure 5.1. Replication across two clusters

Bidirectional replication across two clusters

The MirrorMaker 2.0 architecture supports bidirectional replication in an active/active cluster configuration, so both clusters are active and provide the same data simultaneously. A MirrorMaker 2.0 cluster is required at each target destination.

Remote topics are distinguished by automatic renaming that prepends the name of cluster to the name of the topic. This is useful if you want to make the same data available locally in different geographical locations.

However, if you want to backup or migrate data in an active/passive cluster configuration, you might want to keep the original names of the topics. If so, you can configure MirrorMaker 2.0 to turn off automatic renaming.

Figure 5.2. Bidirectional replication

Example YAML showing MirrorMaker 2.0 configuration

  apiVersion: kafka.strimzi.io/v1alpha1
  kind: KafkaMirrorMaker2
  metadata:
    name: my-mirror-maker2
    spec:
      version: 2.6.0
      connectCluster: "my-cluster-target"
      clusters:
      - alias: "my-cluster-source"
        bootstrapServers: my-cluster-source-kafka-bootstrap:9092
      - alias: "my-cluster-target"
        bootstrapServers: my-cluster-target-kafka-bootstrap:9092
      mirrors:
      - sourceCluster: "my-cluster-source"
        targetCluster: "my-cluster-target"
        sourceConnector: {}
      topicsPattern: ".*"
      groupsPattern: "group1|group2|group3"

MirrorMaker

The earlier version of MirrorMaker uses producers and consumers to replicate data across clusters.

MirrorMaker uses:

Consumer and producer configuration includes any authentication and encryption settings.

A whitelist defines the topics to mirror from a source to a target cluster.

Key Consumer configuration

Key Producer configuration

Example YAML showing MirrorMaker configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    bootstrapServers: my-source-cluster-kafka-bootstrap:9092
    groupId: "my-group"
    numStreams: 2
    offsetCommitInterval: 120000
    # ...
  producer:
    # ...
    abortOnSendFailure: false
    # ...
  whitelist: "my-topic|other-topic"
  # ...

A basic Kafka Connect configuration requires a bootstrap address to connect to a Kafka cluster, and encryption and authentication details.

Kafka Connect instances are configured by default with the same:

If multiple different Kafka Connect instances are used, these settings must reflect each instance.

Example YAML showing Kafka Connect configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: my-connect-cluster
    offset.storage.topic: my-connect-cluster-offsets
    config.storage.topic: my-connect-cluster-configs
    status.storage.topic: my-connect-cluster-status
  # ...

Connectors

Connectors are configured separately from Kafka Connect. The configuration describes the source input data and target output data to feed into and out of Kafka Connect. The external source data must reference specific topics that will store the messages.

Kafka provides two built-in connectors:

You can add other connectors using connector plugins, which are a set of JAR files that define the implementation required to connect to certain types of external system.

You create a custom Kafka Connect image that uses new Kafka Connect plugins.

To create the image, you can use:

Managing connectors

You can use the KafkaConnector resource or the Kafka Connect REST API to create and manage connector instances in a Kafka Connect cluster. The KafkaConnector resource offers an OpenShift-native approach, and is managed by the Cluster Operator.

The spec for the KafkaConnector resource specifies the connector class and configuration settings, as well as the maximum number of connector tasks to handle the data.

Example YAML showing KafkaConnector configuration

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnector
metadata:
  name: my-source-connector
  labels:
    strimzi.io/cluster: my-connect-cluster
spec:
  class: org.apache.kafka.connect.file.FileStreamSourceConnector
  tasksMax: 2
  config:
    file: "/opt/kafka/LICENSE"
    topic: my-topic
    # ...

You enable KafkaConnectors by adding an annotation to the KafkaConnect resource.

Example YAML showing annotation to enable KafkaConnector

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
  annotations:
    strimzi.io/use-connector-resources: "true"
  # ...

A Kafka Bridge configuration requires a bootstrap server specification for the Kafka cluster it connects to, as well as any encryption and authentication options required.

Kafka Bridge consumer and producer configuration is standard, as described in the Apache Kafka configuration documentation for consumers and Apache Kafka configuration documentation for producers.

HTTP-related configuration options set the port connection which the server listens on.

CORS

The Kafka Bridge supports the use of Cross-Origin Resource Sharing (CORS). CORS is a HTTP mechanism that allows browser access to selected resources from more than one origin, for example, resources on different domains. If you choose to use CORS, you can define a list of allowed resource origins and HTTP methods for interaction with the Kafka cluster through the Kafka Bridge. The lists are defined in the http specification of the Kafka Bridge configuration.

CORS allows for simple and preflighted requests between origin sources on different domains.

Example YAML showing Kafka Bridge configuration

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  bootstrapServers: my-cluster-kafka:9092
  http:
    port: 8080
    cors:
      allowedOrigins: "https://strimzi.io"
      allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH"
  consumer:
    config:
      auto.offset.reset: earliest
  producer:
    config:
      delivery.timeout.ms: 300000
  # ...